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CDMs in Vocational Education: Assessment and Usage of Diagnostic Problem-Solving Strategies in Car Mechatronics

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Handbook of Diagnostic Classification Models

Part of the book series: Methodology of Educational Measurement and Assessment ((MEMA))

Abstract

The aim of this chapter is to use psychometric models including DCMs to assess diagnostic problem-solving strategies and to investigate the usage of these strategies in car mechatronics. The present study not only advances research on the strategies’ assessment, but also informs professional and vocational education. From the educational perspective, it is not only important to know how to assess diagnostic problem-solving strategies but also to gather information about the strategies’ usage. Such knowledge helps teaching when and under which conditions the strategies are applicable.

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Notes

  1. 1.

    The occupational field of car mechatronics covers, among other things, troubleshooting, repair and maintenance of cars (Baethge & Arends, 2009, p. 33–47). In Germany, car mechatronic apprentices usually attend a 3.5 years training programme including a school-based and workplace-based training (“dual apprenticeship system”). The training of car mechatronic technicians differs significantly from one country to the next (Baethge & Arends, 2009, p. 34).

References

  • Abele, S. (2014). Modellierung und Entwicklung berufsfachlicher Kompetenz [Modeling and development of vocational competence]. Stuttgart, Germany: Franz Steiner.

    Google Scholar 

  • Abele, S. (2017). Diagnostic problem-solving process in professional contexts: Theory and empirical investigation in the context of car mechatronics using computer-generated log-files. Vocations and Learning, 11, 133–159.

    Article  Google Scholar 

  • Abele, S., & von Davier, M. (2018). Applying cognitive diagnosis models and latent class analysis to computer-generated process data to identify diagnostic problem-solving strategies in car mechatronics. Manuscript in preparation.

    Google Scholar 

  • Abele, S., Walker, F., & Nickolaus, R. (2014). Zeitökonomische und reliable Diagnostik beruflicher Problemlösekompetenzen bei Auszubildenden zum Kfz-Mechatroniker. Zeitschrift für Pädagogische Psychologie, 28, 167–179.

    Article  Google Scholar 

  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Caski (Eds.), Proceedings of the second international symposium on information theory (pp. 267–281). Budapest, Hungary: Akademiai Kiado.

    Google Scholar 

  • Baethge, M., & Arends, L. (2009). Feasibility study VET-LSA: A comparative analysis of occupational profiles and VET programmes in 8 European countries—International report. Vocational training research (Vol. 8). Bielefeld, Germany: Bertelsmann.

    Google Scholar 

  • Billett, S. (2011). Vocational education: Purposes, traditions and prospects. Dordrecht, The Netherlands: Springer.

    Book  Google Scholar 

  • Boshuizen, H. P., & Schmidt, H. G. (2008). The development of clinical reasoning expertise. In J. Higgs, M. A. Jones, S. Loftus, & N. Christensen (Eds.), Clinical reasoning in the health professions (3rd ed., pp. 113–121). Oxford, UK: Elsevier Ltd.

    Google Scholar 

  • Bozdogan, H. (1987). Model selection and Akaike’s information cri- terion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.

    Article  Google Scholar 

  • Coderre, S., Mandin, H., Harasym, P. H., & Fick, G. H. (2003). Diagnostic reasoning strategies and diagnostic success. Medical Education, 37, 695–703.

    Article  Google Scholar 

  • Croskerry, P. (2009). A universal model of diagnostic reasoning. Academic Medicine, 84(8), 1022–1028.

    Article  Google Scholar 

  • Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1990). Medical problem solving: A ten-year retrospective. Evaluation & the Health Professions, 13(1), 5–36.

    Article  Google Scholar 

  • Frey, A., Hartig, J., & Rupp, A. A. (2009). An NCME instructional module on booklet designs in large-scale assessments of student achievement: Theory and practice. Educational Measurement: Issues and Practice, 28(3), 39–53.

    Article  Google Scholar 

  • Goldhammer, F., Kröhne, U., Keßel, Y., Senkbeil, M., & Ihme, J. M. (2014). Diagnostik von ICT-literacy: Multiple-choice- vs. simulationsbasierte Aufgaben. Diagnostica, 60(1), 10–21.

    Article  Google Scholar 

  • Greiff, S., Wüstenberg, S., & Avvisati, F. (2015). Computer-generated log-file analyses as a window into students’ minds? A showcase study based on the PISA 2012 assessment of problem solving. Computers & Education, 91, 92–105.

    Article  Google Scholar 

  • Gschwendtner, T., Abele, S., & Nickolaus, R. (2009). Computersimulierte Arbeitsproben: Eine Validierungsstudie am Beispiel der Fehlerdiagnoseleistungen von Kfz-Mechatronikern Mechatronikern [Computer-simulated work samples: A statistical validation study using the example of trouble-shooting competency of car mechatronics]. Zeitschrift für Berufs- und Wirtschaftspädagogik, 105, 557–578.

    Google Scholar 

  • Hanushek, E. A., Schwerdt, G., Woessmann, L., & Zhang, L. (2017). General education, vocational education, and labor-market outcomes over the lifecycle. Journal of Human Resources, 52(1), 48–87.

    Article  Google Scholar 

  • He, Q., & von Davier, M. (2015). Identifying feature sequences from process data in problem-solving items with N-grams. In A. van der Ark, D. Bolt, S. Chow, J. Douglas, & W. Wang (Eds.), Quantitative psychology research: Proceedings of the 79th annual meeting of the psychometric society (pp. 173–190). New York, NY: Springer. https://doi.org/10.1007/978-3-319-19977-1_13

    Chapter  Google Scholar 

  • He, Q., & von Davier, M. (2016). Analyzing process data from problem-solving items with N-grams: Insights from a computer-based large-scale assessment. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of research on technology tools for real-world skill development (pp. 749–776). Hershey, PA: Information Science Reference. https://doi.org/10.4018/978-1-4666-9441-5.ch029

    Chapter  Google Scholar 

  • Hoc, J.-M., & Amalberti, R. (1995). Diagnosis: Some theoretical questions raised by applied research. Current Psychology of Cognition, 14(1), 73–101.

    Google Scholar 

  • Jonassen, D. H. (2011). Learning to solve problems. A handbook for designing problem-solving learning environments. New York, NY: Routledge.

    Google Scholar 

  • Jonassen, D. H., & Hernandez-Serrano, J. (2002). Case-based reasoning and instructional design using stories to support problem solving. Educational Technology Research and Development, 50, 65–77.

    Article  Google Scholar 

  • Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258–272.

    Article  Google Scholar 

  • Kassirer, J., Wong, J., & Kopelman, R. (2010). Learning clinical reasoning (3rd ed.). Baltimore, MD: Lippincott Williams & Wilkins.

    Google Scholar 

  • Kluwe, R. H., & Haider, H. (1990). Modelle zur internen Repräsentation komplexer technischer Systeme. Sprache & Kognition, 9(4), 173–192.

    Google Scholar 

  • Konradt, U. (1995). Strategies of failure diagnosis in computer-controlled manufacturing systems: Empirical analysis and implications for the design of adaptive decision support systems. International Journal of Human-Computer Studies, 43(4), 503–521.

    Article  Google Scholar 

  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin Company.

    Google Scholar 

  • Lerman, R. I. (2016). Restoring opportunity by expanding apprenticeship. In I. Kirsch & H. Braun (Eds.), The dynamics of opportunity in America. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-25991-8_10

    Chapter  Google Scholar 

  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Menlo Park: Addison-Wesley.

    Google Scholar 

  • McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Moustaki, I., & Knott, M. (2000). Generalised latent trait models. Psychometrika, 65(3), 391–411.

    Article  Google Scholar 

  • Nickolaus, R., Abele, S., Gschwendtner, T., Nitzschke, A., & Greiff, S. (2012). Fachspezifische Problemlösefähigkeit in gewerblich-technischen Ausbildungsberufen—Modellierung, erreichte Niveaus und relevante Einflussfaktoren [Occupation-specific problem solving competency as an essential competency dimension of professional competency: Models, achieved levels and relevant predictors in technical education]. Zeitschrift für Berufs- und Wirtschaftspädagogik, 108, 243–272.

    Google Scholar 

  • Norman, G., Young, M., & Brooks, L. (2007). Non-analytical models of clinical reasoning: The role of experience. Medical Education, 41, 1140–1145.

    Google Scholar 

  • Norman, G. R. (2005). Research in clinical reasoning: Past history and current trends. Medical Education, 39, 418–427.

    Article  Google Scholar 

  • Perez, R. S. (2012). A view from troubleshooting. In M. U. Smith (Ed.), Toward a unified theory of problem solving (pp. 127–166). New York, NY: Routledge.

    Google Scholar 

  • Rasmussen, J. (1981). Models of mental strategies in process plant diagnosis. In J. Rasmussen & W. Rouse (Eds.), Human detection and diagnosis of system failures (NATO conference series, Bd. 3, Bd. 15, S., pp. 241–258). New York, NY: Springer US.

    Google Scholar 

  • Rasmussen, J. (1993). Diagnostic reasoning in action. IEEE Transactions on Systems, Man and Cybernetics, 23(4), 981–992. https://doi.org/10.1109/21.247883

    Article  Google Scholar 

  • Rausch, A., Seifried, J., Wuttke, E., Kögler, K., & Brandt, S. (2016). Reliability and validity of a computer-based assessment of cognitive and non-cognitive facets of problem-solving competence in the business domain. Empirical Research in Vocational Education and Training, 8(1), 1–23.

    Article  Google Scholar 

  • Rouse, W. B. (1983). Models of human problem solving: Detection, diagnosis, and compensation for system failures. Automatica, 19(6), 613–625. https://doi.org/10.1016/0005-1098(83)90025-0

    Article  Google Scholar 

  • Schaper, N., Hochholdinger, S., & Sonntag, K. (2004). Förderung des Transfers von Diagnosestrategien durch computergestütztes Training mit kognitiver Modellierung [Improving transfer of troubleshooting skills by computer-based training with modeling]. Zeitschrift für Personalpsychologie, 3(2), 51–62.

    Article  Google Scholar 

  • Schwartz, A., & Elstein, A. S. (2008). Clinical reasoning in medicine. In J. Higgs, M. A. Jones, S. Loftus & N. Christensen (Eds.), Clinical reasoning in the health professions (3. Aufl., pp. 223–234). Oxford, UK: Elsevier Ltd.

    Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  • Sembill, D., Rausch, A., & Kögler, K. (2013). Non-cognitive facets of competence. Theoretical foundations and implications for measurement. In O. Zlatkin-Troitschanskaia & K. Beck (Eds.), From diagnostics to learning success—Proceedings in vocational education and training (pp. S. 199–S. 212). Rotterdam, The Netherlands: Sense.

    Chapter  Google Scholar 

  • Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.

    Book  Google Scholar 

  • Tatsuoka, K. K. (1983). Rule-space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345–354.

    Article  Google Scholar 

  • van Merriënboer, J. G. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160.

    Article  Google Scholar 

  • von Davier, M. (2005). A general diagnostic model applied to language testing data. ETS Research Report Series, 2005, i–35. https://doi.org/10.1002/j.2333-8504.2005.tb01993.x

    Article  Google Scholar 

  • von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61, 287–307. https://doi.org/10.1348/000711007X193957

    Article  Google Scholar 

  • von Davier, M. (2009, March). Some notes on the reinvention of latent structure models as diagnostic classification models. Measurement—Interdisciplinary Research and Perspectives, 7(1), 67–74.

    Article  Google Scholar 

  • von Davier, M. (2013). The DINA model as a constrained general diagnostic model—Two variants of a model equivalency. BJMSP, 67, 49–71.. http://onlinelibrary.wiley.com/doi/10.1111/bmsp.12003/abstract

    Google Scholar 

  • von Davier, M. (2014), The log-linear cognitive diagnostic model (LCDM) as a special case of the general diagnostic model (GDM). ETS research report series. http://onlinelibrary.wiley.com/doi/10.1002/ets2.12043/abstract

  • von Davier, M., DiBello, L., & Yamamoto, K. (2008). Reporting test outcomes using models for cognitive diagnosis. In J. Hartig, E. Klieme, & D. Leutner (Eds.), Assessment of competencies in educational contexts (pp. 151–176). Göttingen, Germany: Hogrefe & Huber Publishers.

    Google Scholar 

  • von Davier, M., & Rost, J. (2016). Logistic mixture-distribution response models. In W. van der Linden (Ed.), Handbook of item response theory (Vol. 1, 2nd ed., pp. 393–406). Boca Raton, FL: CRC Press. http://www.crcnetbase.com/doi/abs/10.1201/9781315374512-24

    Google Scholar 

  • von Davier, M., Xu, X., & Carstensen, C. H. (2011). Measuring growth in a longitudinal large-scale assessment with a general latent variable model. Psychometrika, 76, 318–336. https://doi.org/10.1007/s11336-011-9202-z

    Article  Google Scholar 

  • von Davier, M., & Yamamoto, K. (2004, October). A class of models for cognitive diagnosis. Paper presented at the 4th Spearman conference “Diagnostics for education: Theory, measurement, applications.” ETS: The Inn at Penn, Philadelphia, PA.

    Google Scholar 

  • von Davier, M., Yamamoto, K., Shin, H.-J., Chen, H., Khorramdel, L., Weeks, J., Davis, S., Kong, N., & Kandathil, M. (2019). Evaluating item response theory linking and model fit for data from PISA 2000–2012. Assessment in Education: Principles, Policy & Practice.

    Google Scholar 

  • von Davier, M. (2007). Hierarchical general diagnostic models. Research Report, RR-07-19. Princeton, NJ: ETS. https://onlinelibrary.wiley.com/doi/abs/10.1002/j.2333-8504.2007.tb02061.x

    Book  Google Scholar 

  • van der Linden, W. (2018). Handbook of item response theory, three volume set. Chapman & Hall/CRC statistics in the social and Behavioral sciences. ISBN: 148228247X, 9781482282474

    Book  Google Scholar 

  • Yamamoto, K., Khorramdel, L., & von Davier, M. (2013). Scaling PIAAC cognitive data. Technical report of the survey of adult skills (PIAAC). Paris: Organisation for Economic Co-operation and Development.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Vocational Education and Vocational Didactics, Technische Universität Dresden, Dresden, Germany

    Stephan Abele

  2. National Board of Medical Examiners (NBME), Philadelphia, PA, USA

    Matthias von Davier

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  1. Stephan Abele
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  2. Matthias von Davier
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Correspondence to Stephan Abele .

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Editors and Affiliations

  1. National Board of Medical Examiners (NBME), Philadelphia, PA, USA

    Matthias von Davier

  2. Teachers College, Columbia University, New York, NY, USA

    Young-Sun Lee

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Abele, S., von Davier, M. (2019). CDMs in Vocational Education: Assessment and Usage of Diagnostic Problem-Solving Strategies in Car Mechatronics. In: von Davier, M., Lee, YS. (eds) Handbook of Diagnostic Classification Models. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-05584-4_22

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