2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT)最新文献

The Impact of Online Peer Feedback on Railway Engineering Students' Academic Writing: The Case of Google Docs 网络同伴反馈对铁路工程专业学生学术写作的影响:以Google Docs为例

2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT) Pub Date : 2022-03-09 DOI: 10.1109/ICeLeT55619.2022.9765437

Mahboubeh Taghizadeh, Kafiyeh Assadollahi

引用次数: 0

The effectiveness of Computer Assisted Language Learning on IELTS learners' proficiency level and Engagement level 计算机辅助语言学习对雅思学习者熟练程度和参与度的影响

2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT) Pub Date : 2022-03-09 DOI: 10.1109/ICeLeT55619.2022.9765438

Mitra Hashemzade, Zohre Mohamadi Zenouzagh

引用次数: 1

A Courses Recommendation System based on Graph Clustering and Ant Colony Optimization in MOOC Environment MOOC环境下基于图聚类和蚁群优化的课程推荐系统

2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT) Pub Date : 2022-03-09 DOI: 10.1109/ICeLeT55619.2022.9765436

Shahla Havas, Nafiseh Imanian, P. Moradi

{"title":"A Courses Recommendation System based on Graph Clustering and Ant Colony Optimization in MOOC Environment","authors":"Shahla Havas, Nafiseh Imanian, P. Moradi","doi":"10.1109/ICeLeT55619.2022.9765436","DOIUrl":"https://doi.org/10.1109/ICeLeT55619.2022.9765436","url":null,"abstract":"Course recommendation is a tricky issue for many students. When they just have a limited information about the courses, they must rely on general course schedule systems for guidance, but the outcome are inadequate. In this research, we propose a course recommender system based on graph clustering and ant colony optimization to solve the problem. Graph representation, graph clustering, user weighting using ant colony optimization, and rating prediction are the four essential steps in the proposed method. The dataset is first represented as a weighted network based on the degree of similarity between each pair of users. The Pearson-r correlation coefficient is used to calculate the similarity between users. Ratings of users who are most similar to the active user are the goal of the clustering phase. As a result, users in the same cluster have a lot of similarities. Through third step, the Ant colony algorithm is used to weight users (students) with the goal of picking a group of highly correlated users associated with their importance values as target user's neighbor users. Finally, unknown ratings are predicted in the fourth phase by combining the rating values of nearby users with their weights of similarity to the target user.","PeriodicalId":138384,"journal":{"name":"2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adaptive educational games based on recommendation strategy and learning style identification 基于推荐策略和学习风格识别的适应性教育博弈

2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT) Pub Date : 2022-03-09 DOI: 10.1109/ICeLeT55619.2022.9765415

Nafiseh Imanian, Shahla Havas, P. Moradi

引用次数: 0

MOOCs Recommender System with Siamese Neural Network 基于Siamese神经网络的mooc推荐系统

2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT) Pub Date : 2022-03-09 DOI: 10.1109/ICeLeT55619.2022.9765439

A. Faroughi, P. Moradi

{"title":"MOOCs Recommender System with Siamese Neural Network","authors":"A. Faroughi, P. Moradi","doi":"10.1109/ICeLeT55619.2022.9765439","DOIUrl":"https://doi.org/10.1109/ICeLeT55619.2022.9765439","url":null,"abstract":"Massive open online courses (MOOCs) are becoming a popular method of education, as they offer students a large-scale learning opportunity. However, the variety of MOOC courses and their frequent changes make it more difficult for students to identify relevant new information. To pique students' attention, a recommendation system (RS) is used to match the learner with the best learning resources. Most research on recommender system relies mainly on the presence of explicit feedback, while this information is commonly scarce or unavailable in MOOCs. Therefore, in this paper we use implicit feedback which is gathered passively by tracking different sorts of students' behavior to model user positive and negative preferences. We propose using Siamese Neural Networks (SNNs) to extract latent representations of students and courses based on a loss function that gives observed courses a higher preference than unobserved courses. Then, users and courses similarity are determined based on new representations. Furthermore, the other challenge is recommending courses to students with little available interaction data (cold start). To solve this problem, we employ user and course content information, which aids in the creation of more accurate representations as well. We analyze the proposed model on a real dataset obtained from XuetangX-one of China's largest MOOCs-. Experiment results show that the proposed algorithm outperforms numerous baseline algorithms.","PeriodicalId":138384,"journal":{"name":"2022 9th International and the 15th National Conference on E-Learning and E-Teaching (ICeLeT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124316511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2