The rapid growth of the Industrial Revolution (IR) 4.0 has prompted the Malaysian Education Institution to transform the current education system into the future education system 4.0. The impact of IR 4.0 has opened a new paradigm for the Malaysian Educational Institution to ensure that all lecturers are capable of using information and communication technologies (ICT) in teaching and learning. However, there is a challenge in identifying appropriate digital learning platforms and tools to engage students in learning at their own pace. In this paper, we aimed to investigate the demand for digital learning platforms and tools according to the needs of students in Polytechnic Malaysia. The study was conducted randomly among 320 students from various fields of study in selected polytechnics. The analysis method used in this study was a quantitative method using questionnaires as an instrument. The results of our study indicated that e-learning platforms were the highest demand students’ preferred compared to other learning platforms and tools. Hence, the implications of this study could be useful as a guideline to assist Malaysian Polytechnic lecturers in strengthening the practice of using digital learning and develop digital proficiency for enabling education 4.0 in the future.

Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in student grade prediction. However, there is a lack of studies in identifying the effective predictive model, especially in addressing imbalanced multi-classification for student grade prediction. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the final student grades in the first semester courses by providing better prediction accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of five well-known machine learning techniques, namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (N.B.), K-Nearest Neighbor (kNN), and Logistic Regression (L.R.), to our real dataset. Second, we proposed a multi-class prediction model for an imbalanced multi-class dataset using two types of data-level solutions to improve the prediction accuracy performance. The obtained results indicate that the proposed Synthetic Minority Oversampling Technique (SMOTE) and wrapper-based feature selection (F.S.) integrates with kNN shows significant improvement with the highest accuracy of 99.6%. In contrast, all F.S. algorithms perform 99.4% robust performance when working with SVM independently. These findings indicate that the proposed model generally discovers various F.S. algorithms and SMOTE in addressing imbalanced multi-classification, which will help the researcher to improve the performance for student grade prediction.

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