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Using Process and Motivation Data to Predict the Quality with Which Preservice Teachers Debugged Higher and Lower Complexity Programs
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Belland, Brian R.; Kim, ChanMin; Zhang, Anna Y.; Baabdullah, Afaf A.; Lee, Eunseo – IEEE Transactions on Education, 2021
Contribution: This study indicates that supporting debugging processes is a strong method to improve debugging outcome quality among preservice, early childhood education (ECE) teachers. Background: Central to preparing ECE teachers to teach computer science is helping them learn to debug. Little is known about how ECE teachers' motivation and…
Descriptors: Student Motivation, Predictor Variables, Preservice Teachers, Early Childhood Teachers
Examining Successful Attributes for Undergraduate Students by Applying Machine Learning Techniques
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Ko, Chia-Yin; Leu, Fang-Yie – IEEE Transactions on Education, 2021
Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how…
Descriptors: Undergraduate Students, Student Characteristics, Academic Achievement, Predictor Variables
More Time or Better Tools? A Large-Scale Retrospective Comparison of Pedagogical Approaches to Teach Programming
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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
Programming Anxiety amongst Computing Students--A Key in the Retention Debate?
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Connolly, C.; Murphy, E.; Moore, S. – IEEE Transactions on Education, 2009
Low retention rates in third-level computing courses, despite continuing research into new and improved computer teaching methods, present a worrying concern. For some computing students learning programming is intimidating, giving rise to lack of confidence and anxiety. The noncognitive domain of anxiety with regard to learning computer…
Descriptors: Computer Science Education, Computer Attitudes, Programming, Anxiety
Predicting Computer Science Ph.D. Completion: A Case Study
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Cox, G. W.; Hughes, W. E., Jr.; Etzkorn, L. H.; Weisskopf, M. E. – IEEE Transactions on Education, 2009
This paper presents the results of an analysis of indicators that can be used to predict whether a student will succeed in a Computer Science Ph.D. program. The analysis was conducted by studying the records of 75 students who have been in the Computer Science Ph.D. program of the University of Alabama in Huntsville. Seventy-seven variables were…
Descriptors: Case Studies, Prediction, Computer Science Education, Doctoral Degrees