Skip to main content
SpringerLink
Log in

Learning Analytics in Higher Education—A Literature Review

  • Chapter
  • First Online:
Learning Analytics: Fundaments, Applications, and Trends

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 94))

Abstract

This chapter looks into examining research studies of the last five years and presents the state of the art of Learning Analytics (LA) in the Higher Education (HE) arena. Therefore, we used mixed-method analysis and searched through three popular libraries, including the Learning Analytics and Knowledge (LAK) conference, the SpringerLink, and the Web of Science (WOS) databases. We deeply examined a total of 101 papers during our study. Thereby, we are able to present an overview of the different techniques used by the studies and their associated projects. To gain insights into the trend direction of the different projects, we clustered the publications into their stakeholders. Finally, we tackled the limitations of those studies and discussed the most promising future lines and challenges. We believe the results of this review may assist universities to launch their own LA projects or improve existing ones.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Online: http://scholar.google.com.

  2. 2.

    Online: https://cran.r-project.org/web/packages/wordcloud/index.html.

Abbreviations

AA:

Academic analytics

ACM:

Association for computing machinery

EDM:

Educational data mining

HE:

Higher education

ITS:

Intelligent tutoring system

LA:

Learning analytics

LAK:

Learning analytics and knowledge

LMS:

Learning management system

MOOC:

Massive open online course

NMC:

New media consortium

PLE:

Personal learning environment

RQ:

Research question

SNA:

Social network analysis

VLE:

Virtual learning environment

WOS:

Web of science

References

  • Abdelnour-Nocera J, Oussena S, Burns C (2015) Human work interaction design of the smart university. In: Human work interaction design. Work analysis and interaction design methods for pervasive and smart workplaces. Springer International Publishing, pp 127–140

    Google Scholar 

  • AbuKhousa E, Atif Y (2016) Virtual social spaces for practice and experience sharing. In: State-of-the-Art and Future Directions of Smart Learning. Springer, Singapore, pp 409–414

    Google Scholar 

  • Aguiar E, Chawla NV, Brockman J, Ambrose GA, Goodrich V (2014) Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 103–112

    Google Scholar 

  • Aguilar S, Lonn S, Teasley SD (2014) Perceptions and use of an early warning system during a higher education transition program. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 113–117

    Google Scholar 

  • Akhtar S, Warburton S, Xu W (2015) The use of an online learning and teaching system for monitoring computer aided design student participation and predicting student success. Int J Technol Des Edu, pp 1–20

    Google Scholar 

  • Arnold KE, Pistilli MD (2012) Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 267–270

    Google Scholar 

  • Arnold KE, Lonn S, Pistilli MD (2014) An exercise in institutional reflection: the learning analytics readiness instrument (LARI). In: Proceedings of the fourth international conference on learning penetrating the black box of time-on-task estimation and knowledge. ACM, pp 163–167

    Google Scholar 

  • Asif R, Merceron A, Pathan MK (2015) Investigating performance of students: a longitudinal study. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 108–112

    Google Scholar 

  • Atif A, Richards D, BilginA, Marrone M (2013) Learning analytics in higher education: a summary of tools and approaches. In: 30th Australasian Society for computers in learning in tertiary education conference, Sydney

    Google Scholar 

  • Barber R, Sharkey M (2012) Course correction: using analytics to predict course success. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 259–262

    Google Scholar 

  • Best M, MacGregor D (2015) Transitioning design and technology education from physical classrooms to virtual spaces: implications for pre-service teacher education. Int J Technol Des Edu, pp 1–13

    Google Scholar 

  • Bichsel J (2012) Analytics in higher education: benefits, barriers, progress, and recommendations. EDUCAUSE Center for Applied Research

    Google Scholar 

  • Bramucci R, Gaston J (2012) Sherpa: increasing student success with a recommendation engine. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 82–83

    Google Scholar 

  • Cambruzzi WL, Rigo SJ, Barbosa JL (2015) Dropout prediction and reduction in distance education courses with the learning analytics multitrail approach. J UCS 21(1):23–47

    Google Scholar 

  • Campbell JP, Oblinger DG (2007) Academic analytics, EDUCAUSE white paper. Retrieved 10 Feb 2016 from https://net.educause.edu/ir/library/pdf/PUB6101.pdf

  • Campbell JP, DeBlois PB, Oblinger DG (2007) Academic analytics: a new tool for a new era. EDUCAUSE Rev 42(4):40–57

    Google Scholar 

  • Casquero O, Ovelar R, Romo J, Benito M (2014) Personal learningenvironments, highereducation and learninganalytics: a study of theeffects of servicemultiplexityonundergraduatestudents’ personal networks/Entornos de aprendizaje personales, educación superior y analítica del aprendizaje: un estudio sobre los efectos de la multiplicidad de servicios en las redes personales de estudiantes universitarios. Cultura y Educación 26(4):696–738

    Article  Google Scholar 

  • Casquero O, Ovelar R, Romo J, Benito M, Alberdi M (2016) Students’ personal networks in virtual and personal learning environments: a case study in higher education using learning analytics approach. Interact Learning Environ 24(1):49–67

    Article  Google Scholar 

  • Clow D (2014) Data wranglers: human interpreters to help close the feedback loop. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 49–53

    Google Scholar 

  • Corrigan O, Smeaton AF, Glynn M, Smyth S (2015) Using educational analytics to improve test performance. In: Design for teaching and learning in a networked world. Springer International Publishing, pp 42–55

    Google Scholar 

  • Delen D (2010) A comparative analysis of machine learning techniques for student retention management. Decis Support Syst 49(4):498–506

    Article  Google Scholar 

  • Drachsler H, Greller W (2012) The pulse of learning analytics understandings and expectations from the stakeholders. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 120–129

    Google Scholar 

  • Elbadrawy A, Studham RS, Karypis G (2015) Collaborative multi-regression models for predicting students’ performance in course activities. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 103–107

    Google Scholar 

  • Elias T (2011) Learning analytics: definitions, processes and potential

    Google Scholar 

  • Ferguson R (2012) Learning analytics: drivers, developments and challenges. Int J Technol Enhanced Learning 4(5/6):304–317

    Article  Google Scholar 

  • Ferguson R, Shum SB (2012) Social learning analytics: five approaches. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 23–33

    Google Scholar 

  • Freitas S, Gibson D, Du Plessis C, Halloran P, Williams E, Ambrose M, Dunwell I, Arnab S (2015) Foundations of dynamic learning analytics: using university student data to increase retention. Br J Educational Technol 46(6):1175–1188

    Article  Google Scholar 

  • Fritz J (2011) Classroom walls that talk: using online course activity data of successful students to raise self-awareness of underperforming peers. Internet Higher Edu 14(2):89–97

    Article  Google Scholar 

  • Gasevic D, Kovanovic V, Joksimovic S, Siemens G (2014) Where is research on massive open online courses headed? A data analysis of the MOOC research initiative. Int Rev Res Open Distrib Learning, 15(5)

    Google Scholar 

  • Gašević D, Dawson S, Siemens G (2015) Let’s not forget: learning analytics are about learning. TechTrends 59(1):64–71

    Article  Google Scholar 

  • Gibson D, de Freitas S (2016) Exploratory analysis in learning analytics. Technol Knowl Learning 21(1):5–19

    Article  Google Scholar 

  • Gibson A, Kitto K, Willis J (2014) A cognitive processing framework for learning analytics. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 212–216

    Google Scholar 

  • Grann J, Bushway D (2014) Competency map: visualizing student learning to promote student success. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 168–172

    Google Scholar 

  • Grau-Valldosera J, Minguillón J (2011) Redefining dropping out in online higher education: a case study from the UOC. In: Proceedings of the 1st international conference on learning analytics and knowledge. ACM, pp 75–80

    Google Scholar 

  • Grau-Valldosera J, Minguillón J (2014) Rethinking dropout in online higher education: The case of the UniversitatOberta de Catalunya. Int Rev Res Open Distrib Learning, 15(1)

    Google Scholar 

  • Greller W, Ebner M, Schön M (2014) Learning analytics: from theory to practice–data support for learning and teaching. In: Computer assisted assessment. Research into e-assessment. Springer International Publishing, pp 79–87

    Google Scholar 

  • Harrison S, Villano R, Lynch G, Chen G (2015) Likelihood analysis of student enrollment outcomes using learning environment variables: a case study approach. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 141–145

    Google Scholar 

  • Hecking T, Ziebarth S, Hoppe HU (2014) Analysis of dynamic resource access patterns in a blended learning course. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 173–182

    Google Scholar 

  • Holman C, Aguilar S, Fishman B (2013) GradeCraft: what can we learn from a game-inspired learning management system? In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 260–264

    Google Scholar 

  • Holman C, Aguilar SJ, Levick A, Stern J, Plummer B, Fishman B (2015) Planning for success: how students use a grade prediction tool to win their classes. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 260–264

    Google Scholar 

  • Ifenthaler D, Widanapathirana C (2014) Development and validation of a learning analytics framework: two case studies using support vector machines. Technol Knowl Learning 19(1–2):221–240

    Article  Google Scholar 

  • Jo IH, Yu T, Lee H, Kim Y (2015) Relations between student online learning behavior and academic achievement in higher education: a learning analytics approach. In: Emerging issues in smart learning. Springer, Berlin, pp 275–287

    Google Scholar 

  • Johnson L, Adams S, Cummins M (2012) The NMC horizon report: 2012 higher education edition. The New Media Consortium, Austin

    Google Scholar 

  • Johnson L, Adams Becker S, Cummins M, Freeman A, Ifenthaler D, Vardaxis N (2013) Technology outlook for Australian tertiary education 2013–2018: an NMC horizon project regional analysis. New Media Consortium

    Google Scholar 

  • Johnson L, Adams S, Cummins M, Estrada V, Freeman A, Hall C (2016) NMC horizon report: 2016 higher education edition. The New Media Consortium, Austin. http://cdn.nmc.org/media/2016-nmc-horizon-report-he-EN.pdf

  • Junco R, Clem C (2015) Predicting course outcomes with digital textbook usage data. Internet High Edu 27:54–63

    Article  Google Scholar 

  • Khalil M, Ebner M (2015) Learning analytics: principles and constraints. In: Proceedings of world conference on educational multimedia, hypermedia and telecommunications, pp 1326–1336

    Google Scholar 

  • Khalil M, Ebner M (2016a) What is learning analytics about? A survey of different methods used in 2013–2015. In: Proceedings of smart learning conference, Dubai, UAE, 7–9 Mar. HBMSU Publishing House, Dubai, pp 294–304

    Google Scholar 

  • Khalil M, Ebner M (2016b) De-identification in learning analytics. J Learning Anal 3(1), pp 129–138 http://dx.doi.org/10.18608/jla.2016.31.8

  • Khousa EA, Atif Y (2014) A learning analytics approach to career readiness development in higher education. In: International conference on web-based learning. Springer International Publishing, pp 133–141

    Google Scholar 

  • Kim J, Jo IH, Park Y (2016) Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pac Edu Rev 17(1):13–24

    Article  Google Scholar 

  • Koulocheri E, Xenos M (2013) Considering formal assessment in learning analytics within a PLE: the HOU2LEARN case. In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 28–32

    Google Scholar 

  • Kovanović V, Gašević D, Dawson S, Joksimović S, Baker RS, Hatala M (2015) Penetrating the black box of time-on-task estimation. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 184–193

    Google Scholar 

  • Kung-Keat T, Ng J (2016) Confused, bored, excited? An emotion based approach to the design of online learning systems. In: 7th International conference on university learning and teaching (InCULT 2014) proceedings. Springer, Singapore, pp 221–233

    Google Scholar 

  • Lauría EJ, Baron JD, Devireddy M, Sundararaju V, Jayaprakash SM (2012) Mining academic data to improve college student retention: an open source perspective. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 139–142

    Google Scholar 

  • Leony D, Muñoz-Merino PJ, Pardo A, Kloos CD (2013) Provision of awareness of learners’ emotions through visualizations in a computer interaction-based environment. Expert Syst Appl 40(13):5093–5100

    Article  Google Scholar 

  • Liñán LC, Pérez ÁAJ (2015) Educational data mining and learning analytics: differences, similarities, and time evolution. Revista de Universidad y SociedaddelConocimiento 12(3):98–112

    Article  Google Scholar 

  • Lockyer L, Dawson S (2011) Learning designs and learning analytics. In: Proceedings of the 1st international conference on learning analytics and knowledge. ACM, pp 153–156

    Google Scholar 

  • Lonn S, Krumm AE, Waddington RJ, Teasley SD (2012) Bridging the gap from knowledge to action: Putting analytics in the hands of academic advisors. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 184–18

    Google Scholar 

  • Lonn S, Aguilar S, Teasley SD (2013) Issues, challenges, and lessons learned when scaling up a learning analytics intervention. In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 235–239

    Google Scholar 

  • Lotsari E, Verykios VS, Panagiotakopoulos C, Kalles D (2014) A learning analytics methodology for student profiling. In: Hellenic conference on artificial intelligence. Springer International Publishing, pp 300–312

    Google Scholar 

  • Ma J, Han X, Yang J, Cheng J (2015) Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: the role of the instructor. Internet High Edu 24:26–34

    Article  Google Scholar 

  • Machi LA, McEvoy BT (2009) The literature review: six steps to success. Corwin Sage, Thousand Oaks

    Google Scholar 

  • Manso-Vázquez M, Llamas-Nistal M (2015) A monitoring system to ease self-regulated learning processes. IEEE RevistaIberoamericana de TecnologiasdelAprendizaje 10(2):52–59

    Google Scholar 

  • Martin F, Whitmer JC (2016) Applying learning analytics to investigate timed release in online learning. Technol Knowl Learning 21(1):59–74

    Article  Google Scholar 

  • McKay T, Miller K, Tritz J (2012) What to do with actionable intelligence: E 2 coach as an intervention engine. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 88–91

    Google Scholar 

  • Menchaca I, Guenaga M, Solabarrieta J (2015) Project-based learning: methodology and assessment learning technologies and assessment criteria. In: Design for teaching and learning in a networked world. Springer International Publishing, pp 601–604

    Google Scholar 

  • Muñoz-Merino PJ, Valiente JAR, Kloos CD (2013) Inferring higher level learning information from low level data for the Khan Academy platform. In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 112–116

    Google Scholar 

  • Nam S, Lonn S, Brown T, Davis CS, Koch D (2014) Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 16–25

    Google Scholar 

  • Nespereira CG, Elhariri E, El-Bendary N, Vilas AF, Redondo RPD (2016) Machine learning based classification approach for predicting students performance in blended learning. In: The 1st International conference on advanced intelligent system and informatics (AISI2015), 28–30 Nov 2015, BeniSuef, Egypt. Springer International Publishing, pp 47–56

    Google Scholar 

  • Øhrstrøm P, Sandborg-Petersen U, Thorvaldsen S, Ploug T (2013) Teaching logic through web-based and gamified quizzing of formal arguments. European conference on technology enhanced learning. Springer, Berlin, pp 410–423

    Google Scholar 

  • Palavitsinis N, Protonotarios V, Manouselis N (2011) Applying analytics for a learning portal: the organic. Edunet case study. In: Proceedings of the 1st international conference on learning analytics and knowledge. ACM, pp 140–146

    Google Scholar 

  • Palmer S (2013) Modelling engineering student academic performance using academic analytics. Int J Eng Educ 29(1):132–138

    Google Scholar 

  • Pardo A, Mirriahi N, Dawson S, Zhao Y, Zhao A, Gašević D (2015) Identifying learning strategies associated with active use of video annotation software. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 255–259

    Google Scholar 

  • Park Y, Yu JH, Jo IH (2016) Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute. Internet High Educ 29:1–11

    Article  Google Scholar 

  • Piety PJ, Hickey DT, Bishop MJ (2014) Educational data sciences: framing emergent practices for analytics of learning, organizations, and systems. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 193–202

    Google Scholar 

  • Pistilli MD, Willis III JE, Campbell JP (2014) Analytics through an institutional lens: definition, theory, design, and impact. In: Learning analytics. Springer New York, pp 79–102

    Google Scholar 

  • Prinsloo P, Slade S, Galpin F (2012) Learning analytics: challenges, paradoxes and opportunities for mega open distance learning institutions. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 130–133

    Google Scholar 

  • Prinsloo P, Archer E, Barnes G, Chetty Y, Van Zyl D (2015) Big (ger) data as better data in open distance learning. Int Rev Res Open Distrib Learning, 16(1)

    Google Scholar 

  • Ramírez-Correa P, Fuentes-Vega C (2015) Factors that affect the formation of networks for collaborative learning: an empirical study conducted at a Chilean university/Factores que afectanla formación de redes para el aprendizajecolaborativo: unestudioempíricoconducidoenunauniversidadchilena. Ingeniare: RevistaChilena de Ingenieria, 23(3), 341

    Google Scholar 

  • Rogers T, Colvin C, Chiera B (2014) Modest analytics: using the index method to identify students at risk of failure. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 118–122

    Google Scholar 

  • Romero C, Ventura S (2013) Data mining in education. Wiley Interdiscip Rev Data Min Knowl Discovery 3(1):12–27

    Article  Google Scholar 

  • Santos JL, Govaerts S, Verbert K, Duval E (2012) Goal-oriented visualizations of activity tracking: a case study with engineering students. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 143–152

    Google Scholar 

  • Santos JL, Verbert K, Govaerts S, Duval E (2013) Addressing learner issues with StepUp!: an evaluation. In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 14–22

    Google Scholar 

  • Santos JL, Verbert K, Klerkx J, Duval E, Charleer S, Ternier S (2015) Tracking data in open learning environments. J Univ Comput Sci 21(7):976–996

    Google Scholar 

  • Scheffel M, Niemann K, Leony D, Pardo A, Schmitz HC, Wolpers M, Kloos CD (2012) Key action extraction for learning analytics. European conference on technology enhanced learning. Springer, Berlin, pp 320–333

    Google Scholar 

  • Sclater N (2014) Code of practice “essential” for learning analytics. http://analytics.jiscinvolve.org/wp/2014/09/18/code-of-practice-essential-for-learning-analytics/

  • Shacklock X (2016) From bricks to clicks: the potential of data and analytics in higher education. The Higher Education Commission’s (HEC) report

    Google Scholar 

  • Sharkey M (2011) Academic analytics landscape at the University of Phoenix. In: Proceedings of the 1st international conference on learning analytics and knowledge. ACM, pp 122–126

    Google Scholar 

  • Siemens G (2010) What are learning analytics. Retrieved 10 Feb 2016 from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/

  • Siemens G, Long P (2011) Penetrating the fog: analytics in learning and education. EDUCAUSE Rev 46(5):30–40

    Google Scholar 

  • Simsek D, Sándor Á, Shum SB, Ferguson R, De Liddo A, Whitelock D (2015) Correlations between automated rhetorical analysis and tutors’ grades on student essays. In: Proceedings of the fifth international conference on learning analytics and knowledge. ACM, pp 355–359

    Google Scholar 

  • Sinclair J, Kalvala S (2015) Engagement measures in massive open online courses. In: International workshop on learning technology for education in cloud. Springer International Publishing, pp 3–15

    Google Scholar 

  • Slade S, Prinsloo P (2013) Learning analytics ethical issues and dilemmas. Am Behav Sci 57(10):1510–1529

    Article  Google Scholar 

  • Softic S, Taraghi B, Ebner M, De Vocht L, Mannens E, Van de Walle R (2013) Monitoring learning activities in PLE using semantic modelling of learner behaviour. Human factors in computing and informatics. Springer, Berlin, pp 74–90

    Chapter  Google Scholar 

  • Strang KD (2016) Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? Education and information technologies, pp 1–21

    Google Scholar 

  • Swenson J (2014) Establishing an ethical literacy for learning analytics. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 246–250

    Google Scholar 

  • Tervakari AM, Marttila J, Kailanto M, Huhtamäki J, Koro J, Silius K (2013) Developing learning analytics for TUT Circle. Open and social technologies for networked learning. Springer, Berlin, pp 101–110

    Chapter  Google Scholar 

  • Tseng SF, Tsao YW, Yu LC, Chan CL, Lai KR (2016) Who will pass? Analyzing learner behaviors in MOOCs. Res Pract Technol Enhanced Learning 11(1):1

    Article  Google Scholar 

  • Vahdat M, Oneto L, Anguita D, Funk M, Rauterberg M (2015) A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In: Design for teaching and learning in a networked world. Springer International Publishing, pp 352–366

    Google Scholar 

  • van Barneveld A, Arnold KE, Campbell JP (2012) Analytics in higher education: establishing a common language. EDUCAUSE Learning Initiative 1:1–11

    Google Scholar 

  • Vozniuk A, Holzer A, Gillet D (2014) Peer assessment based on ratings in a social media course. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 133–137

    Google Scholar 

  • Westera W, Nadolski R, Hummel H (2013) Learning analytics in serious gaming: uncovering the hidden treasury of game log files. In: international conference on games and learning alliance. Springer International Publishing, pp 41–52

    Google Scholar 

  • Wise AF (2014) Designing pedagogical interventions to support student use of learning analytics. In: Proceedings of the fourth international conference on learning analytics and knowledge. ACM, pp 203–211

    Google Scholar 

  • Wolff A, Zdrahal Z, Nikolov A, Pantucek M (2013) Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In: Proceedings of the third international conference on learning analytics and knowledge. ACM, pp 145–149

    Google Scholar 

  • Wu IC, Chen WS (2013) Evaluating the practices in the e-learning platform from the perspective of knowledge management. Open and social technologies for networked learning. Springer, Berlin, pp 81–90

    Chapter  Google Scholar 

  • Yasmin D (2013) Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Dis Educ 34(2):218–231

    Article  Google Scholar 

Download references

Acknowledgements

This research project is co-funded by the European Commission Erasmus+ program, in the context of the project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD.

Author information

Authors and Affiliations

  1. Educational Technology, Graz University of Technology, Münzgrabenstraße 35A/I, 8010, Graz, Austria

    Philipp Leitner, Mohammad Khalil & Martin Ebner

Authors
  1. Philipp Leitner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Ebner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Philipp Leitner or Mohammad Khalil .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, WOLNM & Escuela Superior de Ingeniería,Mecánica y Eléctrica, Zacatenco, México DF, Mexico

    Alejandro Peña-Ayala

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Leitner, P., Khalil, M., Ebner, M. (2017). Learning Analytics in Higher Education—A Literature Review. In: Peña-Ayala, A. (eds) Learning Analytics: Fundaments, Applications, and Trends. Studies in Systems, Decision and Control, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-52977-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-52977-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52976-9

  • Online ISBN: 978-3-319-52977-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation