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Direct linkLinda J. Sax; Kaitlyn N. Stormes; Maxx F. Pereyra – ACM Transactions on Computing Education, 2025
To cultivate more computing talent (including more diverse talent), it is important to understand how college students experience their computing courses and if such experiences vary based on students' gender and racial/ethnic identities. In this paper, we focus on course modality to understand whether taking courses in-person, online, or a hybrid…
Descriptors: Computer Science Education, Electronic Learning, Online Courses, Delivery Systems
Al-Azawei, Ahmed; Parslow, Patrick; Lundqvist, Karsten – Australasian Journal of Educational Technology, 2017
This study assesses learner perceptions of a blended e-learning system (BELS) and the feasibility of accommodating educational hypermedia systems (EHSs) according to learning styles using a modified version of the technology acceptance model (TAM). Recently, Moodle has been adopted by an Iraqi university alongside face-to-face (F2F) classrooms to…
Descriptors: Cognitive Style, Blended Learning, Computer Attitudes, Models
Tran, Khanh Ngo Nhu – Journal of Information Technology Education: Research, 2016
This study examines factors that determine the attitudes of learners toward a blended e-learning system (BELS) using data collected by questionnaire from a sample of 396 students involved in a BELS environment in Vietnam. A theoretical model is derived from previous studies and is analyzed and developed using structural equation modeling…
Descriptors: Foreign Countries, Blended Learning, Electronic Learning, Educational Technology
Chang, Chi-Cheng; Tseng, Kuo-Hung; Liang, Chaoyun; Yan, Chi-Fang – Technology, Pedagogy and Education, 2013
Mobile learning aims to utilise communication devices such as mobile devices and wireless connection in combination with e-learning systems, allowing learners to experience convenient, instant and suitable learning at unrestricted time and place. Participants were 125 Taiwanese senior high school students, whose continuance intention was examined…
Descriptors: Foreign Countries, High School Students, Handheld Devices, Electronic Learning
Contreras, Carlos L. M. – Quarterly Review of Distance Education, 2004
Demographic and personality variables and computer use were used to predict computer self-confidence with a sample of students enrolled in online college-credit classes. Computer self-confidence was measured with one 10-choice question. Demographic variables included age, annual income, geographic region, gender, and ethnicity. Computer use was…
Descriptors: Income, Age Differences, Ethnic Groups, Gender Differences
