期刊:
Journal of Educational Computing Research,2022年60(1):74 - 103 ISSN:0735-6331
通讯作者:
Wanli Xing
作者机构:
[Wang, Xianhui] School of Journalism and Communication, Central China Normal University, Wuhan, China;[Xing, Wanli] School of Teaching and Learning, University of Florida, Gainesville, United States
通讯机构:
[Wanli Xing] S;School of Teaching and Learning, University of Florida, Gainesville, United States
关键词:
game-based learning;collaborative game in 3D virtual environment;social interaction;association rule mining;autism spectrum disorder
摘要:
This study explored youth with Autism Spectrum Disorder (ASD) learning social competence in the context of innovative 3D virtual learning environment and the effects of gaming as a central element of the learning experience. The empirical study retrospectively compared the social interactions of 11 adolescents with ASD in game-and nongame-based 3D collaborative learning activities in the same social competence training curriculum. We employed a learning analytics approach - association rule mining to uncover the associative rules of verbal social interaction and nonverbal social interaction contributors from the large dataset of the coded social behaviors. By comparing the rules across the game and nongame activities, we found a significant difference in youth with ASD’s social performance. The results of the group comparison study indicated that the co-occurrence of verbal and nonverbal behaviors is much stronger in the game-based learning activities. The game activities also yielded more diverse social interaction behavior patterns. On the other hand, in the nongame activities, students’ social interaction behavior patterns are much more limited. Furthermore, the impact of game design principles on learning is then discussed in this paper.
期刊:
Behaviour & Information Technology,2022年41(12):2560-2577 ISSN:0144-929X
通讯作者:
Xianhui Wang
作者机构:
[Xing, Wanli] Univ Florida, Sch Teaching & Learning, Educ Technol, Gainesville, FL USA.;[Wang, Xianhui] Cent China Normal Univ, Sch Journalism & Commun, Wuhan, Hubei, Peoples R China.
通讯机构:
[Xianhui Wang] S;School of Journalism and Communication, Central China Normal University, Wuhan, People’s Republic of China
关键词:
Use of data;data-driven decision;e-learning;ease of use perceptions and behavioural intentions
摘要:
With the advancement of digital technologies, big data and learning analytics have become prevalent in the higher education. Various student-facing systems increased the amount of data available to students, and whether students can use big data and learning analytics effectively will affect their academic success. Most studies, however, have focused on how teachers and administrative personnel use student data to make data-driven instruction and management decisions. As a result, little attention has been given to students' use of relevant data that generated by big data and learning analytics to promote their own learning and growth. This study explored using social cognitive theory to identify possible environmental, personal, and behavioural factors that affect students' data use. We used an online questionnaire that collected 242 completed surveys from Chinese university students. Partial Least Squares (PLS) path modelling was used to analyse the data. The initial findings support the conclusion that university students could be encouraged to effectively use data in three ways: (1) through the promotion of university-wide cultures of data use and sustained improvements in data quality, (2) through the professional development of student data literacy, and (3) through the support of student data autonomy, student data reflectiveness, and students' digital identities.