Open AccessIncreasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators
Author(s) -
Tibor Fauszt,
László Bognár,
Ágnes Sándor
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i24.23923
Subject(s) - computer science , predictability , machine learning , process (computing) , artificial intelligence , openness to experience , goodness of fit , matlab , core (optical fiber) , open source , analytics , learning analytics , data mining , programming language , software , statistics , psychology , social psychology , telecommunications , mathematics
Starting with version 3.4 of Moodle, it has been possible to build educational ML models using predefined indicators in the Analytics API. These models can be used primarily to identify students at risk of failure. Our research shows that the goodness and predictability of models built using predefined core indicators in the API lags far behind the generally acceptable level. Moodle is an open-source system, which on the one hand allows the analysis of algorithms, and on the oth-er hand its modification and further development. Utilizing the openness of the system, we examined the calculation algorithm of the core indicators, and then, based on the experience, we built new models with our own indicators. Our re-sults show that the goodness of models built on a given course can be significant-ly improved. In the article, we discuss the development process in detail and pre-sent the results achieved.