Design and Evaluation of Online Educational Content: AI-Informed Guidelines Based on Machine Learning Analysis of Learners’ Interactions Traces

Abstract

This study proposes an intelligent educational decision support system that empowers instructional designers to evaluate online educational contents in order to improve their design and effectiveness. The key challenge in developing it lies in automating and objectifying the evaluation process. To address this, the study pursues two main objectives. The first one is to propose a Multicriteria Approach for Learning Experience Analysis (MALEA) on which we have based the evaluation through the learners’ traces. The second objective consists of proposing the Approach for Content Success Prediction (ACSP), which can be used to evaluate educational content before its deployment. ACSP combines logistic regression and MALEA. This combination helps to guard against the possible imprecision of human judgment affecting the decision-making process. A case study is conducted and proves that the system meets the objective sought and thus is retained for online educational content evaluation. Results are promising with high values of precision, accuracy, specificity, and sensitivity. Different perspectives are finally proposed.