Background: Video games are very well known for their intrinsic adaptivity; as they adapt the gameplay to the player’s preferences and rhythm. In addition to adapting the gameplay, educational games should also adapt the learning content to match the learner’s competencies, preferences, playing and learning styles. Determining the playing style in an educational game is made possible by collecting certain metrics and information susceptible of monitoring the player’s interactions in the game. However, it is still a challenge to assess the learner’s learning style. Aim: This study examines the correlation between learning and playing styles. It has been acknowledged that both playing and learning styles are related to personality. After examining the personality traits of each style of both Kolb’s learning styles and Bartle’s playing styles, it was hypothesized that there would be a correlation between the two. In that purpose, a quantitative research was conducted to explore the relationship between the two taxonomies. Method: One hundred high school students majoring in science in Morocco have completed Kolb’s learning style inventory (version 3.1) as well as the Bartle test questionnaire developed by Andreasen and Downey. The statistical correlation between the two taxonomies was investigated using cross-tabulation, Chi-square/Fisher’s exact test and Pearson coefficient. Results: Results revealed a relevant interdependency between Kolb’s learning styles and Bartle’s playing styles. The ‘convergent’ type was found to correlate with Bartle’s explorer type; the majority of assimilators adopt the killer type in a game; accomodators tend to be achievers and divergers prefer to be socializers. In terms of learning styles, it was noticed that the majority of participants adopted the converging and the assimilating learning styles; which is in line with what it is indicated in Kolb’s inventory. Results have also shown that learning and playing styles are gender independent. It was concluded that the learning style can be predicted, based on the playing style.

Diversifying learning practices and situations helps learners to better regulate their learning with deep understanding, which improves learning outcomes. Accordingly, this paper presents our vision of a differentiation system of learning paths within MOOC. Promising beginning point for this vision would be to determine new factors that directly affect the success rate. Then, we introduce the theoretical framework of differentiated instruction, which represents the key component of the proposed system. Finally, we implement some key concepts in differentiation and some techniques for assigning learners into groups in order to differentiate learning paths. The main purpose of the proposed contribution is to optimize learning situations of each learner according to his needs. As a result reducing the proportion of learners in a situation of failure and thereby improving the success rate.

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms.

COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode. In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution.

MOOC have opened up learning opportunities to a wide public. With these advantages, they have emerged in the development, promotion, and delivery of courses from leading American universities. Nevertheless, in parallel with this potential, several problems have arisen. The dropout of learners is the most common problem. Therefore, this chapter highlights the high dropout rate in MOOC, according to two visions: learners' heterogeneity and diversification of the learning theories supporting MOOC. On this basis, the authors propose the introduction of differentiated instruction within MOOC to overcome this problem. The main purpose of this chapter is to discuss MOOC from pedagogical and organizational perspectives. More specifically, it is interesting to emphasize how differentiated instruction can be implemented in these courses. The interest is to propose a MOOC model that is more effective and more aligned with the learning objectives and preferences of each learner in order to improve the success rate.