Machine Learning and Statistical Analysis to Enhance Learning Outcomes in Online Learning Environments

Educational Data Mining (EDM) is an emerging field that aims to better understand students' behavior patterns and learning environments by employing statistical and machine learning methods to analyze large repositories of educational data. Analysis of variable data in the early stages of a course might be used to develop a comprehensive prediction model to prevent students' failure or dropout. This will assist instructors in intervening effectively. This dissertation utilizes machine learning models to predict students' learning outcomes based on their interactions with online learning environments. It also focuses on students' learning style detection to enhance their academic performance and personalize learning resources in online learning environments

Personalized Learning in Virtual Learning Environments Using Students’ Behavior Analysis

In recent years, many research studies have focused on personalized e-learning. One of the most crucial parts of any learning environment is having a learning style that focuses on individual learning. In this paper, we propose an approach to personalizing learning resources based on students’ learning styles in a virtual learning environment to enhance their academic performance. Students’ interactions with the learning management system are utilized to analyze learners’ behaviors. The Felder–Silverman Learning Style Model (FSLSM) is used to map students’ interactions with online learning resources to learning style (LS) features. The learning style and demographic features are then utilized for training machine learning models to predict students’ academic performance in each quarter of courses. The most accurate prediction model for each quarter is then used to find learning style features that maximize students’ pass rates. We statistically prove that students whose actual learning style features were close enough to the ones calculated by the approach achieved better grades. To improve students’ academic performance each quarter, we suggest two strategies based on the learning style features calculated by the process

Impacts on Students’ Academic Performance Due to Emergency Transition to Remote Teaching during the COVID-19 Pandemic: A Financial Engineering Course Case Study

The COVID-19 pandemic has enforced higher education institutions to adopt emergency remote teaching (ERT) as the substitution for traditional face-to-face (F2F) classes. A lot of concerns have been raised among education institutions, faculty, and students regarding the effectiveness of this sudden shift to online learning. This study aims to statistically investigate the impacts of such a transition on the academic performance of undergraduate students enrolled in the Financial Engineering course. A novel rank percentage measure is proposed and employed to compare the academic performance of around 500 students who attended the course during the four semesters, including the transitional disrupted semester by the pandemic, two consecutive online semesters, and the traditional face-to-face classroom. Our analysis emphasizes the significance of the differences between specific subgroups of the students. In particular, academically average to good students with cumulative GPAs greater than 2.90 have been negatively impacted by the transition to online learning, whereas the results for students with cumulative GPAs less than 2.90 are not very conclusive. Realizing the effects of such closures on the academic performance of students is considered important, since the results might have some merits for other courses and instructors. The template model can be transferred to other courses, and employed by the university administrators, specifically for developing policies in emergency circumstances that are not limited to pandemics