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MOOCs represent a new concept that offers learning content to participants freely, anywhere and anytime. However, they suffer from several unsolved problems such as high dropout percentage, low completion rate or uncontrollable understanding level of the participants that can be caused by the lack of the practical activities and simulations. This article aims to propose a solution to ensure the integration of virtual manipulations in MOOCs.

This paper proposes the integration of virtual manipulations (simulations and practical activities) relying on augmented reality. To ensure the manipulation of the used 3D objects, two methods have been proposed based on markers or hand gestures. Customized markers are used, facilitating their recognition by the users, to visualize the objects and to ensure their interactions. Hand gestures have been proposed to perform the manipulation easily. Consequently, hand detection and gestures classification using hand contour detection and HSV filter have been applied.

Two MOOCs pedagogically similar were proposed to evaluate the effectiveness of the proposed solution. The only difference is that the second MOOC contains virtual manipulations that the participants can perform to understand better and to interact during the courses. The finding results show that the participants’ understanding and satisfaction levels in the second MOOC were higher, and the dropout rate was lower than the first one.

The integration of practical activities/simulations in MOOCs using augmented reality is the key novelty of our work. To do so, two manipulation methods have been proposed, so the instructor can feel free to choose the adequate method to ensure a better progress of the manipulations.

Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out.

This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC.

The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score.

Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.

The purpose of this paper is to bring together the textual and multimedia opinions, since the use of social data has become the new trend that enables to gather the product reputation traded in social media. Integrating a product reputation process into the companies' strategy will bring several benefits such as helping in decision-making regarding the current and the new generation of the product by understanding the customers’ needs. However, image-centric sentiment analysis has received much less attention than text-based sentiment detection.

In this work, the authors propose a multimedia content-based product reputation framework that helps in detecting opinions from social media. Thus, in this case, the analysis of a certain publication is made by combining their textual and multimedia parts.

To test the effectiveness of the proposed framework, a case study based on YouTube videos has been established, as it brings together the image, the audio and the video processing at the same time.

The key novelty is the implication of multimedia content in addition of the textual one with the goal of gathering opinions about a certain product. The multimedia analysis brings together facial sentiment detection, printed text analysis, opinion detection from speeches and textual opinion analysis.

The pervasiveness of mobile devices has led to the permanent use of their new features by the crowd to perform different tasks. The purpose of this paper is to exploit this massive consumption of new information technologies supported by the concept of crowdsourcing in a governmental context to access citizens as a source of ideas and support. The aim is to find out how crowdsourcing combined with the new technologies can constitute a great force to enhance the performance of the suspect investigation process.

This paper provides a structured view of a suspect investigation framework, especially based on the image processing techniques, including the automatic face analysis. This crowdsourcing framework is mainly based on the personal description as an identification technique to facilitate the suspect investigation and the use of MongoDB as a document-oriented database to store the information.

The case study demonstrates that the proposed framework provides satisfying results in each step of the identification process. The experimental results show how the combination between the crowdsourcing concept and the mobile devices pervasiveness has fruitfully strengthened the identification process with the use of automatic face analysis techniques.

A review of the literature has shown that previous work has focused mainly on the presentation of forensic techniques that can be used in the investigation process steps. However, this paper implements a complete framework whose whole strength is based on the crowdsourcing concept as a new paradigm used by institutions to solve many organizational problems.

This paper aims to detect opinion leaders, who they play a vital role as influencers of their community, which will help companies to improve their image in social media. This idea came with the fast development of social media, where individuals are increasingly sharing their personal experiences, opinions and critiques about products through these platforms. Thus, the new customers can rely on these spontaneous recommendations to proceed with the purchase without risk of disappointment. Therefore, the mismanagement of the e-reputation can cause huge losses for companies.

In this study, a product reputation framework based on the prediction of opinion leaders is presented. To do so, opinion mining has been used to determine the product reputation in social media. In addition to posts processing, the profile information has also exploited to predict opinion leaders. To achieve the authors’ goal, spammers and duplicated profiles have been detected to improve the product reputation results.

The effectiveness of this approach has been tested using a social media simulation. The obtained results show that this approach is efficient and more accurate compared to the classical solutions.

The key novelty is the gathering of spammer detection criteria with different weights and the profiles matching by providing the suitable matching methods that take into account the profile’s attributes types. Consequently, a different similarity measure was assigned for each of the considered four attributes types. These two steps can ensure that the results obtained from social media are actually supported by opinions extracted directly from the real physical consumers.