Adaptive remediation for novice programmers through personalized prescriptive quizzes

Soltanpoor, R, Thevathayan, C and D'Souza, D 2018, 'Adaptive remediation for novice programmers through personalized prescriptive quizzes', in Irene Polycarpou, Janet C. Read, Panayiotis Andreou, and Michal Armoni (ed.) Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018), Larnaca, Cyprus, 2-4 July 2018, pp. 51-56.


Document type: Conference Paper
Collection: Conference Papers

Title Adaptive remediation for novice programmers through personalized prescriptive quizzes
Author(s) Soltanpoor, R
Thevathayan, C
D'Souza, D
Year 2018
Conference name ITiCSE 2018
Conference location Larnaca, Cyprus
Conference dates 2-4 July 2018
Proceedings title Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018)
Editor(s) Irene Polycarpou, Janet C. Read, Panayiotis Andreou, and Michal Armoni
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 51
End page 56
Total pages 6
Abstract Learning to program is a cognitively demanding activity. Students need to combine mental models of various concepts and constructs to solve problems. Many students new to IT and CS programs have little or no prior experience with abstract reasoning and problemsolving. Instructors attempt to present the core concepts early to allow adequate time for students to complete their programming assignments. However, misconceptions of basic concepts formed in the early stages often get propagated blocking any further progress. Such students often begin to form poor opinions about their capability leading to low self-esteem and performance. This paper proposes a framework to help individual students to overcome their misconceptions through personalized prescriptive quizzes. These quizzes are generated by combining the rich meta-data captured by each quiz question with analysis of past responses to class quizzes. The personalized prescriptive quizzes generated helped to improve student engagement and performance substantially. Over 91% of the students surveyed indicated that personalized quizzes helped them to clarify their own misconceptions and made them more confident of their progress. Students using the prescriptive quizzes performed significantly better than others in subsequent class assessments and the final exam.
Subjects Expert Systems
Keyword(s) Personalized Learning
Formative Assessment
Active Learning
Learning Analytics
Prescriptive Analytics
DOI - identifier 10.1145/3197091.3197097
Copyright notice © 2018 Association for Computing Machinery
ISBN 9781450357074
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Created: Fri, 14 Dec 2018, 16:06:00 EST by Catalyst Administrator
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