Ed-Net: Multivariate Time Series Approach for Uncovering Student Learning Outcome in Higher Education Using Blended Deep Learning Technique

Abstract

The higher education system (HES) in every country depicts the progress and prominence of that nation. So, the government endures the most care at all levels to enrich the quality of education on both the educator and learner side. The new academic policies also urge for worthwhile education. The HES abides by multiple steps for students' welfare in the academic curriculum, including a revamped syllabus, teaching methodology, and evaluation system. Nevertheless, students' performance is falling yearly, particularly in undergraduate programs. After COVID-19, students' study behaviour transformed abruptly due to online classes where students use mobile phones for education. They spend valuable time on the internet and gaming applications, which makes the students addicted. The evolution of Artificial Intelligence and massive student data permits us to get back the next generation by doing periodic assessments during their study period. This paper proposed a blended deep learning binary classification model (Ed-Net) using convolutional neural network and bidirectional long short term memory to predict the students' performance. Multivariate time series (MTS) student academic data is employed to train the model. To accomplish this systematic research, we followed two stages in the experiment. Stage one identifies the superlative student input data, approach (tabular/time-series), and algorithm (machine/deep learning) for better classification. Stage two executes the proposed system (Ed-Net) to attain the highest accuracy with less classification error. The synthetic minority oversampling technique (SMOTE) is applied to balance the students' pass-fail ratio. Finally, the experimental result exhibits the proposed method surpassed the baseline models with 98% accuracy, 97% precision, 94% recall, and 95% fl-score. The proposed model also uses a benchmark dataset to simulate the data and evaluate its efficacy. Moreover, any educational institution can quickly fit the academic data into this model to identify the students lacking in studies early for giving proper intervention to the parents, teachers, and students.