BUILT-IN LEARNING ANALYTICS CAPABILITIES IN MOODLE

Gergana Kasabova ()
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Gergana Kasabova: University of Economics - Varna / Department of Informatics, Varna, Bulgaria

Conferences of the department Informatics, 2024, issue 1, 206-212

Abstract: This article examines the possibilities of Learning Analytics within the Learning Management System (LMS) Moodle. With the increasing role of online distance education and technology in educational processes, Learning Analytics is becoming a key tool for enhancing the learning experience. Moodle, as a leading opensource platform, generates a substantial amount of data on learner activities, which can be analyzed to improve educational quality. Learning analytics involves the collection, measurement and evaluation of data regarding learners, with the aim of understanding their outcomes and optimizing the learning process. This allows for informed decisions regarding learning opportunities and teaching methods as well as the identification of students at risk of dropping out. There are four main types of learning data analysis: descriptive, diagnostic, predictive, and prescriptive. Moodle provides built-in learning analytics capabilities through its Moodle Analytics API, which utilizes models based on machine learning and "static" models. The main advantages of Moodle Analytics include predicting learner performance and data-driven decision-making, while some of the challenges are the complexity of setting up and configuring the API, as well as concerns related to data privacy.

Keywords: e-learning; Learning Management System; Learning Analytics; Moodle Analytics API (search for similar items in EconPapers)
JEL-codes: C8 (search for similar items in EconPapers)
Date: 2024
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