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Lunn, Stephanie; Ross, Monique; Hazari, Zahra; Weiss, Mark Allen; Georgiopoulos, Michael; Christensen, Kenneth – ACM Transactions on Computing Education, 2022
Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students' motivation and persistence is critical to helping educators,…
Descriptors: Educational Experience, Prediction, Identification (Psychology), Computer Science Education
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Umar Shehzad; Jody Clarke-Midura; Mimi Recker – ACM Transactions on Computing Education, 2024
Objectives: The increasing demand for computing skills has led to a rapid rise in the development of new computer science (CS) curricula, many with the goal of equitably broadening the participation of underrepresented students in CS. While such initiatives are vital, factors outside of the school environment also play a role in influencing…
Descriptors: Parent Child Relationship, Computer Science Education, Programming, Equal Education
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Liao, Soohyun Nam; Zingaro, Daniel; Thai, Kevin; Alvarado, Christine; Griswold, William G.; Porter, Leo – ACM Transactions on Computing Education, 2019
As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind.…
Descriptors: Prediction, Low Achievement, Tests, Scores
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Lagus, Jarkko; Longi, Krista; Klami, Arto; Hellas, Arto – ACM Transactions on Computing Education, 2018
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in…
Descriptors: Computer Science Education, Transfer of Training, Programming, Educational Objectives
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Carter, Adam S.; Hundhausen, Christopher D.; Adesope, Olusola – ACM Transactions on Computing Education, 2017
Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students' programming behaviors and course outcomes, and (b) students'…
Descriptors: Social Behavior, Academic Achievement, Programming, Computer Science Education