## 2 The Impact of New Technologies in Education
### Online Social Interactions in Education  
In this subsection, we present the research works that have helped in one way or another to focus this research. Some of them have helped to set up platform features, meanwhile others have helped to support the decision of using PRSNs, in order to improve and analyze students' online social interactions. The relationship between online participation and e-learning performance is analyzed in [24]. Learning style is one of the predictors taken into account when analyzing learning performance. A model is proposed and tested for the relationship between learning style and e-learning performance, and the moderating effects of prior knowledge. Data about online participation were extracted from students' records (a function offered by the e-learning platform used). Evaluation was grounded in several indicators, such as: number of discussion board posts, number of file views, session duration, and total number of pages read. This participation was categorized into two classes: active participation and passive participation. It is worth highlighting that the results show learning style predicts performance through online participation, although other types of learning styles with online participation do not affect the performance. In [25], the authors present a data mining approach for improving the prediction of a student's final academic performance. They use participation indicators, including quantitative, qualitative, and social items for this prediction. The three different types of analytic data based on forum activity are: (1) quantitative information, statistical information; (2) qualitative information, evaluation or scores for content given by the teacher;(3) social network information, relationships between students. The research finds that two quantitative measures are the best predictors of students' final results. These two items are: the number of messages (posts or replies) sent, and the number of words written. In addition, the two social network measures were the most important for predicting final student performance. The intention of the study conducted by [26] was to obtain a general view of student's expectations and experiences while using e-learning platforms. In 60% of the subjects, the students interviewed claimed to use e-learning platforms occasionally or frequently. 'What aspects of e-learning do students consider important for their learning achievements and course satisfaction?' Expectations and students' achievement goals were positive predictors of success. The instructor's expertise and involvement was the best predictor of subject satisfaction and learning achievement. This research highlights the key characteristics of an e-learning course and learning behavior for performance, achievement, and satisfaction. According to students, the instructor played a vital role, and counseling and support was especially important in order to facilitate the construction of knowledge, media competence and satisfaction. Based on a 36-question questionnaire about motivation in e-learning, the authors in [27] tried to identify the successful aspects within e-learning. Two of the most important barriers found during e-learning are the progressive discouragement process and the lack of real commitment to the educational process. The most interesting findings of this research are that the way content is presented improves grades and levels of satisfaction and that integrating a motivational system (equalitarian) increases engagement and retention. Accessibility was also found to be a key point. Using digital textbook learning analytics as a starting point, some similarities or relations may be established when relating it to PRSN use in educational environments. In [28], the authors find that textbook engagement is related to better course outcomes. This is relevant to our study, since PRSN use is related to students' outcomes.