Learning activities are considerably supported and improved by the rapid advancement of e-learning systems. This gives students a tremendous chance to participate in learning activities worldwide. The Massive Open Online Courses (MOOCs) platform has emerged as one of the most significant platforms for e-learning as a result of the rapid growth of network information technologies. Due to the increased number of online courses available, it is harder for individual learners to choose the appropriate courses, activities, and learning paths for the actual necessities they want, which reduces their learning performance. Moreover, a sequential Recommender System (RS) can identify the learner’s future interest and suggest the subsequent item or learning content given a sequence of past interactions. This is in contrast to interactive recommendation methods that can create recommendations based on the learner’s feedback via constant interactions. The goal of this paper is to propose a Reinforcement Learning (RL) based smart e-learning framework with Markov Decision Process (MDP) that have the potential to enhance the learning experience for each student by providing them with a personalized and effective learning path. Applying the MDP and RL-based techniques such as Q-learning for Sequential Path Recommendation (SPR) and learning development is more achievable. This is because the MDP allows for adjusting the recommendations method to find new activities and learning paths based on the learners’ feedback on recommendations results. Experimental findings reveal that the suggested model obtains significant improvements and provides viable performance under different parameters optimization. Furthermore, we also show that the proposed method outperforms a long session (long-term rewards such that they maximize learning progress while minimizing frustration and disengagement). This demonstrates the model’s improvements in simulating the learner’s sequential behavior, learning activities, various learning materials, and learning paths simultaneously. These promising initial results provide a possible solution to assess this challenge further in future work.

Smart strategies and intelligent technologies are enabling the designing of a smart learning environment that successfully supports the development of personalized learning and adaptive learning. This trend towards integration is in line with the growing prevalence of Internet of Things (IoT)-enabled smart education systems, which can leverage Machine Learning (ML) techniques to provide Personalized Course Recommendations (PCR) to students. Furthermore, the existing recommendation techniques are based on either explicit or implicit feedback and fail to capture the changes in learners’ preferences while integrating implicit or explicit feedback. To this end, this paper proposes a new model for personalized learning and PCR that is enabled by a smart E-Learning (EL) platform. The model aims to gather data on students’ academic performance, interests, and learning preferences and utilize this data to recommend the courses that will be most beneficial to each student. The proposed approach makes suggestions based on the learner’s interactions with the system and the cosine similarity in related contents by combining explicit (user ratings) and implicit (views and behavior) methodologies. The suggested method makes use of ML algorithms and an EL Recommender System (RecSys) based on Collaborative Filtering (CF).This includes Random Forest Regressor (RFR), Decision Tree Regressor (DTR), K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), eXtreme Gradient Boosting Regressor (XGBR), and Linear Regression (LR). The proposed solution is benchmarked against existing approaches on both predictive accuracy and running time. Experimental results are conducted based on two benchmark datasets (Coursera and Udemy). The proposed model outperforms existing top-K recommendations techniques in terms of accuracy metrics such as precision@k, Mean Average Precision (MAP)@k, recall@k, Normalized Discounted Cumulative Gain (NDCG)@k, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) for PCR. From the experiments, it can be shown that SVD can perform well in terms of higher accuracy and MAP and NDCG and lower MAE, RMSE, and MSE values when contrasted to other proposed algorithms because it is better suited to capture complex student-course interactions. The proposed solutions are promising on two different datasets and can be applied to various RecSys domains.

Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of “big data”. ANN are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarise recent progress, challenges and opportunities for future research. This paper presents a concise view of the state-of-the-art, challenges and future research opportunities regarding the applications of ANN in big data analytics, and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of ANN in big data analytics.