Knowledge Transfer and Deep Learning in Bransford & Schwartz’s vs. Kapur & Bielaczyc’s Views

Bransford & Schwartz’s View of Knowledge Transfer and Deep Learning

The first paper given by Bransford and Schwartz represents the transfer of knowledge into practical skills and problem-solving. They emphasized the importance of transfer in “knowing with,” where students can apply previous experiences and concepts to new problems and find optimal solutions (Bransford & Schwartz, 70). Here, the role of the educator does not lie in direct instruction on knowledge application, but rather in an assisting duty. It includes helping students to better reflect on given tasks and improve their own strategies for learning and problem-solving. In this sense, it also shows the transfer process from one to another.

Deep learning, on its own, was not directly described in the text; however, the author’s overall intention was to demonstrate that learning more broadly than just training for specific tasks is essential in the modern educational system. In addition, the importance of transfer was emphasized, where the student, when faced with tasks, discards previously held ideas to implement new approaches and methods (Bransford & Schwartz, 80). It is consistent with the modern study provided in the following paragraph.

Kapur & Bielaczyc’s Perspective on Knowledge Transfer and Deep Learning

Kapur and Bielaczyc (2012) review the importance of failure in the education process, which is also similar to negative transfer. In the study, they provided experimental observations of students’ performance development in an environment with minimal instruction. Here, the transfer is defined similarly to the previous paper, but with a greater emphasis on instructions.

Students, although failing on tasks with no instructions, performed better with provided instructions compared to students who received direct assistance from the educator (Kapur & Bielaczyc, 45). Here, the author argues that understanding the structure of tasks and problems helped students to be more prepared to solve them. It is consistent with deep learning, where the application and knowledge are insufficient for achieving the optimal educational outcome.

Comparison

The overall perception of transfer and deep learning is not conceptually different in both papers. Still, they focus on different topics that enable the review of transfer as part of the larger education process. In this sense, it is possible to derive some important definitions from the papers. As transfer still plays an essential role in education, its possible applications and models of implementation will vary over time, but the idea itself will remain the same.

Works Cited

Bransford, J. D., & Schwartz, D. L. “Chapter 3: Rethinking Transfer: A Simple Proposal with Multiple Implications”. Review of Research in Education, vol. 24, no.1, 1999, pp. 61-100.

Kapur, M., & Bielaczyc, K. “Designing for Productive Failure”. Journal of the Learning Sciences, vol. 21, no. 1, 2012, pp. 45-83. Web.

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StudyCorgi. (2025) 'Knowledge Transfer and Deep Learning in Bransford & Schwartz’s vs. Kapur & Bielaczyc’s Views'. 30 October.

1. StudyCorgi. "Knowledge Transfer and Deep Learning in Bransford & Schwartz’s vs. Kapur & Bielaczyc’s Views." October 30, 2025. https://studycorgi.com/knowledge-transfer-and-deep-learning-in-bransford-and-schwartzs-vs-kapur-and-bielaczycs-views/.


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StudyCorgi. "Knowledge Transfer and Deep Learning in Bransford & Schwartz’s vs. Kapur & Bielaczyc’s Views." October 30, 2025. https://studycorgi.com/knowledge-transfer-and-deep-learning-in-bransford-and-schwartzs-vs-kapur-and-bielaczycs-views/.

References

StudyCorgi. 2025. "Knowledge Transfer and Deep Learning in Bransford & Schwartz’s vs. Kapur & Bielaczyc’s Views." October 30, 2025. https://studycorgi.com/knowledge-transfer-and-deep-learning-in-bransford-and-schwartzs-vs-kapur-and-bielaczycs-views/.

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