The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
Open accessAbstract
The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings.
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 mechanism resembles prompted self-explanation, an instructional technique that encourages learners to articulate their reasoning and has been shown to support monitoring of understanding, identification of knowledge gaps, and integration of new information with prior knowledge (Bisra et al., 2018;Chi et al., 1989).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Chi et al., 1989
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The self-explanation effect was demonstrated independently of cognitive load theory (Chi et al. 1989) but can be explained by the theory.”
From Cognitive Architecture and Instructional Design: 20 Years Later (2019) · cites this paper as Chi et al. 1989
In the citing paper
“Moreover, researchers have advocated active learning because it enables learners to connect new information with the knowledge they already possess (Chi et al. 1989;Mayer 2001;Wittrock 1974).”
From Interactivity in Video-based Models (2007) · cites this paper as Chi et al. 1989
In the citing paper
“Asking learners to explain to themselves aloud what they have understood from a learning task has proved to be an effective instructional method (Chi et al. 1989;Renkl 1997;Renkl and Atkinson 2002).”
From Interactivity in Video-based Models (2007) · cites this paper as Chi et al. 1989
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
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,886
Categorization and Representation of Physics Problems by Experts and Novices*
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981) · Cognitive Science · doi:10.1207/s15516709cog0502_2 · Cited by 5,248
Why a Diagram is (Sometimes) Worth Ten Thousand Words
Larkin, J. H., & Simon, H. A. (1987) · Cognitive Science · doi:10.1111/j.1551-6708.1987.tb00863.x · Cited by 3,855
The role of theories in conceptual coherence.
Murphy, G. L., & Medin, D. L. (1985) · Psychological Review · doi:10.1037/0033-295x.92.3.289 · Cited by 2,805
Advances in Instructional Psychology
(2019) · Routledge eBooks · doi:10.4324/9781315864341 · Cited by 2,538
(2014) · doi:10.4324/9781315799681 · Cited by 2,174
Advances in the Psychology of Human Intelligence
Sternberg, R. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315808246 · Cited by 1,771
Eye fixations and cognitive processes
Just, M. A., & Carpenter, P. A. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90015-3 · Cited by 1,730
Advances in the psychology of human intelligence
Eysenck, H. J. (1983) · Behaviour Research and Therapy · doi:10.1016/0005-7967(83)90111-0 · Cited by 1,590
Michelene, T. H., Glaser, R. A., & Rees, E. T. (1981) · Defense Technical Information Center (DTIC) · Cited by 1,529
Research works printed but not linked: 4
- Brown A.L., 1983, Cognitive development (Vol. III of P.H. Mussen, Ed., Handbook of child psychology, 77
- Halliday D., 1981, Fundamentals of physics.
- Lewis C.H., 1982, Proceedings of the conference of human factors in computer systems
- 10.1007/BF00116250 · DOI · Paper page
Cited by
Sorted by citations
Items 901–1,000 of 1,022
Learning by self-explaining causal diagrams in high-school biology
Cho, Y. H., & Jonassen, D. H. (2011) · Asia Pacific Education Review · doi:10.1007/s12564-011-9187-4 · Cited by 29
Example-based feedback provision using structured solution spaces
Groß, S., Mokbel, B., Paaßen, B., Hammer, B., & Niels Pinkwart, C. (2014) · International Journal of Learning Technology · doi:10.1504/ijlt.2014.065752 · Cited by 29
Butcher, K. R., & Aleven, V. A. W. M. M. (2013) · Journal of Educational Psychology · doi:10.1037/a0031756 · Cited by 29
Shenderovich, Y., Thurston, A., & Miller, S. J. (2015) · International Journal of Educational Research · doi:10.1016/j.ijer.2015.03.007 · Cited by 36
When Are Tutorial Dialogues More Effective Than Reading?
Matthews, D. E., VanLehn, K. A., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A. M., & RosAc, A. C. P. (2007) · Cognitive Science · doi:10.1207/s15516709cog3101_2 · Cited by 30
Using Screencasts in ChE Courses
Falconer, J. L., deGrazia, J. L., Medlin, J. W., & Holmberg, M. P. (2009) · Chemical Engineering Education · Cited by 28
Graf, E. A. (2009) · ETS Research Report Series · doi:10.1002/j.2333-8504.2009.tb02199.x · Cited by 30
Online collaborative learning in dyads: Effects of knowledge distribution and awareness
Kozlov, M. D., & Große, C. S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.01.043 · Cited by 28
Self-Explanations in Game-Based Learning: From Tacit to Transferable Knowledge
ter Vrugte, J., & de Jong, T. (2016) · Springer eBooks · doi:10.1007/978-3-319-39298-1_8 · Cited by 28
Skuballa, I. T., Dammert, A., & Renkl, A. (2018) · Learning and Instruction · doi:10.1016/j.learninstruc.2018.02.001 · Cited by 28
Predicting Cognitive Load in Future Code Puzzles
Kelleher, C. L., & Hnin, W. Y. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300487 · Cited by 30
Effects of reflection prompts on learning outcomes and learning behaviour in statistics education
Stark, R., & Krause, U.-M. (2009) · Learning Environments Research · doi:10.1007/s10984-009-9063-x · Cited by 30
The role of community feedback in the student example authoring process: An evaluation of AnnotEx
Hsiao, I., & Brusilovsky, P. L. (2010) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2009.01030.x · Cited by 28
One Day, One Problem: PBL at the Republic Polytechnic
O’Grady, G., & Alwis, W. A. M. (2002) · Cited by 28
Improving Students' Conceptual Reasoning by Prompting Cognitive Operations
Meter, P. N. V., Firetto, C. M., Turns, S. R., Litzinger, T., Cameron, C. E., & Shaw, C. W. (2016) · Journal of Engineering Education · doi:10.1002/jee.20120 · Cited by 28
Why and how do middle school students exchange ideas during science inquiry?
Matuk, C. F., & Linn, M. C. (2018) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-018-9282-1 · Cited by 29
Effects of explanation on children’s question asking
Ruggeri, A., Xu, F., & Lombrozo, T. (2019) · Cognition · doi:10.1016/j.cognition.2019.05.003 · Cited by 28
Why set-comparison is vital in early number learning
Muldoon, K. P., Lewis, C. N., & Freeman, N. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.01.010 · Cited by 28
Justification effects on the judgment of analogy
Sieck, W. R., Quinn, C. N., & Schooler, J. W. (1999) · Memory & Cognition · doi:10.3758/bf03198537 · Cited by 31
Introspective multistrategy learning: constructing a learning strategy under reasoning failure
Cox, M. T. (1996) · Defense Technical Information Center (DTIC) · Cited by 28
A Self-Regulated Learning Approach to Teaching Educational Psychology
Bois, N. F. D., & Staley, R. K. (1997) · Educational Psychology Review · doi:10.1023/a:1024792529797 · Cited by 28
Hilbert, T. S., Schworm, S., & Renkl, A. (2004) · Cited by 28
Immediate and Sustained Effects of Planning in a Problem-Solving Task.
Delaney, P. F., Ericsson, K. A., & Knowles, M. E. (2004) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.30.6.1219 · Cited by 29
A Space for Debate. How diagrams support collaborative argumentation-based learning
van Amelsvoort, M. A. A. (2006) · Utrecht University Repository (Utrecht University) · Cited by 28
Analyzing Students' Mathematical Thinking in Technology-supported Environments
Karadağ, Z. (2010) · Library and Archives Canada (Government of Canada) · Cited by 28
The development and analysis of tutorial dialogues in AutoTutor Lite
Wolfe, C. R., Widmer, C. L., Reyna, V. F., Hu, X., Cedillos, E. M., Fisher, C. R., Brust-Renck, P. G., Williams, T. C., Vannucchi, I. D., & Weil, A. M. (2013) · Behavior Research Methods · doi:10.3758/s13428-013-0352-z · Cited by 27
Fiorella, L., Vogel-Walcutt, J. J., & Fiore, S. M. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.11.017 · Cited by 34
Digiacomo, G., & Chen, P. P. (2016) · Psychology in the Schools · doi:10.1002/pits.21929 · Cited by 28
Blogging mathematics: Using technology to support mathematical explanations for learning fractions
Stoyle, K. L., & Morris, B. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.04.007 · Cited by 27
ABBABB or 1212: Abstract language facilitates children’s early patterning skills
Flynn, M. E., Guba, T.-P., & Fyfe, E. R. (2020) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2019.104791 · Cited by 27
Cairns, D., & Areepattamannil, S. (2021) · Research in Science Education · doi:10.1007/s11165-021-10002-0 · Cited by 27
On Pedagogical Effects of Learner-Support Agents in Collaborative Interaction
Hayashi, Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30950-2_3 · Cited by 27
Mathematical Interactions and Their Influence on Learning
Holton, D., & Thomas, G. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-47228-7_5 · Cited by 27
A protocol-analytic study of metacognition in mathematical problem solving
Cai, J. (1994) · Mathematics Education Research Journal · doi:10.1007/bf03217270 · Cited by 27
Why does explaining help learning? Insight from an explanation impairment effect
Williams, J. J., Lombrozo, T., & Rehder, B. (2010) · eScholarship (California Digital Library) · Cited by 27
“Physics with a Smile”—Explaining Phenomena with a Qualitative Problem-Solving Strategy
Mualem, R., & Eylon, B. (2007) · The Physics Teacher · doi:10.1119/1.2709674 · Cited by 27
Analogy construction versus analogy solution, and their influence on transfer
Harpaz-Itay, Y., Kaniel, S., & Ben-Amram, E. (2006) · Learning and Instruction · doi:10.1016/j.learninstruc.2006.10.007 · Cited by 27
Brusilovsky, P. L. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58648-2_38 · Cited by 27
Broers, N. J., & Imbos, T. (2005) · Learning and Instruction · doi:10.1016/j.learninstruc.2005.08.005 · Cited by 27
Learning Consistent, Interactive, and Meaningful Task‐Action Mappings: A Computational Model
Howes, A., & Young, R. M. (1996) · Cognitive Science · doi:10.1016/s0364-0213(99)80009-3 · Cited by 27
Beyond the “Wow” Factor: Using Generative AI for Increasing Generative Sense-Making
Makransky, G., Shiwalia, B. M., Herlau, T., & Blurton, S. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10039-x · Cited by 27
Blech, C., Gaschler, R., & Bilalić, M. (2020) · Thinking & Reasoning · doi:10.1080/13546783.2019.1685001 · Cited by 26
Learning to think like linguists: A think-aloud study of novice phonology students
Anderson, C. A. (2016) · Language · doi:10.1353/lan.2016.0081 · Cited by 26
Learning essays and the reflective learner: supporting assessment in engineering design education
Turns, J. (2002) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.1997.635893 · Cited by 26
Linn, M. C., Gerard, L. F., Matuk, C. F., & McElhaney, K. W. (2016) · Review of Research in Education · doi:10.3102/0091732x16680788 · Cited by 36
Developing a pedagogical photoreal virtual environment to teach civil engineering
Walker, J., Towey, D., Pike, M., Kapogiannis, G., Elamin, A., & Wei, R. (2020) · Interactive Technology and Smart Education · doi:10.1108/itse-10-2019-0069 · Cited by 26
Walker, C. M., & Nyhout, A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108553803 · Cited by 26
La probabilidad en los libros de texto
Haro, J. (2002) · Dialnet (Universidad de la Rioja) · Cited by 26
Questioning in Writing Center Conferences
Thompson, I. K., & Mackiewicz, J. (2014) · The Writing center journal · doi:10.7771/2832-9414.1767 · Cited by 26
Lorch, R. F., Lorch, E. P., Freer, B. D., Dunlap, E. E., Hodell, E. C., & Calderhead, W. J. (2013) · Journal of Educational Psychology · doi:10.1037/a0034375 · Cited by 26
Wolfe, C. R., Reyna, V. F., Widmer, C. L., Cedillos, E. M., Brust-Renck, P. G., Weil, A. M., & Hu, X. (2016) · Learning and Individual Differences · doi:10.1016/j.lindif.2016.06.009 · Cited by 26
Game-Based Practice in a Reading Strategy Tutoring System: Showdown in iSTART-ME
Jackson, G. T., Dempsey, K. B., & McNamara, D. S. (2012) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137005267_7 · Cited by 26
Seipel, B., Carlson, S. E., & Clinton‐Lisell, V. (2016) · Reading Psychology · doi:10.1080/02702711.2016.1216489 · Cited by 26
Schwaighofer, M., Bühner, M., & Fischer, F. (2016) · Journal of Educational Psychology · doi:10.1037/edu0000115 · Cited by 27
Suggesting API Usage to Novice Programmers with the Example Guru
Ichinco, M., Hnin, W. Y., & Kelleher, C. L. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025827 · Cited by 26
Inside the Black Box: Examining Mediators and Moderators of a Middle School Science Intervention
Desimone, L. M., & Hill, K. L. (2017) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373717697842 · Cited by 26
Müldner, K., & Conati, C. (2010) · International Journal of Artificial Intelligence in Education · doi:10.3233/jai-2010-0004 · Cited by 26
Selection and use of propositional knowledge in statistical problem solving
Broers, N. J. (2002) · Learning and Instruction · doi:10.1016/s0959-4752(01)00025-1 · Cited by 26
Guisasola, J., Ceberio, M., & Zubimendi, J. L. (2005) · Research in Science Education · doi:10.1007/s11165-005-9000-7 · Cited by 27
The Importance of Being Interpreted: Grounded Words and Children’s Relational Reasoning
Son, J. Y., Smith, L. B., Goldstone, R. L., & Leslie, M. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00045 · Cited by 26
Self-observation and learning: the effect of watching oneself on problem solving performance
Fireman, G. D., Kose, G., & Solomon, M. J. (2003) · Cognitive Development · doi:10.1016/s0885-2014(03)00038-8 · Cited by 26
Weinberger, A. (2003) · Cited by 26
Making processes visible: Scaffolding learning with reasoning-congruent representations
Merrill, D. C., Reiser, B. J., Beekelaar, R., & Hamid, A. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55606-0_14 · Cited by 26
Talking about Multimedia: a layered design framework
Taylor, J., Sumner, T., & Law, A. (1997) · Journal of Educational Television · doi:10.1080/1358165970230209 · Cited by 26
Intertwining query construction and relevance evaluation
Fischer, G., Henninger, S., & Redmiles, D. (1991) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/108844.108853 · Cited by 26
Understanding How Programmers Can Use Annotations on Documentation
Horvath, A., Liu, M. X., Hendriksen, R., Shannon, C., Paterson, E., Jawad, K., Macvean, A., & Myers, B. A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502095 · Cited by 26
Developing an analytical framework for argumentation on energy consumption issues
Jin, H., Mehl, C. E., & Lan, D. H. (2015) · Journal of Research in Science Teaching · doi:10.1002/tea.21237 · Cited by 25
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 28
Cognitive Effect of Tracing Gesture in the Learning from Mathematics Worked Examples
Yeo, L.-M., & Tzeng, Y. (2019) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-019-09987-y · Cited by 29
An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals
Rosé, C. P., & VanLehn, K. A. (2005) · International Journal of Artificial Intelligence in Education · doi:10.3233/irg-2005-15(4)04 · Cited by 25
Moss, J., Schunn, C. D., Schneider, W., & McNamara, D. S. (2013) · Brain Research · doi:10.1016/j.brainres.2013.09.047 · Cited by 29
Instructional means to overcome transfer problems in the domain of economics: empirical studies
Stark, R., Mandl, H., Gruber, H. G., & Renkl, A. (1999) · International Journal of Educational Research · doi:10.1016/s0883-0355(99)00026-9 · Cited by 25
Studying the Effect of a Competitive Game Show in a Learning by Teaching Environment
Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2013) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0009-1 · Cited by 25
Memory systems within a cognitive architecture
Sun, R. (2011) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2011.11.003 · Cited by 26
The Impact of Problem-Based Learning on Iranian EFL Learners’ Speaking Proficiency
Ansarian, L., Adlipour, A. A., Saber, M. A., & Shafiei, E. (2016) · Advances in Language and Literary Studies · doi:10.7575/aiac.alls.v.7n.3p.84 · Cited by 25
Rau, M. A., Aleven, V., & Rummel, N. (2016) · Journal of Educational Psychology · doi:10.1037/edu0000145 · Cited by 25
Dialogic Instruction: A New Frontier
Clarke, S. N., Resnick, L. Β., & Rosé, C. P. (2015) · Dialnet (Universidad de la Rioja) · doi:10.4324/9781315688244-40 · Cited by 25
Allen, L. K., Perret, C. A., Likens, A. D., & McNamara, D. S. (2017) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3027385.3027445 · Cited by 25
Memory accessibility shapes explanation: Testing key claims of the inherence heuristic account
Hussak, L. J., & Cimpian, A. (2017) · Memory & Cognition · doi:10.3758/s13421-017-0746-8 · Cited by 25
Bolkan, S., & Goodboy, A. K. (2019) · Communication Education · doi:10.1080/03634523.2019.1602275 · Cited by 25
Effects of different concept map activities on chemistry learning
Wang, Z., Adesope, O., Sundararajan, N., & Buckley, P. D. (2020) · Educational Psychology · doi:10.1080/01443410.2020.1749567 · Cited by 25
Learning Theories For Engineering and Technology Education
Schunn, C. D., & Silk, E. (2011) · SensePublishers eBooks · doi:10.1007/978-94-6091-549-9_1 · Cited by 27
Substituting one mystery for another: the role of self-explanations in solving algebra word-problems
Neuman, Y., & Schwarz, B. B. (2000) · Learning and Instruction · doi:10.1016/s0959-4752(99)00027-4 · Cited by 25
Modelling dialogue and beliefs as a basis for generating guidance in a CSCL environment
Lund, K., Baker, M. J., & Baron, M. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61327-7_117 · Cited by 25
Alternative Knowledge Structures in Creative Thought: Schema, Associations, and Cases
Mumford, M. D., Blair, C., & Marcy, R. T. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606915.008 · Cited by 25
Cognitive and Motivational Consequences of Tutoring and Discovery Learning
Reiser, B. J., Copen, W. A., Ranney, M. A., Hamid, A., & Kimberg, D. Y. (1998) · Defense Technical Information Center (DTIC) · doi:10.21236/ada347269 · Cited by 25
Socratic dialogue as a teaching and research method for co-creativity?
Stenning, K., Schmoelz, A. (. O. E., Wren, H., Stouraitis, E., Scaltsas, T., Alexopoulos, K. (. O. E., & Aichhorn, A. (2016) · Phaidra (Universität Wien) · Cited by 24
Supporting concept mapping for learning from text
Hauser, S., Nückles, M., & Renkl, A. (2006) · International Conference of Learning Sciences · Cited by 24
Information processing: a review of implications of Johnstone’s model for science education
Clair‐Thompson, H. S., Overton, T., & Botton, C. (2010) · Research in Science & Technological Education · doi:10.1080/02635141003750479 · Cited by 26
Earley, C. E. (2003) · Behavioral Research in Accounting · doi:10.2308/bria.2003.15.1.111 · Cited by 24
Morrison, J., Bol, L., Ross, S. M., & Watson, G. S. (2015) · Educational Technology Research and Development · doi:10.1007/s11423-015-9397-2 · Cited by 24
Nandagopal, K., & Ericsson, K. A. (2011) · American Psychological Association eBooks · doi:10.1037/13273-010 · Cited by 24
Heitzmann, N., Fischer, F., & Fischer, M. R. (2017) · Instructional Science · doi:10.1007/s11251-017-9432-2 · Cited by 31
Finding the Best Types of Guidance for Constructing Self-Explanations of Subgoals in Programming
Margulieux, L. E., & Catrambone, R. (2018) · Journal of the Learning Sciences · doi:10.1080/10508406.2018.1491852 · Cited by 24
Teaching Hardware Reverse Engineering: Educational Guidelines and Practical Insights
Wiesen, C., Becker, S., Fyrbiak, M., Albartus, N., Elson, M., Rummel, N., & Paar, C. (2018) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale.2018.8615270 · Cited by 24
Designing for learning during collaborative projects online: tools and takeaways
Sankaranarayanan, S., Kandimalla, S. R., Cao, M., Maronna, I., An, H., Bogart, C., Murray, R. C., Hilton, M. L., Sakr, M. F., & Rosé, C. P. (2020) · Information and Learning Sciences · doi:10.1108/ils-04-2020-0095 · Cited by 27
Evaluating a decision-theoretic approach to Tailored example selection
Müldner, K., & Conati, C. (2007) · International Joint Conference on Artificial Intelligence · Cited by 24
The development of self-directed learning strategies in problem-based learning
Hmelo‐Silver, C. E., & Côté, N. (1996) · International Conference of Learning Sciences · Cited by 24
Constructing Quantitative Problem Representations on the Basis of Qualitative Reasoning
Ploetzner, R., & Spada, H. (1998) · Interactive Learning Environments · doi:10.1080/1049482980050107 · Cited by 24
Individual Differences in Patterns of Spontaneous Online Tool Use
Shute, V. J., & Gluck, K. A. (1996) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0504_2 · Cited by 24
Metadata
- Authors
- Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R.
- Affiliations
- RAND Corporation (US)
- TU Bergakademie Freiberg (DE)
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1989-04
- Volume
- 13
- Issue
- 2
- Pages
- 145-182
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W4239816899
- Cited by
- 1,714
- References
- 33
- Citations by year
- 2026: 54 · 2025: 49 · 2024: 41 · 2023: 41 · 2022: 65 · 2021: 80 · 2020: 103 · 2019: 99 · 2018: 58 · 2017: 53 · 2016: 77 · 2015: 69 · 2014: 60 · 2013: 58 · 2012: 59
- FWCI
- 27.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- self-explanation, worked examples, problem solving, mechanics problems, self-monitoring, understanding-based learning, explanation-based learning, student differences
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
