Taxonomy of E-Course Quality Metrics and Automated Evaluation Possibilities

Abstract

Nowadays, the popularity of online learning is growing rapidly, and e-courses are becoming one of the main ways of learning. To successfully conduct online training, it is crucial to ensure the high quality of learning materials, as they have a significant impact on the effectiveness of the learning process, motivation, and engagement of learners. Considering the large number of e-courses and their frequent changes, human evaluation becomes complicated and ineffective. In this situation, it becomes relevant to look for automated methods for assessing the quality of electronic courses, which would not only enhance the evaluation process, but also contribute to objectivity, consistency, and standardization, ensuring high quality of learning content. After analyzing the possibilities for evaluating the quality of electronic courses, it was observed that there is a lack of a unified methodology for the automated assessment of learning materials. Therefore, the objective of this work is to create a taxonomy that summarizes the landscape of various aspects of the quality of electronic courses and their automated evaluation possibilities. The proposed taxonomy allows the identification of the most important criteria and elements of the quality of learning materials, contributing to a standardized and objective evaluation process.