Abstract
Critical Thinking (CrT) is generally characterized as an abstract thinking process, detached from the (bodily) actions one engages in during the process. Though recent cognitive theories assert that all thinking is action-based, the embodied and distributed cognitive processes underlying CrT have not been identified. We present preliminary findings from the first iteration of a design-based research project which involves probing possible connections between CrT and one’s (bodily) action sequences. We performed sequential pattern mining and qualitative analysis on the study participants’ actions logs to find differences in participants CrT processes. Our analysis showed that only a subset of participants contextualized their assumptions, inferences, and implications in the different information resources available in the environment. A majority of participants’ actions performed within the interface were incoherent. These results have implications for automated analyses of the CrT process, and for the design of AI-based scaffolds to support CrT development.
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References
Larson, L.C., Miller, T.N.: 21st century skills: prepare students for the future. Kappa Delta Pi Rec. 47(3), 121–123 (2011)
Paul, R., Elder, L.: The Miniature Guide to Critical Thinking Concepts and Tools. Rowman & Littlefield (2019)
Worsley, M., Blikstein, P.: A multimodal analysis of making. Int. J. Artif. Intell. Educ. 28(3), 385–419 (2017). https://doi.org/10.1007/s40593-017-0160-1
Segedy, J.R., Biswas, G., Sulcer, B.: A model-based behavior analysis approach for Open-Ended Environments. J. Educ. Technol. Soc. 17(1), 272–282 (2014)
Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., Koller, D.: programming pluralism: using learning analytics to detect patterns in the learning of computer programming. J. Learn. Sci. 23(4), 561–599 (2014). https://doi.org/10.1080/10508406.2014.954750
Vieira, C., Hathaway Goldstein, M., Purzer, Ş., Magana, A.J.: Using learning analytics to characterize student experimentation strategies in engineering design. J. Learn. Analytics 3(3), 291–317 (2016). https://doi.org/10.18608/jla.2016.33.14
Kovanović, V., et al.: Understand students’ self-reflections through learning analytics. In: Proceedings Of The 8th International Conference on Learning Analytics and Knowledge, pp. 389–398 (2018)
Koh, E., Jonathan, C., Tan, J.P.L.: Exploring conditions for enhancing critical thinking in networked learning: findings from a secondary school learning analytics environment. Educ. Sci. 9(4), 287 (2019)
Buckingham Shum, S., Crick, R.D.: Learning analytics for 21st century competencies. J. Learn. Analytics 3(2), 6–21 (2016)
Newen, A., De Bruin, L., Gallagher, S. (eds.): The Oxford Handbook of 4E Cognition. Oxford University Press, Oxford (2018)
Landy, D., Allen, C., Zednik, C.: A perceptual account of symbolic reasoning. Front. Psychol. 5, 275 (2014)
Kirsh, D.: Thinking with external representations. AI & Soc. 25(4), 441–454 (2010). https://doi.org/10.1007/s00146-010-0272-8
Aurigemma, J., Chandrasekharan, S., Nersessian, N.J., Newstetter, W.: Turning experiments into objects: the cognitive processes involved in the design of a lab-on-a-chip device. J. Eng. Educ. 102(1), 117–140 (2013)
Winne, P.H.: Construct and consequential validity for learning analytics based on trace data. Comput. Hum. Behav. 112, 106457 (2020). https://doi.org/10.1016/j.chb.2020.106457
Mishra, S., Majumdar, R., Kothiyal, A., Pande, P., Warriem, J.M.: ENaCT: An Action-based Framework for the Learning and Analytics of Critical Thinking. in the Proceedings of ICCE 2020, pp.144–153 (2020)
Majumdar, R., et al.: Design of a Critical Thinking Task Environment based on ENaCT framework. In: The Proceedings of ICALT 2021 (2021)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh IEEE International Conference on Data Engineering (ICDE), pp. 3–14 (1995)
Kinnebrew, J.S., Loretz, K.M., Biswas, G.: A contextualized, differential sequence mining method to derive students’ learning behavior patterns. 5(1), 190–219 (2013)
Crossley, S.A., Kyle, K., Dascalu, M.: The tool for the automatic analysis of cohesion 2.0: integrating semantic similarity and text overlap. Behav. Res. Methods 51(1), 14–27 (2018). https://doi.org/10.3758/s13428-018-1142-4
Segedy, J.R., Kinnebrew, J.S., Biswas, G.: Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments. J. Learn. Analytics 2(1), 13–48 (2015)
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This collaborative research is partially funded by the SPIRITS 2020 grant of Kyoto University.
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Mishra, S., Majumdar, R., Kothiyal, A., Pande, P., Warriem, J.M. (2021). Tracing Embodied Narratives of Critical Thinking. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_48
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DOI: https://doi.org/10.1007/978-3-030-78270-2_48
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