The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Conceptual Understanding of Electrical Circuits in Secondary Vocational Engineering Education: Combining Traditional Instruction with Inquiry Learning in a Virtual Lab
Abstract
Background: Traditionally, engineering curricula about electrical circuits use textbook instruction and hands‐on lessons, which are effective approaches for teaching terms and definitions, the procedural use of formulas, and how to build circuits. Nonetheless, students often lack conceptual understanding. Purpose (Hypothesis): The aim of this study was to discover how to facilitate the acquisition of conceptual understanding. We hypothesized that adding an instructional approach in the form of inquiry learning in a virtual lab would be more effective than relying on traditional instruction alone. Design/Method: Students in secondary vocational engineering education were randomly assigned to one of two conditions in a quasi‐experimental study. In the traditional condition, the curriculum was supplemented with computer‐based practice. In the virtual lab condition, the traditional curriculum was supplemented with inquiry learning in a virtual lab. Results: Results showed that students in the virtual lab condition scored significantly higher on conceptual understanding (Cohen's d = 0.65) and on procedural skills (d = 0.76). In particular, students in this condition scored higher (d = 1.19) on solving complex problems. This result occurred for both complex conceptual and procedural problems. Conclusion: Since students in the virtual lab condition acquired better conceptual understanding and also developed better procedural skills than students in the traditional condition, it appears that conceptual understanding and procedural skills develop in an iterative fashion.
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.
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
Scientific discovery learning with computer simulations of conceptual domains
De Jong, T., & Van Joolingen, W. R. (1998) · Review of Educational Research · doi:10.3102/00346543068002179 · Cited by 1,593
Inquiry, modeling, and metacognition: making science accessible to all students
White, B. Y., & Frederiksen, J. R. (1998) · Cognition and Instruction · doi:10.1207/s1532690xci1601_2 · Cited by 1,394 · cited 2×
“Eventually, this process of testing mental models on the basis of empirical observations can help students bring their mental models in line with the real phenomena (Papadouris & Constantinou, 2009; White & Frederiksen, 1998).”
BackgroundDoes discovery-based instruction enhance learning?
Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011) · Journal of Educational Psychology · doi:10.1037/a0021017 · Cited by 1,338
Saying the wrong thing: improving learning with multimedia by including misconceptions
Muller, D. A., Bewes, J., Sharma, M. D., & Reimann, P. (2008) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2007.00248.x · Cited by 145
Cited by
Physical and Virtual Laboratories in Science and Engineering Education
de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013) · Science · doi:10.1126/science.1230579 · Cited by 1,083
Self-citationEstriegana, R., Medina-Merodio, J.-A., & Barchino, R. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.02.010 · Cited by 334
Riopel, M., Nenciovici, L., Potvin, P., Chastenay, P., Charland, P., Sarrasin, J. B., & Masson, S. (2019) · Studies in Science Education · doi:10.1080/03057267.2019.1722420 · Cited by 127
Nolen, S. B., & Koretsky, M. D. (2018) · IEEE Transactions on Education · doi:10.1109/te.2018.2791445 · Cited by 76
May, D., Reeves, B., Trudgen, M., & Alweshah, A. (2020) · 2020 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9274121 · Cited by 33
Metadata
- Authors
- Kollöffel, B., & de Jong, T.
- Venue
- Journal of Engineering Education
- Publisher
- Wiley
- Published
- 2013-07
- Volume
- 102
- Issue
- 3
- Pages
- 375-393
- ISSN
- 1069-4730 (ISSN-L)
- 2168-9830
- OpenAlex
- W1850015201
- Cited by
- 171
- References
- 4
- FWCI
- 15.54
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.