12 Digital Transformation Trends & Use Cases in Education in '24

case study for education industry

The COVID-19 pandemic has accelerated digital transformation in education as nearly 1.5 billion students across the world became distanced from their classrooms. However, online education is not the only way digital technologies transform the teaching and learning experience. We explore how digital transformation affects the education sector with key technologies and trends.

What does digital transformation mean for education?

Digital transformation in education means digitalizing processes and products to improve the teaching and learning experience for everyone involved.

Digital transformation in education focuses on:

  • Accessibility: Digital technologies enable learners (e.g. students, employees) to access learning resources more easily and less expensively than traditional education. People across the world, from all ages, with different socioeconomic statuses have access to classes and resources through the internet. Technologies such as text-to-speech remove the barriers for students with disabilities.
  • Interactive learning: Micro lessons, videos, interactive tests, gamification, etc. are all different learning formats that are transforming education with a more interactive learning environment. For example, interactive language teaching apps like Duolingo claim to reach more US learners interested in foreign languages than the school system.
  • Customized learning: Computer technology and AI enable educational methods such as adaptive learning where each learner is allowed to learn in a way appropriate to them.

Why is digital transformation in education important now?

School shutdowns and distance education are some of the most profound effects of COVID-19 which has demonstrated the importance and urgency of incorporating digital technologies into education. Even before the pandemic, the education industry was in the process of digital transformation. The image below from research by HolonIQ shows that global EdTech (education technology) venture capital funding had increased from $500 million to $7 billion between 2010 and 2019. The effect of the pandemic is also staggering as the investments almost tripled in 2021.

EdTech venture capital funding had significantly increased, highlighting the trend of digital transformation in education

What are the key technologies and trends enabling digital transformation in education?

1- artificial intelligence.

Artificial intelligence applications can undertake simple but time-consuming tasks in education to ease the workload of educators or school staff. They can also be used to deliver an improved and custom learning experience to students. The applications include:

Improving student performance

  • Voice-to-text  technologies transforming classes to notes are helpful to students with hearing impairment
  • Text-to-voice technologies help dyslexic students learn more effectively by listening instead of reading.
  • Personalized learning  can involve a diverse set of technologies including AI to elicit how a student learns best and tailor the education accordingly. Blended and adaptive learning are examples of methods that combine face-to-face instruction with digital learning tools that encourage students to learn by discovery.

Increasing the effectiveness of staff

  • Intelligent FAQ chatbots  to answer questions about class, homework, campus, etc. Chatbots can act as virtual advisors for college students which can free up professors’ time.
  • Domain specific chatbots: College admission is a complex and stressful process for high school students. College counsellors have limited time to support hundreds of students. Chatbots focused on the admission process can support students in this challenging and important process
  • Educational businesses also have back office functions like finance. Process mining can help identify inefficiencies in the back office functions. Read our article on educational process mining to learn more about the applications of process mining in education.
  • Individual automation technologies like RPA or combining multiple automation technologies (also called hyperautomation) can help save the time of support staff.

Explore the top 20 use cases of RPA in education in more detail.

2- Analytics

Digital technologies enable schools to collect and analyze a wealth of data about their students to monitor and enhance their performance. Using traditional and advanced analytics, they can determine where students struggle and succeed, develop new methods, and test whether these methods yield expected results.

3- Augmented reality/Virtual reality

Augmented reality and virtual reality (AR/VR) technologies can create interactive and virtual environments for students and help them better engage with the subject. These technologies can enable virtual field trips to historical locations or facilitate learning-by-doing for applied sciences and medicine. The distance learning experience can also be improved with AR/VR technologies.

4- Internet of Things (IoT)

The increasing use of smartphones and other edge devices improves the connectivity between students and their educational institutions by enabling real-time communication and data transfer. IoT devices can also be used to track young children’s absence or presence in class and alert teachers and parents for their security.

5- Online learning

Distance learning (or remote learning) through Zoom or Skype was an emergency response from schools and colleges to the pandemic. Educational institutions can also build their own online class systems, commonly called learning management systems (LMS), and integrate them into their websites or platforms. This will allow them to customize the online learning experience according to the needs of learners or the subject of the course.

6- Smart classes

Digital technologies have also improved face-to-face learning. Smart classes equipped with smart boards, computers, internet connections, projectors, etc. unlock the ways of delivering learning resources to students that were impossible with a blackboard and chalks.

What are some case studies?

  • Google Expedition is an education app that contains 1000 VR and 100 AR tours. It helps teachers and students to explore art galleries, museums, underwater, or outer space. Google is now sunsetting the Expedition app and migrating the tours to Google Arts & Culture and making it available to everyone.

  • Arizona State University has leveraged Amazon Echo Dot devices in their campus and student resident halls as voice assistants that provide information about the university for students, faculty, staff, and alumni.
  • EdTech company Carnegie Learning provides technology solutions to K-12 schools. Their math learning platform MATHia uses artificial intelligence to act as a personal tutor that adjusts itself continually to each student and delivers a personalized learning experience.

How can educational institutions transform digitally?

We outlined the steps to achieve digital transformation and AI transformation . These steps are similar across industries. These involve understanding the challenges of your business and buying or building solutions to resolve these challenges. When it comes to building custom solutions, working with agencies that have done it before can help.

For more on digital transformation:

  • Digital Transformation
  • Digital Transformation Statistics
  • Digital Transformation Consulting

You can also check our data-driven, sortable/filterable list of digital transformation consultant companies .

If you have more questions about digital transformation or digital education, let us know:

case study for education industry

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Education case studies, around-the-world case studies on unicef's education programme.

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Case studies

Adolescent education and skills.

Improving the quality of lower secondary through inquiry-based learning and skills development (Argentina)

An online career portal strengthens career guidance among secondary students in India and helps them plan for future educational and work opportunities (India)

Lessons on youth-led action towards climate advocacy and policy (India)

Learning, life skills and citizenship education and social cohesion through game-based sports – Nashatati Programme (Jordan)

Mental health promotion and suicide prevention in schools (Kazakhstan)

A multi-level, cross-sectoral response to improving adolescent mental health (Mongolia)

The Personal Project (Morocco)  

Improving adolescents’ learning in violence-affected areas through blended in-person and online learning opportunities - Communities in Harmony for Children and Adolescents (Mexico)

A community-based approach to support the psychosocial wellbeing of students and teachers (Nicaragua)

Flexible pathways help build the skills and competencies of vulnerable out-of-school adolescents (United Republic of Tanzania)

Climate change and education

Schools as platforms for climate action (Cambodia)

Paving the way for a climate resilient education system (India)

Youth act against climate and air pollution impacts (Mongolia)

Early childhood education

Early environments of care: Strengthening the foundation of children’s development, mental health and wellbeing (Bhutan)

Native language education paves the way for preschool readiness (Bolivia)

Developing cross-sector quality standards for children aged 0-7 (Bulgaria)

Expanding quality early learning through results-based financing (Cambodia)

Harnessing technology to promote communication, education and social inclusion for young children with developmental delays and disabilities (Croatia, Montenegro, and Serbia)

Scaling up quality early childhood education in India by investing in ongoing professional development for officials at the state, district and local levels (India)

Strengthening early childhood education in the national education plan and budget in Lesotho to help children succeed in primary and beyond (Lesotho)

Enhancing play-based learning through supportive supervision (Nigeria)

Learning social and emotional skills in pre-school creates brighter futures for children (North Macedonia)

How developing minimum standards increased access to pre-primary education (Rwanda)

Expanding access to quality early childhood education for the most excluded children (Serbia)

Advancing early learning through results-based financing (Sierra Leone)

Lessons learned from designing social impact bonds to expand preschool education (Uzbekistan)

Equity and inclusion

Inclusive education for children with disabilities.

National early screening and referrals are supporting more young children with disabilities to learn (Jamaica)

Ensuring inclusive education during the pandemic and beyond (Dominican Republic)

Championing inclusive practices for children with disabilities (Ghana)

Accessible digital textbooks for children in Kenya (Kenya)

Planning for inclusion (Nepal)

Harnessing the potential of inclusive digital education to improve learning (Paraguay)

Gender equality in education

Sparking adolescent girls' participation and interest in STEM (Ghana)

Non-formal education and the use of data and evidence help marginalized girls learn in Nepal (Nepal)

Getting girls back to the classroom after COVID-19 school closures (South Sudan)

Education in emergencies

Return to school (Argentina)

Learning from the education sector’s COVID-19 response to prepare for future emergencies (Bangladesh)

Prioritising learning for Rohingya children (Bangladesh)

Prioritizing children and adolescents’ mental health and protection during school reopening (Brazil)

Learning where it is difficult to learn: Radio programmes help keep children learning in Cameroon

Reaching the final mile for all migrant children to access education (Colombia)

Supporting the learning and socio-emotional development of refugee children (Colombia)

Mission Recovery (Democratic Republic of the Congo)

The National Building the Foundations for Learning Program, CON BASE (Dominican Republic)

Mental health and psychosocial well-being services are integrated in the education system (Ecuador)

Improving access to quality education for refugee learners (Ethiopia)

The Learning Passport and non-formal education for vulnerable children and youth (Lebanon)

Accelerated Learning Programme improves children’s learning in humanitarian settings (Mozambique)

Responding to multiple emergencies – building teachers’ capacity to provide mental health and psychosocial support before, during, and after crises (Mozambique)

Teaching at the right level to improve learning in Borno State (Nigeria)

Remedial catch-up learning programmes support children with COVID-19 learning loss and inform the national foundational learning strategy (Rwanda)

Learning solutions for pastoralist and internally displaced children (Somalia)

Recovering learning at all levels (South Africa)

How radio education helped children learn during the COVID-19 pandemic and aftermath (South Sudan)

Addressing learning loss through EiE and remedial education for children in Gaza (State of Palestine)

Providing psychosocial support and promoting learning readiness during compounding crises for adolescents in Gaza (State of Palestine)

Inclusion of South Sudanese refugees into the national education system (Sudan)

Inclusion of Syrian refugee children into the national education system (Turkey)

Including refugee learners so that every child learns (Uganda)

Learning assessments

Assessment for learning (Afghanistan)

Formative assessment places student learning at the heart of teaching (Ethiopia)

Strengthening teacher capacity for formative assessment (Europe and Central Asia)

All students back to learning (India)

Strengthening the national assessment system through the new National Achievement Survey improves assessment of children’s learning outcomes (India)

A new phone-based learning assessment targets young children (Nepal)

Adapting a remote platform in innovative ways to assess learning (Nigeria)

Assessing children's reading in indigenous languages (Peru)

Southeast Asia primary learning metrics: Assessing the learning outcomes of grade 5 students (Southeast Asia)

Minimising learning gaps among early-grade learners (Sri Lanka)

Assessing early learning (West and Central Africa)

Primary education / Foundational Literacy and Numeracy

Improving child and adolescent health and nutrition through policy advocacy (Argentina)

Online diagnostic testing and interactive tutoring (Bulgaria)

Supporting the socio-emotional learning and psychological wellbeing of children through a whole-school approach (China)

Engaging parents to overcome reading poverty (India)

Integrated school health and wellness ensure better learning for students (India)

Instruction tailored to students’ learning levels improves literacy (Indonesia)

A whole-school approach to improve learning, safety and wellbeing (Jamaica)

Multi-sectoral programme to improve the nutrition of school-aged adolescents (Malawi)

Parents on the frontlines of early grade reading and math (Nigeria)

Training, inspiring and motivating early grade teachers to strengthen children’s skills in literacy and numeracy (Sierra Leone) Life skills and citizenship education through Experiential Learning Objects Bank (State of Palestine)

Curriculum reform to meet the individual needs of students (Uzbekistan)

Improving early grade reading and numeracy through ‘Catch-Up,’ a remedial learning programme (Zambia)

Reimagine Education / Digital learning

Education 2.0: skills-based education and digital learning (Egypt)

Empowering adolescents through co-creation of innovative digital solutions (Indonesia)

Virtual instructional leadership course (Jamaica)

Learning Bridges accelerates learning for over 600,000 students (Jordan)

Unleashing the potential of youth through the Youth Learning Passport (Jordan)

Lessons learned from the launch of the Learning Passport Shkollat.org (Kosovo)

Opening up the frontiers of digital learning with the Learning Passport (Lao PDR)

Building teachers’ confidence and capacity to provide online learning (Maldives)

Mauritania’s first digital learning program: Akelius Digital French Course (Mauritania)

Mitigating learning loss and strengthening foundational skills through the Learning Passport (Mexico)

Expanding digital learning opportunities and connectivity for all learners (Tajikistan)

For COVID-19 education case studies, please click here and filter by area of work (Education) and type (Case Study / Field Notes).

Resources for partners

Learning at the heart of education

Key Asks 2021 - National Reviews - SDG 4 Quality Education

More from UNICEF

Transforming education in africa.

An evidence-based overview and recommendations for long-term improvements

Early Childhood Education for All

It is time for a world where all children enter school equipped with the skills they need to succeed.

A world ready to learn

Prioritizing quality early childhood education

Mission: Recovery education in humanitarian countries

Updates on UNICEF’s work to deliver education to children in crisis-affected countries, with support from the US Government

15 EdTech research papers that we share all the time

We hope you saw our recent blog post responding to questions we often get about interesting large-scale EdTech initiatives. Another question we are often asked is: “What EdTech research should I know about?” 

As Sara’s blog post explains, one of the Hub’s core spheres of work is research, so we ourselves are very interested in the answer to this question. Katy’s latest blog post explains how the Hub’s research programme is addressing this question through a literature review to create a foundation for further research.  While the literature review is in progress, we thought we would share an initial list of EdTech papers that we often reach for. At the Hub we are fortunate enough to have authors of several papers on this list as members of our team. 

All papers on this list are linked to a record in the EdTech Hub’s growing document library – where you will find the citation and source to the full text. This library is currently an alpha version. This means it’s the first version of the service and we’re testing how it works for you. If you have any feedback or find any issues with our evidence library, please get in touch.

Tablet use in schools: a critical review of the evidence for learning outcomes

This critical review by our own Bjӧrn Haßler, Sara Hennessy, and Louis Major has been cited over 200 times since it was published in 2016. It examines evidence from 23 studies on tablet use at the primary and secondary school levels. It discusses the fragmented nature of the knowledge base and limited rigorous evidence on tablet use in education. 

Haßler, B., Major, L., & Hennessy, S. (2016) Tablet use in schools: a critical review of the evidence for learning outcomes . Journal of Computer Assisted Learning, 32(2), 139-156.

The impact and reach of MOOCs: a developing countries’ perspective

This article challenges the narrative that Massive Open Online Courses (MOOCs) are a solution to low and middle-income countries’ (LMIC) lack of access to education, examining the features of MOOCs from their perspectives. It argues that a complicated set of conditions, including access, language, and computer literacy, among others, challenge the viability of MOOCs as a solution for populations in LMIC. 

Liyanagunawardena, T., Williams, S., & Adams, A. (2013) The impact and reach of MOOCs: a developing countries’ perspective. eLearning Papers , 33(33).

Technology and education – Why it’s crucial to be critical

A thought-provoking read, Selwyn’s book chapter argues that technology and education should continuously be viewed through a critical lens. It points to how the use of technology in education is entwined with issues of inequality, domination, and exploitation, and offers suggestions for how to grapple with these issues. 

Selwyn, N. (2015) Technology and education – Why it’s crucial to be critical. In S. Bulfin, N. F. Johnson & L. Rowan (Eds.), Critical Perspectives on Technology and Education (pp. 245-255). Basingstoke and St. Martins, New York: Palgrave Macmillan.

Moving beyond the predictable failure of Ed-Tech initiatives

This article argues that a narrow vision of digital technology, which ignores the complexity of education, is becoming an obstacle to improvement and transformation of education. Specifically, the authors critically reflect on common approaches to introducing digital technology in education under the guise of promoting equality and digital inclusion.

Sancho-Gil, J.M., Rivera-Vargas, P. & Miño-Puigcercós, R. (2019) Moving beyond the predictable failure of Ed-Tech initiatives. Learning, Media and Technology , early view. DOI: 10.1080/17439884.2019.1666873

Synergies Between the Principles for Digital Development and Four Case Studies

The REAL Centre’s report, which includes contributions from the Hub’s own ranks, is one of the few we’ve seen that provides an in-depth exploration of how the Principles for Digital Development apply to the education sector. It uses four case studies on the work of the Aga Khan Foundation, Camfed, the Punjab Education and Technology Board, and the Varkey Foundation. 

REAL Centre (2018). Synergies Between the Principles for Digital Development and Four Case Studies. Cambridge, UK: Research for Equitable Access and Learning (REAL) Centre, Faculty of Education, University of Cambridge .

Education technology map: guidance document

This report by the Hub’s Jigsaw colleagues accompanies a comprehensive map of 401 resources with evidence on the use of EdTech in low-resource environments. The evidence mapping reviews certain criteria of the resources from sources such as journal indices, online research, evaluation repositories, and resource centres and experts. The type of criteria it maps include: the geographical location of study, outcomes studied, and type of EdTech introduced.  While not inclusive of the latest EdTech research and evidence (from 2016 to the present), this mapping represents a strong starting point to understand what we know about EdTech as well as the characteristics of existing evidence.

Muyoya, C., Brugha, M., Hollow, D. (2016). Education technology map: guidance document. Jigsaw, United Kingdom.

Scaling Access & Impact: Realizing the Power of EdTech

Commissioned by Omidyar Network and written by RTI, this executive summary (with the full report expected soon) is a useful examination of the factors needed to enable, scale, and sustain equitable EdTech on a national basis. Four country reports on Chile, China, Indonesia, and the United States examine at-scale access and use of EdTech across a broad spectrum of students. It also provides a framework for an ecosystem that will allow EdTech to be equitable and able to be scaled.  

S caling Access & Impact: Realizing the Power of EdTech (Executive Summary). Omidyar Network.

Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development

If you are interested in how technology can be used in the classroom and to support teacher professional development, this report by the Hub’s Björn Haßler and members of the Faculty of Education at the University of Cambridge emphasizes the key point that technology should be seen as complementary to, rather than as a replacement for, teachers. As the authors put it, “the teacher and teacher education are central for the successful integration of digital technology into the classroom.” The report is also accompanied by a toolkit (linked below) with questions that can be used to interrogate EdTech interventions.

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626440

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). A short guide on the use of technology in learning: Perspectives and Toolkit for Discussion . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626660

Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa

In this paper, the Hub’s Sara Hennessy and co-authors synthesise literature on teachers’ use of ICT, with a focus on using ICT to improve the quality of teaching and learning. They find evidence to support the integration of ICT into subject learning, instead of treating it as a discrete subject, and to provide relevant preparation to teachers during pre- and in-service training to use ICT in classrooms. Although this evidence has been available for a decade, the implications of the paper’s findings are still not often reflected in practice.  

Hennessy, S., Harrison, D., & Wamakote, L. (2010). Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa. Itupale Online Journal of African Studies, 2, 39- 54.

Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations

This landscape review by Burns and co-authors offers a useful descriptive starting point for understanding technology use in sub-Saharan Africa in secondary education, including the policy environment, key actors, promising practices, challenges, trends, and opportunities. The report includes four case studies on South Africa, Mauritius, Botswana, and Cape Verde. 

Burns, M., Santally, M. I., Halkhoree, R., Sungkur, K. R., Juggurnath, B., Rajabalee, Y. B. (2019) Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations. Mastercard Foundation.

The influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya

This study examines the impact of training teachers to use ICT, on the success of NEPAD’S e-Schools. The e-Schools objectives were to impart ICT skills to students, enhance teachers’ capacities through the use of ICT in teaching, improve school management and increase access to education. Unlike other studies on the subject, Nyawoga, Ocholla, and Mutula crucially recognise that while teachers received technical ICT training, they did not receive training on pedagogies for integrating ICT in teaching and learning. 

Nyagowa, H. O., Ocholla, D. N., & Mutula, S. M. (2014). T he influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya . Information Development, 30(3), 235-246 .

Education in Conflict and Crisis: How Can Technology Make a Difference?

This landscape review identifies ICT projects supporting education in conflict and crisis settings. It finds that most of the projects operate in post-conflict settings and focus on the long-term development of such places. The report hones in on major thematic areas of professional development and student learning. It also presents directions for further research, including considerations of conflict sensitivity and inclusion in the use of ICT. 

Dahya, N. (2016) Education in Conflict and Crisis: How Can Technology Make a Difference? A Landscape Review . GIZ.

Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya

This randomized controlled trial contributes to the limited evidence base on the effects of different types of ICT investments on learning outcomes. All groups participated in the ‘base’ initiative which focused on training teachers and headteachers in literacy and numeracy, books for every student, teacher guides that matched closely with the content of the students’ book, and modest ICT intervention with tablets provided only for government-funded instructional supervisors. The RCT then compared outcomes from three interventions:  (1) base program plus e-readers for students, (2) base program plus tablets for teachers, and (3) the control group who were treated only with the base program. The paper finds that the classroom-level ICT investments do not improve literacy outcomes significantly more than the base program alone, and that cost considerations are crucial in selecting ICT investments in education.

Piper, B., Zuilkowski, S., Kwayumba, D., & Strigel, C. (2016). Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya. International Journal of Educational Development (49), 204-214.

[FORTHCOMING] Technology in education in low-income countries: Problem analysis and focus of the EdTech Hub’s work

Informed by the research cited in this list (and much more) – the Hub will soon publish a problem analysis. It will define our focus and the scope of our work. To give a taste of what is to come, the problem analysis will explain why we will prioritise teachers, marginalised groups, and use a systems lens. It will also explore emergent challenges in EdTech research, design, and implementation.

EdTech Hub. (2020). Technology in education in low-income countries: Problem analysis and focus of the Hub’s work (EdTech Hub Working Paper No. 5). London, UK. https://doi.org/10.5281/zenodo.3377829

It is important to note that we have included a mix of research types at varying levels of rigour, from landscape reviews and evidence maps, to critical reviews and case studies. Our list is not comprehensive and has some obvious limitations (they are all in English, for one). If you are interested in exploring more papers and evidence, don’t forget to check out the EdTech Hub’s growing document library , where you will find not just links to the full papers in this list but over 200 resources, with more being added each day.

What interesting EdTech research have you recently read, and what did you take away from it? Let us know in the comments section or on Twitter at @GlobalEdTechHub and use #EdTechHub

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Case Study in Education Research by Lorna Hamilton LAST REVIEWED: 21 April 2021 LAST MODIFIED: 27 June 2018 DOI: 10.1093/obo/9780199756810-0201

It is important to distinguish between case study as a teaching methodology and case study as an approach, genre, or method in educational research. The use of case study as teaching method highlights the ways in which the essential qualities of the case—richness of real-world data and lived experiences—can help learners gain insights into a different world and can bring learning to life. The use of case study in this way has been around for about a hundred years or more. Case study use in educational research, meanwhile, emerged particularly strongly in the 1970s and 1980s in the United Kingdom and the United States as a means of harnessing the richness and depth of understanding of individuals, groups, and institutions; their beliefs and perceptions; their interactions; and their challenges and issues. Writers, such as Lawrence Stenhouse, advocated the use of case study as a form that teacher-researchers could use as they focused on the richness and intensity of their own practices. In addition, academic writers and postgraduate students embraced case study as a means of providing structure and depth to educational projects. However, as educational research has developed, so has debate on the quality and usefulness of case study as well as the problems surrounding the lack of generalizability when dealing with single or even multiple cases. The question of how to define and support case study work has formed the basis for innumerable books and discursive articles, starting with Robert Yin’s original book on case study ( Yin 1984 , cited under General Overview and Foundational Texts of the Late 20th Century ) to the myriad authors who attempt to bring something new to the realm of case study in educational research in the 21st century.

This section briefly considers the ways in which case study research has developed over the last forty to fifty years in educational research usage and reflects on whether the field has finally come of age, respected by creators and consumers of research. Case study has its roots in anthropological studies in which a strong ethnographic approach to the study of peoples and culture encouraged researchers to identify and investigate key individuals and groups by trying to understand the lived world of such people from their points of view. Although ethnography has emphasized the role of researcher as immersive and engaged with the lived world of participants via participant observation, evolving approaches to case study in education has been about the richness and depth of understanding that can be gained through involvement in the case by drawing on diverse perspectives and diverse forms of data collection. Embracing case study as a means of entering these lived worlds in educational research projects, was encouraged in the 1970s and 1980s by researchers, such as Lawrence Stenhouse, who provided a helpful impetus for case study work in education ( Stenhouse 1980 ). Stenhouse wrestled with the use of case study as ethnography because ethnographers traditionally had been unfamiliar with the peoples they were investigating, whereas educational researchers often worked in situations that were inherently familiar. Stenhouse also emphasized the need for evidence of rigorous processes and decisions in order to encourage robust practice and accountability to the wider field by allowing others to judge the quality of work through transparency of processes. Yin 1984 , the first book focused wholly on case study in research, gave a brief and basic outline of case study and associated practices. Various authors followed this approach, striving to engage more deeply in the significance of case study in the social sciences. Key among these are Merriam 1988 and Stake 1995 , along with Yin 1984 , who established powerful groundings for case study work. Additionally, evidence of the increasing popularity of case study can be found in a broad range of generic research methods texts, but these often do not have much scope for the extensive discussion of case study found in case study–specific books. Yin’s books and numerous editions provide a developing or evolving notion of case study with more detailed accounts of the possible purposes of case study, followed by Merriam 1988 and Stake 1995 who wrestled with alternative ways of looking at purposes and the positioning of case study within potential disciplinary modes. The authors referenced in this section are often characterized as the foundational authors on this subject and may have published various editions of their work, cited elsewhere in this article, based on their shifting ideas or emphases.

Merriam, S. B. 1988. Case study research in education: A qualitative approach . San Francisco: Jossey-Bass.

This is Merriam’s initial text on case study and is eminently accessible. The author establishes and reinforces various key features of case study; demonstrates support for positioning the case within a subject domain, e.g., psychology, sociology, etc.; and further shapes the case according to its purpose or intent.

Stake, R. E. 1995. The art of case study research . Thousand Oaks, CA: SAGE.

Stake is a very readable author, accessible and yet engaging with complex topics. The author establishes his key forms of case study: intrinsic, instrumental, and collective. Stake brings the reader through the process of conceptualizing the case, carrying it out, and analyzing the data. The author uses authentic examples to help readers understand and appreciate the nuances of an interpretive approach to case study.

Stenhouse, L. 1980. The study of samples and the study of cases. British Educational Research Journal 6:1–6.

DOI: 10.1080/0141192800060101

A key article in which Stenhouse sets out his stand on case study work. Those interested in the evolution of case study use in educational research should consider this article and the insights given.

Yin, R. K. 1984. Case Study Research: Design and Methods . Beverley Hills, CA: SAGE.

This preliminary text from Yin was very basic. However, it may be of interest in comparison with later books because Yin shows the ways in which case study as an approach or method in research has evolved in relation to detailed discussions of purpose, as well as the practicalities of working through the research process.

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Hertz CEO Kathryn Marinello with CFO Jamere Jackson and other members of the executive team in 2017

Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

  • Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
  • Fifty four percent of raw case users came from outside the U.S..
  • The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
  • Twenty-six of the cases in the list are raw cases.
  • A third of the cases feature a woman protagonist.
  • Orders for Yale SOM case studies increased by almost 50% compared to 2020.
  • The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

  • AI Use Cases
  • Current post

5 main use cases of AI in education

Irina Kolesnikova July 31st, 2022

  • Introduction

1. Personalized learning

  • 2. Task automation

3. Smart content creation

4. 24/7 educational and administrative assistance, 5. inclusive and universal access to education.

Educational leaders, thinking of efficiency growth, may find that artificial intelligence (AI) could provide a personalized approach, and happier students, thus gaining more traction and better results. The pandemic proved that idea right and brought AI in education as well as in many other fields to bloom.

As everything was remote, it appeared that although teachers’ presence was vital, technology would change how they do their jobs for the better. AI in education is not just enhancing the teachers’ jobs but also revolutionizing the way students learn. But don’t picture yourself in the rise of the machines: AI systems usually take care of the repetitive and time-consuming tasks, leaving the most creative (and complex) challenges for humans.

Personalized learning, tailored for specific students’ interests, abilities, knowledge levels, and talents, provides deeper involvement. Thus helping students stay motivated and be more successful and empowering their voices and choices. That is why personalized learning is one of the biggest trends in education.

With artificial intelligence, students now have a personalized approach to learning programs based on their unique experiences and preferences. Moreover, students with special needs can get more with personalized AI-based recommendations. AI is giving them more attention than teachers can provide, which is a win-win scenario. Personalized use of AI is probably one of the most significant achievements in education as learning is more comfortable, smoother, and cut across personal knowledge.

An example of AI-driven personalized learning paths: the National Project of Estonia.

This project aims to implement AI-driven solutions to personalize learning paths by using their and other students’ data points created throughout their studies.

AI can adapt to each student’s level of knowledge, preferred style of learning, speed of learning, and desired goals, so they’re getting the most out of their education.

Moreover, AI-powered solutions can analyze students’ previous learning backgrounds, identify weaknesses, and offer courses best suited for improvement, providing many opportunities for a personalized learning experience.

Teaching the teacher

Just as AI can plan students’ lessons and personalize their learning courses, it can also recommend improvements to teachers’ plans. Through this, teachers can expand their view on their subject using the best material from other tutors without additional effort. In addition, each educator can also contribute with their best materials and provide unique expert knowledge to the network to help students understand the subject better.

For course updates or development, artificial intelligence can provide teachers with a detailed picture of subjects and learning materials to be reviewed. Thus, teachers and professors can improve their courses to address the most common knowledge gaps or challenge areas before someone falls too far behind.

Identify classroom weakness

As already mentioned, AI in education is not meant to replace teachers. Instead, it’s meant to add some superpowers.

Artificial intelligence can assist the teacher’s work in a classroom to identify some weaknesses or gaps. For example, artificial intelligence can detect when some students miss specific questions or topics. The teachers will be notified; thus, they will know which of their students need explanations and the subject of such explanations and can give them in a more personalized manner.

Such predictive systems, also known as early warning systems , can identify if and when students are in danger of not reaching their goals. For that, nearly 50% of the public high schools in the US and 90 percent of colleges use an early warning system to track student grades, attendance, and other factors. Moreover, these AI systems are reliable as they use precise performance data — such as attendance — to predict students’ progress in studies, allowing teachers to intervene early.

Finally, there are voice-based AI solutions or, in other words, speech recognition algorithms. For example, these systems are the backbone of tools such as Siri or Alexa. Those algorithms have recently been explored as ways to diagnose reading issues. Besides, NLP technologies can also grade student reading scores more accurately than traditional formulas do, such as the Flesch-Kincaid Grade Level test.

2. Task automation in education

The Telegraph survey claims that teachers spend up to 31% of their time planning lessons, grading tests, and doing administrative work. These iterative tasks could be delegated to artificial intelligence: using supportive automation tools, teachers can streamline manual processes, receive more time for personalized focus, and use AI-recommended materials to learn the core competencies.

Administrative and organizational tasks automation

AI in the education sector can ease the professors and teachers‘ most common duties, which are managing the classroom environment as well as numerous organizational and administrative tasks, such as filling out the necessary paperwork, making a student progress report, and organizing resources and materials for lectures, etc. If done manually, they require much work.

Luckily, the processes can be automated or at least semi-automated using natural language processing solutions . Artificial intelligence, in those cases, could go through the fields of a report, partially or fully prefilling them. As artificial intelligence automates those tedious tasks, it provides the teachers with the possibility to spend more time with each student.

Automated test grading and homework evaluating

Old news: an educator spends considerable time grading homework and tests. New world: AI steps in and speeds up the grading process.

Although machines can already grade multiple-choice tests, they are also ready to assess free text written responses. For instance, they are drawing the teacher’s attention to specific places of an essay while at the same time offering recommendations for how to close the gaps in learning. There is much potential for AI to create more efficient enrollment and admissions processes.

For example, admission offices of higher education institutions applying artificial intelligence can reduce biases in testing systems and provide educational institutions with diversity. Moreover, with AI systems, the admissions process can become faster and more personalized. Colleges and universities using artificial intelligence for administrative activities can provide customizable experiences for students during the admissions process, including visa processing, student housing selection, and course registration.

One subfield revolves around the technology already mentioned above: natural language processing. This technology is capable of processing and grading written essays. For example, students can use spell-checking or plagiarism detection apps to improve their writing before it is submitted for the assessment.

Usually, those apps provide actionable advice on how to improve the text. Moreover, artificial intelligence can not only look for the exact word-to-word content but also can assist you in finding content that is paraphrased or re-worded or lacks the arguments to cover your point of view. Popular online educational platforms allowing unlimited participation, such as Coursera and Udacity, have also integrated AI scoring to analyze essays within their courses, thus improving student performance. A vision-based artificial intelligence called AI image recognition is also an important field that can help with assessment. For example, instead of an educator manually grading a math equation a student wrote, the student can snap a picture of the equation and send it to the teacher. Or the teacher could do the same snap and then let a machine grade it. Example:

AI in education can assist with the content that will make teaching and learning more comfortable. Starting with question-answer pairs that could be used for texting or training, and ending with complex study environments.

Example: Duolingo

Digital lessons

AI can analyze a big topic and then divide it into smaller, easy-to-grasp parts; make study guides or digital textbooks from those pieces, all within the framework of digital learning. Exercises as a part of these digital lessons could also be produced with the help of AI: computer vision can be used to take the necessary parts from learning materials, and natural language processing algorithms afterward can be trained to create different types of learning materials: fill-the-gap questions, flashcards with definitions, multiple and single choice questions.

Information visualization

To perceive information, students can lean on AI-powered simulations, visualizations, and study environments. Moreover, those educational environments are integrated with AI-based stealth assessment. It means that the process of assessments is integrated seamlessly into the environment so that the student is unaware of being assessed.

It is usual for students to look for extra help after class hours. However, teachers don’t have the time for all their students.

If you haven’t met the “teen and algebra” situation yet, you could ask any parents who have struggled with that. Probably, they will be the most excited about the potential of AI to support their children when they are fighting algebra home tasks.

For example, a virtual learning assistant or a chatbot could help. Although chatbots cannot replace human educators, AI tools can still provide feedback on students’ tasks, thus improving their skills during after-school instruction.

It can be a personalized one-on-one learning experience with the answers to urgent or not-so-clear questions at all hours of the day. At the same time, an educator can spend precious time on other tasks.

AI-powered chatbots can swiftly assist student learning by providing answers to their most frequently asked questions. Conversational artificial intelligence (the one that mimics human interaction) can bring a win-win result: time-saving for tutors answering questions, as well as for students reducing a waiting time for a response to their questions or searching the answer through the learning materials.

Several AI-powered chatbots were specifically built for the education sector. They support students round the clock by answering their queries, and thanks to machine learning getting more and more effective over time. In addition, chatbots provide accessibility for all students, anytime and anywhere, allowing each student to learn at their own pace, providing focused, result-oriented and personalized learning.

AI in education opens up new possibilities for students who need to learn at different levels, who want to learn a subject unavailable in their school, or who are unable to attend school.

Moreover, educational classrooms can become globally available to all students through AI tools, even to those who have visual or hearing impairments or speak different languages. For example, with a PowerPoint plugin such as Presentation Translator , students have real-time subtitles for everything the lector says.

Accessibility checkers help educators increase access for low-vision students . Artificial intelligence and computer vision machine learning can define what is in an image and generate a caption. Then, using natural language generation, the technology allows machines to speak human language, describing the picture. Such machine learning technology saves much time with auto-captioning on images, as well as being much more likely to make the content accessible.

Well-designed artificial intelligence in education allows both teachers and students to take advantage of the technology that can facilitate and raise the quality of educational processes. Artificial intelligence in education can provide a wide variety of ways to use this technology, creating engaging tools, such as intelligent tutoring, stealth assessments, games, and even virtual reality. All these AI-powered tools can help tutors to guide students to better knowledge and academic results, developing their critical thinking skills.

However, the basis of the educational system is always good teaching and human interactions: AI-powered education tools and bots provide (and will hopefully increase the helping capacities) support with routine and repetitive tasks, letting teachers spend their time on creative and more important things.

ai plan execution

  • Review article
  • Open access
  • Published: 02 November 2020

Big data in education: a state of the art, limitations, and future research directions

  • Maria Ijaz Baig 1 ,
  • Liyana Shuib   ORCID: orcid.org/0000-0002-7907-0671 1 &
  • Elaheh Yadegaridehkordi 1  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  44 ( 2020 ) Cite this article

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Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studies covered four main research themes under big data in education, mainly, learner’s behavior and performance, modelling and educational data warehouse, improvement in the educational system, and integration of big data into the curriculum. Most of the big data educational researches have focused on learner’s behavior and performances. Moreover, this study highlights research limitations and portrays the future directions. This study provides a guideline for future studies and highlights new insights and directions for the successful utilization of big data in education.


The world is changing rapidly due to the emergence of innovational technologies (Chae, 2019 ). Currently, a large number of technological devices are used by individuals (Shorfuzzaman, Hossain, Nazir, Muhammad, & Alamri, 2019 ). In every single moment, an enormous amount of data is produced through these devices (ur Rehman et al., 2019 ). In order to cater for this massive data, current technologies and applications are being developed. These technologies and applications are useful for data analysis and storage (Kalaian, Kasim, & Kasim, 2019 ). Now, big data has become a matter of interest for researchers (Anshari, Alas, & Yunus, 2019 ). Researchers are trying to define and characterize big data in different ways (Mikalef, Pappas, Krogstie, & Giannakos, 2018 ).

According to Yassine, Singh, Hossain, and Muhammad ( 2019 ), big data is a large volume of data. However, De Mauro, Greco, and Grimaldi ( 2016 ) referred to it as an informational asset that is characterized by high quantity, speed, and diversity. Moreover, Shahat ( 2019 ) described big data as large data sets that are difficult to process, control or examine in a traditional way. Big data is generally characterized into 3 Vs which are Volume, Variety, and Velocity (Xu & Duan, 2019 ). The volume refers to as a large amount of data or increasing scale of data. The size of big data can be measured in terabytes and petabytes (Herschel & Miori, 2017 ). In order to cater for the large volume of data, high capacity storage systems are required. The variety refers to as a type or heterogeneity of data. The data can be in a structured format (databases) or unstructured format (images, video, emails). Big data analytical tools are helpful in handling unstructured data. Velocity refers to as the speed at which big data can access. The data is virtually present in a real-time environment (Internet logs) (Sivarajah, Kamal, Irani, & Weerakkody, 2017 ).

Currently, the concept of 3 V’s is inflated into several V’s. For instance, Demchenko, Grosso, De Laat, and Membrey ( 2013 ) classified big data into 5vs, which are Volume, Velocity, Variety, Veracity, and Value. Similarly, Saggi and Jain ( 2018 ) characterized big data into 7 V’s namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value.

Big data demand is significantly increasing in different fields of endeavour such as insurance and construction (Dresner Advisory Services, 2017 ), healthcare (Wang, Kung, & Byrd, 2018 ), telecommunication (Ahmed et al., 2018 ), and e-commerce (Wu & Lin, 2018 ). According to Dresner Advisory Services ( 2017 ), technology (14%), financial services (10%), consulting (9%), healthcare (9%), education (8%) and telecommunication (7%) are the most active sectors in producing a vast amount of data.

However, the educational sector is not an exception in this situation. In the educational realm, a large volume of data is produced through online courses, teaching and learning activities (Oi, Yamada, Okubo, Shimada, & Ogata, 2017 ). With the advent of big data, now teachers can access student’s academic performance, learning patterns and provide instant feedback (Black & Wiliam, 2018 ). The timely and constructive feedback motivates and satisfies the students, which gives a positive impact on their performance (Zheng & Bender, 2019 ). Academic data can help teachers to analyze their teaching pedagogy and affect changes according to students’ needs and requirement. Many online educational sites have been designed, and multiple courses based on individual student preferences have been introduced (Holland, 2019 ). The improvement in the educational sector depends upon acquisition and technology. The large-scale administrative data can play a tremendous role in managing various educational problems (Sorensen, 2018 ). Therefore, it is essential for professionals to understand the effectiveness of big data in education in order to minimize educational issues.

So far, several review studies have been conducted in the big data realm. Mikalef et al. ( 2018 ) conducted a systematic literature review study that focused on big data analytics capabilities in the firm. Mohammad & Torabi ( 2018 ), in their review study on big data, observed the emerging trends of big data in the oil and gas industry. Furthermore, another systematic literature review was conducted by Neilson, Daniel, and Tjandra ( 2019 ) on big data in the transportation system. Kamilaris, Kartakoullis, and Prenafeta-Boldú ( 2017 ), conducted a review study on the use of big data in agriculture. Similarly, Wolfert, Ge, Verdouw, and Bogaardt ( 2017 ) conducted a review study on the use of big data in smart farming. Moreover, Camargo Fiorini, Seles, Jabbour, Mariano, and Sousa Jabbour ( 2018 ) conducted a review study on big data and management theory. Even though that many fields have been covered in the previous review studies, yet, a comprehensive review of big data in the education sector is still lacking today. Thus, this study aims to conduct a systematic review of big data in education in order to identify the primary studies, their trends & themes, as well as limitations and possible future directions. This research can play a significant role in the advancement of big data in the educational domain. The identified limitations and future directions will be helpful to the new researchers to bring encroachment in this particular realm.

The research questions of this study are stated below:

What are the trends in the papers published on big data in education?

What research themes have been addressed in big data in education domain?

What are the limitations and possible future directions?

The remainder of this study is organized as follows: Section 2 explains the review methodology and exposes the SLR results; Section 3 reports the findings of research questions; and finally, Section 4 presents the discussion and conclusion and research implications.

Review methodology

In order to achieve the aforementioned objective, this study employs a systematic literature review method. An effective review is based on analysis of literature, find the limitations and research gap in a particular area. A systematic review can be defined as a process of analyzing, accessing and understanding the method. It explains the relevant research questions and area of research. The essential purpose of conducting the systematic review is to explore and conceptualize the extant studies, identification of the themes, relations & gaps, and the description of the future directions accordingly. Thus, the identified reasons are matched with the aim of this study. This research applies the Kitchenham and Charters ( 2007 ) strategies. A systematic review comprised of three phases: Organizing the review, managing the review, and reporting the review. Each phase has specific activities. These activities are: 1) Develop review protocol 2) Formulate inclusion and exclusion criteria 3) Describe the search strategy process 4) Define the selection process 5) Perform the quality evaluation procedure and 6) Data extraction and synthesis. The description of each activity is provided in the following sections.

Review protocol

The review protocol provides the foundation and mechanism to undertake a systematic literature review. The essential purpose of the review protocol is to minimize the research bias. The review protocol comprised of background, research questions, search strategy, selection process, quality assessment, and extraction of data and synthesis. The review protocol helps to maintain the consistency of review and easy update at a later stage when new findings are incorporated. This is the most significant aspect that discriminates SLR from other literature reviews.

Inclusion and exclusion criteria

The aim of defining the inclusion and exclusion criteria is to be rest assured that only highly relevant researches are included in this study. This study considers the published articles in journals, workshops, conferences, and symposium. The articles that consist of introductions, tutorials and posters and summaries were eliminated. However, complete and full-length relevant studies published in the English language between January 2014 to 2019 March were considered for the study. The searched words should be present in title, abstract, or in the keywords section.

Table  1 shows a summary of the inclusion and exclusion criteria.

Search strategy process

The search strategy comprised of two stages, namely S1 (automatic stage) and S2 (manual stage). Initially, an automatic search (S1) process was applied to identify the primary studies of big data in education. The following databases and search engines were explored: Science Direct, SAGE.

Journals, Emerald Insight, Springer Link, IEEE Xplore, ACM Digital Library, Taylor and Francis and AIS e-Library. These databases were considered as it possessed highest impact journals and germane conference proceedings, workshops and symposium. According to Kitchenham and Charters ( 2007 ), electronic databases provide a broad perspective on a subject rather than a limited set of specific journals and conferences. In order to find the relevant articles, keywords on big data and education were searched to obtain relatable results. The general words correlated to education were also explored (education OR academic OR university OR learning.

OR curriculum OR higher education OR school). This search string was paired with big data. The second stage is a manual search stage (S2). In this stage, a manual search was performed on the references of all initial searched studies. Kitchenham ( 2004 ) suggested that manual search should be applied to the primary study references. However, EndNote was used to manage, sort and remove the replicate studies easily.

Selection process

The selection process is used to identify the researches that are relevant to the research questions of this review study. The selection process of this study is presented in Fig.  1 . By applying the string of keywords, a total number of 559 studies were found through automatic search. However, 348 studies are replica studies and were removed using the EndNote library. The inclusion and exclusion criteria were applied to the remaining 211 studies. According to Kitchenham and Charters ( 2007 ), recommendation and irrelevant studies should be excluded from the review subject. At this phase, 147 studies were excluded as full-length articles were not available to download. Thus, 64 full-length articles were present to download and were downloaded. To ensure the comprehensiveness of the initial search results, the snowball technique was used. In the second stage, manual search (S2) was performed on the references of all the relevant papers through Google Scholar (Fig. 1 ). A total of 1 study was found through Google Scholar search. The quality assessment criteria were applied to 65 studies. However, 25 studies were excluded, as these studies did not fulfil the quality assessment criteria. Therefore, a total of 40 highly relevant primary studies were included in this research. The selection of studies from different databases and sources before and after results retrieval is shown in Table  2 . It has been found that majority of research studies were present in Science Direct (90), SAGE Journals (50), Emerald Insight (81), Springer Link (38), IEEE Xplore (158), ACM Digital Library (73), Taylor and Francis (17) and AIS e-Library (52). Google Scholar was employed only for the second round of manual search.

figure 1

Selection Process

Quality assessment

According to (Kitchenham & Charters, 2007 ), quality assessment plays a significant role in order to check the quality of primary researches. The subtleties of assessment are totally dependent on the quality of the instruments. This assessment mechanism can be based on the checklist of components or a set of questions. The primary purpose of the checklist of components and a set of questions is to analyze the quality of every study. Nonetheless, for this study, four quality measurements standard was created to evaluate the quality of each research. The measurement standards are given as:

QA1. Does the topic address in the study related to big data in education?

QA2. Does the study describe the context?

QA3. Does the research method given in the paper?

QA4. Does data collection portray in the article?

The four quality assessment standards were applied to 65 selected studies to determine the integrity of each research. The measurement standards were categorized into low, medium and high. The quality of each study depends on the total number of score. Each quality assessment has two-point scores. If the study meets the full standard, a score of 2 is awarded. In the case of partial fulfillment, a score of 1 is acquired. If none of the assessment standards is met, then a score of 0 is awarded. In the total score, if the study gets below 4, it is counted as ‘low’ and exact 4 considered as ‘medium’. However, the above 4 is reflected as ‘high’. The details of studies are presented in Table 11 in Appendix B . The 25 studies were excluded as it did not meet the quality assessment standard. Therefore, based on the quality assessment standard, a total of 40 primary studies were included in this systemic literature review (Table 10 in Appendix A ). The scores of the studies (in terms of low, medium and high) are presented in Fig.  2 .

figure 2

Scores of studies

Data extraction and synthesis

The data extraction and synthesis process were carried by reading the 65 primary studies. The studies were thoroughly studied, and the required details extracted accordingly. The objective of this stage is to find out the needed facts and figure from primary studies. The data was collected through the aspects of research ID, names of author, the title of the research, its publishing year and place, research themes, research context, research method, and data collection method. Data were extracted from 65 studies by using this aspect. The narration of each item is given in Table  3 . The data extracted from all primary studies are tabulated. The process of data synthesizing is presented in the next section.

Figure  3 presented the allocation of studies based on their publication sources. All publications were from high impact journals, high-level conferences, and workshops. The primary studies are comprised of 21 journals, 17 conferences, 1 workshop, and 1 symposium. However, 14 studies were from Science Direct journals and conferences. A total of 5 primary studies were from the SAGE group, 1 primary study from SpringerLink. Whereas 6 studies were from IEEE conferences, 2 studies were from IEEE symposium and workshop. Moreover, 1 primary study from AISeL Conference. Hence, 4 studies were from Emraldinsight journals, 5 studies were from ACM conferences and 2 studies were from Taylor and Francis. The summary of published sources is given in Table  4 .

figure 3

Allocation of studies based on publication

Temporal view of researches

The selection period of this study is from January 2014–March 2019. The yearly allocation of primary studies is presented in Fig.  4 . The big data in education trend started in the year 2014. This trend gradually gained popularity. In 2015, 8 studies were published in this domain. It has been found that a number of studies rise in the year 2017. Thus, the highest number of publication in big data in the education realm was observed in the year 2017. In 2017, 12 studies were published. This trend continued in 2018, and in that year, 11 studies that belong to big data in education were published. In 2019, the trend of this domain is still continued as this paper covers that period of March 2019. Thus, 4 studies were published until March 2019.

figure 4

Temporal view of Papers

In order to find the total citation count for the studies, Google Scholar was used. The number of citation is shown in Fig.  5 . It has been observed that 28 studies were cited by other sources 1–50 times. However, 11 studies were not cited by any other source. Thus, 1 study was cited by other sources 127 times. The top cited studies with their titles are presented in Table  5 , which provides general verification. The data provided here is not for comparison purpose among the studies.

figure 5

Research methodologies

The research methods employed by primary studies are shown in Fig.  6 . It has been found that majority of them are review based studies. These reviews were conducted in a different educational context and big data. However, reviews covered 28% of primary studies. The second most used research method was quantitative. This method covered 23% of the total primary studies. Only 3% of the study was based on a mix method approach. Moreover, design science method also covered 3% of primary studies. Nevertheless, 20% of the studies used qualitative research method, whereas the remaining 25% of the studies were not discussed and given in the articles.

figure 6

Distribution of Research Methods of Primary Studies

Data collection methods

The data collection methods used by primary studies are shown in Fig.  7 . The primary studies employed different data collection methods. However, the majority of studies used extant literature. The 5 types of research conducted surveys which covered 13% of primary Studies. The 4 studies carried experiments for data collection, which covered 10% of primary studies. Nevertheless, 6 studies conducted interviews for data collection, which is based on 15% of primary studies. The 4 studies used data logs which are based on 10% of primary studies. The 2 studies collected data through observations, 1 study used social network data, and 3 studies used website data. The observational, social network data and website-based researches covered 5%, 3% and 8% of primary studies. Moreover, 11 studies used extant literature and 1 study extracted data from a focus group discussion. The extant literature and focus group-based studies covered 28% and 3% of primary studies. However, the data collection method is not available for the remaining 3 studies.

figure 7

Distribution of Data Collection Methods of Primary Studies

What research themes have been addressed in educational studies of big data?

The theme refers to an idea, topic or an area covered by different research studies. The central idea reflects the theme that can be helpful in developing real insight and analysis. A theme can be in single or combination of more words (Rimmon-Kenan, 1995 ). This study classified big data research themes into four groups (Table  6 ). Thus, Fig.  8 shows a mind map of big data in education research themes, sub-themes, and the methodologies.

figure 8

Mind Map of big data in education research themes, sub-themes, and the methodologies

Figure  9 presents, research themes under big data in education, namely learner’s behavior and performance, modelling, and educational data warehouse, improvement of the educational system, and integration of big data into the curriculum.

figure 9

Research Themes

The first research theme was based on the leaner’s behavior and performance. This theme covers 21 studies, which consists of 53% of overall primary studies (Fig.  9 ). The theme studies are based on teaching and learning analytics, big data frameworks, user behaviour, and attitude, learner’s strategies, adaptive learning, and satisfaction. The total number of 8 studies relies on teaching and learning analytics (Table  7 ). Three (3) studies deal with big data framework. However, 6 studies concentrated on user behaviour and attitude. Nevertheless, 2 studies dwell on learning strategies. The adaptive learning and satisfaction covered 1 study, respectively. In this theme, 2 studies conducted surveys, 4 studies carried out experiments and 1 study employed the observational method. The 5 studies reported extant literature. In addition, 4 studies used event log data and 5 conducted interviews (Fig.  10 ).

figure 10

Number of Studies and Data Collection Methods

In the second theme, studies conducted focused on modeling and educational data warehouses. In this theme, 6 studies covered 15% of primary studies. This theme studies investigated the cloud environment, big data modeling, cluster analysis, and data warehouse for educational purpose (Table  8 ). Three (3) studies introduced big data modeling in education and highlighted the potential for organizing data from multiple sources. However, 1 study analyzed data warehouse with big data tools (Hadoop). Moreover, 1 study analyzed the accessibility of huge academic data in a cloud computing environment whereas, 1 study used clustering techniques and data warehouse for educational purpose. In this theme, 4 studies reported extant review, 1 study conduct survey, and 1 study used social network data.

The third theme concentrated on the improvement of the educational system. In this theme, 9 studies covered 23% of the primary studies. They consist of statistical tools and measurements, educational research implications, big data training, the introduction of the ranking system, usage of websites, big data educational challenges and effectiveness (Table  9 ). Two (2) studies considered statistical tools and measurements. Educational research implications, ranking system, usage of websites, and big data training covered 1 study respectively. However, 3 studies considered big data effectiveness and challenges. In this theme, 1 study conducted a survey for data collection, 2 studies used website traffic data, and 1 study exploited the observational method. However, 3 studies reported extant literature.

The fourth theme concentrated on incorporating the big data approaches into the curriculum. In this theme, 4 studies covered 10% of the primary studies. These 4 studies considered the introduction of big data topics into different courses. However, 1 study conducted interviews, 1 study employed survey method and 1 study used focus group discussion.

The 20% of the studies (Fig. 6 ) used qualitative research methods (Dinter et al., 2017 ; Veletsianos et al., 2016 ; Yang & Du, 2016 ). Qualitative methods are mostly applicable to observe the single variable and its relationship with other variables. However, this method does not quantify relationships. In qualitative researches, understanding is attained through ‘wording’ (Chaurasia & Frieda Rosin, 2017 ). The behaviors, attitude, satisfaction, performance, and overall learning performance are related with human phenomenons (Cantabella et al., 2019 ; Elia et al., 2018 ; Sedkaoui & Khelfaoui, 2019 ). Qualitative researches are not statistically tested (Chaurasia & Frieda Rosin, 2017 ). Big data educational studies which employed qualitative methods lacks some certainties that are present in quantitative research methods. Therefore, future researches might quantify the educational big data applications and its impact on higher education.

The six studies conducted interviews for data collection (Chaurasia et al., 2018 ; Chaurasia & Frieda Rosin, 2017 ; Nelson & Pouchard, 2017 ; Troisi et al., 2018 ; Veletsianos et al., 2016 ). However, 2 studies used observational method (Maldonado-Mahauad et al., 2018 ; Sooriamurthi, 2018 ) and one (1) study conducted focus group discussion (Buffum et al., 2014 ) for data collection (Fig.  10 ). The observational studies were conducted in uncontrolled environments. Sometimes results of these studies lead to self-selection biased. There is a chance of ambiguities in data collection where human language and observation are involved. The findings of interviews, observations and focus group discussions are limited and cannot be extended to a wider population of learners (Dinter et al., 2017 ).

The four big data educational studies analyzed the event log data and conducted interviews (Cantabella et al., 2019 ; Hirashima et al., 2017 ; Liang et al., 2016 ; Yang & Du, 2016 ). However, longitudinal data are more appropriate for multidimensional measurements and to analyze the large data sets in the future (Sorensen, 2018 ).

The eight studies considered the teaching and learning analytics (Chaurasia et al., 2018 ; Chaurasia & Frieda Rosin, 2017 ; Dessì et al., 2019 ; Roy & Singh, 2017 ). There are limited researches that covered the aspects of learning environments, ethical and cultural values and government support in the adoption of educational big data (Yang & Du, 2016 ). In the future, comparison of big data in different learning environments, ethical and cultural values, government support and training in adopting big data in higher education can be covered through leading journals and conferences.

The three studies are related to big data frameworks for education (Cantabella et al., 2019 ; Muthukrishnan & Yasin, 2018 ). However, the existed frameworks did not cover the organizational and institutional cultures, yet lacking robust theoretical grounds (Dubey & Gunasekaran, 2015 ; Muthukrishnan & Yasin, 2018 ). In the future, big data educational framework that concentrates on theories and adoption of big data technology is recommended. The extension of existed models and interpretation of data models are recommended. This will help in better decision and ensure the predictive analysis in the academic realm. Moreover, further relations can be tested by integrating other constructs like university size and type (Chaurasia et al., 2018 ).

The three studies dwelled on big data modeling (Pardos, 2017 ; Petrova-Antonova et al., 2017 ; Wassan, 2015 ). These models do not incorporate with the present systems (Santoso & Yulia, 2017 ). Therefore, efficient research solutions that can manage the educational data, new interchanging and resources are required in the future. One (1) study explored a cloud-based solution for managing academic big data (Logica & Magdalena, 2015 ). However, this solution is expensive. In the future, a combination of LMS that is supported by open-source applications and software’s can be used. This development will help universities to obtain benefits from unified LMS and to introduce new trends and economic opportunities for the academic industry. The data warehouse with big data tools was investigated by one (1) study (Santoso & Yulia, 2017 ). Nevertheless, a manifold node cluster can be implemented to process and access the structural and un-structural data in future (Ramos et al., 2015 ). In addition, new techniques that are based on relational and nonrelational databases and development of index catalogs are recommended to improve the overall retrieval system. Furthermore, the applicability of the least analytical tools and parallel programming models are needed to be tested for academic big data. MapReduce, MongoDB, pig,

Cassandra, Yarn, and Mahout are suggested for exploring and analysis of educational big data (Wassan, 2015 ). These tools will improve the analysis process and help in the development of reliable models for academic analytics.

One (1) study detected ICT factors through data mining techniques and tools in order to enhance educational effectiveness and improves its system (Martínez-Abad et al., 2018 ). Additionally, two studies also employed big data analytic tools on popular websites to examine the academic user’s interest (Martínez-Abad et al., 2018 ; Qiu et al., 2015 ). Thus, in future research, more targeted strategies and regions can be selected for organizing the academic data. Similarly, in-depth data mining techniques can be applied according to the nature of the data. Thus, the foreseen research can be used to validate the findings by applying it on other educational websites. The present research can be extended by analyzing the socioeconomic backgrounds and use of other websites (Qiu et al., 2015 ).

The two research studies were conducted on measurements and selection of statistical software for educational big data (Ozgur et al., 2015 ; Selwyn, 2014 ). However, there is no statistical software that is fit for every academic project. Therefore, in future research, all in one’ type statistical software is recommended for big data in order to fulfill the need of all academic projects. The four research studies were based on incorporating the big data academic curricula (Buffum et al., 2014 ; Sledgianowski et al., 2017 ). However, in order to integrate the big data into the curriculum, the significant changes are required. Firstly, in future researches, curricula need to be redeveloped or restructured according to the level and learning environment (Nelson & Pouchard, 2017 ). Secondly, the training factor, learning objectives, and outcomes should be well designed in future studies. Lastly, comparable exercises, learning activities and assessment plan need to be well structured before integrating big data into curricula (Dinter et al., 2017 ).

Discussion and conclusion

Big data has become an essential part of the educational realm. This study presented a systematic review of the literature on big data in the educational sector. However, three research questions were formulated to present big data educational studies trends, themes, and identification of the limitations and directions for further research. The primary studies were collected by performing a systematic search through IEEE Xplore, ScienceDirect, Emerald Insight, AIS Electronic Library, Sage, ACM Digital Library, Springer Link, Taylor and Francis, and Google Scholar databases. Finally, 40 studies were selected that meet the research protocols. These studies were published between the years 2014 (January) and 2019 (April). Through the findings of this study, it can be concluded that 53% of extant studies were conducted on learner’s behavior and performance theme. Moreover, 15% of the studies were on modeling and educational Data Warehouse, and 23% of the studies were on the improvement of educational system themes. However, only 10% of the studies were on the integration of big data into the curriculum theme.

Thus, a large number of studies were conducted in learner’s behavior and performance theme. However, other themes gained lesser attention. Therefore, more researches are expected in modeling and educational Data Warehouse in the future, in order to improve the educational system and integration of big data into the curriculum, related themes.

It has been found that 20% of the studies used qualitative research methods. However, 6 studies conducted interviews, 2 studies used observational method and 1 study conducted focus group discussion for data collection. The findings of interviews, observations and focus group discussions are limited and cannot be extended to a wider population of learners. Therefore, prospect researches might quantify the educational big data applications and its impact in higher education. The longitudinal data are more appropriate for multidimensional measurements and future analysis of the large data sets. The eight studies were carried out on teaching and learning analytics. In the future, comparison of big data in different learning environments, ethical and cultural values, government support and training to adopt big data in higher education can be covered through leading journals and conferences.

The three studies were related to big data frameworks for education. In the future, big data educational framework that dwells on theories and extension of existed models are recommended. The three studies concentrated on big data modeling. These models cannot incorporate with present systems. Therefore, efficient research solutions are that can manage the educational data, new interchanging and resources are required in a future study. The two studies explored a cloud-based solution for managing academic big data and investigated data warehouse with big data tools. Nevertheless, in the future, a manifold node cluster can be implemented for processing and accessing of the structural and un-structural data. The applicability of the least analytical tools and parallel programming models needs to be tested for academic big data.

One (1) study considered the detection of ICT factors through data mining technique and 2 studies employed big data analytic tools on popular websites to examine the academic user’s interest. Thus, more targeted strategies and regions can be selected for organizing the academic data in future. Four (4) research studies featured on incorporating the big data academic curricula. However, the big data based curricula need to be redeveloped by considering the learning objectives. In the future, well-designed learning activities for big data curricula are suggested.

Research implications

This study has two folded implications for stakeholders and researchers. Firstly, this review explored the trends published on big data in education realm. The identified trends uncover the studies allocation, publication sources, sequential view and most cited papers. In addition, it highlights the research methods used in these studies. The described trends can provide opportunities and new ideas to researchers to predict the accurate direction in future studies.

Secondly, this research explored the themes, sub-themes, and the methodologies in big data in education domain. The classified themes, sub-themes, and the methodologies present a comprehensive overview of existing literature of big data in education. The described themes and sub-themes can be helpful for researchers to identify new research gap and avoid using repeated themes in future studies. Meanwhile, it can help researchers to focus on the combination of different themes in order to uncover new insights on how big data can improve the learning and teaching process. In addition, illustrated methodologies can be useful for researchers in the selection of method according to nature of the study in future.

Identified research can be an implication for stakeholders towards the holistic expansion of educational competencies. The identified themes give new insight to universities to plan mixed learning programs that combine conventional learning with web-based learning. This permits students to accomplish focused learning outcomes, engrossing exercises at an ideal pace. It can be helpful for teachers to apprehend the ways to gauge students learning behaviour and attitude simultaneously and advance teaching strategy accordingly. Understanding the latest trends in big data and education are of growing importance for the ministry of education as they can develop flexible possibly to support the institutions to improve the educational system.

Lastly, the identified limitations and possible future directions can provide guidelines for researchers about what has been explored or need to explore in future. In addition, stakeholders can also extract ideas to impart the future cohort and comprehend the learning and academic requirements.

Availability of data and materials

Not applicable.

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Baig, M.I., Shuib, L. & Yadegaridehkordi, E. Big data in education: a state of the art, limitations, and future research directions. Int J Educ Technol High Educ 17 , 44 (2020). https://doi.org/10.1186/s41239-020-00223-0

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This lack of around-the-clock support for their global locations was another leading factor in the inconsistency in their service desk support.</li></ul><h2>Solution & Approach</h2><p>The university sought a cost-effective partner skilled in English and with a shared time zone</p><p>Enter Auxis.</p><p>The service management consultant and outsourcing leader quickly emerged as the university’s ideal choice, aligning seamlessly with expressed requirements and demonstrating a commitment to staff augmentation and training while providing exceptional service from the Costa Rica location.</p><p>While many service desk solutions are characterized by “black box” operating models that provide little visibility into operations, the client also appreciated Auxis’ transparent business practices. 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This phased approach allowed for careful evaluation and performance monitoring during the initial six-month pilot.</p><p>Key change factors:</p><h2>Dedicated Level 1 Agent Deployment.</h2><p>To address the inconsistency in service quality stemming from the use of student employees, Auxis introduced a dedicated team of highly experienced Level 1 agents to support current and past students and/or parents with accessing student accounts and necessary documents.</p><p>These agents bring a depth of knowledge and professionalism beyond what part-time student workers can provide, ensuring a standardized and reliable service experience. Some of the unique advantages of working with our agents are our ITIL best practices, best-in-class tech, proactive approach instead of reactive approach, deep experience streamlining and improving processes, optimized operational models, and clearly documented procedures for incident and escalation management. 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The use of data science for education: The case of social-emotional learning

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The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.


Recently, AlphaGo, an artificially intelligent (AI) computer system built by Google, was able to beat world champion Lee Sedol at a complex strategy game called Go. AlphaGo’s victory shocked not only artificial intelligence experts, who thought such an event was 10 to 15 years away, but also educators, who worried that today’s high-value human skills will rapidly be sidelined by advancing technology, possibly even by 2020 (World Economic Forum 2016 ). Such potential technologies also catch some reflections of the relevance of certain educational practices in the future.

At the same time, emerging AI technologies not only pose threats but also create opportunities of producing a wide variety of data types from human interactions with these platforms. The broad availability of data has led to increasing interest in methods for exploring useful knowledge relevant to education—the realm of data science (Heckman and Kautz 2013 ; Levin 2013 ; Moore et al. 2015 ). In other words, data-driven decision-making through the collection and analysis of educational data is increasingly used to inform policy and practice, and this trend is only likely to grow in the future (Ghazarian and Kwon 2015 ).

The literature on education data analytics has many materials on the assessment and prediction of students’ academic performance, as measured by standardized tests (Fernández et al. 2014 ; Linan and Perez 2015 ; Papamitsiou and Economides 2014 ; Romero and Ventura 2010 ). However, research on education data analytics should go beyond explaining student success with the typical three Rs (reading, writing and arithmetic) of literacy in the current economy (Lipnevich and Roberts 2012 ). Furthermore, the availability of data alone does not ensure successful data-driven decision-making (Provost and Fawcett 2013 ). Consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this paper is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we used the data science research methodology.

Defining data science

Dhar ( 2013 ) defines data science as the study of the generalizable extraction of knowledge from data. At a high level, Provost and Fawcett ( 2013 ) defines data science as a set of fundamental principles that support and guide the principled extraction of information and knowledge from data. Furthermore, Wikipedia defines data science (DS) as extracting useful knowledge from data by employing techniques and theories drawn from many fields within the broad areas of mathematics, statistics, and information technology. The field of statistics is the core building block of DS theory and practice, and many of the techniques for extracting knowledge from data have their roots in this. Traditional statistical analytics mainly have mathematical foundations (Cobb 2015 ); while DS analytics emphasize the computational aspects of pragmatically carrying out data analysis, including acquisition, management, and analysis of a wide variety of data (Hardin et al. 2015 ). More importantly, DS analytics follow frameworks for organizing data-analytic thinking (Baumer 2015 ; Provost and Fawcett 2013 ).

Vision for future education

Character. Disposition. Grit. Growth mindset. Non-cognitive skills. Soft skills. Social and emotional learning. People use these words and phrases to describe skills that they also often refer to as nonacademic skills (Kamenetz 2015 ; Moore et al. 2015 ). Among these various terms, the social-emotional skills promoted by the Collaborative for Academic, Social and Emotional Learning ( http://www.casel.org/ ) have mostly been accepted by the broader educational community (Brackett et al. 2012 ). A growing number of studies show that these nonacademic factors play an important role in shaping student achievement, workplace readiness, and adult well-being (Child Trends 2014 ). For example, Mendez ( 2015 ) finds that nonacademic factors play a prominent role in explaining variation in 15-years-old school children’s’ scholastic performance, as measured by the Program for International Students Assessment (PISA) achievement tests. Lindqvist and Vestman ( 2011 ) also find strong evidence that men who fare poorly in the labor market—in the sense of unemployment or low annual earnings—lack non-cognitive rather than cognitive abilities. Furthermore, Moffitt et al. ( 2011 ) find that the emotional skill of self-control in childhood is associated with better physical health, less substance dependence, better personal finances, and fewer instances of criminal offending in adulthood.

Due to a new understanding of the impact of nonacademic factors in the global economy, a growing movement in education has raised the focus on building social-emotional competencies in national curricula. In fact, countries like China, Finland, Israel, Korea, Singapore, the United States, and the United Kingdom currently mandate that a range of social-emotional skills be part of the standard curriculum (Lipnevich and Roberts 2012 ; Ren 2015 ; Sparks 2016 ). The movement involves some complex issues ranging from the establishment of social and emotional learning standards to the development of social and emotional learning programs for students, and to the offering of professional development programs for teachers, and to the carrying out of social and emotional learning assessments (Kamenetz 2015 ).

However, as argued by Sparks ( 2016 ), research studying these skills has not quite caught up with their growing popularity. A number of authors raise various directions for future research in social and emotional learning. Child Trends ( 2014 ), for instance, conducted a systematic literature review of different social-emotional skills and highlighted the need for further research on the importance of the following five skills: self-control, persistence, mastery orientation, academic self-efficacy, and social competence. Moreover, Moore et al. ( 2015 ) provide conceptual and empirical justification for the inclusion of nonacademic outcome measures in longitudinal education surveys to avoid omitted variable bias, inform the development of new intervention strategies, and support mediating and moderating analyses. Likewise, Levin ( 2013 ) and Sellar ( 2015 ) both suggest that the development of data infrastructure in education should select a few nonacademic skill measures in conjunction with the standard academic performance measures. Furthermore, Duckworth and Yeager ( 2015 ) note that how multidimensional data on personal qualities can inform action in educational practice is another topic that will be increasingly important in this context.

Although all those issues have varying significances regarding the measurement and development of social and emotional learning, the following two research goals are priorities for studies of social and emotional learning:

Developing assessment techniques,

Providing intervention approaches.

These two research areas strongly affect the development of social-emotional skills, which are the principal concerns of the domains of education and data science, and which can be studied to derive evidence-based policies. To consider these issues, this paper focuses on (a) the suggested data science research methodology that is applicable to reach these goals, and (b) the case study of social-emotional learning in which we used the data science research methodology.

Methodology review for data science

To better pursue those goals, it could be useful to formalize the knowledge discovery processes within a standardized framework in DS. There are several objectives to keep in mind when applying a systemic approach (Cios et al. 2007 ): (1) help ensure that the quality of results can contribute to solving the user’s problems; (2) a well-defined DS model should have logical, well-thought-out substeps that can be presented to decision-makers who may have difficulty understanding the techniques behind the model; (3) standardization of the DS model would reduce the amount of extensive background knowledge required for DS, thereby leading directly to a knowledge discovery process that is faster, more reliable, and more manageable.

In the context of DS, the Cross-Industry Standard Process for Data Mining (CRISP-DM) model is the most widely used methodology for knowledge discovery (Guruler and Istanbullu 2014 ; Linan and Perez 2015 ; Shearer 2000 ). It has also been incorporated into commercial knowledge discovery systems, such as SPSS Modeler. To meet the needs of the academic research community, Cios et al. ( 2007 ) further develop a process model based on the CRISP-DM model by providing a more general, research-oriented description of the steps. Applications of Cios et al. process model follow six steps, as shown in Fig.  1 .

Cios et al.’s process model. Source: adapted from Cios and Kurgan ( 2005 )

Understanding of the problem domain

This initial step involves thinking carefully about the use scenario, understanding the problem to be solved and determining the research goals. Working closely with educational experts helps define the fundamental problems. Research goals are structured into one or more DS subtasks, and thus, the initial selection of the DS tools (e.g., classification and estimation) can be performed in the later step of the process. Finally, a description of the problem domain is generated.

An example research goal would be: Since meaningful learning requires motivation to learn, researchers are interested in real-time modeling of students’ motivational orientations (e.g., approach vs. avoidance). Similarly, researchers might be interested in developing models that can automatically detect affective states (e.g., anxiety, frustration, boredom) from machine-readable signals (Huang et al. In Press ; Lai et al. 2016 ; Liu et al. 2015 ).

Understanding of the data

This step includes collecting sample data that are available and deciding which data, including format and size, will be needed. To better understand the strengths and limitations of the data, it also includes checking data completeness, redundancy, missing values, the plausibility of attribute values. Background knowledge can be used to guide these checks. Another critical part of this step is estimating the costs and benefits of each data source and deciding whether further investment in collection is worthwhile. Finally, this step includes verifying that the data matches one or more DS subtasks in the last step.

For example, researchers may decide to analyze log traces in an online learning session to make inferences about students’ motivational orientations. Moreover, researchers may choose to collect physiological data (such as facial expression, blood volume pulse, and skin conductance data) to develop models that can automatically detect affective states.

To date, DS has relied heavily on two data sources (Siemens 2013 ): student information systems (SIS, for in generating learner profiles, such as grade point averages) and learning management systems (LMS). For example, Moodle ( https://moodle.org/ ) and Blackboard ( http://www.blackboard.com/ ) can record logs for user activity in courses, forums, and groups. Linan and Perez ( 2015 ) suggest using Google Analytics to gather information about a site, such as the number of visits, pages visited, the average duration of each visit, and demographics. Massive open online courses (MOOCs) may also provide additional data sets to understand the learning process. For instance, Leony et al. ( 2015 ) show how to infer the learners’ emotions (i.e., boredom, confusion, frustration, and happiness) by analyzing their actions on the Khan Academy Platform. Moreover, a variety of physiological sensors have been used to increase the quality and depth of analysis (Kaklauskas et al. 2015 ), such as wearable technologies (Schaefer et al. 2016 ).

Social computing systems refer to the interplay between people’s social behaviors and their interactions with computing technologies (Cheng et al. 2015 ; Lee and Chen 2013 ). These systems can extract various kinds of behavioral cues and social signals, such as physical appearance, gesture and posture, gaze and face, vocal behavior, and use of space and environment (Zhou et al. 2012 ). Analyzing this information can enable the visually representation of social features, such as identity, reputation, trust, accountability, presence, social role, expertise, knowledge, and ownership (Zhou et al. 2012 ).

There are also open datasets that can be used for research on social and emotional analytics, such as PhysioBank, which includes digital recordings of physiological signals and related data for use by the biomedical research community (Goldberger et al. 2000 ); DEAP, a database for emotion analysis using physiological signals (Koelstra et al. 2012 ); and DECAF, a multimodal dataset for decoding user physiological responses to affective multimedia content (Abadi et al. 2015 ). Verbert et al. ( 2012 ) further review the availability of such open educational datasets, including dataTEL ( http://www.teleurope.eu/pg/pages/view/50630/ ), DataShop ( https://pslcdatashop.web.cmu.edu/ ) and Mulce ( http://mulce.univ-bpclermont.fr:8080/PlateFormeMulce/ ). As highlighted by Siemens ( 2013 ), taking multiple data sources into account provides more information to educators and students than a single data source.

Preparation of the data

This step concerns manipulating and converting the raw data materials into suitable forms that will meet the specific input requirements for the DS tools. For example, some DS techniques are designed for symbolic and categorical data, while others handle only numeric values. Typical examples of manipulation include converting data to different types and discretizing or summarizing data to derive new attributes. Moreover, numerical values must often be normalized or scaled so that they are comparable. Preparation also involves sampling, running correlation and significance tests, and data cleaning, which includes removing or inferring missing values. Feature selection and data reduction algorithms may further be used with the cleaned data. The end results are then usually converted to a tabular format for the next step.

Cios and Kurgan ( 2005 ) demonstrate that the data preparation step is by far the most time-consuming part of the DS process model, but educational DS research rarely examines this. Cristóbal Romero et al. ( 2014 ) survey the literature on pre-processing educational data to provide a guide or tutorial for educators and DS practitioners. Their results showed these seven pre-processing tasks: (1) data gathering, bringing together all the available data into a set of instances; (2) data aggregation/integration, grouping together all the data from different sources; (3) data cleaning, detecting erroneous or irrelevant data and discarding it; (4) user and session identification; identifying individual users; (5) attribute/variable selection, choosing a subset of relevant attributes from all the available attributes; (6) data filtering, selecting a subset of representative data to convert large data sets into smaller data sets; and (7) data transformation, deriving new attributes from the already available ones.

Mining of the data

At this point, various mining techniques are applied to derive knowledge from preprocessed data (see Table  1 ). This usually involves the calibration of the parameters to the optimal values. The output of this step is some model parameters or pattern capturing regularities in the data.

Evaluation of the discovered knowledge

The evaluation stage serves to help ensure that the discovered knowledge satisfies the original research goals before moving on. Only approved models are retained for the next step, otherwise the entire process is revisited to identify which alternative actions could be taken to improve the results (e.g., adjusting the problem definition or getting different data). The researchers will assess the results rigorously and thus gain confidence as to whether or not they are qualified. Scheffel et al. ( 2014 ) conduct brainstorming with experts from the field of learning analytics and gather their ideas about specific quality indicators to evaluate the effects of learning analytics. We summarize the results in Table  2 . The criteria provide a way to standardize the evaluation of learning analytics tools.

In addition, the domain experts will help interpret the results and check whether the discovered knowledge is novel, interesting, and influential. To facilitate their understanding, the research team must think about the comprehensibility of the models to domain experts (and not just to the DS researchers).

As suggested by Romero and Ventura ( 2010 ), visualizing models in compelling ways can make analytics data straightforward for non-specialists to observe and understand. For example, Leony et al. ( 2013 ) propose four categories of visualizations for an intelligent system, including time-based visualizations, context-based visualizations, visualizations of changes in emotion, and visualizations of accumulated information. The main objective of these visualizations is to provide teachers with knowledge about their learner’s emotions, learning causes, and the relationships that learning has with emotions. Verbert et al. ( 2014 ) also review works on capturing and visualizing traces of learning activities as dashboard applications. They present examples to demonstrate how visualization can not only promote awareness, reflection, and sense-making, but also represent learner’s goals and enable them to track progress toward these. Epp and Bull ( 2015 ) explored 21 visual variables (e.g., arrangement, boundary, connectedness, continuity, depth, motion, orientation, position, and shape) that have been employed to communicate a learner’s abilities, knowledge, and interests. Manipulating such visual variables should provide a reasonable starting point from which to visualize educational data.

Use of the discovered knowledge

This final step consists of planning where and how to put the discovered knowledge into real use. A plan can be obtained by simply documenting the action principles being used to impact and improve teaching, learning, administrative adoption, culture, resource allocation and decision making on investment. The discovered knowledge may also be reported in educational systems, where the learner can see the related visualizations. These visualizations can provide learners with information about several factors, including their knowledge, performance, and abilities (Epp and Bull 2015 ). Moreover, the results from the current context may be extended to other cases to assess their robustness. The discovered knowledge is then finally deployed.

However, according to the findings of Romero and Ventura ( 2010 ) survey, only a small minority of studies can apply the discovered knowledge to institutional planning processes. One of the barriers to this is individual and group resistance to innovation and change. Macfadyen and Dawson ( 2012 ) thus highlight that the accessibility and presentation of analytics processes and findings are the keys to motivating participants to feel positive about the change. Furthermore, the initial iteration may not be complete or good enough to deploy, and so a second iteration may be necessary to yield an improved solution. Therefore, the diagram shown in Fig.  1 represents this process as a cycle and describes several explicit feedback loops, rather than as a simple, linear process.

The case of social-emotional learning

In this section, we describe a case study in which we used the data science research methodology. The research was initiated with an instructor who wanted to understand university students’ motivation for learning during a semester. We thus started to help this instructor through understanding the problem (Step 1). The instructor explained that university students’ motivation for learning varies over a long semester. Monitoring their motivation can help in providing the right motivated strategies at the right time. We thus went on to the next step: understanding the data (Step 2). Although the use of the motivated strategies for learning questionnaire (MSLQ) (Garcia and Pintrich 1996 ) can gather data about students’ motivation, the questionnaire measures were quite long and were not sensitive to change over time. Inspired by the concept of teaching opinion survey implemented at the end of a semester, we decided to collect text data to evaluate university students’ motivation to learn. After repeatedly going through Steps 1 and 2, the research problem became “predicting university students’ motivation to learn based on teaching opinion mining.”

In this experiment, we employed the motivated strategies for learning questionnaire to collect the respondents’ motivation states. In addition, an open-ended opinion survey about the challenges they faced on the F2F course and recommendations to the teacher with regard to adjusting instruction was utilized to collect the text data. One hundred and fifty-two university students (62 females, 90 males; mean age ± S.D. = 21.1 ± 7.5 years) completed the survey for this study. They were taking face-to-face computer courses at four universities in southern Taiwan.

In the data preparation step (Step 3), we first calculated the mean score of MSLQ. Those respondents with a score less than the mean were labeled as low motivation (LM) students, while those with more than the mean were labeled as high motivation (HM) students. The sample consisted of 76 LM and 76 HM students (the mean was equal to the median).

We then continued to process the textual data. Because textual data is unstructured, the aim of data preparation is to represent the raw text by numeric values. This process contained two steps: tokenizing and counting. In the tokenizing step, we used the CKIP Chinese word segmentation system (Ma and Chen 2003 ) to handle the text segmentation. In the counting step, term frequency-inverse document frequency (TF-IDF) was used as an indicator parameter to extract text features. TF-IDF is a measure of how frequent a term is in a document, and how rare a term is in many documents.

In mining the data (Step 4), we applied a support vector machine (SVM) to classify the respondents. The dataset was randomly split into two groups: a training set and a testing set. The training set consisted of 138 instances (90%) and the testing set of 14 instances (10%). We constructed a model based on the training set and made predictions on the testing set to evaluate the prediction performance. In the evaluation of the model (Step 5), the rate of correct predictions over all instances was measured to represent the accuracy of the prediction model. Through removing the 1074 stop words and substituting the 39 words having similar meanings, the results revealed that the accuracy of the prediction model could be up to 85.7%. We used a free data analysis software, RapidMiner, to perform the analysis (See Fig.  2 ). Therefore, in the final step the instructor could predict students’ motivation to learn during the whole semester using computer-mediated communication, such as instant messaging (Step 6).

The analysis process in RapidMiner

We further iterated the process by redefining the research problem as “finding groups of respondents using similar terms to describe an opinion.” In mining the data, the K-Means clustering method was used to partition the respondents into two clusters. The cluster model revealed that Cluster 1 had 89 respondents, and Cluster 2 had 63. ANOVA was performed to determine how the score of MSLQ was influenced by participant’s clusters (see Table  3 ). Significant effects across different work methods were found for the two clusters, F (1, 150) = 14.33, p  = .000. Table  3 indicates that the Cluster 2 had a higher mean score of MSLQ than Cluster 1. The cluster model also found that the top three important terms for were “考試(exam)”, “報告(presentation)”, and “作業(homework)” for Cluster 1 and “老師(instructor)”, “同學(peer)”, and “自己(oneself)” for Cluster 2. In other words, the terms used in Cluster 1 concerned more about the value component of MSLQ. However, the terms used in Cluster 2 concerned more about the expectancy component of MSLQ. Therefore, the instructor could use these terms to roughly provide interventions to improve students’ motivation for learning.

The broad availability of data has led to the development of data science. This paper’s research goals are to stimulate further research and practice in the use of data science for education. It also presents a DS research methodology that is applicable to achieve these goals. A well-defined DS research model can help ensure that quality of results, contribute to better understanding the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Through an examination of large data sets, a DS methodology can help us to acquire more knowledge about how people learn (Koedinger et al. 2015 ). This is important, as it contributes to the development of better intervention support for more effective learning.

This paper also describes the emerging field of social-emotional learning and its challenges. It has been proposed that the social-emotional competencies that occur between people will become very important to education in the future. Although research suggests that social-emotional qualities have a positive influence on academic achievement, most related studies examine these qualities in relation to outcome measurement and prediction, and more work is needed to develop interventions based on this research (Levin 2013 ). Therefore, this paper presents a case study of social-emotional learning in which we used the data science research methodology.

Several large problems remain to be addressed by researchers in this field. Before incorporating the approaches recommended in this work in large-scale education settings, we should select a few social-emotional skill areas and measures. This investment in data acquisition and knowledge discovery by DS will enable a deeper understanding of school effects and school policy in this context, and would avoid pulling reform efforts in unproductive or detrimental directions (Whitehurst 2016 ). Moreover, explicit privacy regulations, such as anonymity in data collection and consent from the parents in a K-12 setting, also need to be addressed. Slade and Prinsloo ( 2013 ) recommend collaborating with students on voluntarily providing data and allowing them to access DS outcomes to aid in their learning and development. We hope the issues we have highlighted in this paper help stimulate further research and practice in education.

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This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 105-2511-S-006 -015 -MY2.

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case study for education industry

7 Favorite Business Case Studies to Teach—and Why

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The Army Crew Team . Emily Michelle David of CEIBS

ATH Technologies . Devin Shanthikumar of Paul Merage School of Business

Fabritek 1992 . Rob Austin of Ivey Business School

Lincoln Electric Co . Karin Schnarr of Wilfrid Laurier University

Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth . Gary Pisano of Harvard Business School

The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron . Francesca Gino of Harvard Business School

Warren E. Buffett, 2015 . Robert F. Bruner of Darden School of Business

To dig into what makes a compelling case study, we asked seven experienced educators who teach with—and many who write—business case studies: “What is your favorite case to teach and why?”

The resulting list of case study favorites ranges in topics from operations management and organizational structure to rebel leaders and whodunnit dramas.

1. The Army Crew Team

Emily Michelle David, Assistant Professor of Management, China Europe International Business School (CEIBS)

case study for education industry

“I love teaching  The Army Crew Team  case because it beautifully demonstrates how a team can be so much less than the sum of its parts.

I deliver the case to executives in a nearby state-of-the-art rowing facility that features rowing machines, professional coaches, and shiny red eight-person shells.

After going through the case, they hear testimonies from former members of Chinese national crew teams before carrying their own boat to the river for a test race.

The rich learning environment helps to vividly underscore one of the case’s core messages: competition can be a double-edged sword if not properly managed.

case study for education industry

Executives in Emily Michelle David’s organizational behavior class participate in rowing activities at a nearby facility as part of her case delivery.

Despite working for an elite headhunting firm, the executives in my most recent class were surprised to realize how much they’ve allowed their own team-building responsibilities to lapse. In the MBA pre-course, this case often leads to a rich discussion about common traps that newcomers fall into (for example, trying to do too much, too soon), which helps to poise them to both stand out in the MBA as well as prepare them for the lateral team building they will soon engage in.

Finally, I love that the post-script always gets a good laugh and serves as an early lesson that organizational behavior courses will seldom give you foolproof solutions for specific problems but will, instead, arm you with the ability to think through issues more critically.”

2. ATH Technologies

Devin Shanthikumar, Associate Professor of Accounting, Paul Merage School of Business

case study for education industry

“As a professor at UC Irvine’s Paul Merage School of Business, and before that at Harvard Business School, I have probably taught over 100 cases. I would like to say that my favorite case is my own,   Compass Box Whisky Company . But as fun as that case is, one case beats it:  ATH Technologies  by Robert Simons and Jennifer Packard.

ATH presents a young entrepreneurial company that is bought by a much larger company. As part of the merger, ATH gets an ‘earn-out’ deal—common among high-tech industries. The company, and the class, must decide what to do to achieve the stretch earn-out goals.

ATH captures a scenario we all want to be in at some point in our careers—being part of a young, exciting, growing organization. And a scenario we all will likely face—having stretch goals that seem almost unreachable.

It forces us, as a class, to really struggle with what to do at each stage.

After we read and discuss the A case, we find out what happens next, and discuss the B case, then the C, then D, and even E. At every stage, we can:

see how our decisions play out,

figure out how to build on our successes, and

address our failures.

The case is exciting, the class discussion is dynamic and energetic, and in the end, we all go home with a memorable ‘ah-ha!’ moment.

I have taught many great cases over my career, but none are quite as fun, memorable, and effective as ATH .”

3. Fabritek 1992

Rob Austin, Professor of Information Systems, Ivey Business School

case study for education industry

“This might seem like an odd choice, but my favorite case to teach is an old operations case called  Fabritek 1992 .

The latest version of Fabritek 1992 is dated 2009, but it is my understanding that this is a rewrite of a case that is older (probably much older). There is a Fabritek 1969 in the HBP catalog—same basic case, older dates, and numbers. That 1969 version lists no authors, so I suspect the case goes even further back; the 1969 version is, I’m guessing, a rewrite of an even older version.

There are many things I appreciate about the case. Here are a few:

It operates as a learning opportunity at many levels. At first it looks like a not-very-glamorous production job scheduling case. By the end of the case discussion, though, we’re into (operations) strategy and more. It starts out technical, then explodes into much broader relevance. As I tell participants when I’m teaching HBP's Teaching with Cases seminars —where I often use Fabritek as an example—when people first encounter this case, they almost always underestimate it.

It has great characters—especially Arthur Moreno, who looks like a troublemaker, but who, discussion reveals, might just be the smartest guy in the factory. Alums of the Harvard MBA program have told me that they remember Arthur Moreno many years later.

Almost every word in the case is important. It’s only four and a half pages of text and three pages of exhibits. This economy of words and sparsity of style have always seemed like poetry to me. I should note that this super concise, every-word-matters approach is not the ideal we usually aspire to when we write cases. Often, we include extra or superfluous information because part of our teaching objective is to provide practice in separating what matters from what doesn’t in a case. Fabritek takes a different approach, though, which fits it well.

It has a dramatic structure. It unfolds like a detective story, a sort of whodunnit. Something is wrong. There is a quality problem, and we’re not sure who or what is responsible. One person, Arthur Moreno, looks very guilty (probably too obviously guilty), but as we dig into the situation, there are many more possibilities. We spend in-class time analyzing the data (there’s a bit of math, so it covers that base, too) to determine which hypotheses are best supported by the data. And, realistically, the data doesn’t support any of the hypotheses perfectly, just some of them more than others. Also, there’s a plot twist at the end (I won’t reveal it, but here’s a hint: Arthur Moreno isn’t nearly the biggest problem in the final analysis). I have had students tell me the surprising realization at the end of the discussion gives them ‘goosebumps.’

Finally, through the unexpected plot twist, it imparts what I call a ‘wisdom lesson’ to young managers: not to be too sure of themselves and to regard the experiences of others, especially experts out on the factory floor, with great seriousness.”

4. Lincoln Electric Co.

Karin Schnarr, Assistant Professor of Policy, Wilfrid Laurier University

case study for education industry

“As a strategy professor, my favorite case to teach is the classic 1975 Harvard case  Lincoln Electric Co.  by Norman Berg.

I use it to demonstrate to students the theory linkage between strategy and organizational structure, management processes, and leadership behavior.

This case may be an odd choice for a favorite. It occurs decades before my students were born. It is pages longer than we are told students are now willing to read. It is about manufacturing arc welding equipment in Cleveland, Ohio—a hard sell for a Canadian business classroom.

Yet, I have never come across a case that so perfectly illustrates what I want students to learn about how a company can be designed from an organizational perspective to successfully implement its strategy.

And in a time where so much focus continues to be on how to maximize shareholder value, it is refreshing to be able to discuss a publicly-traded company that is successfully pursuing a strategy that provides a fair value to shareholders while distributing value to employees through a large bonus pool, as well as value to customers by continually lowering prices.

However, to make the case resonate with today’s students, I work to make it relevant to the contemporary business environment. I link the case to multimedia clips about Lincoln Electric’s current manufacturing practices, processes, and leadership practices. My students can then see that a model that has been in place for generations is still viable and highly successful, even in our very different competitive situation.”

5. Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth

Gary Pisano, Professor of Business Administration, Harvard Business School

case study for education industry

“My favorite case to teach these days is  Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth .

I love teaching this case for three reasons:

1. It demonstrates how a company in a super-tough, highly competitive business can do very well by focusing on creating unique operating capabilities. In theory, Pal’s should have no chance against behemoths like McDonalds or Wendy’s—but it thrives because it has built a unique operating system. It’s a great example of a strategic approach to operations in action.

2. The case shows how a strategic approach to human resource and talent development at all levels really matters. This company competes in an industry not known for engaging its front-line workers. The case shows how engaging these workers can really pay off.

3. Finally, Pal’s is really unusual in its approach to growth. Most companies set growth goals (usually arbitrary ones) and then try to figure out how to ‘backfill’ the human resource and talent management gaps. They trust you can always find someone to do the job. Pal’s tackles the growth problem completely the other way around. They rigorously select and train their future managers. Only when they have a manager ready to take on their own store do they open a new one. They pace their growth off their capacity to develop talent. I find this really fascinating and so do the students I teach this case to.”

6. The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron

Francesca Gino, Professor of Business Administration, Harvard Business School

case study for education industry

“My favorite case to teach is  The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron .

The case surprises students because it is about a leader, known in the unit by the nickname Chaos , who inspired his squadron to be innovative and to change in a culture that is all about not rocking the boat, and where there is a deep sense that rules should simply be followed.

For years, I studied ‘rebels,’ people who do not accept the status quo; rather, they approach work with curiosity and produce positive change in their organizations. Chaos is a rebel leader who got the level of cultural change right. Many of the leaders I’ve met over the years complain about the ‘corporate culture,’ or at least point to clear weaknesses of it; but then they throw their hands up in the air and forget about changing what they can.

Chaos is different—he didn’t go after the ‘Air Force’ culture. That would be like boiling the ocean.

Instead, he focused on his unit of control and command: The 99th squadron. He focused on enabling that group to do what it needed to do within the confines of the bigger Air Force culture. In the process, he inspired everyone on his team to be the best they can be at work.

The case leaves the classroom buzzing and inspired to take action.”

7. Warren E. Buffett, 2015

Robert F. Bruner, Professor of Business Administration, Darden School of Business

case study for education industry

“I love teaching   Warren E. Buffett, 2015  because it energizes, exercises, and surprises students.

Buffett looms large in the business firmament and therefore attracts anyone who is eager to learn his secrets for successful investing. This generates the kind of energy that helps to break the ice among students and instructors early in a course and to lay the groundwork for good case discussion practices.

Studying Buffett’s approach to investing helps to introduce and exercise important themes that will resonate throughout a course. The case challenges students to define for themselves what it means to create value. The case discussion can easily be tailored for novices or for more advanced students.

Either way, this is not hero worship: The case affords a critical examination of the financial performance of Buffett’s firm, Berkshire Hathaway, and reveals both triumphs and stumbles. Most importantly, students can critique the purported benefits of Buffett’s conglomeration strategy and the sustainability of his investment record as the size of the firm grows very large.

By the end of the class session, students seem surprised with what they have discovered. They buzz over the paradoxes in Buffett’s philosophy and performance record. And they come away with sober respect for Buffett’s acumen and for the challenges of creating value for investors.

Surely, such sobriety is a meta-message for any mastery of finance.”

More Educator Favorites


Emily Michelle David is an assistant professor of management at China Europe International Business School (CEIBS). Her current research focuses on discovering how to make workplaces more welcoming for people of all backgrounds and personality profiles to maximize performance and avoid employee burnout. David’s work has been published in a number of scholarly journals, and she has worked as an in-house researcher at both NASA and the M.D. Anderson Cancer Center.

case study for education industry

Devin Shanthikumar  is an associate professor and the accounting area coordinator at UCI Paul Merage School of Business. She teaches undergraduate, MBA, and executive-level courses in managerial accounting. Shanthikumar previously served on the faculty at Harvard Business School, where she taught both financial accounting and managerial accounting for MBAs, and wrote cases that are used in accounting courses across the country.

case study for education industry

Robert D. Austin is a professor of information systems at Ivey Business School and an affiliated faculty member at Harvard Medical School. He has published widely, authoring nine books, more than 50 cases and notes, three Harvard online products, and two popular massive open online courses (MOOCs) running on the Coursera platform.

case study for education industry

Karin Schnarr is an assistant professor of policy and the director of the Bachelor of Business Administration (BBA) program at the Lazaridis School of Business & Economics at Wilfrid Laurier University in Waterloo, Ontario, Canada where she teaches strategic management at the undergraduate, graduate, and executive levels. Schnarr has published several award-winning and best-selling cases and regularly presents at international conferences on case writing and scholarship.

case study for education industry

Gary P. Pisano is the Harry E. Figgie, Jr. Professor of Business Administration and senior associate dean of faculty development at Harvard Business School, where he has been on the faculty since 1988. Pisano is an expert in the fields of technology and operations strategy, the management of innovation, and competitive strategy. His research and consulting experience span a range of industries including aerospace, biotechnology, pharmaceuticals, specialty chemicals, health care, nutrition, computers, software, telecommunications, and semiconductors.

case study for education industry

Francesca Gino studies how people can have more productive, creative, and fulfilling lives. She is a professor at Harvard Business School and the author, most recently, of  Rebel Talent: Why It Pays to Break the Rules at Work and in Life . Gino regularly gives keynote speeches, delivers corporate training programs, and serves in advisory roles for firms and not-for-profit organizations across the globe.

case study for education industry

Robert F. Bruner is a university professor at the University of Virginia, distinguished professor of business administration, and dean emeritus of the Darden School of Business. He has also held visiting appointments at Harvard and Columbia universities in the United States, at INSEAD in France, and at IESE in Spain. He is the author, co-author, or editor of more than 20 books on finance, management, and teaching. Currently, he teaches and writes in finance and management.

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Success Factors in Higher Education–Industry Collaboration: A case study of collaboration in the engineering field

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This paper addresses the question of what potential success factors are relevant when developing and managing higher education–business partnerships. To shed light on this question, the paper presents a review of research literature on the possible success factors in university–industry relations. To shed further light on the factors identified in the literature, this paper reports on an empirical study of cross-sector collaboration between four regional universities and energy firms in Norway. The empirical study should be seen as a “relevance check” of the factors identified in the extant literature on university–industry collaboration, within the particular context of educationrelated partnerships. Based on the review and case studies, implications for management and further research are discussed.

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Thune, T. Success Factors in Higher Education–Industry Collaboration: A case study of collaboration in the engineering field. Tert Educ Manag 17 , 31–50 (2011). https://doi.org/10.1080/13583883.2011.552627

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Received : 13 December 2010

Published : 01 March 2011

Issue Date : March 2011

DOI : https://doi.org/10.1080/13583883.2011.552627

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Watch CBS News

Supreme Court turns away affirmative action dispute over Virginia high school's admissions policies

By Melissa Quinn

February 20, 2024 / 10:34 AM EST / CBS News

Washington —  The Supreme Court on Tuesday turned away a challenge to the admissions policy at a prestigious Virginia high school that administrators say is designed to mitigate socioeconomic and geographic barriers for prospective students.

The decision from the high court not to take up the appeal by a group of parents challenging the admissions policies at Thomas Jefferson High School for Science and Technology leaves intact a lower court decision upholding the criteria, which school officials argue is race neutral. The U.S. Court of Appeals for the 4th Circuit concluded last year that the goal of the program is to foster diversity among the school's student body, though the parents that brought the case said it impermissibly discriminated against Asian-American students. 

Justices Samuel Alito and Clarence Thomas dissented from the court's decision not to hear the case. In a dissenting  opinion  joined by Thomas, Alito said the admissions model adopted by the high school "has been trumpeted to potential replicators as a blueprint for evading" the Supreme Court's affirmative action decision.

"The holding below effectively licenses official actors to discriminate against any racial group with impunity as long as that group continues to perform at a higher rate than other groups. That is indefensible," Alito wrote. He concluded that "the Court's willingness to swallow the aberrant decision below is hard to understand. We should wipe the decision off the books."

Affirmative action at the Supreme Court

The case is the latest involving affirmative action to arrive at the court since it issued its landmark ruling last June invalidating the race-conscious admissions policies at Harvard and the University of North Carolina. In the wake of its 6-3 decision, the Supreme Court has already been asked to temporarily stop the U.S. Military Academy at West Point from considering race in its admissions process, but declined to do so .

The challenge to West Point's policies arose out of a footnote in the majority opinion authored by Chief Justice John Roberts in the Harvard and University of North Carolina cases, in which he said the Supreme Court's decision did not apply to the nation's service academies. Roberts' opinion also warned that schools shouldn't try to get around the court's affirmative action ruling through application essays or other means, writing "'[w]hat cannot be done directly cannot be done indirectly.'"

This case involves the admissions process at an Alexandria, Virginia-based high school, which is considered to be one of the best in the country. A group of parents in Fairfax County, a wealthy enclave of Washington, D.C., argued that admissions criteria imposed at Thomas Jefferson High School seek to "indirectly" use race as a factor, which the Supreme Court said would be unlawful.

Admission to the magnet school, known as TJ, was previously based on standardized tests and a combination of GPA, teacher recommendations and essays until 2020. But that year, the Fairfax County School Board, which oversees the high school, eliminated entrance exams from Thomas Jefferson's admissions process and put in place a holistic system.

Thomas Jefferson High School in Alexandria, Virginia, on July 1, 2020.

The school board argued in court filings that under the old admissions processes, admitted classes overwhelmingly were made up of students from a small subset of Fairfax County's wealthiest areas. But under the new program, seats are reserved for top students from each of the county's middle schools.  The remaining spots are awarded to highest-evaluated applicants, as well as to students based on a number of socioeconomic factors, including whether students are from low-income families, are learning English as a second language or attended a "historically underrepresented" middle school.

The policy is race-neutral, according to the Fairfax County School Board, and admissions evaluators do not know an applicant's name, gender, race or ethnicity. They also cannot keep track of the racial composition of an incoming class during the admissions process, the board said in court papers.

But a grassroots group of parents called The Coalition for TJ sued the Fairfax County School Board in 2021, arguing that the revamped admissions policy is unconstitutional because it discriminated against Asian-American applicants.

In 2021, the first year under the new system, fewer Asian-American applicants were admitted than the prior year and the share of Asian-American students receiving admissions offers fell from 73% to 54%. Every other racial group saw an increase in admissions numbers, according to court filings: Admissions offers to White students rose from 18% to 22%; offers to Black students grew from less than 2% to nearly 8%; and offers to Hispanic students jumped from 3% to 11%.

A federal district court in Alexandria ruled for the coalition in February 2022, finding that the board's redesigned policy was "designed to increase Black and Hispanic enrollment which would, by necessity, decrease the representation of Asian-Americans at TJ," and adopted with discriminatory intent.

U.S. District Judge Claude Hilton blocked the board from implementing the policy, but a divided panel of three federal appeals court judges eventually reversed the ruling and upheld the admissions program. 

The 4th Circuit concluded that "the undisputed facts show only that the Board intended to improve the overall socioeconomic and geographic diversity of TJ's student body," and found that the coalition failed to prove that the board was motivated by discriminatory intent.

"The challenged admissions policy's central aim is to equalize opportunity for those students hoping to attend one of the nation's best public schools, and to foster diversity of all stripes among TJ's student body," the 4th Circuit said in its 2-1 decision. It continued: "Expanding the array of student backgrounds in the classroom serves, at minimum, as a legitimate interest in the context of public primary and secondary schools. And that is the primary and essential effect of the challenged admissions policy."

The Supreme Court was asked to weigh in at an earlier stage in the proceedings and denied a request from the Coalition for TJ for emergency relief in April 2022, more than a year before its affirmative action ruling. Justices Clarence Thomas, Samuel Alito and Neil Gorsuch said they would have granted the group's request to block the admissions policy.

The parents returned to the Supreme Court in August, asking the justices to decide whether the board violated the Constitution's Equal Protection Clause when it overhauled the admissions criteria at the high school. Citing the court's June affirmative action decision, the group warned that its "guarantees … might mean little if schools could accomplish the same discriminatory result through race-neutral proxies."

The coalition told the court in a filing that while it has said racial balancing through racial classifications is impermissible, it has not yet explicitly addressed whether student body diversity can be achieved through race-neutral means.

"The longer this question is not resolved, the more incentive school districts (and now, universities) will have to develop workarounds that enable them to racially discriminate without using racial classifications," its lawyers wrote.

But the Fairfax County School Board argued that the new policy removes socioeconomic barriers to admission to Thomas Jefferson High School and is race neutral and race blind. 

"The policy did not in fact result in a student body that matches the demographics of the County, maintains predetermined percentages of any racial group, or otherwise reflects racial balance of any sort," they said in a filing .

The board said there was no evidence supporting the coalition's "reckless" claim that Thomas Jefferson's admissions criteria were changed to discriminate against Asian-Americans, and noted that more Asian-American students from poor families living in less affluent areas of Fairfax County were admitted under the new policy.

Melissa Quinn is a politics reporter for CBSNews.com. She has written for outlets including the Washington Examiner, Daily Signal and Alexandria Times. Melissa covers U.S. politics, with a focus on the Supreme Court and federal courts.

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Blog The Education Hub


Degree apprenticeships: How you could get a degree for free

Degree apprenticeships

If you want to get a degree, but you’re worried about finances or just not sure that a traditional university course is right for you, a degree apprenticeship could be a great option.  

A degree apprenticeship allows you to study towards an undergraduate or master’s degree while you work, getting invaluable industry experience and earning a salary. Your off-the-job training takes place in your working hours, and you won’t have to pay for your tuition.  

Find out everything you need to know about degree apprenticeships here.  

What are degree apprenticeships?  

Degree apprenticeships are jobs with training. On completion of the apprenticeship, you’ll achieve an undergraduate or master’s degree – just like someone who has got their degree through a traditional route.  

Your training is paid for by apprenticeship funding, so unlike with traditional university courses, you won’t have to pay for your tuition yourself. What’s more, you’ll earn a competitive salary while learning.   

Though you’ll study part-time at a university , around 80% of your time will be spent doing practical work. This allows you to get real-life work experience and still gain a recognised qualification.  

Degree apprenticeships are a Level 6 or 7 apprenticeship.  

Who can do degree apprenticeships and what are the requirements?  

The requirements for each degree apprenticeship are different.   

Some Level 6 apprenticeships will ask for at least five GCSEs at 9-4 (or A*- C on the old grading scale), including English and maths. But you could also progress into a degree apprenticeship from a lower-level apprenticeship, or another qualification like a T Level .  

It does mean that you’ll need to be at least 18-years-old to participate in most degree apprenticeship, though there is no upper age limit.  

However, as well as considering your grades, employers will be looking for other skills like communication, teamwork and passion. They might also value prior industry experience as much as formal qualifications.    

How much do degree apprentices earn?  

The amount you can earn depends on the specific apprenticeship. However, many employers offer apprentices a salary of at least £20,000 a year.   

Apprentices often go on to well-paid jobs on completion of their training. Recent analysis showed that one year after finishing their programmes, the median salary for a former Level 6 apprentice was £34,620.  

What kind of degree apprenticeships are available?  

Degree apprenticeships are available in a range of industries, from engineering, to science, to law, to marketing and digital.  

For example, you could gain a BSc (Bachelor of Science) degree through a civil engineering, nursing or biomedical apprenticeship. Other apprenticeships could lead to a BA (Bachelor of Arts) in digital marketing, or an LLB (Bachelor of Laws).    

The NHS recruitment and training body, Health Education England (HEE), has also confirmed funding for a new Medical Doctor Degree Apprenticeship .

There are hundreds of degree apprenticeships on offer throughout the year.  

Browsing apprenticeships and filtering them by level and location could be a good way to get inspiration for the kind of degree apprenticeship that excites you.  

How to apply for a degree apprenticeship   

You apply to degree apprenticeships direct with the employer. You can now search for degree apprenticeships via the Find an Apprenticeship website or via UCAS.  

Applying for a degree apprenticeship is similar to applying for a job. This means that unlike with university courses, you can apply for degree apprenticeships at any time.   

Applications will differ depending on the apprenticeship, but it’s likely that you’ll have to submit a CV and do an interview.   

To get personalised advice on your application, you can talk to a careers adviser for free via the National Career Service. There are lots of ways to  get in touch  including by phone, webchat or in person.  

You may also be interested in:

  • What are T Levels and how can they benefit me?
  • NHS doctor apprenticeships: Everything you need to know
  • Get the facts on student loans

Tags: apprentices , Apprenticeship , degree , degree apprenticeships , Higher education , national apprenticeships week , NAW , undergraduate degrees

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