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Adopting learning management systems (LMS) in higher education has become a major focus of interest to implement e-learning. Evaluating the quality of LMS is important to improve learner outcomes and promote teaching strategy. Many LMSs are emerging and thus assisting higher institutions to choose the adequate LMS becomes crucial especially under fuzzy environment where uncertainties and subjectivities are considered. Because of this, the paper proposes a quality framework inspired from ISOLIEC 9126 to evaluate and rank proprietary, open source and cloud-based LMSs. Then a Fuzzy Vikor (VlseKriterijumska Optimizacija I Kompromisno Resenje) technique is applied for instantiating the proposed framework criteria and selecting alternatives from three LMSs adopted in Saudi Arabia universities. The obtained results show that the most important criteria for decision makers in these institutions are equally understandability and time behavior. In addition, the open source Moodle was set as the appropriate LMS to meet higher institutions standards.

Universities around the world are keen to develop study plans that will guide their graduates to success in the job market. The internship course is one of the most significant courses that provides an experiential opportunity for students to apply knowledge and to prepare to start a professional career. However, internships do not guarantee employability, especially when a graduate's internship performance is not satisfactory and the internship requirements are not met. Many factors contribute to this issue making the prediction of employability an important challenge for researchers in the higher education field. In this paper, our aim is to introduce an effective method to predict student employability based on context and using Gradient Boosting classifiers. Our contributions consist of harnessing the power of gradient boosting algorithms to perform context-aware employability status prediction processes. Student employability prediction relies on identifying the most predictive features impacting the hiring opportunity of graduates. Hence, we define two context models, which are the student context based on the student features and the internship context based on internship features. Experiments are conducted using three gradient boosting classifiers: eXtreme Gradient Boosting (XGBoost), Category Boosting (CatBoost) and Light Gradient Boosted Machine (LGBM). The results obtained showed that applying LGBM classifiers over the internship context performs the best compared to student context. Therefore, this study provides strong evidence that the employability of graduates is predictable from the internship context.

One of the challenges in e-learning is the customization of the learning environment to avoid learners’ failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students’ results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students’ interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students’ classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations.

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