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Direct linkLin, Yu-Tzu; Wu, Cheng-Chih; Chen, Zhi-Hong; Ku, Pei-Yi – Educational Technology & Society, 2020
This study aimed to investigate the effects of gender pairings on collaborative problem-solving performance, processes, and attitudes in a social learning context. Three types of pairings (i.e., male-male, female-female, and mixed pairings) were considered in an empirical study with 222 tenth-grade students. The selection of three different…
Descriptors: Gender Differences, Cooperative Learning, Problem Solving, High School Students
Zimmerman, Thomas G.; Johnson, David; Wambsgans, Cynthia; Fuentes, Antonio – ACM Transactions on Computing Education, 2011
This article reports on a public school that is succeeding in encouraging Latino high school students to select Computer Science (CS) as a major. The students attend a charter high school designed to encourage low-income Latino students to attend college and attain proficiency in English, Spanish, and computers. Using data from surveys and by…
Descriptors: High Schools, Private Schools, Intervention, Student Interests
Lau, Wilfred Wing Fat; Yuen, Allan Hoi Kau – Australasian Journal of Educational Technology, 2010
The gender digital divide has been widely discussed and researched over the years. Previous studies have focused on a number of factors such as computer attitude, computer anxiety, computer self-efficacy, and computer experience. This study empirically tested the sensitivity of a learning style instrument, the "Gregorc Style Delineator"…
Descriptors: Gender Differences, Cognitive Style, Computer Science Education, Programming
Cheong, Yuk Fai; Pajares, Frank; Oberman, Paul S. – Computer Science Education, 2004
The primary objective of this study was to determine the degree to which academic motivation predicted the executive help-seeking, instrumental help-seeking, perceived benefits of help-seeking, and avoidance of help-seeking of high school students enrolled in computer science (n = 314). Task goals were positively associated with instrumental…
Descriptors: African American Students, Self Efficacy, Computer Science, Asian American Students
