The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Differentiated Overt Learning Activities for Effective Instruction in Engineering Classrooms
Abstract
Background: Similar to other domains, engineering education lacks a framework to classify active learning methods used in classrooms, which makes it difficult to evaluate when and why they are effective for learning. Purpose/Hypothesis: This study evaluated the effectiveness and applicability of the Differentiated Overt Learning Activities (DOLA) framework, which classifies learning activities as interactive, constructive, or active, for engineering classes. We tested the ICAP hypothesis that student learning is more effective in interactive than constructive activities, which are more effective than active activities, which are more effective than passive activities. Design/Method: We conducted two studies to determine how and to what degree differentiated activities affected student learning outcomes; we measured student knowledge and understanding of materials science and engineering concepts. Results: Study 1 showed that students scored higher on all postclass quiz questions after participating in interactive and constructive activities than after the active activities. Student scores on more difficult, inference questions suggested that interactive activities provided significantly deeper learning than constructive or active activities. Study 2 showed that student learning, in terms of gain scores, increased systematically from passive to active to constructive to interactive, as predicted by the ICAP hypothesis. All the increases, from condition to condition, were significant. Conclusions: Our analyses of classroom activities in the engineering domain showed that they fit within the taxonomy of the DOLA framework. The results of the two studies provided evidence to support the predictions of the ICAP hypothesis.
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Citations
Cites
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Cited by
The ICAP framework: Linking cognitive engagement to active learning outcomes
Chi, M. T. H., & Wylie, R. (2014) · Educational Psychologist · doi:10.1080/00461520.2014.965823 · Cited by 2,911
Self-citationHartikainen, S., Rintala, H., Pylväs, L., & Nokelainen, P. (2019) · Education Sciences · doi:10.3390/educsci9040276 · Cited by 299
Eddy, S. L., Converse, M., & Wenderoth, M. P. (2015) · CBE—Life Sciences Education · doi:10.1187/cbe.14-06-0095 · Cited by 182
“Second, a deeper understanding of the material is often gained when students share and explain their answers to other students (i.e., work in small groups; L4; Wood et al., 1995, 1999; Renkl, 2002; Sampson and Clark, 2009; Menekse et al., 2013).”
BackgroundTeach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education
Jin, H., Lee, S., Shin, H., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642349 · Cited by 99
The Role of Robotics Teams’ Collaboration Quality on Team Performance in a Robotics Tournament
Menekse, M., Higashi, R., Schunn, C. D., & Baehr, E. (2017) · Journal of Engineering Education · doi:10.1002/jee.20178 · Cited by 80
Metadata
- Authors
- Menekse, M., Stump, G. S., Krause, S., & Chi, M. T. H.
- Venue
- Journal of Engineering Education
- Publisher
- Wiley
- Published
- 2013-07
- Volume
- 102
- Issue
- 3
- Pages
- 346-374
- ISSN
- 1069-4730 (ISSN-L)
- 2168-9830
- OpenAlex
- W2148922214
- Cited by
- 264
- References
- 38
- FWCI
- 41.50
- Type
- journal article
- Language
- en
- Open access
- closed
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