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Technology, Knowledge and Learning
Published in: Technology, Knowledge and Learning 4/2020

12-08-2019 | Original research

Advance in Detecting Key Concepts as an Expert Model: Using Student Mental Model Analyzer for Research and Teaching (SMART)

Authors: Min Kyu Kim, Cassandra J. Gaul, So Mi Kim, Reeny J. Madathany

Published in: Technology, Knowledge and Learning | Issue 4/2020

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Abstract

While key concepts embedded within an expert’s textual explanation have been considered an aspect of expert model, the complexity of textual data makes determining key concepts demanding and time consuming. To address this issue, we developed Student Mental Model Analyzer for Teaching and Learning (SMART) technology that can analyze an expert’ textual explanation to elicit an expert concept map from which key concepts are automatically derived. SMART draws on four graph-based metrics (i.e., clustering coefficient, betweenness, PageRank, and closeness) to automatically filter key concepts from experts’ concept maps. This study investigated which filtering method extract key concepts most accurately. Using 18 expert textual data, we compared the accuracy levels of those four competing filtering methods by referring to four accuracy measures (i.e., precision, recall, F-measure, and N-similarity). The results showed the PageRank filtering method outperformed the other methods in all accuracy measures. For example, on average, PageRank derived 79% of key concepts as accurately as human experts. SMART’s automatic filtering methods can help human experts save time when building an expert model, and it can validate their decision making on a list of key concepts.
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Footnotes
Literature
Allen, L. K., Snow, E. L., & McNamara, D. S. (2015). Are you reading my mind? In Proceedings of the fifth international conference on learning analytics and knowledge—LAK ’15 (pp. 246–254). New York, NY: ACM Press.
Anthonisse, J. M. (1971). The rush in a graph. Amsterdam: Mathematische Centrum.
Anzai, Y., & Yokoyama, T. (1984). Internal models in physics problem-solving. Cognition and Instruction, 1(4), 397–450.
Axelrod, R. (1976). Structure of decision: The cognitive maps of political elites. Princeton: Princeton University Press.
Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don’t count, predict! A systematic comparison of context-counting versus context-predicting semantic vectors. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers) (Vol. 1, pp. 238–247).
Beamer, B., Rozovskaya, A., & Girju, R. (2008). Automatic semantic relation extraction with multiple boundary generations. In Proceedings of AAAI (pp. 824–829). Chicago: AAAI Press.
Boleda, G., & Herbelot, A. (2016). Formal distributional semantics: Introduction to the special issue. Computational Linguistics, 42(4), 619–635.
Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2), 163–177.
Burstein, J., Tetreault, J., & Madnani, N. (2013). The e-rater automated essay scoring system. In M. D. Shermis & J. Burstein (Eds.), Handbook of automated essay evaluation: Current applications and new directions (pp. 55–67). New York: Routledge.
Carley, K., & Palmquist, M. (1992). Extracting, representing, and analyzing mental models. Social Forces, 70(3), 601–636.
Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2007). Social networks, communication styles, and learning performance in a CSCL community. Computers & Education, 49(2), 309–329.
Clariana, R., Wallace, P., & Godshalk, V. (2009). Deriving and measuring group knowledge structure from essays: The effects of anaphoric reference. Educational Technology Research and Development, 57(6), 725–737.
Clariana, R. B. (2010). Multi-decision approaches for eliciting knowledge structure. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 41–59). New York: Springer.
Clark, S. (2015). Vector space models of lexical meaning. In S. Lappin & C. Fox (Eds.), Handbook of contemporary semantics (2nd ed., pp. 493–534). Malden, MA: Blackwell.
Cohen, E., Delling, D., Pajor, T., & Werneck, R. F. (2014). Computing classic closeness centrality, at scale. In Proceedings of the second ACM conference on Online social networks (pp. 37–50). ACM.
Collins, A., & Gentner, D. (1987). How people construct mental models. In D. Holland & N. Quinn (Eds.), Cultural models in language and thought (pp. 243–265). Cambridge: Cambridge University Press.
Collins, A. M., & Loftus, E. F. (1975). A spreading–activation theory of semantic processing. Psychological Review, 82, 407–428.
Coronges, K. A., Stacy, A. W., & Valente, T. W. (2007). Structural comparison of cognitive associative networks in two populations. Journal of Applied Social Psychology, 37(9), 2097–2129.
D’Mello, S., Hays, P., Williams, C., Cade, W., Brown, J., & Olney, A. (2010). Collaborative lecturing by human and computer tutors. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent tutoring systems (pp. 178–187). Berlin: Springer.
Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128.
Erk, K. (2012). Vector space models of word meaning and phrase meaning: A survey. Language and Linguistics Compass, 6(10), 635–653.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Garnham, A. (1987). Mental models as representations of discourse and text (1st ed.). Chichester: Ellis Horwood Ltd.
Garnham, A. (2001). Mental models and the interpretation of anaphora. Hove: Psychology Press.
Girju, R., Nakov, P., Nastase, V., Szpakowicz, S., Turney, P., & Yuret, D. (2009). Classification of semantic relations between nominals. Language Resources and Evaluation, 43(2), 105–121.
Glaser, R., Chi, M. T., & Farr, M. J. (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.
Greeno, J. G. (1989). Situation, mental models, and generative knowledge. In D. Klahr & K. Kotovsky (Eds.), Complex information processing (1st ed., pp. 285–318). Hillsdale, NJ: Lawrence Erlbaum Associates.
Hage, P., & Harary, F. (1983). Structural models in anthropology. Cambridge: Cambridge University Press.
Hansen, D., Schneiderman, B., & Smith, M. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Burlington, MA: Morgan Kaufmann.
Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.
Johnson-Laird, P. N. (2005a). Mental models and thoughts. In K. J. Holyoak (Ed.), The Cambridge handbook of thinking and reasoning (pp. 185–208). Cambridge: Cambridge University Press.
Johnson-Laird, P. N. (2005b). The history of mental models. In K. I. Manktelow & M. C. Chung (Eds.), Psychology of reasoning: Theoretical and historical perspectives (pp. 179–212). New York: Psychology Press.
Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.
Jonassen, D. H., Beissner, K., & Yacci, M. (Eds.). (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale: Lawrence Erlbaum Associates Inc.
Jonassen, D. H., & Henning, P. (1996). Mental models: Knowledge in the head and knowledge in the world. In Proceedings of the 1996 international conference on learning sciences (pp. 433–438). International Society of the Learning Sciences.
Kim, K. (2017). Visualizing first and second language interactions in science reading: A knowledge structure network approach. Language Assessment Quarterly, 14, 328–345.
Kim, K. (2018). An automatic measure of cross-language text structures. Technology, Knowledge and Learning, 23(2), 301–314.
Kim, K., Clarianay, R. B., & Kim, Y. (2018). Automatic representation of knowledge structure: Enhancing learning through knowledge structure reflection in an online course. Educational Technology Research and Development, 67(1), 105–122.
Kim, M., Zouaq, A., & Kim, S. (2016). Automatic detection of expert models: The exploration of expert modeling methods applicable to technology-based assessment and instruction. Computers & Education, 101, 55–69.
Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95(2), 163.
Knoke, D., & Kuklinski, J. H. (1982). Network analysis. Newbury Park: Sage Publications.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.
Lintean, M., Rus, V., & Azevedo, R. (2012). Automatic detection of student mental models based on natural language student input during metacognitive skill training. International Journal of Artificial Intelligence in Education, 21(3), 169–190.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge: Cambridge University Press.
Montague, R. (1974). In R. Thomason (Ed.), Formal philosophy: The selected papers of Richard Montague. New Haven: Yale University Press.
Narayanan, V. K. (2005). Causal mapping: An historical overview. In V. K. Narayanan & D. J. Armstrong (Eds.), Causal mapping for research in information technology (pp. 1–19). Hershey: Idea Group Publishing.
Newman, M. (2010). Networks: An introduction. Oxford: Oxford University Press.
Newman, M. E. (2004). Analysis of weighted networks. Physical Review E, 70(5), 56–131.
Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and Family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469.
Partee, B. H. (1984). Compositionality. In F. Landman & F. Veltman (Eds.), Varieties of formal semantics: Proceedings of the 4th Amsterdam colloquium (Groningen-Amsterdam Studies in Semantics, No. 3) (pp. 281–311). Dordrecht: Foris.
Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3–18.
Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63.
Pretz, J. E., Naples, A. J., & Sternberg, R. J. (2003). Recognizing, defining, and representing problems. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 3–30). New York, NY: Cambridge University Press.
Quillian, M. R. (1985). Word concepts. A theory and simulation of some basic capabilities. Behavioral Science, 12(5), 410–430.
Rupp, A. A., Gushta, M., Mislevy, R. J., & Shaffer, D. W. (2010). Evidence-centered design of epistemic games: Measurement principles for complex learning environments. The Journal of Technology, Learning and Assessment, 8(4), 4–47.
Rus, V., D’Mello, S., Hu, X., & Graesser, A. (2013). Recent advances in conversational intelligent tutoring systems. AI Magazine, 34(3), 42–54.
Schvaneveldt, R. W. (1990). Pathfinder associative networks: Studies in knowledge organizations. Norwood, NJ: Ablex Publishing Corp.
Seel, N. (2003). Model-centered learning and instruction. Technology, Instruction, Cognition, and Learning, 1(1), 59–85.
Seel, N. M. (1999). Semiotics and structural leaning theory. Journal of Structural Learning and Intelligent Systems, 14(1), 11–28.
Seel, N. M. (2001). Epistemology, situated cognition, and mental models: “Like a bridge over troubled water”. Instructional Science, 29(4/5), 403–427.
Seel, N. M. (2004). Model-centered learning environments: Theory, instructional design, and effects. In N. M. Seel & S. Dijkstra (Eds.), Curriculum, plans, and processes in instruction design: International perspectives (1st ed., pp. 49–74). New York: Routledge.
Seel, N. M., & Dinter, F. R. (1995). Instruction and mental model progression: Learner-dependent effects of teaching strategies on knowledge acquisition and analogical transfer. Educational Research and Evaluation, 1(1), 4–35.
Shermis, M. D. (2010). Automated essay scoring in a high stakes testing environment. In Innovative assessment for the twenty-first century (pp. 167–185). Boston, MA: Springer US.
Siemens, G., & Baker, R. (2012). Learning analytics and educational data mining. In Proceedings of the 2nd international conference on learning analytics and knowledge—LAK ’12 (pp. 252–254). New York, NY: ACM Press.
Smith, J. P., III, diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115–163.
Snow, R. E. (1990). New approaches to cognitive and conative assessment in education. International Journal of Educational Research, 14(5), 455–473.
Spector, J. M. (2008). Complex domain learning. In H. H. Adelsberger, Kinshuk, & J. M. Pawlowski (Eds.), Handbook of information technologies for education and training (pp. 261–275). Heidelberg: Springer.
Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37, 141–188.
Villalon, J., & Calvo, R. A. (2011). Concept maps as cognitive visualizations of writing assignments. Journal of Educational Technology & Society, 14(3), 16–27.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Zimmerman, W. A., Kang, H. B., Kim, K., Gao, M., Johnson, G., Clariana, R., et al. (2018). Computer-automated approach for scoring short essays in an introductory statistics course. Journal of Statistics Education, 26(1), 40–47.
Zouaq, A., Gasevic, D., & Hatala, M. (2011). Ontologizing concept maps using graph theory. In Proceedings of the 2011 ACM symposium on applied computing (pp. 1687–1692). New York, NY: ACM.
Zouaq, A., Gasevic, D., & Hatala, M. (2012). Linguistic patterns for information extraction in Ontocmaps. In Proceedings of the 3rd international conference on ontology patterns—Volume 929 (pp. 61–72). CEUR-WS.​org.
Zouaq, A., Joksimovic, S., & Gasevic, D. (2013). Ontology learning to analyze research trends in learning analytics publications. In CEUR WS Proceedings of the LAK Data Challenge, 974. Accessible under http://​lak.​linkededucation.​org. Accessed 19 Sept 2018.
Metadata
Title
Advance in Detecting Key Concepts as an Expert Model: Using Student Mental Model Analyzer for Research and Teaching (SMART)
Authors
Min Kyu Kim
Cassandra J. Gaul
So Mi Kim
Reeny J. Madathany
Publication date
12-08-2019
Publisher
Springer Netherlands
Published in
Technology, Knowledge and Learning / Issue 4/2020
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-019-09418-5

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