MARC

LEADER 00000nab a2200000uu 4500
001 3275510491
003 UK-CbPIL
022 |a 2227-7102 
022 |a 2076-3344 
024 7 |a 10.3390/educsci15111492  |2 doi 
035 |a 3275510491 
045 2 |b d20250101  |b d20251231 
084 |a 231457  |2 nlm 
100 1 |a Gutiérrez de Ravé Simón 
245 1 |a Integrating CAD and Orthographic Projection in Descriptive Geometry Education: A Comparative Analysis with Monge’s System 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Descriptive geometry plays a fundamental role in developing spatial reasoning and geometric problem-solving skills in engineering education. This study investigates the comparative effectiveness of two instructional methodologies—Monge’s traditional projection system and the CADOP method, which integrates computer-aided design tools with orthographic projection principles. A quasi-experimental design was implemented with 90 undergraduate engineering students randomly assigned to two groups. Both groups followed the same instructional sequence and were evaluated using baseline surveys, rubric-based performance assessments, and post-training reflections. Quantitative analysis included mean comparisons, t-tests, and effect sizes, while inter-rater reliability confirmed scoring consistency. The results showed that CADOP students significantly outperformed those taught with Monge’s method across all criteria—conceptual under-standing, graphical accuracy, procedural consistency, and spatial reasoning—with very large effect sizes. Qualitative data indicated that CADOP enhanced clarity, efficiency, and confidence, while Monge promoted conceptual rigor but higher cognitive effort. The findings confirm that CADOP effectively reduces procedural complexity and cognitive load, supporting deeper spatial comprehension. Integrating CADOP with selected manual practices offers a balanced pedagogical approach for modernizing descriptive geometry instruction in engineering education. 
653 |a Software 
653 |a Coding theory 
653 |a Cognition & reasoning 
653 |a Computer aided design--CAD 
653 |a Learning 
653 |a Quantitative analysis 
653 |a Comprehension 
653 |a College students 
653 |a Research design 
653 |a Cognitive load 
653 |a Education 
653 |a Reasoning 
653 |a Problem solving 
653 |a Quasi-experimental methods 
653 |a Orthography 
653 |a Qualitative research 
653 |a Geometry 
653 |a Comparative analysis 
653 |a High Achievement 
653 |a Undergraduate Students 
653 |a Geometric Concepts 
653 |a Prior Learning 
653 |a Educational Methods 
653 |a Computer Assisted Design 
653 |a Interrater Reliability 
653 |a Computers 
653 |a Learning Theories 
653 |a Performance Tests 
653 |a Educational Change 
653 |a Computer Assisted Instruction 
653 |a Feedback (Response) 
653 |a Coding 
653 |a Accuracy 
653 |a Career and Technical Education 
653 |a Comparative Education 
653 |a Outcomes of Education 
653 |a Engineering Education 
653 |a Cognitive Ability 
653 |a Learner Engagement 
653 |a Educational Strategies 
700 1 |a Gutiérrez de Ravé Eduardo 
700 1 |a Jiménez-Hornero, Francisco J 
773 0 |t Education Sciences  |g vol. 15, no. 11 (2025), p. 1492-1518 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3275510491/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3275510491/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3275510491/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch