%0 Conference Paper %1 citeulike:14390289 %A Houbraken, Mara %A Sun, Chang %A Smirnov, Evgueni %A Driessens, Kurt %B Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2017 %I ACM %K competence datamining e-learning edrecsys2017 umap2017 %P 147--152 %R 10.1145/3099023.3099034 %T Discovering Hidden Course Requirements and Student Competences from Grade Data %U http://dx.doi.org/10.1145/3099023.3099034 %X This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a "Data Science and Knowledge Engineering" Bachelor study, Maastricht University. %@ 978-1-4503-5067-9

@inproceedings{citeulike:14390289, abstract = {{This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a "Data Science and Knowledge Engineering" Bachelor study, Maastricht University.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Houbraken, Mara and Sun, Chang and Smirnov, Evgueni and Driessens, Kurt}, biburl = {https://www.bibsonomy.org/bibtex/22861cfb6d17ffb3516f5e618d3ea1895/aho}, booktitle = {Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization}, citeulike-article-id = {14390289}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3099034}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/3099023.3099034}, doi = {10.1145/3099023.3099034}, interhash = {2091d5e2f24d4c63b083480d2262e854}, intrahash = {2861cfb6d17ffb3516f5e618d3ea1895}, isbn = {978-1-4503-5067-9}, keywords = {competence datamining e-learning edrecsys2017 umap2017}, location = {Bratislava, Slovakia}, pages = {147--152}, posted-at = {2017-07-09 08:50:34}, priority = {2}, publisher = {ACM}, series = {UMAP '17}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Discovering Hidden Course Requirements and Student Competences from Grade Data}}, url = {http://dx.doi.org/10.1145/3099023.3099034}, year = 2017 }