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Roslyn Arlin Mickelson; Ian Mikkelsen; Mohsen Dorodchi; Bojan Cukic; Caitlin Petro; Zelaya Al Ayeisha; Shakayla Alston; Anthony Teddy; Myat Win; Sandra Wiktor; Barry Sherman; Jeffrey Cook – School Community Journal, 2024
Students from underrepresented populations--females, working class, and youth from marginalized racial/ethnic groups--are less likely than their middle-class Asian and White male peers to study computer science (CS) in college. The dearth of CS undergraduates from these groups contributes to projected labor force shortages. Sources of the dilemma…
Descriptors: Middle School Students, Computer Science Education, Partnerships in Education, Minority Group Students
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Saito, Tomohiro; Watanobe, Yutaka – International Journal of Distance Education Technologies, 2020
Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to…
Descriptors: Programming, Computer Science Education, Electronic Learning, Instructional Materials
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Rohde, Jacqueline; Musselman, Lisa; Benedict, Brianna; Verdin, Dina; Godwin, Allison; Kirn, Adam; Benson, Lisa; Potvin, Geoff – IEEE Transactions on Education, 2019
Contribution: This paper found that design experiences can foster engineering identity and belongingness for early career electrical and computer engineering students. Students had different interpretations of what it meant to be an engineer (identity) and their belongingness in engineering. This paper provides novel insights into how students may…
Descriptors: Design, Engineering Education, Self Concept, Engineering
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Sentance, Sue; Waite, Jane; Kallia, Maria – Computer Science Education, 2019
Background and Context: Vygotsky's sociocultural theory emphasises the importance of language, mediation, and the transfer of skills and knowledge from the social into the cognitive plane. This perspective has influenced the development of PRIMM (Predict, Run, Investigate, Modify, Make), a structured approach to teaching programming. Objective:…
Descriptors: Computer Science Education, Teaching Methods, Comparative Analysis, 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
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Lau, Wilfred W. F.; Yuen, Allan H. K. – Computers & Education, 2011
In the 21st century, the ubiquitous nature of technology today is evident and to a large extent, most of us benefit from the modern convenience brought about by technology. Yet to be technology literate, it is argued that learning to program still plays an important role. One area of research in programming concerns the identification of…
Descriptors: Foreign Countries, Academic Achievement, Information Technology, Least Squares Statistics
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Lau, Wilfred W. F.; Yuen, Allan H. K. – British Journal of Educational Technology, 2009
Computer programming has been taught in secondary schools for more than two decades. However, little is known about how students learn to program. From the curriculum implementation perspectives, learning style helps address the issue of learner differences, resulting in a shift from a teacher-centred approach to a learner-focused approach. This…
Descriptors: Cognitive Style, Instructional Effectiveness, Gender Differences, Academic Ability
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Robins, Anthony – Computer Science Education, 2010
Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and…
Descriptors: Programming, Learning Processes, Grading, Simulation