Factors Influencing the Learner's Cognitive Engagement in a Language MOOC: Feature Selection Approach

Abstract

This study aims to predict the cognitive engagement rate in a Language MOOC (Massive Open Online Course) based on the features extracted from learners' engagement behaviors within the content and activities. The features were extracted from the data of the Language MOOC 'Türkçe Öǧreniyorum (I learn Turkish)' which aims to provide self-paced learning materials for those interested in developing their skills in Turkish as a foreign language. After the data preprocessing processes were carried out with the data set obtained for cognitive engagement classification feature selection processes were performed using filtering and wrapper methods. Afterward the machine learning model trained using the Logistic Regression (LR) algorithm performed the classification with 94% accuracy. The model evaluation metrics also support the classification result obtained. Based on the extracted features and the classification results obtained the model will be able to capture learners' interaction behaviors with the content and activities in a Language MOOC and detect changes in learner behavior over time. Prediction accuracy is essential to offer dynamic content and activities in a Language MOOC for adjusting the individual needs of each learner providing personalized learning experiences that are tailored to their skills knowledge and preferences. © 2023 Elsevier B.V. All rights reserved.