فهرست مطالب omid fatemi

Online learning platforms have become commonplace in modern society today, but high dropout rates and decrement students’ performance still require more attention in such online learning environments. The purpose of this research is to accelerate the identification of students at risk of academic failure in order to take appropriate corrective action. Therefore, we have proposed model to achieve this goal and ultimately improve the performance of students and faculty. Then, for early prediction of students at risk of academic failure, the short-term memory neural network (LSTM) and the widely used support vector algorithm have been used to analyze students’ time based behaviors using data from the University of Tehran e-learning system. To demonstrate the optimal performance of the predictive algorithm, we compared the LSTM network with the support vector algorithm with different evaluation criteria. The results show that the use of LSTM network for early prediction of students at risk provides higher predictive accuracy compared to the support vector machine algorithm. In this research, our method in predicting students’ performance with LSTM network has achieved 94% accuracy and with support vector machine algorithm has achieved 88% accuracy. In addition, the Area Under the Curve (AUC) was 0.936 and 0.882, respectively, using the LSTM algorithm and the support vector machine. Therefore, according to the obtained results, it can be seen that our proposed algorithm has an important and effective contribution to improving the final performance of teachers and students during the course.

In recent years, with the advancement of information technology in education, e-learning quality promotion has received increased attention. Numerous criteria exist for promoting learning quality, such as fitness for purpose, which refers to the extent to which service fits its intended purpose. Multiple purposes are considered in e-learning. One is reducing the knowledge gap between the learner’s perception of educational concepts and what should be understood of training concepts. Identifying and calculating the learner’s knowledge gap is the first step in reducing the knowledge gap. Consequently, this paper presents a new method for calculating the learner’s knowledge gap concerning each concept in the training video content based on the learner’s click behavior. The association between the learner’s knowledge gap and click behavior was determined by categorizing the learner’s click behaviors. Similarly, the Apriori algorithm extracted rules for each behavioral category. The results demonstrated that learning outcome correlated with the learner’s click behavior. Therefore, four behavioral rules regarding the compatibility between the knowledge gap and learner’s click behavior are presented. Experiments were performed by 52 students enrolled in the micro-processing course at Tehran University’s e-Learning Center.

Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as co-author dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented.