Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning
Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where online (web-based electronic interaction) and offline (direct face-to-face interaction in classrooms) learning are combined. This paper presents a Machine Learning (ML) based classification approach for students learning achievement behavior in Higher Education. In the proposed approach, Random Forests (RF) and Support Vector Machines (SVM) classification algorithms are being applied for developing prediction models in order to discover the underlying relationship between students past course interactions with Learning Management Systems (LMS) and their tendency to pass/fail. In this paper, we considered daily students interaction events, based on time series, with a number of Moodle LMS modules as the leading characteristics to observe students learning performance. The dataset used for experiments is constructed based on anonymized real data samples traced from web-log files of students access behavior concerning different modules in a Moodle online LMS throughout two academic years. Experimental results showed that the proposed RF classification system has outperformed the typical SVMs classification algorithm.
KeywordsLearning analytics Blended learning Features extraction Learning management systems (LMS) Moodle Random forests (RF) Support vector machines (SVMS)
This work is funded by: the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC); the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA; and the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R). This work is also partially funded by the European Commission under the Erasmus Mundus GreenIT project (3772227-1-2012-ES-ERA MUNDUS-EMA21). The authors also thank GRADIANT for its computing support and the University of Vigo for its e-learning service for their support.
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