Book contents
- Frontmatter
- Contents
- List of Tables and Figures
- List of Contributors
- 1 Introduction
- PART 1 PAST
- 2 Why Learning Sciences?
- 3 The Prehistory of the Learning Sciences
- 4 Some Early Contributions to the Situative Perspective on Learning and Cognition
- 5 The Group as Paradigmatic Unit of Analysis: The Contested Relationship of Computer-Supported Collaborative Learning to the Learning Sciences
- PART 2 PRESENT
- PART 3 FUTURE
- Name Index
- Subject Index
- References
2 - Why Learning Sciences?
from PART 1 - PAST
Published online by Cambridge University Press: 05 February 2016
Book contents
- Frontmatter
- Contents
- List of Tables and Figures
- List of Contributors
- 1 Introduction
- PART 1 PAST
- 2 Why Learning Sciences?
- 3 The Prehistory of the Learning Sciences
- 4 Some Early Contributions to the Situative Perspective on Learning and Cognition
- 5 The Group as Paradigmatic Unit of Analysis: The Contested Relationship of Computer-Supported Collaborative Learning to the Learning Sciences
- PART 2 PRESENT
- PART 3 FUTURE
- Name Index
- Subject Index
- References
Summary
I started to work in Artificial Intelligence (AI) in the mid-1960s – AI had been around for about ten years by that time. Within a few years, I began to understand that I was likely to be perpetually out of step with most other people working in AI. The other AI people I met wanted to create intelligent machines that did intelligent things. They didn't much care if the machine played chess the way a person would play chess. They just wanted to build a program that played chess well. Similarly, in what was then called computational linguistics, they wanted computers to parse sentences and translate the sentences, but they didn't much care about whether the programs operated the way people did when they understand or compose language, nor did they try to shed any light on how people did it.
I wanted to know how individuals’ minds worked. Also, I thought that there might not be any way to process natural language (this is a term, now widely in use, that I coined to get the AI work on language out of the linguistics camp) other than the way individuals did it. At least we knew individuals had some way in which they could understand and respond. Why not study individuals instead of enhancing algorithms?
I was an undergraduate at Carnegie Tech (now Carnegie Mellon) but there was no psychology major nor was there a computer science major. I majored in mathematics. It was difficult to find colleagues who cared about what I cared about. I wanted to study learning, but in the 1960s, the study of learning was dominated by behaviorist theory and methodology. Learning experiments focused on animals to study stimulus and response relationships. There were lots of rats and pigeons in the psychology department. At that time, no one believed there was any connection between the behaviorist approach to learning, which focused on observable behavior and was opposed to any theories or models about cognitive structures or processes, and the new field of artificial intelligence, which was focused on exactly the opposite: understanding and modeling cognitive structures and processes.
By the late 1960s, when I had become a professor, many of my students wanted to work on learning as well. I had many students who independently proposed to build a computer that started out knowing nothing and learned little by little.
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- Reflections on the Learning Sciences , pp. 19 - 31Publisher: Cambridge University PressPrint publication year: 2016
References
, & (2010). Academically adrift: Limited learning on college campuses. Chicago: University of Chicago Press.CrossRefGoogle Scholar
, , & (2014). Make it stick: The science of successful learning. Cambridge, MA: Belknap Press.CrossRefGoogle Scholar
(2014). Excellent sheep: The miseducation of the American elite and the way to a meaningful life. New York: Free Press.Google Scholar
, & (2012). Generation on a tightrope: A portrait of today's college student. San Francisco: Jossey-Bass.Google Scholar
. (1958). The complete essays of Montaigne. Translated by . Stanford, CA: Stanford University Press.Google Scholar
(1982a). Dynamic memory: A theory of reminding and learning in computers and people. New York: Cambridge University Press.Google Scholar
(1982b). Reading and understanding: Teaching from the perspective of artificial intelligence. New York: Psychology Press.Google Scholar
(2001). Designing world-class e-learning: How IBM, GE, Harvard Business School, and Columbia University are succeeding at e-learning. New York: McGraw-Hill.Google Scholar
, & (1977). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
(2008). The quiet crisis: How higher education is failing America. San Francisco: Jossey-Bass.Google Scholar
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