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Students’ Complex Problem Solving Profiles

Author

Listed:
  • Michela Gnaldi

    (University of Perugia)

  • Silvia Bacci

    (University of Florence)

  • Thiemo Kunze

    (University of Luxembourg)

  • Samuel Greiff

    (University of Luxembourg)

Abstract

Complex problem solving (CPS) is an up-and-coming twenty-first century skill that requires test-takers to solve dynamically changing problems, often assessed using computer-based tests. The log data that users produce when interacting with a computer-based test provide valuable information about each individual behavioral action they undertake, but such data are rather difficult to handle from a statistical point of view. This paper addresses this issue by building upon recent research focused on decoding log data and aims to identify homogeneous student profiles with regard to their ability to solve CPS tasks. Therefore, we estimated a discrete two-tier item response theory model, which allowed us to profile units (i.e., students) while taking into account the multidimensionality of the data and the explanatory effect of individual characteristics. The results indicate that: (1) CPS can be thought of as a three-dimensional latent variable; (2) there are ten latent classes of students with homogenous profiles regarding the CPS dimensions; (3) students in the higher latent classes generally demonstrate higher cognitive and non-cognitive performances; (4) some of the latent classes seem to profit from learning-by-doing within tasks, whereas others seem to exhibit the reverse behavior; (5) cognitive and non-cognitive skills, as well as gender and to some extent age, contribute to distinguishing among the latent classes.

Suggested Citation

  • Michela Gnaldi & Silvia Bacci & Thiemo Kunze & Samuel Greiff, 2020. "Students’ Complex Problem Solving Profiles," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 469-501, June.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:2:d:10.1007_s11336-020-09709-2
    DOI: 10.1007/s11336-020-09709-2
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    References listed on IDEAS

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    1. Robert Gibbons & Donald Hedeker, 1992. "Full-information item bi-factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 423-436, September.
    2. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.
    3. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    4. Li Cai, 2010. "A Two-Tier Full-Information Item Factor Analysis Model with Applications," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 581-612, December.
    5. Formann, Anton K., 2007. "Mixture analysis of multivariate categorical data with covariates and missing entries," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5236-5246, July.
    6. Eric Bradlow & Howard Wainer & Xiaohui Wang, 1999. "A Bayesian random effects model for testlets," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 153-168, June.
    7. Robert Mislevy & Norman Verhelst, 1990. "Modeling item responses when different subjects employ different solution strategies," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 195-215, June.
    8. Lotz, Christin & Scherer, Ronny & Greiff, Samuel & Sparfeldt, Jörn R., 2017. "Intelligence in action – Effective strategic behaviors while solving complex problems," Intelligence, Elsevier, vol. 64(C), pages 98-112.
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    Cited by:

    1. Nicolay, Björn & Krieger, Florian & Kuhn, Jörg-Tobias & Graesser, Arthur C. & Ifenthaler, Dirk & Baker, Ryan & Greiff, Samuel, 2023. "Unsuccessful and successful complex problem solvers – A log file analysis of complex problem solving strategies across multiple tasks," Intelligence, Elsevier, vol. 101(C).

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