Predicting student academic performance in Video-Based learning
The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era due to the impact of COVID-19 pandemic has promoted the rise of the big data era in the e-learning platform. A new educational norm has been created due to the emerge of the educationa...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2023
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era due to the impact of COVID-19 pandemic has promoted the rise of the big data era in the e-learning platform. A new educational norm has been created due to the emerge of the educational technology domain where many students have accessing e-learning web platforms such as Edpuzzle, Coursera, and Udemy as well as Youtube for learning new knowledge. However, there are some drawbacks especially in the asynchronous video-based learning. Sense of isolation could be occurred between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that delivered by the teacher may not reach to students effectively and cause a drop in student performance in the coming examination. Moreover, growing of video-based learning has create the huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93. |
|---|