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Learning sciences and learning engineering: A natural or artificial distinction?

delete2022-08-12
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Victor R. Lee *
DOI:10.1080/10508406.2022.2100705delete
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摘要

摘要

En 中文
Learning engineering has gained popularity as term connected to the work of learning sciences. However, the nature of that connection is not entirely clear. For some, learning engineering represents distinct, industry-inspired practices enabled by data abundance and digital platformization of learning technologies. That view is presented as one where learning engineers apply learning research that has resided in experimental studies. For others, learning engineering should refer to the use of the full breadth of knowledge developed within the learning sciences research community. This second view is more inclusive of the fundamentally situated, design-oriented, and real-world commitments that are the backbone of the learning sciences, as reflected in this journal. The two views differ even as far as whether the academic field is labeled learning science or learning sciences. This article examines and articulates these differences. It also argues that without course correction, many who identify with learning engineering will conduct technology-supported learning improvement work that, at its own risk, will neglect the full and necessary scope of what has already been and continues to be discovered in the learning sciences. Moreover, it behooves all to consider recently elevated, but deeply fundamental questions being asked in the learning sciences about what is important to learn and toward what ends. With some more clarity around what is actually encompassed by the learning sciences and how all interested in design and educational improvement can build upon that knowledge, we can make greater collective process to understanding and supporting human learning.

期刊

J
Journal of the Learning Sciences
IF:
3.9
论文数:
650
被引数:
3.0K

机构

S
Stanford University
学者数:
9.6W
论文数: 8.2W
被引数: 17.0W
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