Abstract
Over the past years, there has been a significant increase in the number of students attending online courses in higher education, in some cases due to the COVID-19 pandemic. Online higher education provides greater flexibility in selecting courses in the preferred order, but it also presents challenges due to the amount of different combinations during the enrollment process. In this paper, we describe a dashboard for supporting enrollment in higher education using a map as a visual metaphor, in order to provide students and their advisors with a clear overview of their situation within the degree, and help them to make better decisions. The enrollment dashboard has been evaluated through an initial series of interviews with advisors, which showed positive results in terms of usability and usefulness. However, several concerns were raised about the parameters used to create the map. These results will be used in future rounds of interviews with students and advisors.
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References
Bodily, R., Verbert, K.: Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Trans. Learn. Technol. 10(4), 405–418 (2017). Conference Name: IEEE Transactions on Learning Technologies https://doi.org/10.1109/TLT.2017.2740172
Duval, E.: Attention please! learning analytics for visualization and recommendation. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK 2011, pp. 9–17. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/2090116.2090118
Karga, S., Satratzemi, M.: Using explanations for recommender systems in learning design settings to enhance teachers’ acceptance and perceived experience. Educ. Inf. Technol. 24(5), 2953–2974 (2019). https://doi.org/10.1007/s10639-019-09909-z
Ma, B., Lu, M., Taniguchi, Y., Konomi, S.: CourseQ: the impact of visual and interactive course recommendation in university environments. Res. Pract. Technol. Enhanc. Learn. 16(1), 18 (2021). https://doi.org/10.1186/s41039-021-00167-7
Maphosa, M., Doorsamy, W., Paul, B.: A review of recommender systems for choosing elective courses. Int. J.Adv. Comput. Sci. Appl. 11(9) (2020). https://doi.org/10.14569/IJACSA.2020.0110933
Maphosa, V., Maphosa, M.: Fifteen years of recommender systems research in higher education: current trends and future direction. Appl. Artif. Intell. 37(1), 2175106 (2023). https://doi.org/10.1080/08839514.2023.2175106
Minguillón, J., Rivas, N., Chacón, J.: Supporting enrollment in higher education through a visual recommendation system. In: Artificial Intelligence Research and Development: Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence, vol. 339, p. 177. IOS Press (2021). https://doi.org/10.3233/FAIA210131
Othman, M.H., Mohamad, N., Barom, M.N.: Students’ decision making in class selection and enrolment. Int. J. Educ. Manage. 33(4), 587–603 (2019). Publisher: Emerald Publishing Limited https://doi.org/10.1108/IJEM-06-2017-0143
Rivas, N., Minguillón, J., Chacón, J.: Enrolling habits in higher education. what sources of information do students have and what are missing? In: INTED2021 Proceedings, pp. 4980–4988. 15th Int. Technology, Education and Development Conf, IATED (8–9 March, 2021 2021). https://doi.org/10.21125/inted.2021.1025
Schwendimann, B.A., Rodriguez-Triana, M.J., Vozniuk, A., Prieto, L.P., Boroujeni, M.S., Holzer, A., Gillet, D., Dillenbourg, P.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2016). https://doi.org/10.1109/TLT.2016.2599522
Scott, M., Savage, D.A.: Lemons in the university: asymmetric information, academic shopping and subject selection. High. Educ. Res. Dev. 41(4), 1247–1261 (2022). https://doi.org/10.1080/07294360.2021.1887094
Stevens, M., Harrison, M., Thompson, M.E., Lifschitz, A., Chaturapruek, S.: Choices, identities, paths: Understanding college students’ academic decisions (2018). https://doi.org/10.2139/ssrn.3162429
Wladis, C., Wladis, K., Hachey, A.C.: The role of enrollment choice in online education: Course selection rationale and course difficulty as factors affecting retention. Online Learn. 18(3) (2014). https://eric.ed.gov/?id=EJ1043163, publisher: Online Learning Consortium, Inc ERIC Number: EJ1043163
Zhang, M., Ma, J., Liu, Z., Sun, J., Silva, T.: A research analytics framework-supported recommendation approach for supervisor selection. Br. J. Edu. Technol. 47(2), 403–420 (2016). https://doi.org/10.1111/bjet.12244
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Rivas, N., Minguillón, J., Chacón-Pérez, J. (2023). An Enrollment Dashboard to Reinforce Decision-Making for Students and Advisors. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_71
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