A Course Recommender System Built on Success to Support Students at Risk in Higher Education
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Published
Jun 27, 2024
Kerstin Wagner
Agathe Merceron
Petra Sauer
Niels Pinkwart
https://orcid.org/0000-0002-6182-2142
Abstract
In this paper, we present an extended evaluation of a course recommender system designed to support
students who struggle in the first semesters of their studies and are at risk of dropping out. The system,
which was developed in earlier work using a student-centered design, is based on the explainable
k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of
successful neighbors, that is, students who graduated from the study program. In terms of the number of
recommended courses, we found a discrepancy between the number of courses that struggling students
are recommended to take and the actual number of courses they take. This indicates that there may be
an alternative path that these students could consider. However, the recommended courses align well
with the courses taken by students who successfully graduated. This suggests that even students who are
performing well could still benefit from the course recommender system designed for at-risk students.
In the present work, we investigate a second type of success—a specific minimum number of courses
passed—and compare the results with our first approach from previous work. With the second type, the
information about success might be already available after one semester instead of after graduation which
allows faster growth of the database and faster response to curricular changes. The evaluation of three
different study programs in terms of dropout risk reduction and recommendation quality suggests that
course recommendations based on students passing at least three courses in the following semester can
be an alternative to guide students on a successful path. The aggregated result data and results explorations
are available at: https://kwbln.github.io/jedm23.
How to Cite
Wagner, K., Merceron, A., Sauer, P., & Pinkwart, N. (2024). A Course Recommender System Built on Success to Support Students at Risk in Higher Education. Journal of Educational Data Mining, 16(1), 330–364. https://doi.org/10.5281/zenodo.11384083
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Keywords
course recommender system, course set recommendation, student success, nearest neighbors, user-centered design, dropout prediction, two-step dropout risk prediction, university records
References
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BERENS, J., SCHNEIDER, K., GORTZ, S., OSTER, S., AND BURGHOFF, J. 2019. Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining 11, 3, 1–41. https://doi.org/10.5281/zenodo.3594771.
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DU, F., PLAISANT, C., SPRING, N., AND SHNEIDERMAN, B. 2017. Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI 2017). Association for Computing Machinery, New York, NY, USA, 5498–5544. https://doi.org/10.1145/3025453.3025777.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-Aware Grade Prediction and Top-n Course Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys 2016). Association for Computing Machinery, New York, NY, USA, 183–190. https://doi.org/10.1145/2959100.2959133.
HEUBLEIN, U., EBERT, J., HUTZSCH, C., ISLEIB, S., KÖNIG, R., RICHTER, J., AND WOISCH, A. 2017. Zwischen Studienerwartungen und Studienwirklichkeit. Ursachen des Studienabbruchs, beruflicher Verbleib der Studienabbrecherinnen und Studienabbrecher und die Entwicklung der Studienabbruchquote an deutschen Hochschulen. [Between study expectations and study reality. Causes of dropping out of university, where dropouts remain in their careers and the development of the dropout rate at German universities.]. Collection, Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW). June. https://www.bildungsserver.de/onlineressource.html?onlineressourcen_id=58641.
HEUBLEIN, U., HUTZSCH, C., AND SCHMELZER, R. 2022. Die Entwicklung der Studienabbruchquoten in Deutschland [The development of student drop-out rates in Germany]. Tech. rep., Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW). https://www.dzhw.eu/publikationen/pub_show?pub_id=7922&pub_type=kbr.
KEMPER, L., VORHOFF, G., AND WIGGER, B. U. 2020. Predicting Student Dropout: A Machine Learning Approach. European Journal of Higher Education 10, 1, 28–47. https://doi.org/10.1080/21568235.2020.1718520.
LEMAÎTRE, G., NOGUEIRA, F., AND ARIDAS, C. K. 2017. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research 18, 17, 1–5.
MA, B., TANIGUCHI, Y., AND KONOMI, S. 2020. Course Recommendation for University Environments. In Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, Online, 460–466. https://eric.ed.gov/?id=ED607802.
MANRIQUE, R., NUNES, B. P., MARINO, O., CASANOVA, M. A., AND NURMIKKO-FULLER, T. 2019. An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK 2019). Association for Computing Machinery, New York, NY, USA, 401–410. https://doi.org/10.1145/3303772.3303800.
MOLNAR, C. 2023. 5.7 Other Interpretable Models. https://christophm.github.io/interpretable-mlbook/ other-interpretable.html.
MORSY, S. AND KARYPIS, G. 2019. Will This Course Increase or Decrease Your GPA? Towards Grade-Aware Course Recommendation. Journal of Educational Data Mining 11, 2, 20–46. https://doi.org/10.5281/zenodo.3554677.
NEUGEBAUER, M., HEUBLEIN, U., AND DANIEL, A. 2019. Studienabbruch in Deutschland: Ausmaß, Ursachen, Folgen, Präventionsmöglichkeiten [Higher education dropout in Germany: extent, causes, consequences, prevention]. Zeitschrift für Erziehungswissenschaft 22, 5 (Oct.), 1025–1046. https://doi.org/10.1007/s11618-019-00904-1.
PARDOS, Z. A., FAN, Z., AND JIANG, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling and User- Adapted Interaction 29, 2, 487–525. https://doi.org/10.1007/s11257-019-09218-7.
PARDOS, Z. A. AND JIANG, W. 2020. Designing for serendipity in a university course recommendation system. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). Association for Computing Machinery, New York, NY, USA, 350–359. https://doi.org/10.1145/3375462.3375524.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., VANDERPLAS, J., PASSOS, A., COUR- NAPEAU, D., BRUCHER, M., PERROT, M., AND DUCHESNAY, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.
POLYZOU, A. AND KARYPIS, G. 2016. Grade Prediction with Course and Student Specific Models. In Advances in Knowledge Discovery and Data Mining. 20th Pacific-Asia Conference (PAKDD 2016), J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, and R. Wang, Eds. Springer International Publishing, Cham, Auckland, New Zealand, 89–101. https://doi.org/10.1007/978-3-319-31753-3_8.
POLYZOU, A., NIKOLAKOPOULOS, A. N., AND KARYPIS, G. 2019. Scholars Walk: A Markov Chain Framework for Course Recommendation. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montreal, Canada, 396–401. https://eric.ed.gov/?id=ED599254.
URDANETA-PONTE, M. C., MENDEZ-ZORRILLA, A., AND OLEAGORDIA-RUIZ, I. 2021. Recommendation Systems for Education: Systematic Review. Electronics 10, 14, 1611. https://doi.org/10.3390/electronics10141611.
VIRTANEN, P., GOMMERS, R., OLIPHANT, T. E., HABERLAND, M., REDDY, T., COURNAPEAU, D., BUROVSKI, E., PETERSON, P., WECKESSER, W., BRIGHT, J., VAN DER WALT, S. J., BRETT, M., WILSON, J., MILLMAN, K. J., MAYOROV, N., NELSON, A. R. J., JONES, E., KERN, R., LARSON, E., CAREY, C. J., POLAT, ., FENG, Y., MOORE, E. W., VANDERPLAS, J., LAXALDE, D., PERKTOLD, J., CIMRMAN, R., HENRIKSEN, I., QUINTERO, E. A., HARRIS, C. R., ARCHIBALD, A. M., RIBEIRO, A. H., PEDREGOSA, F., VAN MULBREGT, P., AND SCIPY 1.0 CONTRIBUTORS. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261–272.
WAGNER, K., HILLIGER, I., MERCERON, A., AND SAUER, P. 2021. Eliciting Students’ Needs and Concerns about a Novel Course Enrollment Support System. In Companion Proceedings of the 11th International Learning Analytics and Knowledge Conference (LAK 2021). Online, 294–304. https://www.solaresearch.org/core/lak21-companion-proceedings/.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2022. Personalized and Explainable Course Recommendations for Students at Risk of Dropping out. In Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 657–661. https://doi.org/10.5281/zenodo.6853008.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2023. Can the Paths of Successful Students Help Other Students With Their Course Enrollments? In Proceedings of the 16th International Conference on Educational Data Mining (EDM 2023), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 171–182. https://zenodo.org/record/8115719.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2024. About the Quality of a Course Recommender System as Perceived by Students. In Proceedings of the 16th International Conference on Computer Supported Education, O. Poquet, A. Ortega-Arranz, O. Viberg, I.-A. Chounta, B. McLaren, and J. Jovanovic, Eds. SCITEPRESS - Science and Technology Publications, Angers, France, 238–246.
WAGNER, K., VOLKENING, H., BASYIGIT, S., MERCERON, A., SAUER, P., AND PINKWART, N. 2023. Which Approach Best Predicts Dropouts in Higher Education? In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023), J. Jovanovic, I.-AChounta, J. Uhomoibhi, and B. M. McLaren, Eds. SciTePress, Prague, Czech Republic, 15–26. https://doi.org/10.5220/0011838100003470.
BERENS, J., SCHNEIDER, K., GORTZ, S., OSTER, S., AND BURGHOFF, J. 2019. Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining 11, 3, 1–41. https://doi.org/10.5281/zenodo.3594771.
CHAWLA, N. V., BOWYER, K. W., HALL, L. O., AND KEGELMEYER, W. P. 2002. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357. http://arxiv.org/abs/1106.1813.
DU, F., PLAISANT, C., SPRING, N., AND SHNEIDERMAN, B. 2017. Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI 2017). Association for Computing Machinery, New York, NY, USA, 5498–5544. https://doi.org/10.1145/3025453.3025777.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-Aware Grade Prediction and Top-n Course Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys 2016). Association for Computing Machinery, New York, NY, USA, 183–190. https://doi.org/10.1145/2959100.2959133.
HEUBLEIN, U., EBERT, J., HUTZSCH, C., ISLEIB, S., KÖNIG, R., RICHTER, J., AND WOISCH, A. 2017. Zwischen Studienerwartungen und Studienwirklichkeit. Ursachen des Studienabbruchs, beruflicher Verbleib der Studienabbrecherinnen und Studienabbrecher und die Entwicklung der Studienabbruchquote an deutschen Hochschulen. [Between study expectations and study reality. Causes of dropping out of university, where dropouts remain in their careers and the development of the dropout rate at German universities.]. Collection, Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW). June. https://www.bildungsserver.de/onlineressource.html?onlineressourcen_id=58641.
HEUBLEIN, U., HUTZSCH, C., AND SCHMELZER, R. 2022. Die Entwicklung der Studienabbruchquoten in Deutschland [The development of student drop-out rates in Germany]. Tech. rep., Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW). https://www.dzhw.eu/publikationen/pub_show?pub_id=7922&pub_type=kbr.
KEMPER, L., VORHOFF, G., AND WIGGER, B. U. 2020. Predicting Student Dropout: A Machine Learning Approach. European Journal of Higher Education 10, 1, 28–47. https://doi.org/10.1080/21568235.2020.1718520.
LEMAÎTRE, G., NOGUEIRA, F., AND ARIDAS, C. K. 2017. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research 18, 17, 1–5.
MA, B., TANIGUCHI, Y., AND KONOMI, S. 2020. Course Recommendation for University Environments. In Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, Online, 460–466. https://eric.ed.gov/?id=ED607802.
MANRIQUE, R., NUNES, B. P., MARINO, O., CASANOVA, M. A., AND NURMIKKO-FULLER, T. 2019. An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK 2019). Association for Computing Machinery, New York, NY, USA, 401–410. https://doi.org/10.1145/3303772.3303800.
MOLNAR, C. 2023. 5.7 Other Interpretable Models. https://christophm.github.io/interpretable-mlbook/ other-interpretable.html.
MORSY, S. AND KARYPIS, G. 2019. Will This Course Increase or Decrease Your GPA? Towards Grade-Aware Course Recommendation. Journal of Educational Data Mining 11, 2, 20–46. https://doi.org/10.5281/zenodo.3554677.
NEUGEBAUER, M., HEUBLEIN, U., AND DANIEL, A. 2019. Studienabbruch in Deutschland: Ausmaß, Ursachen, Folgen, Präventionsmöglichkeiten [Higher education dropout in Germany: extent, causes, consequences, prevention]. Zeitschrift für Erziehungswissenschaft 22, 5 (Oct.), 1025–1046. https://doi.org/10.1007/s11618-019-00904-1.
PARDOS, Z. A., FAN, Z., AND JIANG, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling and User- Adapted Interaction 29, 2, 487–525. https://doi.org/10.1007/s11257-019-09218-7.
PARDOS, Z. A. AND JIANG, W. 2020. Designing for serendipity in a university course recommendation system. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). Association for Computing Machinery, New York, NY, USA, 350–359. https://doi.org/10.1145/3375462.3375524.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., VANDERPLAS, J., PASSOS, A., COUR- NAPEAU, D., BRUCHER, M., PERROT, M., AND DUCHESNAY, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.
POLYZOU, A. AND KARYPIS, G. 2016. Grade Prediction with Course and Student Specific Models. In Advances in Knowledge Discovery and Data Mining. 20th Pacific-Asia Conference (PAKDD 2016), J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, and R. Wang, Eds. Springer International Publishing, Cham, Auckland, New Zealand, 89–101. https://doi.org/10.1007/978-3-319-31753-3_8.
POLYZOU, A., NIKOLAKOPOULOS, A. N., AND KARYPIS, G. 2019. Scholars Walk: A Markov Chain Framework for Course Recommendation. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montreal, Canada, 396–401. https://eric.ed.gov/?id=ED599254.
URDANETA-PONTE, M. C., MENDEZ-ZORRILLA, A., AND OLEAGORDIA-RUIZ, I. 2021. Recommendation Systems for Education: Systematic Review. Electronics 10, 14, 1611. https://doi.org/10.3390/electronics10141611.
VIRTANEN, P., GOMMERS, R., OLIPHANT, T. E., HABERLAND, M., REDDY, T., COURNAPEAU, D., BUROVSKI, E., PETERSON, P., WECKESSER, W., BRIGHT, J., VAN DER WALT, S. J., BRETT, M., WILSON, J., MILLMAN, K. J., MAYOROV, N., NELSON, A. R. J., JONES, E., KERN, R., LARSON, E., CAREY, C. J., POLAT, ., FENG, Y., MOORE, E. W., VANDERPLAS, J., LAXALDE, D., PERKTOLD, J., CIMRMAN, R., HENRIKSEN, I., QUINTERO, E. A., HARRIS, C. R., ARCHIBALD, A. M., RIBEIRO, A. H., PEDREGOSA, F., VAN MULBREGT, P., AND SCIPY 1.0 CONTRIBUTORS. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261–272.
WAGNER, K., HILLIGER, I., MERCERON, A., AND SAUER, P. 2021. Eliciting Students’ Needs and Concerns about a Novel Course Enrollment Support System. In Companion Proceedings of the 11th International Learning Analytics and Knowledge Conference (LAK 2021). Online, 294–304. https://www.solaresearch.org/core/lak21-companion-proceedings/.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2022. Personalized and Explainable Course Recommendations for Students at Risk of Dropping out. In Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 657–661. https://doi.org/10.5281/zenodo.6853008.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2023. Can the Paths of Successful Students Help Other Students With Their Course Enrollments? In Proceedings of the 16th International Conference on Educational Data Mining (EDM 2023), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 171–182. https://zenodo.org/record/8115719.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2024. About the Quality of a Course Recommender System as Perceived by Students. In Proceedings of the 16th International Conference on Computer Supported Education, O. Poquet, A. Ortega-Arranz, O. Viberg, I.-A. Chounta, B. McLaren, and J. Jovanovic, Eds. SCITEPRESS - Science and Technology Publications, Angers, France, 238–246.
WAGNER, K., VOLKENING, H., BASYIGIT, S., MERCERON, A., SAUER, P., AND PINKWART, N. 2023. Which Approach Best Predicts Dropouts in Higher Education? In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023), J. Jovanovic, I.-AChounta, J. Uhomoibhi, and B. M. McLaren, Eds. SciTePress, Prague, Czech Republic, 15–26. https://doi.org/10.5220/0011838100003470.
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Extended Articles from the EDM 2023 Conference
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