Early Warning System of Students Failing a Course : A Binary Classification Modelling Approach at Upper Secondary School Level

Sammanfattning

Only 70% of the Swedish students graduate from upper secondary school within the given time frame. Earlier research has shown that unfinished degrees disadvantage the individual student, policy makers and society. A first step for preventing dropouts is to indicate students about to fail courses. Thus the purpose is to identify tendencies whether a student will pass or not pass a course. In addition, the thesis accounts for the development of an Early Warning System to be applied to signal which students need additional support from a professional teacher. The used algorithm Random Forest functioned as a binary classification model of a failed grade against a passing grade. Data in the study are in samples of approximately 700 students from an upper secondary school within the Stockholm municipality. The chosen method originates from a Design Science Research Methodology that allows the stakeholders to be involved in the process. The results showed that the most dominant indicators for classifying correct were Absence, Previous grades and Mathematics diagnosis. Furthermore, were variables from the Learning Management System predominant indicators when the system also was utilised by teachers. The prediction accuracy of the algorithm indicates a positive tendency for classifying correctly. On the other hand, the small number of data points imply doubt if an Early Warning System can be applied in its current state. Thus, one conclusion is in further studies, it is necessary to increase the number of data points. Suggestions to address the problem are mentioned in the Discussion. Moreover, the results are analysed together with a review of the potential Early Warning Systemfrom a didactic perspective. Furthermore, the ethical aspects of the thesis are discussed thoroughly.