2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)最新文献_第10页

Impact of Learning Style based Teaching on Students Performance in a Formal Methods Course for Computer Scientists 基于学习风格的教学对计算机科学正式方法课程学生表现的影响

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00145

R. Schorr

引用次数: 0

The Role of Basic Psychological Needs Satisfaction in Primary Student’s Computational Thinking Development 基本心理需求满足在小学生计算思维发展中的作用

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00147

Stella Xin Yin

引用次数: 0

Instructional Design Assistant Mentorship Scheme for Innovations on Virtual Instruction 虚拟教学创新的教学设计助理师友计划

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00078

Xinyi Liang, Chi-Un Lei

引用次数: 0

The use of interactive whiteboard drawing and concept/mind mapping to promote active learning in online/hybrid classes – a case study for science education 利用互动白板绘图和概念/思维导图促进在线/混合课程的主动学习-科学教育的个案研究

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00110

Kim-hung Lam, Wai Ki Vicky Chan, Long-yung Ivan Chung, Wai-chun Boris Leung, Kwong-chak Gary Cheung, Yoon-suk Lawrence Lee, Wing-yiu Yu

{"title":"The use of interactive whiteboard drawing and concept/mind mapping to promote active learning in online/hybrid classes – a case study for science education","authors":"Kim-hung Lam, Wai Ki Vicky Chan, Long-yung Ivan Chung, Wai-chun Boris Leung, Kwong-chak Gary Cheung, Yoon-suk Lawrence Lee, Wing-yiu Yu","doi":"10.1109/TALE54877.2022.00110","DOIUrl":"https://doi.org/10.1109/TALE54877.2022.00110","url":null,"abstract":"The COVID-19 pandemic has upended the conventional mode of class delivery. With the need to maintain social distancing and strict quarantine measures for local and non-local students, all teaching activities must be conducted in online mode, which in turn limited the interaction among students and instructors. In this study, we reported the use of interactive whiteboard on facilitating teaching and learning activities via drawing and handwriting during 2021/2022 Semester 1,2 and 3 (9/2021 to 6/2022). In general, nonprogramme major (NPM) students (ABCT1D09 and ABCT1D10) were satisfied with the use of interactive whiteboard with average score over 4.0, where more than 80% of them chose 4 and 5. For programme-major (PM) students (ABCT1742, ABCT3623 and ABCT4752), they were mostly satisfied with the use of interactive whiteboard with average score over 3.3, where more than 57% of them chose 4 and 5. To conclude, both NPM and PM students were satisfied with the use of interactive whiteboard during learning with overall average score around 4.0, where greater than 74% of them chose 4 and 5.","PeriodicalId":369501,"journal":{"name":"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256197","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: 0

Design, Development, Implementation and Evaluation of Female-friendly Learning Design on Enhancing Grade 1 and 2 Girls’ Motivation in STEM education 提高一二年级女生STEM学习动机的女性友好学习设计的设计、开发、实施与评价

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00097

Rebecca Rosemary Self, A. Tse

引用次数: 0

Algorithmic Bias in a Student Success Prediction Models: Two Case Studies 学生成功预测模型中的算法偏差:两个案例研究

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00058

Feiya Xiang, Xinzhi Zhang, Jiali Cui, Morgan Carlin, Yang Song

{"title":"Algorithmic Bias in a Student Success Prediction Models: Two Case Studies","authors":"Feiya Xiang, Xinzhi Zhang, Jiali Cui, Morgan Carlin, Yang Song","doi":"10.1109/TALE54877.2022.00058","DOIUrl":"https://doi.org/10.1109/TALE54877.2022.00058","url":null,"abstract":"Machine learning algorithms are increasingly being used in today’s society. However, growth in these algorithms means growth in algorithmic bias, and it is imperative that we work to understand the bias that may result. One area of study in which these algorithms are widely used is in educational institutions. The algorithms are often used to predict student success or retention. In our research, we aim to uncover the biases that may result from building and using a machine learning student success models. To do so, we used two publicly available student datasets from educational settings (one from a MOOC and another one from secondary education in Portugal) and built models of our own. We then compared the accuracy and the fairness of each model type to observe which models performed best on each subcategory of students. Among the models we built, we found that while it is easy to use accuracy to evaluate models and find the most accurate ones, the most accurate predictive model overall for a dataset may not perform fairly in predicting student success for all subcategories of students. To better tune models for fairness, we found that it is possible to tune models that also take fairness into consideration, and these models could perform more fairly on almost all subcategories of students, but it slightly took away from the accuracy of the algorithm. Our results demonstrate the importance of creating and tuning several model types in order to choose a balanced model that balances accuracy and fairness.","PeriodicalId":369501,"journal":{"name":"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129258007","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: 0

Graduate Attribute Assessment for Each Student of Remote Sensing Science and Technology Program 遥感科学与技术专业学生毕业属性评价

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00081

Y. Zhang, Yan Gong, W. Cui

{"title":"Graduate Attribute Assessment for Each Student of Remote Sensing Science and Technology Program","authors":"Y. Zhang, Yan Gong, W. Cui","doi":"10.1109/TALE54877.2022.00081","DOIUrl":"https://doi.org/10.1109/TALE54877.2022.00081","url":null,"abstract":"Taking the program of remote sensing science and technology as example, this paper calculated the degree of achievement of the 12 graduate attributes (GAs) adopted by Washington Accord for each student using the direct assessment method based on supporting courses’ grades. To make full use of the elective science courses and elective engineering program courses the course combos were adopted as supporting courses linking the GAs to the learning activities. The achievement scores of 12 GAs for 614 students of 2019-2021 were calculated. The results show that on average the 12 scores are all above 0.8. There is a small and steady improvement of the overall achievement scores except for GA6 Engineer and society of the past 3 years. The attributes with high scores are attribute 7 Environment and sustainability, 5 Modern tool usage. However, the low scores are from some soft competency requirement such as 10 Communication, 12 Life-long learning, and 11 Project management, which are the relatively deficient parts in the program of remote sensing science and technology. Comparison of the distribution in different achievement score ranges indicates that the students numbers of a score above 0.89 has increased, from 20% to 22% in 2020 and to 32% in 2021.","PeriodicalId":369501,"journal":{"name":"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977238","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: 0

Work-in-Progress: Virtual Reality Trainer for Vector Network Analyzer Calibration 正在进行的工作:矢量网络分析仪校准的虚拟现实培训师

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00119

I. Rumyancev, Margarita O. Aivazova, D. Akhmetov

引用次数: 0

Digital Narratives: Alternative Object-based Learning During the COVID-19 Pandemic 数字叙事:COVID-19大流行期间基于对象的替代学习

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00130

Sayuri Tanabashi

引用次数: 0

Development of a Chatbot to Train Physiotherapy Students in Clinical Questioning and Reasoning 开发聊天机器人训练物理治疗学生临床提问和推理

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Pub Date : 2022-12-01 DOI: 10.1109/TALE54877.2022.00084

Chien-Hong Lee, M. Low, Benjamin Tze Chin Soon, LiMing Lu, H. Lee, Nadya Shaznay Patel

引用次数: 0