Data Collection & Analysis
Mental ModelsAfter data has been collected through knowledge elicitation methods, one method to manage and make sense of this data is to use mental models. Mental models can make sure expertise from the past is not lost or to compare the knowledge structures of novices and experts. Once information is more appropriately organized using methods such as concept maps, conceptual analysis, and relational analysis it can be used to generate valuable tools such as training programs or decision aids.
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What are Mental Models?
Mental models are knowledge represented in the form of a mental network, which allows for a more efficient representation of knowledge as well as a visualization of the relationships between concepts.
Why Use Mental Models
Why Use Mental ModelsMental models make large amounts of information more manageable so that it can be used for a purpose instead of being wasted over the years in many disparate files.
When Use Mental Models
Knowledge elicitation methods can generate substantial amounts of data. When there is a need to organize information to utilize it for some purpose, mental models become a valuable tool.
Common scenarios where mental models are valuable:
Common scenarios where mental models are valuable:
- Knowledge recovery (Hoffman, Feltovich, and Eccles, 2007)
- Understanding skill acquisition by comparing mental models of experts and novices (Schvaneveldt et al., 1985)
- Creating expert systems
- Developing training programs
- Decision aids (Hoffman, Feltovich, and Eccles, 2007)
- Assessing knowledge (Davis, Curtis, & Tchetter, 2003; Beatty & Gerace, 2002)
- Representing user behavior (HCI) and clarifying user requirements early on (Kudikyala & Vaughn, 2005)
How to Use Mental Models
Three important ways researchers use mental models are concept maps, conceptual analysis, and relational analysis.
References & Resources
1. (2013). An introduction to content analysis. Writing@CSU. Retrieved December 8, 2013 from http://www.umsl.edu/~wilmarthp/mrpc-web-resources/content-analysis.pdf.
2. Beatty, I.D., & Gerace, W.J. (2002). Probing physics students' conceptual knowledge through term association. American Journal of Physics, 70(7), 750-758.
3. Carley, K. (1993). Coding choices for textual analysis: A comparison of content analysis and map analysis. Sociological Methodology, 23, 75-126.
4. Davis, M.A., Curtis, M.B., & Tschetter, J.D. (2003). Evaluating cognitive training outcomes: Validity and utility of structural knowledge assessment. Journal of Business and Psychology, 18(2), 191-206.
5. Hoffman, R.R., Feltovich, P.J., & Eccles, D.W. (2007). The cost of knowledge recovery: A challenge for the application of concept mapping. Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting, 328-332.
6. Kudikyala, U.K., & Vaughn, R.B. (2005). Software requirement understanding using Pathfinder networks: Discovering and evaluating mental models. The Journal of Systems and Software, 74, 101-108.
7. Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts. Lawrence Erlbaum Associates. Hillsdale. NJ. USA.
8. Schvaneveldt, R. W., Durso, F. T., Goldsmith, T. E., Breen, T. J., Cooke, N. M., Tucker, R. G., & De Maio, J. C. (1985). Measuring the structure of expertise. International Journal of Man-Machine Studies, 23, 699-728.
2. Beatty, I.D., & Gerace, W.J. (2002). Probing physics students' conceptual knowledge through term association. American Journal of Physics, 70(7), 750-758.
3. Carley, K. (1993). Coding choices for textual analysis: A comparison of content analysis and map analysis. Sociological Methodology, 23, 75-126.
4. Davis, M.A., Curtis, M.B., & Tschetter, J.D. (2003). Evaluating cognitive training outcomes: Validity and utility of structural knowledge assessment. Journal of Business and Psychology, 18(2), 191-206.
5. Hoffman, R.R., Feltovich, P.J., & Eccles, D.W. (2007). The cost of knowledge recovery: A challenge for the application of concept mapping. Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting, 328-332.
6. Kudikyala, U.K., & Vaughn, R.B. (2005). Software requirement understanding using Pathfinder networks: Discovering and evaluating mental models. The Journal of Systems and Software, 74, 101-108.
7. Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts. Lawrence Erlbaum Associates. Hillsdale. NJ. USA.
8. Schvaneveldt, R. W., Durso, F. T., Goldsmith, T. E., Breen, T. J., Cooke, N. M., Tucker, R. G., & De Maio, J. C. (1985). Measuring the structure of expertise. International Journal of Man-Machine Studies, 23, 699-728.