{"title":"学习科学和学习工程:自然的还是人为的区别?","authors":"Victor R. Lee","doi":"10.1080/10508406.2022.2100705","DOIUrl":null,"url":null,"abstract":"ABSTRACT “Learning engineering” has gained popularity as a 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 progress to understanding and supporting human learning.","PeriodicalId":48043,"journal":{"name":"Journal of the Learning Sciences","volume":"24 1","pages":"288 - 304"},"PeriodicalIF":3.0000,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Learning sciences and learning engineering: A natural or artificial distinction?\",\"authors\":\"Victor R. Lee\",\"doi\":\"10.1080/10508406.2022.2100705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT “Learning engineering” has gained popularity as a 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 progress to understanding and supporting human learning.\",\"PeriodicalId\":48043,\"journal\":{\"name\":\"Journal of the Learning Sciences\",\"volume\":\"24 1\",\"pages\":\"288 - 304\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2022-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Learning Sciences\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1080/10508406.2022.2100705\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Learning Sciences","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/10508406.2022.2100705","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Learning sciences and learning engineering: A natural or artificial distinction?
ABSTRACT “Learning engineering” has gained popularity as a 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 progress to understanding and supporting human learning.
期刊介绍:
Journal of the Learning Sciences (JLS) is one of the two official journals of the International Society of the Learning Sciences ( www.isls.org). JLS provides a multidisciplinary forum for research on education and learning that informs theories of how people learn and the design of learning environments. It publishes research that elucidates processes of learning, and the ways in which technologies, instructional practices, and learning environments can be designed to support learning in different contexts. JLS articles draw on theoretical frameworks from such diverse fields as cognitive science, sociocultural theory, educational psychology, computer science, and anthropology. Submissions are not limited to any particular research method, but must be based on rigorous analyses that present new insights into how people learn and/or how learning can be supported and enhanced. Successful submissions should position their argument within extant literature in the learning sciences. They should reflect the core practices and foci that have defined the learning sciences as a field: privileging design in methodology and pedagogy; emphasizing interdisciplinarity and methodological innovation; grounding research in real-world contexts; answering questions about learning process and mechanism, alongside outcomes; pursuing technological and pedagogical innovation; and maintaining a strong connection between research and practice.