The Readiness of Universities to Use Predictive Learning Analytics for Students’ Retention and Academic Success
DOI:
https://doi.org/10.18662/lumenss/9.1/40Keywords:
learning analytics, students retention, processual perspective of learning, qualitative variables, learning predictors, “big data”/”small data”, academic success, holistic approachAbstract
The article proposes an analysis of the readiness level of universities for using the predictions available through systems of learning analytics, in order to ensure the academic success of students, especially for those at risk of dropout.
The proposed approach aims to highlight the need for connections to be established between the determinant factors of the success of the studies, as it turns out from the categories of already collected data through Learning Management Systems, versus information derived from recent studies conducted by using the students' perspective. They show fewer links with measures based on meritocracy or employment prospects in the labour market, than with qualitative indicators connected with the development of the skills that students consider relevant, in particular, the orientation towards embodied and emotional success, the formation of critical and reflective thinking that conditions the satisfaction of completing the studies seen as a "continuous challenge", especially to make authentic choices for the professional future. Based on an improved inventory of indicators, including qualitative ones, which also value students’ opinions, data collection through early warning systems can be significantly improved so that intervention strategies can be configured to support students in situations "of risk".
Beginning with this premise, we conclude by highlighting some challenges for the management of human resources necessary for the implementation of the predictions of learning analytics in universities, in order to increase the retention rate of students, devoting special attention to stimulating teachers’ metacognitions in order to identify the areas of intervention at the level of the didactic activities.
References
Beilin, I. (2016). Student Success and the Neoliberal Academic Library. Canadian Journal of Academic Librarianship, 1(1), 10–23. https://doi.org/10.33137/cjal-rcbu.v1.24303
Burke, P., Bennett, A., Burgess, C., Gray, K., & Southgate, E. (2016). Capability, belonging and equity in higher education: Developing inclusive approaches. Centre for Excellence for Equity in Higher Education (CEEHE): University of Newcastle. https://www.newcastle.edu.au/__data/assets/pdf_file/0011/243992/CAPABILITY-ONLINE.pdf
Fritz, J., & Carpenter, R. (2019). Triangulating Student Engagement with “Built & Bought” Learning Analytics. In Khine, M. S. (Ed.), Emerging Trends in Learning Analytics. Leveraging the Power of Education Data (pp. 158-174). Leiden: Koninklijke Brill NV. https://doi.org/10.1163/9789004399273_009
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large‑scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Education Technology Research and Development, 67, 1273-1306. https://doi.org/10.1007/s11423-019-09685-0
Huang, Q., Nathawitharana, N., Ong, K. L., Susan K., & Alahakoon, D. (2018). Mind the Gap: From Analytics to Action in Student Retention. In Miah, S. J., & Yeoh, W. (Eds.), Applying Business Intelligence Initiatives in Healthcare and Organizational Settings (pp. 218-236). IGI Global. https://doi.org/10.4018/978-1-5225-5718-0.ch012
Khine, M. S. (2019). Big Data Analytics in Education for Dynamic Personalised Learning Design. In Khine, M. S. (Ed.), Emerging Trends in Learning Analytics. Leveraging the Power of Education Data (pp. 3-10). Leiden: Koninklijke Brill NV. https://doi.org/10.1163/9789004399273_001
McKenney, S., & Mor, Y. (2015). Supporting teachers in data-informed educational design. British Journal of Educational Technology, 46(2), 265–279. https://doi.org/10.1111/bjet.12262
McNair, B., Bensimon, E., Cooper, M., McDonald, N., & Major, T. (2016). Becoming a Student-Ready College: A New Culture of Leadership for Student Success. Jossey-Bass.
O'Shea, S., & Delahunty, J. (2018). Getting through the day and still having a smile on my face! How do students define success in the university learning environment? Higher Education Research & Development, 37(5), 1062-1075. https://doi.org/10.1080/07294360.2018.1463973
Sahlberg, P. (2018). Four Big, Inexpensive Ideas to Transform Education. Thousand Oaks, California: Corwin, a SAGE company.
Siemens, G. (2011). Learning and Academic Analytics. http://www.learninganalytics.net/?p=131.
Southern Oregon University. (2019). Learner Satisfaction and Success. Professional Learning Community. https://sou.edu/strategic-planning/wp-content/uploads/sites/28/2019/04/Final-Report-on-Learner-Satisfaction-and-Success.pdf
Sparkman, L., Maulding, W., & Roberts, J. (2012). Non-Cognitive Predictors of Student Success in College. College Student Journal, 46(3), 642–652.
van Leeuwen, A. (2018). Teachers’ perceptions of the usability of learning analytics reports in a fipped university course: When and how does information become actionable knowledge? Educational Technology Research and Development, 67, 1043-1064. https://doi.org/10.1007/s11423-018-09639-y
Villano, R. A., Harrison, S., Lynch, G., & Chen, G. (2018). Linking early alert systems and student retention: a survival analysis approach. Higher Education, 76, 903–920. https://doi.org/10.1007/s10734-018-0249-y
Vossensteyn, H., Kottmann, A., Jongbloed, B., Kaiser, F., Cremonini, L., Stensaker, B., Hovdhaugen, E., & Wollscheid, S. (2015). Dropout and Completion in Higher Education in Europe. Luxembourg: Publications Office of the European Union. https://doi.org/10.2766/826962
Walker, M. (2003). Framing social justice in education: What does the capabilities approach offer? British Journal of Educational Studies, 51(2), 168–187. https://doi.org/10.1111/1467-8527.t01-2-00232
Walker, M. (2008). Widening participation; Widening capability. London Review of Education, 6(3), 267–279. https://doi.org/10.1080/14748460802489397
Wong, B. T., & Li, K. C. (2019). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7, 7-28. https://doi.org/10.1007/s40692-019-00143-7
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant this journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work, with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as an earlier and greater citation of published work (See The Effect of Open Access).
LUMEN SS Journal has an Attribution-NonCommercial-NoDerivs
CC BY-NC-ND
