TY - CHAP
T1 - Using causal inference in learning processes to predict student proficiency
AU - Saleh, Hanan
AU - Carrero, Gustavo
AU - Kumar, Vivekanandan Suresh
N1 - Publisher Copyright:
© 2023, IGI Global. All rights reserved.
PY - 2023/10/24
Y1 - 2023/10/24
N2 - The search for causal relations from observational data is an open problem that spans many fields. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is always desirable. In computer science, integrated development environments (IDE) offer students many features promising to instill the necessary competency skills for migration to industry. In this chapter, current causal discovery methods are applied to investigate a causal link between IDE consultations and student competency which is measured by the number of issues at the end of the coding timeline. The coding activities of students were timestamped over the coding lifetime. Due to the nature of the data, the authors were able to test for causality using methods for static and methods for dynamic data. The authors show the presence of a causal link between IDE consults and student improvement. In addition, they show the time it takes to see the effect of a system consult.
AB - The search for causal relations from observational data is an open problem that spans many fields. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is always desirable. In computer science, integrated development environments (IDE) offer students many features promising to instill the necessary competency skills for migration to industry. In this chapter, current causal discovery methods are applied to investigate a causal link between IDE consultations and student competency which is measured by the number of issues at the end of the coding timeline. The coding activities of students were timestamped over the coding lifetime. Due to the nature of the data, the authors were able to test for causality using methods for static and methods for dynamic data. The authors show the presence of a causal link between IDE consults and student improvement. In addition, they show the time it takes to see the effect of a system consult.
UR - http://www.scopus.com/inward/record.url?scp=85177611535&partnerID=8YFLogxK
U2 - 10.4018/978-1-6684-9527-8.ch010
DO - 10.4018/978-1-6684-9527-8.ch010
M3 - Chapter
AN - SCOPUS:85177611535
SN - 9781668495278
SP - 188
EP - 207
BT - Perspectives on Learning Analytics for Maximizing Student Outcomes
ER -