IEEE Global Engineering Education Conference最新文献_第5页

Teamwork competence and academic motivation in computer science engineering studies

IEEE Global Engineering Education Conference Pub Date : 1900-01-01 DOI: 10.1109/EDUCON.2014.6826182

J. Pérez-Martínez, J. Martín, A. Sierra-Alonso

{"title":"Teamwork competence and academic motivation in computer science engineering studies","authors":"J. Pérez-Martínez, J. Martín, A. Sierra-Alonso","doi":"10.1109/EDUCON.2014.6826182","DOIUrl":"https://doi.org/10.1109/EDUCON.2014.6826182","url":null,"abstract":"The present work is focused on studying two issues: the “teamwork” generic competence and the “academic motivation”. Currently the professional profile of engineers has a strong component of teamwork. On the other hand, motivational profile of students determines their tendencies when they come to work in team, as well as their performance at work. In this context we suggest four hypotheses: (H1) students improve their teamwork capacity by specific training and carrying out a set of activities integrated into an active learning process; (H2) students with higher mastery motivation have better attitude towards team working; (H3) students with higher mastery motivation obtain better results in academic performance; and (H4) students show different motivation profiles in different circumstances: type of courses, teaching methodologies, different times of the learning process. This study was carried out with computer science engineering students from two Spanish universities. The first results point to an improvement in teamwork competence of students if they have previously received specific training in facets of that competence. Other results indicate that there is a correlation between the motivational profiles of students and their perception about teamwork competence. Finally, and contrary to the initial hypothesis, these profiles appear to not influence significantly the academic performance of students.","PeriodicalId":235118,"journal":{"name":"IEEE Global Engineering Education Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382468","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: 15

Laboratory education experiences in the development of advanced digital TV services 实验室教育的发展经验为先进的数字电视服务

IEEE Global Engineering Education Conference Pub Date : 1900-01-01 DOI: 10.1109/EDUCON.2012.6201163

A. Papadakis, M. Panaousis, Costas Alafodimos

引用次数: 0

Avatars and badges, are there differences between genders? 头像和徽章，性别之间有区别吗?

IEEE Global Engineering Education Conference Pub Date : 1900-01-01 DOI: 10.1109/EDUCON46332.2021.9454126

Brenda Nelly Santos Guevara, E. G. R. Flores

引用次数: 2

Exploring Computer Science student engagement factors within the learning analytics context to increase their academic achievement 在学习分析的背景下探索计算机科学学生的参与因素，以提高他们的学术成就

IEEE Global Engineering Education Conference Pub Date : 1900-01-01 DOI: 10.1109/EDUCON54358.2023.10125203

Hafsa Al Ansari

{"title":"Exploring Computer Science student engagement factors within the learning analytics context to increase their academic achievement","authors":"Hafsa Al Ansari","doi":"10.1109/EDUCON54358.2023.10125203","DOIUrl":"https://doi.org/10.1109/EDUCON54358.2023.10125203","url":null,"abstract":"The study aims to improve student retention rate and maintain student engagement level by exploring the student motivation factors within a learning analytics context. This research is an exploratory case study within the Computer Science Department at the University of Huddersfield, UK. The research focuses to develop a model to explore undergraduate CS student motivation factors through Self-Determination Theory and train the identified factors using learning analytics records. The identified factors will contribute to enhance the student academic achievement and their engagement with the course. In addition, the factors can be used to assist the future designer of learning analytics (LA) tools to adapt the human center design approach. Therefore, the new LA tools can incorporate cognitive and noncognitive factors that can enhance student retention rate and engagement toward computer science department. The study adapts a mixed-method approach using survey, interview and learning analytics records. More than 10,000 thousand virtual learning environment records were analyzed from the year 2018 to 2021. The analysis findings revealed that there are 8 significant factors that can have an impact on CS student engagement and therefore affecting their final grade.","PeriodicalId":235118,"journal":{"name":"IEEE Global Engineering Education Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126445860","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