The Effects of Worked-Out Example and Metacognitive Scaffolding on Problem-Solving Programming
Abstract
This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners’ knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: 1) task performance with a traditional WOE (TW), 2) task performance with a faded WOE (FW), 3) task performance with traditional WOE and metacognitive scaffolding (TWM), and 4) task performance with a faded WOE and metacognitive scaffolding (FWM). Over the course of 3 weeks, participants in these four groups were asked to solve programming problems using Python with WOE and metacognitive scaffolding. The results demonstrate that the provision of metacognitive scaffolding with faded WOE (FWM) is the most effective for problem-solving programming and self-regulation skills. In addition, an interaction effect exists between the two treatments for the germane load in FWM. Therefore, results in this study provide empirical insights into ways to effectively apply WOE and metacognitive scaffolding to problem-solving processes for programming-based complex problem-solving, especially for novice learners.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Cognitive Architecture and Instructional Design
Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998) · Educational Psychology Review · doi:10.1023/a:1022193728205 · Cited by 6,023 · see citing works
Cognitive Load Measurement as a Means to Advance Cognitive Load Theory
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_8 · Cited by 2,527 · see citing works
Handbook of Self-Regulation of Learning and Performance
Schunk, D. H. (2011) · Routledge eBooks · doi:10.4324/9780203839010 · Cited by 2,191 · see citing works
Paas, F. G. W. C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.4.429 · Cited by 2,150 · see citing works
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_4 · Cited by 1,927 · see citing works
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989) · Cognitive Science · doi:10.1207/s15516709cog1302_1 · Cited by 1,714 · see citing works
Schraw, G., Crippen, K. J., & Hartley, K. (2006) · Research in Science Education · doi:10.1007/s11165-005-3917-8 · Cited by 1,529 · see citing works
Development of an instrument for measuring different types of cognitive load
Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013) · Behavior Research Methods · doi:10.3758/s13428-013-0334-1 · Cited by 1,066 · cited 1× · see citing works
“Participants ’ cognitive load levels were measured using a 10-point Likert scale (Leppink et al., 2013), ranging from “ Extremely Easy ” (0) to “ Extremely Dif fi cult ” (10).”
MethodPaas, F., Renkl, A., & Sweller, J. (2004) · Instructional Science · doi:10.1023/b:truc.0000021806.17516.d0 · Cited by 1,054 · see citing works
Instructional control of cognitive load in the training of complex cognitive tasks
Paas, F. G. W. C., & Van Merriënboer, J. J. G. (1994) · Educational Psychology Review · doi:10.1007/bf02213420 · Cited by 968 · see citing works
Puntambekar, S., & Hubscher, R. (2005) · Educational Psychologist · doi:10.1207/s15326985ep4001_1 · Cited by 787 · see citing works
Worked-out examples: Instructional explanations support learning by self-explanations
Renkl, A. (2002) · Learning and Instruction · doi:10.1016/s0959-4752(01)00030-5 · Cited by 452 · see citing works
Motivated Strategies for Learning Questionnaire
Pintrich, P. R., & De Groot, E. V. (2012) · PsycTESTS Dataset · doi:10.1037/t09161-000 · Cited by 399 · see citing works
How People Learn II: Learners, Contexts, and Cultures
Limson, S. L. I. (2023) · Academy of Management Learning and Education · doi:10.5465/amle.2022.0312 · Cited by 373 · see citing works
How Fading Worked Solution Steps Works – A Cognitive Load Perspective
Renkl, A., Atkinson, R. K., & Große, C. S. (2004) · Instructional Science · doi:10.1023/b:truc.0000021815.74806.f6 · Cited by 272 · see citing works
Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance
Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J., & Burnett, M. M. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858252 · Cited by 223 · see citing works
Chao, P.-Y. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.01.010 · Cited by 211 · see citing works
Crippen, K. J., & Earl, B. L. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.11.018 · Cited by 202 · see citing works
van Merriënboer, J. J. G., & Kirschner, P. A. (2012) · Routledge eBooks · doi:10.4324/9780203096864 · Cited by 157 · see citing works
Herrington, J., Viteli, J., & Leikomaa, M. (2014) · Murdoch University Research Portal · Cited by 150 · see citing works
A theory of instruction for introductory programming skills
Xie, B., Loksa, D., Nelson, G. L., Davidson, M. J., Dong, D., Kwik, H., Tan, A. H., Hwa, L., Li, M., & Ko, A. J. (2019) · Computer Science Education · doi:10.1080/08993408.2019.1565235 · Cited by 148 · see citing works
Self-Regulated Learning and Performance
Zimmerman, B. J., & Schunk, D. H. (2015) · Routledge eBooks · doi:10.4324/9780203839010.ch1 · Cited by 147 · see citing works
The Role of Self-Regulation in Programming Problem Solving Process and Success
Loksa, D., & Ko, A. J. (2016) · Proceedings of the 2016 ACM Conference on International Computing Education Research · doi:10.1145/2960310.2960334 · Cited by 143 · see citing works
What Do We Think We Think We Are Doing?
Prather, J. E., Becker, B. A., Craig, M., Denny, P. C., Loksa, D., & Margulieux, L. E. (2020) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3372782.3406263 · Cited by 137 · see citing works
Not seeing the forest for the trees
Lister, R., Simon, B., Thompson, E., Whalley, J. L., & Prasad, C. (2006) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/1140124.1140157 · Cited by 115 · see citing works
When worked examples don't work: Is cognitive load theory at an Impasse?
Moreno, R. (2006) · Learning and Instruction · doi:10.1016/j.learninstruc.2006.02.006 · Cited by 109 · see citing works
Metacognitive scaffolding in an innovative learning arrangement
Molenaar, I., van Boxtel, C., & Sleegers, P. J. C. (2010) · Instructional Science · doi:10.1007/s11251-010-9154-1 · Cited by 105 · see citing works
Defining a New 21st Century Skill-Computational Thinking: Concepts and Trends
Haseski, H. I., Ilic, U., & Tugtekin, U. (2018) · International Education Studies · doi:10.5539/ies.v11n4p29 · Cited by 105 · see citing works
Agbo, F. J., Oyelere, S. S., Suhonen, J. T., & Adewumi, S. E. (2019) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3364510.3364521 · Cited by 103 · see citing works
Learning programming via worked-examples: Relation of learning styles to cognitive load
Abdul-Rahman, S.-S., & du Boulay, B. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.09.007 · Cited by 92 · see citing works
Managing Cognitive Load in Introductory Programming Courses: A Cognitive Aware Scaffolding Tool
Stachel, J., Marghitu, D., Brahim, T. B., Sims, R., Reynolds, L., & Czelusniak, V. (2013) · Journal of Integrated Design and Process Science · doi:10.3233/jid-2013-0004 · Cited by 74 · see citing works
Shin, Y., & Song, D. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331211052632 · Cited by 42 · see citing works
Self-citationInvestigating Novices' In Situ Reflections on Their Programming Process
Loksa, D., Xie, B., Kwik, H., & Ko, A. J. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366846 · Cited by 39 · see citing works
Magana, A. J., Fennell, H. W., Vieira, C., & Falk, M. L. (2019) · Journal of Engineering Education · doi:10.1002/jee.20264 · Cited by 35 · see citing works
Jung, J., Shin, Y., & Zumbach, J. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2019.1619592 · Cited by 30 · see citing works
Self-citationJung, J. H., Jung, M. K., Kim, K. E., et al. (2016) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2016.21.2.75 · Cited by 28 · see citing works
Self-citationSelf-regulated Learning in Computer Programming: Strategies Students Adopted During an Assignment
Pedrosa, D., Cravino, J., Morgado, L. C., & Barreira, C. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-41769-1_7 · Cited by 28 · see citing works
Exploring Design Characteristics of Worked Examples to Support Programming and Algorithm Design
Magana, A. J., Vieira, C., & Yan, J. (2015) · The Journal of Computational Science Education · doi:10.22369/issn.2153-4136/6/1/1 · Cited by 25 · see citing works
A Tool to Support the Use of Part-Complete Solutions in the Learning of Programming
Garner, S. (2001) · Informing Science and IT Education Conference · doi:10.28945/2385 · Cited by 15 · see citing works
The Effect of Faded Information Based on Learners’ Prior Knowledge in Problem-Solving Learning
Jung, J., Hong, J., & Shin, Y. (2019) · Korean Association For Learner-Centered Curriculum And Instruction · doi:10.22251/jlcci.2019.19.15.799 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- Ge X., 2016, The pre-conference workshop at the 12th international conference of the learning sciences, 19
- López-Vargas O., 2017, Journal of Educational Technology and Society, 20, 145
- Renkl A., 2000, In proceeding of the 22nd annual Conference of the cognitive science society, 393
- Salleh S. M., 2018, International Journal of Pure and Applied Mathematics, 118, 77
- van Rossum, 2018, Python Tutorial Release 3.7.0
- Yousoof M., 2007, International Journal of Computer and Information Engineering, 1, 4100
Cited by
Sorted by citations
Jung, J., Shin, Y., Chung, H., & Fanguy, M. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-024-09407-3 · Cited by 11 · see citing works
Self-citation
- Authors
- Shin, Y., Jung, J., Zumbach, J., & Yi, E.
- Affiliations
- Hanyang University (KR)
- Korean Educational Development Institute (KR)
- University of Salzburg (AT)
- Corresponding author
- Jung, Jaewon
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2023-05-16
- Volume
- 61
- Issue
- 6
- Pages
- 1312-1331
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W4376875422
- Semantic Scholar
- 5fa52e9545e6ea1e791ea7b59473206dc56487d9
- Cited by
- 18
- References
- 40
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 2 · 2023: 1
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- worked examples, metacognitive scaffolding, cognitive load, self-regulation, germane load, novice learners, programming education, Python
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review