Publication details

Course Recommendation from Social Data



Year of publication 2014
Type Article in Proceedings
Conference 6th International Conference on Computer Supported Education - CSEDU 2014
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System
Description This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommend students such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.

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