Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T00:54:19.625Z Has data issue: false hasContentIssue false
  • English
  • Français

Teacher and learner: Supervised and unsupervised learning in communities

Published online by Cambridge University Press:  08 June 2015

Michael G. Shafto  and
Colleen M. Seifert
Michael G. Shafto
Affiliation:
Cognitive Science Associates, 2850 Easy St., Ann Arbor, MI 48104.
Colleen M. Seifert
Affiliation:
Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1043. [email protected]@umich.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

How far can teaching methods go to enhance learning? Optimal methods of teaching have been considered in research on supervised and unsupervised learning. Locally optimal methods are usually hybrids of teaching and self-directed approaches. The costs and benefits of specific methods have been shown to depend on the structure of the learning task, the learners, the teachers, and the environment.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 38 , 2015 , e64
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atran, S. & Medin, D. L. (2008) The native mind and the cultural construction of nature. MIT Press.Google Scholar
Beller, S., Bender, A. & Medin, D. L. (2012) tShould anthropology be part of cognitive science? Topics in Cognitive Science 4(3):342–53.Google Scholar
Boud, D., Cohen, R. & Sampson, J., eds. (2014) Peer learning in higher education: Learning from and with each other. Routledge.Google Scholar
Chi, M., VanLehn, K., Litman, D. & Jordan, P. (2011) An evaluation of pedagogical tutorial tactics for a natural language tutoring system: A reinforcement learning approach. International Journal of Artificial Intelligence in Education 21(1):83–113.Google Scholar
Duda, R. O., Hart, P. E. & Stork, D. G. (2000) Pattern classification, 2nd edition. Wiley.Google Scholar
Hutchins, E. L. (1995a) How a cockpit remembers its speeds. Cognitive Science 19:265–88.Google Scholar
Hutchins, E. L. (1995b) Cognition in the wild. MIT Press.Google Scholar
Hutchins, E. L. (2005) Material anchors for cognitive blends. Journal of Pragmatics 37:1555–77.Google Scholar
Resendes, M., Chen, B., Acosta, A. & Scardamalia, M. (2013) The effect of formative feedback on vocabulary use and distribution of vocabulary knowledge in a grade two knowledge building class. In: To see the world and a grain of sand: Learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings Volume 1 – Full Papers & Symposia , ed. Rummel, N., Kapur, M., Nathan, M., & Puntambekar, S., pp. 391–398, International Society of the Learning Sciences.Google Scholar
Roberts, S. L. (2013) “Georgia on My Mind”: Writing the “new” state history textbook in the post-Loewen world. The History Teacher 47(1):41–60.Google Scholar
Seifert, C. M. & Hutchins, E. L. (1992) Error as opportunity: Learning in a cooperative task. Human–Computer Interaction 7(4):409–35.Google Scholar
Settles, B. (2010) Active learning literature survey. Computer Sciences Technical Report 1648. University of Wisconsin–Madison.Google Scholar
VanLehn, K. (2011) The relative effectiveness of human tutoring, Intelligent Tutoring Systems, and other tutoring systems. Educational Psychologist 46(4):197–221.Google Scholar
Zhu, X. (2008) Semi-supervised learning literature survey. Computer Sciences Technical Report 1530. University of Wisconsin, Madison.Google Scholar