MARC

LEADER 00000nab a2200000uu 4500
001 3093914534
003 UK-CbPIL
022 |a 1055-3096 
022 |a 2574-3872 
022 |a 1055-3104 
024 7 |a 10.62273/GMIV1698  |2 doi 
035 |a 3093914534 
045 2 |b d20240701  |b d20240930 
084 |a 50955  |2 nlm 
100 1 |a Sandoval-Medina, Carlos 
245 1 |a Self-Explanation Effect of Cognitive Load Theory in Teaching Basic Programming 
260 |b EDSIG  |c Summer 2024 
513 |a Journal Article 
520 3 |a Learning basic programming concepts in computer science-related fields poses a challenge for students, to the extent that it becomes an academic-social problem, resulting in high failure and dropout rates. Proposed solutions to the problem can be found in the literature, such as the development of new programming languages and environments, the inclusion of virtual and augmented reality, gamification, automatic grading tools, and intelligent tutoring systems, among others. However, most of these solutions do not explicitly describe the application of some learning theory, instead, they focus on new technologies. Cognitive Load Theory (CLT) is an instructional design theory that aligns the design of instructional materials with human cognitive architecture using 17 design guidelines to optimize learning. The goal of this research is to design, develop, and test instructional materials to support the teaching and learning of basic programming, measuring their effectiveness compared to traditional materials, based on the selfexplanation effect of CLT. To compare the instructional materials, a quasi-experimental design with homogeneous groups was used, involving students from the Autonomous University of Aguascalientes. The results indicate a positive impact of the use of CLT-based instructional materials, either through the application of a single effect or the combination of two effects such as worked example and self-explanation. 
610 4 |a Autonomous University of Aguascalientes 
653 |a Augmented reality 
653 |a Students 
653 |a Teaching methods 
653 |a Computer science 
653 |a Memory 
653 |a Instructional materials 
653 |a Instructional design 
653 |a Software industry 
653 |a Learning theory 
653 |a Cognitive load 
653 |a Cognitive ability 
653 |a Cognition & reasoning 
653 |a Colleges & universities 
653 |a Tutoring 
653 |a Programming languages 
653 |a Virtual reality 
653 |a Educational objectives 
653 |a Knowledge 
653 |a Educational materials 
653 |a Design of experiments 
653 |a Information processing 
653 |a Design optimization 
653 |a Gamification 
653 |a Learning 
653 |a Virtual environments 
653 |a Computer assisted instruction--CAI 
653 |a College students 
653 |a Research design 
653 |a Quasi-experimental methods 
653 |a Cognition 
653 |a Theory 
653 |a Homogeneity 
653 |a Teaching 
653 |a Languages 
653 |a Learning theories 
653 |a Dropping out 
653 |a Intelligence 
653 |a Communicative language teaching 
653 |a Materials 
653 |a Long Term Memory 
653 |a Learning Processes 
700 1 |a Arévalo-Mercado, Carlos Argelio 
700 1 |a Muñoz-Andrade, Estela Lizbeth  |u Department of Electronic Systems Autonomous University of Aguascalientes Aguascalientes, México 
700 1 |a Muñoz-Arteaga, Jaime  |u Department of Information Systems Autonomous University of Aguascalientes Aguascalientes, México 
773 0 |t Journal of Information Systems Education  |g vol. 35, no. 3 (Summer 2024), p. 303 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3093914534/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3093914534/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3093914534/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch