ACM Home Page
Please provide us with feedback. Feedback
Measuring conceptual understanding: a case study
Full text PdfPdf (529 KB)
Source
Conference On Information Technology Education (formerly CITC) archive
Proceedings of the 9th ACM SIGITE conference on Information technology education table of contents
Cincinnati, OH, USA
SESSION: Session 1.1 table of contents
Pages 11-16  
Year of Publication: 2008
ISBN:978-1-60558-329-7
Authors
Steven Rigby  Brigham Young University - Idaho, Rexburg, ID, USA
Melissa J. Dark  Purdue University, West Lafayette, IN, USA
J. Ekstrom  Brigham Young University, Provo, UT, USA
Marcus Rogers  Purdue University, West Lafayette, IN, USA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 69,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1414558.1414563
What is a DOI?

ABSTRACT

IT Educators are challenged with the task of providing learning experiences that helps learners build abstract mental models of IT concepts to help solve the complex, ill-defined problems they will face. But of the many instructional strategies discussed in the literature how can we tell which methods are better for the different types of IT knowledge presented. Can we assess how well different instructional strategies affect student's conceptual understanding of IT concepts? This descriptive study examined one possible way of measuring conceptual understanding of a specific learning activity called Model Eliciting Activates (MEAs).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Chi, M., Glaser, R., & Farr, M. (1998). The Nature of Expertise. Lawrence Erlbaum.
 
2
Chase, W. C., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology,4, pp. 55--81.
 
3
National Research Council. (2000). How People Learn: Brain, Mind, Experience, and School (Expanded Edition ed.). Washington, D.C.: National Academy Press.
 
4
Perkins, D., and Salomon, G. (1989). Are Cognitive Skills Context-Bound? Educational Researcher. pp. 16--25.
 
5
Brown, J., Collins, A., & Duguid, P. (1989). Situation Cognition and the Culture of Learning. Educational Researcher, 32--42.
 
6
Kinchin, I., & Hay, D. (2000). How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development. Educational Research, 42(1), 43--57.
 
7
Rice, D. C., Ryan, J. M., & Samson, S. M. (1998). Using Concept Maps to Assess Student Learning in the Science Classroom: Must Different Methods Compete? Journal of Research in Science Teaching, 35(10), 1103--1127.
 
8
Mestre, J. (2002). Transfer of Learning: Issues and Research Agenda. National Science Foundation Report #NSF03-212.
 
9
Lesh, R., & Doerr, H. (2003). Model development sequences. In R. Lesh, K. Cramer, H. Doerr, T. Post & J. Zawojewski (Eds.), Beyond Constructivism (pp. 35--58): Mahwah, NJ: Erlbaum.
 
10
Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. New York and Cambridge, UK: Cambridge University Press.
 
11
Markham, K. M., Mintzes, J. J., & Jones, M. G. (1994). The concept map as aresearch and evaluation tool: Further evidence of validity. Journal of Research in Science Teaching, 31(91), 101.

Collaborative Colleagues:
Steven Rigby: colleagues
Melissa J. Dark: colleagues
J. Ekstrom: colleagues
Marcus Rogers: colleagues