Guiding the Transition to University Mathematics with Learning Analytics

Heleen van der Zaag*, Fulya Kula

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In response to a recognised problem in higher education, being the limited use of scalable learning analytics (LA) (e.g. Hernandez-de-Menendez et al., 2022, Viberg et al., 2018 ), this study has designed and implemented a transition course for mathematics. This transition course aims to support first year university students in their transition from secondary to tertiary education with a focus on providing real time feedback to students and teachers through the use of LA (Kula et al., 2023). Feedback was provided with the data that was gathered throughout the course via students’ answers and attempts to questions and data from the interactive videos. Students were presented with an individual overview of their effort using a star based visualisation per sub-topic of the transition course indicating their level of understanding to be adequate for the university mathematics. Moreover, the teacher is presented with a whole class heatmap visualisation of the mathematical subjects which is based on the student attempts and their current result (adapted from Lee et al., 2016). The bridging course was implemented in the Civil Engineering bachelor program, and will be adapted to many bachelor programs at the University of Twente. We have received interest from various national and international universities, both in this course and its use of LA. This use of LA, to give students and teachers the feedback based on question attempts and results, is replicable to different courses. We would like to scale up the transition course as well as its use of LA both horizontally to a large audience of students and teachers national and international dimensions, and also vertically to various disciplines to adapt the use of LA. References: Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16, 1209–1230. Kula, F. , Horstman, E. M., Lanting, L. S., & Ten Klooster, L. R. (2023). Exploring Strategies To Promote Engagement And Active Learning Through Digital Course Design In Engineering Mathematics. Paper presented at 51st Annual Conference of the European Society for Engineering Education, SEFI 2023, Dublin, Ireland. Lee, J. E., Recker, M., Bowers, A. J., & Yuan, M. (2016, June). Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data. In EDM (pp. 603-604). Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(December 2018), 98-110.
Original languageEnglish
Title of host publicationLAK 24: The Fourteenth International Conference on Learning Analytics & Knowledge
Subtitle of host publicationWorkshop: Challenges and Opportunities of Learning Analytics Adoption in Higher Education Institutes: A European Perspective
EditorsLudo W. van Meeuwen, Bart Rienties, Hendrik Drachsler, Olga Viberg, Rianne Conijn, Caroline Vonk, Jan Willem Brijan, Marcus Specht
Publication statusPublished - 2024
Event14th International Learning Analytics and Knowledge Conference, LAK 2024 - Kyoto, Japan
Duration: 18 Mar 202422 Mar 2024
Conference number: 14

Conference

Conference14th International Learning Analytics and Knowledge Conference, LAK 2024
Abbreviated titleLAK 2024
Country/TerritoryJapan
CityKyoto
Period18/03/2422/03/24

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