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

被引:5
|
作者
Lee, Victor R. [1 ]
机构
[1] Stanford Univ, Grad Sch Educ, Stanford, CA 94305 USA
关键词
D O I
10.1080/10508406.2022.2100705
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
"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.
引用
收藏
页码:288 / 304
页数:17
相关论文
共 50 条
  • [21] Artificial learning companionusing machine learning and natural language processing
    R. Pugalenthi
    A Prabhu Chakkaravarthy
    J Ramya
    Samyuktha Babu
    R. Rasika Krishnan
    International Journal of Speech Technology, 2021, 24 : 553 - 560
  • [22] Learning and assessment in natural and artificial systems
    Marocco, Davide
    Dell'Aquila, Elena
    Gigliotta, Onofrio
    QWERTY, 2019, 14 (02): : 5 - 10
  • [23] Learning by thinking in natural and artificial minds
    Lombrozo, Tania
    TRENDS IN COGNITIVE SCIENCES, 2024, 28 (11) : 1011 - 1022
  • [24] Engineering and natural sciences
    Cardenas-Guevara, Daniel Humberto
    REVISTA FACULTAD DE INGENIERIA, UNIVERSIDAD PEDAGOGICA Y TECNOLOGICA DE COLOMBIA, 2015, 24 (40): : 7 - 8
  • [25] Learning based on artificial intelligence for engineering courses
    Rodriguez-Calderon, Rosalino
    Gonzalez-Garcia, Salvador
    VIII IEEE WORLD ENGINEERING EDUCATION CONFERENCE, EDUNINE 2024, 2024,
  • [26] Roles for learning sciences and learning technologies in biomedical engineering education: A review of recent advances
    Harris, TR
    Bransford, JD
    Brophy, SP
    ANNUAL REVIEW OF BIOMEDICAL ENGINEERING, 2002, 4 : 29 - 48
  • [27] E-learning Natural Sciences and Visual Imagery
    Draghici, Florentina
    Dulama, Maria Eliza
    Ilovan, Oana-Ramona
    Voicu, Cristina-Georgiana
    PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON VIRTUAL LEARNING (ICVL-2020), 2020, : 120 - 124
  • [28] TEACHING AND LEARNING THE NATURAL-SCIENCES IN IMMERSION PROGRAMS
    LAPLANTE, B
    PROCEEDINGS OF THE CONFERENCE ON LINGUISTICS 1990: FOCUS ON RESEARCH IN LINGUISTICS ; TERMINOLOGY AND EDUCATION, 1989, 172 : 79 - 86
  • [29] Use of digital educational resources for learning in Natural Sciences
    Alonzo, Paola Ysabel Garcia
    Pita, Yulexy Navarrete
    ATENAS, 2022, 3 (59): : 96 - 112
  • [30] American learning when peace comes "The natural sciences"
    Glockler, G.
    SCIENCE, 1943, 98 (2555) : 525 - 530