學術產出-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 Developing a Computer-Mediated Communication Competence Forecasting Model Based on Learning Behavior Features
作者 陳志銘
Chen, Chih-Ming 
LianYing-You
貢獻者 圖檔所
關鍵詞 Computer-mediated communication ; Cooperative/collaborative learning ; Evaluation methodologies ; Interactive learning environments
日期 2020-01
上傳時間 22-Apr-2021 15:32:50 (UTC+8)
摘要 This study aims to develop a computer-mediated communication (CMC) competence forecasting model (CMCCFM) based on five well-known machine learning schemes- linear regression algorithm, M5P algorithm, sequence minimum optimization regression algorithm, convolutional neural network algorithm, and multi-layer perceptron algorithm and learning behavior features collected by a micro-behavior recorder from the learners who used a web-based collaborative problem-based learning (WCPBL) system to perform a problem-solving learning activity. To explore the big data generated from a huge amount of learners’ micro-behaviors as the user behavior features for establishing a good CMCCFM, this study developed the learning micro-behavior classification structure according to the collected data features and the concepts of CMC competence. Additionally, an effective method for constructing a CMCCFM with high correctness and stableness was proposed and examined its forecasting effectiveness. The effects of learning situations on the prediction accuracy of CMCCFM were also explored. Analytical results show that the CMCCFM developed by the five considered machine learning schemes all have good prediction performance to some degree. Among the five considered machine learning schemes, the sequence minimum optimization regression algorithm with the model complexity parameter set to 0.1 has the highest prediction accuracy and stability. The key features that influence the prediction accuracy most are “communication behavior” and “communication objective.” Moreover, the development of the CMCCFM must consider the learning situations and learners’ traits, such as discussion trait and familiar degree with the problem-solving subject, because those would affect the prediction accuracy of the developed CMCCFM.
關聯 Computers & Education: Artificial Intelligence, Volume 1, 2020, 100004
資料類型 article
DOI https://doi.org/10.1016/j.caeai.2020.100004
dc.contributor 圖檔所
dc.creator (作者) 陳志銘
dc.creator (作者) Chen, Chih-Ming 
dc.creator (作者) LianYing-You
dc.date (日期) 2020-01
dc.date.accessioned 22-Apr-2021 15:32:50 (UTC+8)-
dc.date.available 22-Apr-2021 15:32:50 (UTC+8)-
dc.date.issued (上傳時間) 22-Apr-2021 15:32:50 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/134853-
dc.description.abstract (摘要) This study aims to develop a computer-mediated communication (CMC) competence forecasting model (CMCCFM) based on five well-known machine learning schemes- linear regression algorithm, M5P algorithm, sequence minimum optimization regression algorithm, convolutional neural network algorithm, and multi-layer perceptron algorithm and learning behavior features collected by a micro-behavior recorder from the learners who used a web-based collaborative problem-based learning (WCPBL) system to perform a problem-solving learning activity. To explore the big data generated from a huge amount of learners’ micro-behaviors as the user behavior features for establishing a good CMCCFM, this study developed the learning micro-behavior classification structure according to the collected data features and the concepts of CMC competence. Additionally, an effective method for constructing a CMCCFM with high correctness and stableness was proposed and examined its forecasting effectiveness. The effects of learning situations on the prediction accuracy of CMCCFM were also explored. Analytical results show that the CMCCFM developed by the five considered machine learning schemes all have good prediction performance to some degree. Among the five considered machine learning schemes, the sequence minimum optimization regression algorithm with the model complexity parameter set to 0.1 has the highest prediction accuracy and stability. The key features that influence the prediction accuracy most are “communication behavior” and “communication objective.” Moreover, the development of the CMCCFM must consider the learning situations and learners’ traits, such as discussion trait and familiar degree with the problem-solving subject, because those would affect the prediction accuracy of the developed CMCCFM.
dc.format.extent 1897986 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Computers & Education: Artificial Intelligence, Volume 1, 2020, 100004
dc.subject (關鍵詞) Computer-mediated communication ; Cooperative/collaborative learning ; Evaluation methodologies ; Interactive learning environments
dc.title (題名) Developing a Computer-Mediated Communication Competence Forecasting Model Based on Learning Behavior Features
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.caeai.2020.100004
dc.doi.uri (DOI) https://doi.org/10.1016/j.caeai.2020.100004