Résumé: While some studies indicate that flipped classrooms offer many positive educational outcomes, other studies draw attention to limitations associated with flipped classroom (students' limited preparation prior to class, students' need for guidance at home, students' inability to get immediate feedback while they study at home, and little research has focused on students' learning outcomes, such as: satisfaction and motivation). This paper attempts to address several of these limitations through exploratory studies conducted in an Algerian University. The approach proposed in this paper called Flipped classroom based on Context-Aware mobile learning system (FC-CAMLS) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The system was implemented in an English Language course. It was expected that the FC-CAMLS increased the management of students' heterogeneity. A quantitative analysis by means of structural equation modeling was performed to analyze the causal relationships between knowledge, skills, motivation and students' satisfaction. The results show that the system has positive effects on students' knowledge, skills, and motivation. Finally, our research provides useful results that the use of the context dimensions and learner's feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

Résumé: Students nowadays are hard to be motivated to solve logical problems with traditional teaching methods. Computers, Smartphone's, tablets and other smart devices disturb their attention. But those smart devices can be used as auxiliary tools of modern teaching methods. The flipped classroom is one such innovative method that moves the solving problems outside the classroom via technology and reinforces solving problems inside the classroom via learning activities. In this paper, the authors implement flipped classroom as an element of Internet of Things (IOT) into learning process of mathematical logic course. In the flipped classroom, an Intelligent Tutoring System (ITS) was used to help students work with the problems in the course outside the classroom. This study showed that perceived usefulness, self-efficacy, compatibility, and perceived support for enhancing social ties are important antecedents to continuance intention to use flipped classroom.

Résumé: In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). “NSN-AP-CF” processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the Apriori algorithm. Finally, it groups dynamically the users based on the collaborative filtering. The participants in this study consisted of 80 university students who were asked to analyze the differences in skill level when using various learning activities. Moreover, 40 students were included in this study in order to examine the effectiveness of NSN-AP-CF. The experiment results proved that the proposed algorithm, which considers the grouping dynamically the users and the discovery of all frequent episodes, generates better precisions compared with the other algorithms (F1 = 0.649)

Résumé: A large number of studies attest that learning is facilitated if the teaching strategies are in accordance with the students learning styles (LS), making the learning process more effective and considerably improving student's performances. But, traditional approaches for detection of LS are inefficient. This work determines the current preferences through dynamic Bayesian network that represent the matches between LS and teaching strategies in order to determine how much a given strategy is interesting to a student. The LS theory that supports this approach is the LS model proposed by Felder-Silverman's learning styles model (FSLSM). Our approach gradually and constantly adjusts the student model, taking into account students' performances, student's effort, student's intensity, student's resistance and student's attention. Promising results were obtained from experiments, and some of them are discussed in this paper.

Résumé: Adaptive E-learning Hypermedia Systems (AEHS) are an innovative approach to a web learning experience delivery. They try to solve main shortcomings of classical hypermedia e-learning applications: “one-size-fits-all” approach and “lost-in-hyperspace” phenomena by adapting the learning content and its presentation to needs, goals, thinking styles and learning styles of every individual learner. This paper outlines a new approach to automatically detect learner's thinking and learning styles, and taking into account that thinking and learning styles may change during the learning process in an unexpected and unpredictable way. The authors' approach is based on the Felder Learning Styles Model and Hermann thinking styles model.

Résumé: An Adaptive E-learning Hypermedia System (AEHS) is an approach whose target is to personalize the learner's learning experience. A number of AEHS have been developed to support thinking styles as a source for adaptation. However, these systems suffer from several problems, namely: lack of maintenance adaptation to thinking style and the insertion of specific teaching strategies into learning content. This paper proposes an AEHS model based on thinking styles and knowledge level. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her thinking style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and applying them to a suitable pedagogical strategy. Keywords: Adaptive hypermedia, thinking style, ontology, intelligent system, learner model

Résumé: Recent years have shown increasing awareness for the importance of adaptivity in e-learning. Since the learning style of each learner is different. Adaptive e-learning hypermedia system (AEHS) must fit different learner’s needs. A number of AEHS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: lack of maintenance, adaptation to learning style, less attention was paid to thinking styles and the insertion of specific teaching strategies into learning content. This paper proposes an AEHS model based on thinking styles and knowledge level. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her thinking style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

Résumé: A distinct feature of an adaptive e-learning hypermedia system (AEHS) is the learner model it employs, that is, a representation of information about an individual learner. Learner modeling and adaptation are strongly correlated, in the sense that the amount and nature of the information represented in the learner model depend largely on the kind of adaptation effect that the system has to deliver. In fact, we see a problem arising when teachers assume similar learning styles, thinking styles, levels of knowledge and abilities for learners. This is because learners that are less able will feel that it is too difficult for them to follow and those that are more capable will feel as though the learning method is too easy. Teachers can adjust the standards; however, there may be conflicts between learners with varied styles. Thinking style, learning style, level of knowledge, preferences and ability of learner are part of learner’s characteristics, which have significant influence on the activity of learners in the learning process. In this paper we have focused our attention on the learner model, which allows for the discovery of preferences, needs and interests of users that have access to an AEHS. In order to observe the psychological and pedagogical characteristics of a learner, a quantitative and qualitative research is conducted based on a questionnaire. The thinking style of a learner is analyzed. Based on the statistical results, we figure out the rules about pedagogical activity decision-making. This study presents two subsequent experiments. The first experiment explores the relationship of thinking style and pedagogical activities to validate this specific psychological construct in the context of an AEHS. The second experiment reduces the questionnaire to 60 questions, using a filtering principle keeping the validity of the original questionnaire.

Résumé: A number of adaptive e-learning hypermedia systems (AEHS) have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: lack of maintenance adaptation to learning style, less attention was paid to thinking styles and the insertion of specific teaching strategies into learning content. This paper proposes an AEHS model based on thinking styles and domain ontology. The experiment was completed in three phases for both experimental and control groups. In the first phase all the students were informed that they will participate in an experimental process. The students received a short introduction on how to use the system and to create a user account for login purposes into the system. Then, information about thinking styles categories were given to the experimental group and were asked to complete the questionnaire. In the second phase, the students followed regularly the lessons until the completion of the course; meanwhile taking a quiz at the end of each lesson. In the third phase, learners followed a link to do the post-test.

Résumé: The approach proposed in this paper called Flipped classroom based on Context-Aware mobile learning system (FC-CAMLS ) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The contribution was applied in the context of the flipped learning in order to manage the heterogeneity of context imposed by this approach. Firstly, we present a quantitative analysis by means of structural equation modeling to analyze the causal relationships of knowledge, skills, and motivation with students' satisfaction. Secondly, we confirm that the proposed flipped classroom has positive effects on students' knowledge, skills, and motivation. Finally, our research provides useful results that the use of the context dimensions and learner’s feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

Résumé: Flipped classroom can be used to encourage teachers to prepare their own stories for their students and connect with peers to build their own collaborative learning spaces. Teachers can create digital storytelling from the content or have their students do it to demonstrate their understanding of the content. The greatest benefit in the flipped classroom may be found when students may be given assignments in which they are asked to research a topic, look for pictures, record their voice and then choose a particular point of view. This paper explores storytellers’ experiences of digital storytelling (DST) through a flipped classroom approach. A mixed research method was employed, using multiple sources of data collection, including pre-and post-tests , Perception of Flipped Learning Experience questionnaire, the teachers’ in-class observations, and semi-structured focus-group interviews. The results revealed that the flipped classroom not only enhanced the participants’ motivation, making them more active, but also significantly improved their ability.

Résumé: Learners usually meet cognitive overload and disorientation problems when using e-learning system. At present, most of the studies in e-learning either concentrate on the technological aspect or focus on adapting learner's interests or browsing behaviors, while, learner's skill level and learners' success rate is usually neglected. In this paper, the authors propose an online course generation based not only on the difficulty level of a learning unit, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learners' success rate can promote personalized learning performance. Learners' skill level is obtained from pre-test result analysis, while learners' success rate is acquired through specific tests after completing a learning unit. After computing success rate of a learning unit, the system then modifies the difficulty level of the corresponding learning unit to update courseware material sequencing. Experiment results indicate that applying the proposed intelligent e-learning system can generate high quality learning paths, and help learners to learn more effectively.

Résumé: Adaptive and context-aware learning provides learning content according to a learner's context. In order to achieve this goal, the dimensions that constitute the context in the current learner's state have to be determined. There are several existing works within this field and each of these are taking care of a subset of context parameters - like learning styles, learner location, etc. But, a standardized model that helps to capture a learner's context in its entirety is not available. The requirement to define context more precisely and in a uniform way has been identified by several researchers because a general and precise definition of context can facilitate the identification of what does and does not constitute context and can enable reuse and share of contextual data over applications. To this end, this work proposes a reference context model that helps to define a learner's context. The proposed model is developed by consolidating the various context parameters used in the existing adaptive systems and organizing them into an appropriate structure.