The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.
Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance
Open accessAbstract
More people are learning to code than ever, but most learning opportunities do not explicitly teach the problem solving skills necessary to succeed at open-ended programming problems. In this paper, we present a new approach to impart these skills, consisting of: 1) explicit instruction on programming problem solving, which frames coding as a process of translating mental representations of problems and solutions into source code, 2) a method of visualizing and monitoring progression through six problem solving stages, 3) explicit, on-demand prompts for learners to reflect on their strategies when seeking help from instructors, and 4) context-sensitive help embedded in a code editor that reinforces the problem solving instruction. We experimentally evaluated the effects of our intervention across two 2-week web development summer camps with 48 high school students, finding that the intervention increased productivity, independence, programming self-efficacy, metacognitive awareness, and growth mindset. We discuss the implications of these results on learning technologies and classroom instruction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This growing body of evidence underscores the importance of metacognitive capabilities-specifically, an ability to maintain agency and critical judgment when presented with external solutions-as essential to the effective use of GenAI for programming.”
From The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Coding Tasks (2025) · cites this paper as [23]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1978) · Advances in Behaviour Research and Therapy · doi:10.1016/0146-6402(78)90002-4 · Cited by 10,586
Social Foundations of Thought and Action
Bandura, A. (2002) · SAGE Publications Ltd eBooks · doi:10.4135/9781446221129.n6 · Cited by 9,948
Mindset: The New Psychology of Success
(2006) · Choice Reviews Online · doi:10.5860/choice.44-2397 · Cited by 7,804
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007) · Child Development · doi:10.1111/j.1467-8624.2007.00995.x · Cited by 3,784
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., et al. (2009) · Communications of the ACM · doi:10.1145/1592761.1592779 · Cited by 3,333
(1994) · The MIT Press eBooks · doi:10.7551/mitpress/4561.001.0001 · Cited by 1,770
Schraw, G., Crippen, K. J., & Hartley, K. (2006) · Research in Science Education · doi:10.1007/s11165-005-3917-8 · Cited by 1,529
Promoting General Metacognitive Awareness
Schraw, G. (2001) · Neuropsychology and Cognition · doi:10.1007/978-94-017-2243-8_1 · Cited by 1,245
Promoting general metacognitive awareness
Schraw, G. (1998) · Instructional Science · doi:10.1023/a:1003044231033 · Cited by 1,088
Initial trends in enrolment and completion of massive open online courses
Jordan, K. (2014) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v15i1.1651 · Cited by 978
Occupational outlook handbook, 2014-2015
(2015) · Choice Reviews Online · doi:10.5860/choice.190249 · Cited by 822
Failure rates in introductory programming
Bennedsen, J., & Caspersen, M. E. (2007) · ACM SIGCSE Bulletin · doi:10.1145/1272848.1272879 · Cited by 677
Metacognition, cognition, and human performance
Forrest-Pressley, D.-L., MacKinnon, G. E., & Waller, T. G. (1985) · Cited by 609
Whitebread, D., Coltman, P., Pino‐Pasternak, D., Sangster, C., Grau, V., Bingham, S., Al-Meqdad, Q., & Demetriou, D. (2008) · Metacognition and Learning · doi:10.1007/s11409-008-9033-1 · Cited by 563
Six Learning Barriers in End-User Programming Systems
Ko, A. J., Myers, B. A., & Aung, H. H. (2004) · doi:10.1109/vlhcc.2004.47 · Cited by 552
Self-citationFisher, A. L., & Margolis, J. (2002) · ACM SIGCSE Bulletin · doi:10.1145/543812.543836 · Cited by 508
Storytelling alice motivates middle school girls to learn computer programming
Kelleher, C. L., Pausch, R. F., & Kiesler, S. B. (2007) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1240624.1240844 · Cited by 505
Developmental and instructional analyses of children's metacognition and reading comprehension.
Cross, D., & Paris, S. G. (1988) · Journal of Educational Psychology · doi:10.1037/0022-0663.80.2.131 · Cited by 497
Measures of Children's Knowledge and Regulation of Cognition
Sperling, R. A., Howard, B. C., Miller, L. A., & Murphy, C. (2002) · Contemporary Educational Psychology · doi:10.1006/ceps.2001.1091 · Cited by 469
(2009) · doi:10.1145/1518701.1518944 · Cited by 447
The effects of pair-programming on performance in an introductory programming course
McDowell, C., Werner, L., Bullock, H. E., & Fernald, J. L. (2002) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/563340.563353 · Cited by 353
Estimating the Numbers of End Users and End User Programmers
Scaffidi, C., Shaw, M., & Myers, B. A. (2005) · Proceedings/Proceedings -- IEEE Symposium on Visual Languages and Human-Centric Computing · doi:10.1109/vlhcc.2005.34 · Cited by 342
Effects of computer programming on young children's cognition.
Clements, D. H., & Gullo, D. F. (1984) · Journal of Educational Psychology · doi:10.1037/0022-0663.76.6.1051 · Cited by 339
Practicing Representation: Learning with and about Representational Forms
Greeno, J. G., & Hall, R. P. (1997) · Phi Delta Kappan · Cited by 267
How effective developers investigate source code: an exploratory study
Robillard, M. P., Coelho, W., & Murphy, G. C. (2004) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2004.101 · Cited by 263
Technology as amplifier in international development
Toyama, K. (2011) · Proceedings of the 2011 iConference · doi:10.1145/1940761.1940772 · Cited by 258
The Effect of Knowledge and Strategy Training on Monitoring Accuracy
Nietfeld, J. L., & Schraw, G. J. (2002) · The Journal of Educational Research · doi:10.1080/00220670209596583 · Cited by 208
Examining the role of self-regulated learning on introductory programming performance
Bergin, S., Reilly, R., & Traynor, D. (2005) · doi:10.1145/1089786.1089794 · Cited by 205
(2009) · Bilkent University Institutional Repository (Bilkent University) · Cited by 203
Retaining nearly one-third more majors with a trio of instructional best practices in CS1
Porter, L., & Simon, B. (2013) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2445196.2445248 · Cited by 164
Self-efficacy, metacognition, and performance.
Coutinho, S. A. (2008) · North American journal of psychology · Cited by 126
Evaluating metacognitive scaffolding in Guided Invention Activities
Roll, I., Holmes, N. G., Day, J., & Bonn, D. A. (2012) · Instructional Science · doi:10.1007/s11251-012-9208-7 · Cited by 126
Designing for metacognition—applying cognitive tutor principles to the tutoring of help seeking
Roll, I., Aleven, V. A. W. M. M., McLaren, B. M., & Koedinger, K. R. (2007) · Metacognition and Learning · doi:10.1007/s11409-007-9010-0 · Cited by 124
Principles of a debugging-first puzzle game for computing education
Lee, M. J., Bahmani, F., Kwan, I., LaFerte, J., Charters, P., Horvath, A., Luor, F., Cao, J., Law, C., Beswetherick, M., et al. (2014) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2014.6883023 · Cited by 120
Self-citationScott, M., & Ghinea, G. (2013) · IEEE Transactions on Education · doi:10.1109/te.2013.2288700 · Cited by 115
Comparing the Effectiveness of Online Learning Approaches on CS1 Learning Outcomes
Lee, M. J., & Ko, A. J. (2015) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2787622.2787709 · Cited by 109
Self-citationLanguage Semantics, Mental Models and Analogy
Hoc, J., & Nguyen‐Xuan, A. (1990) · Elsevier eBooks · doi:10.1016/b978-0-12-350772-3.50014-8 · Cited by 107
The effects of pair-programming on performance in an introductory programming course
McDowell, C., Werner, L., Bullock, H., & Fernald, J. (2002) · ACM SIGCSE Bulletin · doi:10.1145/563517.563353 · Cited by 88
Metacognition and the Expertise of Computer Program Comprehension
Eteläpelto, A. (1993) · Scandinavian Journal of Educational Research · doi:10.1080/0031383930370305 · Cited by 83
Sloan, R. H., & Troy, P. (2008) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1352135.1352230 · Cited by 82
Perceptions of non-CS majors in intro programming: The rise of the conversational programmer
Chilana, P. K., Alcock, C., Dembla, S., Ho, A. T. Y., Hurst, A., Armstrong, B., & Guo, P. J. (2015) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2015.7357224 · Cited by 73
What Makes a Great Software Engineer
Li, P. L. (2016) · ResearchWorks at the University of Washington (University of Washington) · Cited by 66
Palumbo, D. B., & Reed, W. M. (1991) · Journal of Research on Computing in Education · doi:10.1080/08886504.1991.10781967 · Cited by 60
Skill Progression Demonstrated by Users in the Scratch Animation Environment
Scaffidi, C. P., & Chambers, C. (2011) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2011.595621 · Cited by 55
Geek genes and bimodal grades.
Lister, R. (2010) · ACM Inroads · Cited by 37
Metacognitive Strategy Training Improves Driving Situation Awareness
Soliman, A. M., & Mathna, E. K. (2009) · Social Behavior and Personality An International Journal · doi:10.2224/sbp.2009.37.9.1161 · Cited by 34
Flanigan, A. E., Peteranetz, M. S., Shell, D. F., & Soh, L. (2015) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2787622.2787722 · Cited by 32
The role of conceptual knowledge in API usability
Ko, A. J., & Riche, Y. (2011) · 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2011.6070395 · Cited by 30
Self-citationHelping programmers understand computer programs
Shaft, T. M. (1995) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/223278.223280 · Cited by 28
A principled evaluation for a principled idea garden
Jernigan, W., Horvath, A., Lee, M., Burnett, M., Cuilty, T., Kuttal, S., Peters, A., Kwan, I., Bahmani, F., & Ko, A. (2015) · 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2015.7357222 · Cited by 28
Self-citationBecker, H. J. (1987) · Educational Researcher · doi:10.3102/0013189x016005011 · Cited by 25
Idea Garden: Situated Support for Problem Solving by End-User Programmers
Cao, J., Fleming, S. D., Burnett, M., & Scaffidi, C. P. (2014) · Interacting with Computers · doi:10.1093/iwc/iwu022 · Cited by 22
Self-citationSloan, R. H., & Troy, P. (2008) · ACM SIGCSE Bulletin · doi:10.1145/1352322.1352230 · Cited by 21
An exploration of design opportunities for “gardening” end-user programmers' ideas
Cao, J., Fleming, S. D., & Burnett, M. (2011) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2011.6070375 · Cited by 19
Self-citationAttitudes and self-efficacy in young adults' computing autobiographies
Ko, A. J. (2009) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2009.5295297 · Cited by 19
Self-citationProgramming, syntax and cognitive load (part 2)
Lister, R. (2011) · ACM Inroads · doi:10.1145/2003616.2003622 · Cited by 12
End-user programmers in trouble: Can the Idea Garden help them to help themselves?
Cao, J., Kwan, I., Bahmani, F., Burnett, M., Fleming, S. D., Jordahl, J., Horvath, A., & Yang, S. (2013) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2013.6645260 · Cited by 11
Self-citationAn idea garden for end-user programmers
Cao, J. (2012) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2212776.2212864 · Cited by 1
(2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781839100222.00008 · Cited by 1
Research works printed but not linked: 3
- James G. Greeno and Rogers P. Hall. 1997. Practicing representation. Phi Delta Kappan 78, 5: 361.
- M. Havenga 2011. Problem-solving processes in computer programming: a case study." In Southern African Computer Lecturers' Association (SACLA) Conference Proceedings 91--99.
- 10.5555/2818754.2818839 · DOI
Cited by
Sorted by citations
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3617367 · Cited by 256
“In the context of programming, it involves how programmers solve problems and the process they go through to do so [53] and the programming environment likely plays a role in this [41].”
BackgroundBecker, B. A., Denny, P., Pettit, R., Bouchard, D., Bouvier, D. J., Harrington, B., Kamil, A., Karkare, A., McDonald, C., Osera, P.-M., et al. (2019) · Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3344429.3372508 · Cited by 199
Kong, S. C., Lai, M., & Sun, D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103872 · Cited by 237
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024) · Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3632620.3671116 · Cited by 196
Novice Programmers and Introductory Programming
Robins, A. V. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108654555.013 · Cited by 191
Metadata
- Authors
- Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J., & Burnett, M. M.
- Affiliations
- University of Washington (US)
- Oregon State University, Corvallis, OR, USA
- Oregon State University (US)
- Venue
- Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2016-05-07
- Pages
- 1449-1461
- OpenAlex
- W2407505401
- Cited by
- 223
- References
- 62
- Citations by year
- 2026: 23 · 2025: 18 · 2024: 29 · 2023: 22 · 2022: 20 · 2021: 17 · 2020: 17 · 2019: 55 · 2018: 10 · 2017: 11 · 2016: 1
- FWCI
- 20.48
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation (1240786, 1314399, 1153625)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Problem and Project Based Learning
- Keywords
- programming problem solving, self-awareness, metacognition, growth mindset, programming self-efficacy, problem-solving stages, context-sensitive help, high school students, learner autonomy, programming education
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
