BUILT-IN LEARNING ANALYTICS CAPABILITIES IN MOODLE
BUILT-IN LEARNING ANALYTICS CAPABILITIES IN MOODLE

Author(s): Gergana Kasabova
Subject(s): Social Sciences, Education, Higher Education , Distance learning / e-learning
Published by: Икономически университет - Варна
Keywords: e-learning; Learning Management System; Learning Analytics; Moodle Analytics API
Summary/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 process-es, Learning Analytics is becoming a key tool for enhancing the learning experience. Moodle, as a leading open-source 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 ad-vantages 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.

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