ُSama Ghoreyshi; Seyed Omid Fatemi; Zahra Shaterzadeh-Yazdi
Abstract
During the pandemic of COVID-19, face to face class sessions, in education systems, have been suspended due to various recommendations of governments. To continue teaching and learning, universities have switched to online learning. In the past two years, implementation of online learning, despite its ...
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During the pandemic of COVID-19, face to face class sessions, in education systems, have been suspended due to various recommendations of governments. To continue teaching and learning, universities have switched to online learning. In the past two years, implementation of online learning, despite its benefits for higher education, has had its drawbacks and students have faced to a variety of challenges. The primary purpose of this study is to investigate the perceptions and opinions of undergraduate engineering students at the University of Tehran about the existing challenges, and their suggestions for making the online learning more efficient in engineering education. This research is performed using a mixed model. A conceptual research model was designed that categorized the main elements of effective online learning into three categories: learners, instructors, and content, and then based on this conceptual model, a questionnaire including Likert and open-ended questions was developed to determine the challenges facing engineering students in online learning in the Corona era. By distributing this questionnaire among incoming engineering students of Tehran University in 2019 and 2020 and analyzing quantitative and qualitative data, to two questions, "What have been the challenges of engineering students in online learning during the Corona era?" And "How has the quality of online engineering education been during the corona pandemic?", was answered. For analysis, qualitative data were coded and categorized using the thematic analysis method and using MAXQDA, and the frequency of each category and code was determined. Quantitative data were also analyzed by statistical analysis methods including descriptive statistics and t-test of independent groups in the SPSS, and then two sets of findings obtained from quantitative and qualitative data were summarized and combined. Based on the results, students' challenges during the pandemic were categorized into five groups: personal challenges, limited social interaction, technology problems, evaluation issues, and concerns about content and teaching methods. Students’ feedback is an important tool in assessing the quality of online courses but other stakeholders’ conceptions and feedbacks should be studied in future research as well.
Hamid zangooei; Omid fatemi
Abstract
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 ...
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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.
