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Silva-Maceda, Gabriela; Arjona-Villicaña, P. David; Castillo-Barrera, F. Edgar – IEEE Transactions on Education, 2016
Learning to program is a complex task, and the impact of different pedagogical approaches to teach this skill has been hard to measure. This study examined the performance data of seven cohorts of students (N = 1168) learning programming under three different pedagogical approaches. These pedagogical approaches varied either in the length of the…
Descriptors: Programming, Teaching Methods, Intermode Differences, Cohort Analysis
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Lau, Wilfred W. F.; Yuen, Allan H. K. – Journal of Educational Computing Research, 2009
Recent years have seen a shift in focus from assessment of learning to assessment for learning and the emergence of alternative assessment methods. However, the reliability and validity of these methods as assessment tools are still questionable. In this article, we investigated the predictive validity of measures of the Pathfinder Scaling…
Descriptors: Concept Mapping, Student Evaluation, Alternative Assessment, Scaling
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Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses
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Ventura, Philip R., Jr. – Computer Science Education, 2005
The paper reports on an examination of predictors of success for an "objects-first" course. The predictors considered included prior programming experience, mathematical ability, academic and psychological variables, gender, and measures of student effort. Cognitive and academic factors such as SAT scores and critical thinking ability…
Descriptors: Academic Achievement, Predictor Variables, Computer Science Education, Programming