Development of the Cognitive Programming Engagement (CPE) Scale: A Learning Strategy

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Publicado en:Journal of Information Systems Education vol. 36, no. 4 (Fall 2025), p. 400-417
Autor principal: Mahatanankoon, Pruthikrai
Otros Autores: Wolf, James R
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100 1 |a Mahatanankoon, Pruthikrai 
245 1 |a Development of the Cognitive Programming Engagement (CPE) Scale: A Learning Strategy 
260 |b EDSIG  |c Fall 2025 
513 |a Journal Article 
520 3 |a Advances in information and communication technologies (ICT) coupled with artificial intelligence have made computer programming skills indispensable for IT majors and for an increasing number of other science, technology, engineering, and mathematics (STEM) disciplines. Like any hands-on skill, mastering computer programming requires dedicated time, patience, focus, and persistent effort. Understanding students' learning strategies as they engage in computer programming activities can reduce attrition and lay a solid foundation for a successful career in IT/STEM disciplines. This paper focuses on developing the cognitive programming engagement (CPE) scale, which builds on existing cognitive engagement measures. Self-reported data from undergraduate IT students who are learning computer programming show that CPE supports four-dimensional learning strategies: memorization, practice, analysis, and visualization, which aligns with the levels of Bloom's Taxonomy. The new scale supports confirmatory, discriminant, and predictive validity and tests on programming self-efficacy and coding grit with acceptable predictive validity. IT/STEM educators can use the scale to assess and evaluate students" learning and improve their teaching strategies. 
610 4 |a National Survey of Student Engagement 
653 |a Measures 
653 |a Attrition 
653 |a Mathematics 
653 |a Science and technology 
653 |a Classification 
653 |a College students 
653 |a Blooms taxonomy 
653 |a Cognitive development 
653 |a Student participation 
653 |a Classroom communication 
653 |a Taxonomy 
653 |a Test validity and reliability 
653 |a Cognition 
653 |a Teachers 
653 |a Strategies 
653 |a Artificial intelligence 
653 |a Computers 
653 |a Reflective teaching 
653 |a Computer mediated communication 
653 |a Predictive validity 
653 |a Information technology 
653 |a Learning 
653 |a Students 
653 |a Computer programming 
653 |a Visualization 
653 |a Deep learning 
653 |a Teaching 
653 |a Success 
653 |a Communications technology 
653 |a STEM education 
653 |a Learning strategies 
653 |a Teaching methods 
653 |a Skills 
653 |a Memorization 
653 |a Self-efficacy 
653 |a Advanced Courses 
653 |a Critical Thinking 
653 |a Attrition (Research Studies) 
653 |a Cognitive Processes 
653 |a Academic Achievement 
653 |a College Entrance Examinations 
653 |a Majors (Students) 
653 |a Learner Engagement 
653 |a Educational Strategies 
653 |a Low Achievement 
653 |a Accreditation (Institutions) 
653 |a High Achievement 
653 |a Instructional Innovation 
653 |a Influence of Technology 
653 |a Learning Processes 
653 |a Educational Technology 
653 |a Student Experience 
653 |a Engineering Technology 
653 |a Computer Science 
653 |a Cognitive Measurement 
653 |a Information Systems 
653 |a Educational Experience 
653 |a Higher Education 
700 1 |a Wolf, James R 
773 0 |t Journal of Information Systems Education  |g vol. 36, no. 4 (Fall 2025), p. 400-417 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3272440732/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
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856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3272440732/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch