Systematic Literature Review of Predictive Analysis Tools in Higher Education
Source abstract
Abstract
The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Telematic Systems Engineering Group, atlanTTic Research Center, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
Funding
Consellería de Economía, Emprego e Industria, Xunta de Galicia · Agencia Estatal de Investigación
References (38)
Show source-provided references
1. Earth Science Data Analytics: Definitions, Techniques and Skills10.5334/dsj-2017-006 ↗
2. Beyond the hype: Big data concepts, methods, and analytics10.1016/j.ijinfomgt.2014.10.007 ↗
3. Global early warning systems for natural hazards: Systematic and people-centred
4. Towards a new early warning system of financial crises10.1016/j.jimonfin.2006.07.007 ↗
5. Smith, M.E.B., Chiovaro, J.C., O’Neil, M., Kansagara, D., Quinones, A., Freeman, M., Motu’apuaka, M., and Slatore, C.G. (2014). Early Warning System Scores: A Systematic Review, VA Evidence-Based Synthesis Program Reports.
6. Siemens, G. (2019, January 12). About: Learning Analytics & Knowledge: February 27–March 1, 2011 in Banff, Alberta. Available online: https://web.archive.org/web/20190112095031/https://tekri.athabascau.ca/analytics/.
7. Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., and Mikic-Fonte, F. (2019). Predictors and Early Warning Systems in Higher Education — A Systematic Literature Review. Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education, CEUR. LASI Spain 2019.
8. Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., Gøtzsche, P.C., Ioannidis, J.P.A., Clarke, M., Devereaux, P.J., Kleijnen, J., and Moher, D. (2009). The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Med., 6.10.2427/5768 ↗
9. Guest Editorial: Trends and Research Issues of Learning Analytics and Educational Big Data
10. Predicting Student Success: A Naïve Bayesian Application to Community College Data10.1007/s10758-017-9334-z ↗
11. Predicting Student Success in a Major’s Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores10.1007/s11165-016-9563-5 ↗
12. Benablo, C.I.P., Sarte, E.T., Dormido, J.M.D., and Palaoag, T. (2018, January 8–10). Higher Education Student’s Academic Performance Analysis Through Predictive Analytics. Proceedings of the 2018 7th International Conference on Software and Computer Applications, Kuantan, Malaysia.10.1145/3185089.3185102 ↗
13. Umer, R., Susnjak, T., Mathrani, A., and Suriadi, S. (2018, January 12–13). A learning analytics approach: Using online weekly student engagement data to make predictions on student performance. Proceedings of the 2018 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan.10.1109/ICECUBE.2018.8610959 ↗
14. Multi-view Learning for Early Prognosis of Academic Performance: A Case Study10.1109/TLT.2019.2911581 ↗
15. Hirose, H. (2018, January 8–13). Success/Failure Prediction for Final Examination Using the Trend of Weekly Online Testing. Proceedings of the 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI), Yonago, Japan.10.1109/IIAI-AAI.2018.00036 ↗
16. Evaluating the Impact of Crime and Discipline on Student Success in Postsecondary Education10.1007/s11162-016-9419-x ↗
17. Tsiakmaki, M., Kostopoulos, G., Koutsonikos, G., Pierrakeas, C., Kotsiantis, S., and Ragos, O. (2018, January 23–25). Predicting University Students’ Grades Based on Previous Academic Achievements. Proceedings of the 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece.10.1109/IISA.2018.8633618 ↗
18. The impact of engineering students’ performance in the first three years on their graduation result using educational data mining10.1016/j.heliyon.2019.e01250 ↗
19. Predictive power of regularity of pre-class activities in a flipped classroom10.1016/j.compedu.2019.02.011 ↗
20. Demography and student success: Early warning tools to drive intervention10.1080/08832323.2018.1496893 ↗
21. The Effects of College Students’ In-Class and After-Class Lecture Note-Taking on Academic Performance10.1007/s40299-012-0010-8 ↗
22. Stress overload as a red flag for freshman failure and attrition10.1016/j.cedpsych.2018.07.004 ↗
23. Arnold, K.E., and Pistilli, M.D. (2012, January 29). Course Signals at Purdue: Using Learning Analytics to Increase Student Success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12), Vancouver, BC, Canada.10.1145/2330601.2330666 ↗
24. Signals: Applying Academic Analytics
25. (2010, July 17). Signals Tells Students How They’re Doing Even Before the Test. Available online: https://web.archive.org/web/20100717072052/http://news.uns.purdue.edu/x/2009b/090827ArnoldSignals.html.
26. Larusson, J.A., and White, B. (2014). A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering. Learning Analytics: From Research to Practice, Springer.
27. Waddington, R.J., and Nam, S. (2014, January 24–28). Practice Exams Make Perfect: Incorporating Course Resource Use into an Early Warning System. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (LAK ’14), Indianapolis, IN, USA.10.1145/2567574.2567623 ↗
28. Brown, M.G., DeMonbrun, R.M., Lonn, S., Aguilar, S.J., and Teasley, S.D. (2016, January 25–29). What and when: The Role of Course Type and Timing in Students’ Academic Performance. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK ’16), Edinburgh, UK.10.1145/2883851.2883907 ↗
29. Brown, M.G., DeMonbrun, R.M., and Teasley, S.D. (2017, January 13–17). Don’t Call It a Comeback: Academic Recovery and the Timing of Educational Technology Adoption. Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK ’17), Vancouver, BC, Canada.10.1145/3027385.3027393 ↗
30. Brown, M.G., DeMonbrun, R.M., and Teasley, S.D. (2018, January 7–9). Conceptualizing Co-enrollment: Accounting for Student Experiences Across the Curriculum. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), Sydney, New South Wales, Australia.10.1145/3170358.3170366 ↗
31. Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., and Verbert, K. (2018). LADA: A learning analytics dashboard for academic advising. Comput. Hum. Behav.
32. The use of an online learning and teaching system for monitoring computer aided design student participation and predicting student success10.1007/s10798-015-9346-8 ↗
33. Contrasting prediction methods for early warning systems at undergraduate level10.1016/j.iheduc.2018.02.001 ↗
34. Wang, Z., Zhu, C., Ying, Z., Zhang, Y., Wang, B., Jin, X., and Yang, H. (2018, January 10–12). Design and Implementation of Early Warning System Based on Educational Big Data. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.10.1109/ICSAI.2018.8599357 ↗
35. Analysis of student activity in web-supported courses as a tool for predicting dropout10.1007/s11423-017-9524-3 ↗
36. Developing an early-warning system for spotting at-risk students by using eBook interaction logs10.1186/s40561-019-0083-4 ↗
37. Plak, S., Cornelisz, I., Meeter, M., and van Klaveren, C. (2019, January 6–9). Early Warning Systems for More Effective Student Counseling in Higher Education—Evidence from a Dutch Field Experiment. Proceedings of the SREE Spring 2019 Conference, Washington, DC, USA.
38. Jovanovic, J., Gasevic, D., Dawson, S., Pardo, A., and Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. Internet Higher Educ., 33.10.1016/j.iheduc.2017.02.001 ↗