E-learning systems and recommending learning materials

Bavcon David (2017) E-learning systems and recommending learning materials. MSc thesis.

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Abstract

Within our thesis, we developed a simple e-learning system which recommends learning materials to high school students. We began by preparing a short overview of appropriate e-curriculums. We continued by implementing our own system that includes real-time learning analytics. To test the system, we also created some learning materials on high school information science. We tested three different recommendation techniques, i.e. learning materials that are the closest match to previous materials and the furthest match from previous materials and materials in random order. Each material assignment strategy was tested on one third of students. Based on observing precision, recall and F1 measure, we established that the best system turns out to be random order material assignment, followed by matrix factorisation that offers the least matching materials to previous materials. The technique offering the closest matching materials proved to be the least useful. The results therefore indicate that we should offer students random materials. Other strategies provided considerably worse results when we compared them by precision.

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