Analysis of two pedagogical approaches to foster discipline integrations in an educational data mining class using communities of practice
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| Publicat a: | International Journal of STEM Education vol. 12, no. 1 (Dec 2025), p. 17 |
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| Publicat: |
Springer Nature B.V.
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 001 | 3174223943 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2196-7822 | ||
| 024 | 7 | |a 10.1186/s40594-025-00538-2 |2 doi | |
| 035 | |a 3174223943 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 084 | |a 243237 |2 nlm | ||
| 245 | 1 | |a Analysis of two pedagogical approaches to foster discipline integrations in an educational data mining class using communities of practice | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundThis paper describes research into two pedagogical approaches to foster transdisciplinarity in a graduate engineering course that involves education and computer science. Leveraging the Communities of Practice framework, we examine how students majoring in computer science can integrate new knowledge from education and computer science to engage in an educational data mining project. The first course iteration sought to connect students from education and computer science disciplines through a blend of problem-based learning and traditional lectures. The second course iteration involved computer science students only, but included two instructors, one from computer science and the other from education. To evaluate these approaches, we conducted multiple student interviews and classroom observations.ResultsWe found that pursuing interdisciplinary through student brokers had a localized student impact on discipline integration without creating an entire class transdisciplinary environment, proving particularly effective for students with backgrounds outside of computer science. However, it fell short of achieving an overarching integration of education knowledge across the entire class. In contrast, the co-teaching approach influenced class dynamics significantly as instructors honed their brokerage skills and introduced crucial components to the multidisciplinary toolkit. Students reinterpreted these elements within the context of their projects, leading to a deeper integration of education and computer science disciplines. However, while students did acquire more knowledge from both disciplines, they did not always achieve a comprehensive practical understanding of the class outcomes.ConclusionsFindings suggest that differences in instructional design can significantly impact how interdisciplinary integration forms within a class. Using CoP, we identified various models to foster disciplinary integration. The two pedagogical approaches used—student brokers and co-instructors—achieved some disciplinary integration, highlighting multidisciplinary, interdisciplinary, and transdisciplinary integration. Engaging in projects with multidisciplinary teams allows students to interact one-on-one while working on real projects, enabling them to negotiate their participation with peers and resulting in a deeper integration of the involved disciplines. This paper discusses the merits and the drawbacks of employing both approaches to build an interdisciplinary class. | |
| 610 | 4 | |a National Science Foundation International Journal of STEM Education | |
| 651 | 4 | |a United Kingdom--UK | |
| 651 | 4 | |a Australia | |
| 653 | |a Students | ||
| 653 | |a Engineering education | ||
| 653 | |a Data mining | ||
| 653 | |a Knowledge management | ||
| 653 | |a Problem based learning | ||
| 653 | |a Teachers | ||
| 653 | |a Education | ||
| 653 | |a Integration | ||
| 653 | |a Teaching | ||
| 653 | |a Pedagogy | ||
| 653 | |a Collaboration | ||
| 653 | |a Computer science | ||
| 653 | |a Instructional design | ||
| 653 | |a Interdisciplinary aspects | ||
| 653 | |a Communities of practice | ||
| 653 | |a Mathematics education | ||
| 653 | |a STEM education | ||
| 653 | |a Data science | ||
| 653 | |a Technology education | ||
| 653 | |a Councils | ||
| 653 | |a Science education | ||
| 653 | |a Participation | ||
| 653 | |a Knowledge | ||
| 653 | |a Engineering | ||
| 653 | |a Professionals | ||
| 653 | |a Learning | ||
| 653 | |a Social | ||
| 653 | |a Team Teaching | ||
| 653 | |a Teacher Collaboration | ||
| 653 | |a Teaching Methods | ||
| 653 | |a Student Participation | ||
| 653 | |a Interdisciplinary Approach | ||
| 653 | |a Learner Engagement | ||
| 773 | 0 | |t International Journal of STEM Education |g vol. 12, no. 1 (Dec 2025), p. 17 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3174223943/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3174223943/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |