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A curriculum sequence represents a match between learners’ preferences, needs, and surroundings from one side, and the learning content characteristics and the pedagogical requirements from the other side. The curriculum sequence adaptation problem (CSA) is considered as an important issue in adaptive and personalized learning field. It concerns the dynamic generation of a personal optimal learning path for a specific learner. This problem has gained an increased research interest in the last decade, and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents a novel GA-based approach modeled as an objective optimization problem to deal with this problem. The experimental results from simulations showed that the proposed GA could outperform particle swarm optimization (PSO) and a random search approach in many simulated datasets. Moreover, from a pedagogical perspective, positive learners’ feedback and high acceptance towards the proposed approach is indicated.

Nowadays web users are vulnerable to various cyber-attacks, among these attacks we find phishing. Phishing is a social engineering cyberattack, which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number, etc, based on imitating legitimate websites or sending a malicious URL in an email to deceive users. Several anti-phishing solutions have been developed such as black/whitelist, visual similarity, machine learning methods ... etc, but phishing attacks keep on growing, which has made phishing detection a primary concern of developers. In this paper, we present a taxonomy of an anti-phishing solutions. The classification helps in understanding various anti-phishing approaches and algorithms developed for phishing detection.

Recent years have brought the need for new pedagogical approaches that appeal to the involvement and participation of learners in the learning process. One of these approaches is the flipped classroom, which gives to learners the possibility to prepare for the next class, through pre-recorded video lectures and close-ended problems. Many studies merely replace in-class instructions by videos and use class time for group discussions. Nevertheless, what instructional design framework should we use in planning the overall flipped classroom approach? This article answers this question through exploratory studies conducted at Algeria University. In this work, the authors have used a flipped classroom concept for an algorithmic course like java programming, based on learner's learning style and learner's skill level.

Social networkings are relatively recent innovations. Literature on other social technologies in education strongly suggests that such technologies can be educationally beneficial to teachers and learners in supporting the sharing of resources, enhancing motivation, and facilitating reflection, social interaction and knowledge building. However, thus far there have been few empirical studies detailing application of social networking technology in educational contexts based on learners’ interests.

The approach proposed in this chapter 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, the authors present a quantitative analysis by means of structural equation modeling to analyze the causal relationships of knowledge, skills, and motivation with students' satisfaction. Secondly, they confirm that the proposed flipped classroom has positive effects on students' knowledge, skills, and motivation. Finally, the research provides useful results that the use of the context dimensions and learner feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

The 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 chapter 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.

E-learning systems have benefited greatly from the concepts of the social web, where learners and tutors are living and interacting mainly in social networks. Our goal is to show that this technology can be adopted to improve the e-learning user experience and to provide a full automatic learning platform called Netlearn. We present a learning environment formulated as a social network, including the interactions between users as well as their relationships with the provided learning resources. In particular, we propose an automatic learning environment based on the analysis of the social interactions that takes place between users-users and users-resources. The analysis is based on the history of interactions made by learners within the environment to deduce their interests in relation to a module. Learners with similar interests will then be assigned to the same learning group in order to propose recommendations regarding their preferences, interests and needs. This system ensures that these recommendations will certainly improve the learning process by providing students with the best learning practices, the desirable collaborators, and the relevant resources that fit better their needs.

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.

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.

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.

Adaptive social network sites (ASNS) are an innovative approach to a web learning experience delivery. They try to solve the main shortcomings of classical social networks—“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 chapter outlines a new approach to automatically detect learners' thinking and learning styles, and takes into account that thinking and learning styles may change during the learning process in unexpected and unpredictable ways. The approach is based on the Felder learning styles model and Hermann thinking styles model.