Toward an Epistemology of Physics

diSessa, Andrea A.·1993·Cognition and Instruction

DOI 10.1080/07370008.1985.9649008

Open this paper in CoChat — free account

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

Crossref · 10.1080/07370008.1985.9649008

Cites

Statistics

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.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2012.01245.x · Cited by 772
“Others have argued for the importance for educational design of analyzing learning goals into components of knowledge (e.g., Minstrell, 2001; diSessa, 1993).”
Background
Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences
Jacobson, M. J., & Wilensky, U. (2006) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1501_4 · Cited by 679
Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529
Expertise-Based Intuition and Decision Making in Organizations
Salas, E., Rosen, M. A., & DiazGranados, D. (2010) · Journal of Management · doi:10.1177/0149206309350084 · Cited by 509
Scientific knowledge suppresses but does not supplant earlier intuitions
Shtulman, A., & Valcarcel, J. (2012) · Cognition · doi:10.1016/j.cognition.2012.04.005 · Cited by 418
When Are Tutorial Dialogues More Effective Than Reading?
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rosé, C. P. (2007) · Cognitive Science · doi:10.1080/03640210709336984 · Cited by 378
Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1096257 · Cited by 272
Goals and Learning in Microworlds
Miller, C. S., Lehman, J. F., & Koedinger, K. R. (1999) · Cognitive Science · doi:10.1207/s15516709cog2303_2 · Cited by 160
Rule-Learning Events in the Acquisition of a Complex Skill: An Evaluation of Cascade
VanLehn, K. (1999) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0801_3 · Cited by 150
The intertwined histories of artificial intelligence and education
Doroudi, S. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00313-2 · Cited by 129
ChatGPT and the frustrated Socrates
Gregorcic, B., & Pendrill, A.-M. (2023) · Physics Education · doi:10.1088/1361-6552/acc299 · Cited by 125
Educational data augmentation in physics education research using ChatGPT
Kieser, F., Wulff, P., Kuhn, J., & Küchemann, S. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.020150 · Cited by 108
Animation as feedback in a computer-based simulation: Representation matters
Rieber, L. P. (1996) · Educational Technology Research and Development · doi:10.1007/bf02300323 · Cited by 89
Counter-Arguing During Curriculum-Supported Peer Interaction Facilitates Middle-School Students’ Science Content Knowledge
Larrain, A., Freire, P., López, P., & Grau, V. (2019) · Cognition and Instruction · doi:10.1080/07370008.2019.1627360 · Cited by 60
Semiotics, memory and augmented reality: History education with learner‐generated augmentation
Lim, K. Y. T., & Lim, R. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12904 · Cited by 56
Taking on the Heat—a Narrative Account of How Infrared Cameras Invite Instant Inquiry
Haglund, J., Jeppsson, F., & Schönborn, K. J. (2016) · Research in Science Education · doi:10.1007/s11165-015-9476-8 · Cited by 45
Using Targeted Feedback to Address Common Student Misconceptions in Introductory Programming: A Data-Driven Approach
Qian, Y., & Lehman, J. D. (2019) · Sage Open · doi:10.1177/2158244019885136 · Cited by 42
Exploring the mathematics of motion through construction and collaboration
Simpson, G., Hoyles, C., & Noss, R. (2006) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2006.00164.x · Cited by 34
Targeting disciplinary practices in an online learning environment
Jaber, L. Z., Dini, V., Hammer, D., & Danahy, E. (2018) · Science Education · doi:10.1002/sce.21340 · Cited by 17

Authors: diSessa, A. A.
Venue: Cognition and Instruction
Publisher: Informa UK Limited
Published: 1993-04
Volume: 10
Issue: 2-3
Pages: 105-225
ISSN: 0737-0008 (ISSN-L)
1532-690X
DOI: 10.1080/07370008.1985.9649008
OpenAlex: W4239613992
Cited by: 1,352
References: 0
FWCI: 54.22
Type: journal article
Language: en
Open access: closed

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