Language learning achievement depends on student engagement which is at the center of attention these days. To assist students to become autonomous and independent learners, providing a social and supportive context is beneficial through autonomy-supportive and interaction. When learners are given the freedom to make choices about their education, they are likely to feel more enthusiastic and engaged. Moreover, learners' academic and social practices are largely influenced by educators, who play a major role as social agents and the function of the educators as the most dominant figures is the cornerstone of the language classroom. As there is a dearth of studies that have considered teachers and student interactions among all other effective issues and their significant effect on students' autonomy and engagement from the perspective of self-determination theory (autonomy support), the present review endeavors to focus on teacher-student interaction from the social perspective and their effects on student engagement in EFL classrooms. Subsequently, some implications are presented to elucidate the practice of teachers, students, teacher educators, materials developers.
A remarkable point in previous decades in every aspect of life is well-being which is also effective in academic settings, and it is consistent with positive psychology, in which one can recognize how to make everything pleasing. Moreover, grit is another noteworthy point in the process of learning, which is at the center of researchers’ attention in last years as a result of its long-term eminence. In addition, school connectedness is another important factor that was found to be positively related to students’ well-being. Therefore, the current review endeavors to emphasize the mediating role of these two constructs, grit and school connectedness on students’ well-being. Successively, some implications are proposed for educators, learners, teacher educators, and materials developers.
Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.
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