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Exploring the relationship between students’ information problem solving patterns and epistemic beliefs: a mixed methods sequential analysis study

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Abstract

Information problem solving (IPS) is an important twenty-first century skill, but it is lacking at all age levels. One type of information problem, those of an ill-structured nature that require multiple iterations of (re)defining problems and formulating emerging solutions, can be particularly challenging but have received less attention in the IPS literature. Further, the process of solving such problems often reveals, while simultaneously being impacted by, problem solvers’ epistemic beliefs. Using a self-regulated problem-solving model as an analytic framework and taking advantage of multiple data sources, this study examined college students’ self-regulatory patterns in performing an ill-structured IPS task, and compared the patterns displayed by two groups of students with more and less adaptive epistemic beliefs. Sequential analysis of behavioral data revealed different patterns between the two groups. Think-aloud data, interviews, and students’ IPS products showed three key differences between the two groups: difference in the roles of IPS task instructions, difference in the numbers and triggers of queries, and qualitative difference in iterations between page viewing and writing. The findings yielded important insights into the self-regulatory processes of IPS and the role of epistemic beliefs at different problem-solving stages. Implications are drawn for educators and learning designers for developing IPS in higher education.

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References

  • Argelagos, E., Garcia, C., Privado, J., & Wopereis, I. (2022). Fostering information problem solving skills through online task-centred instruction in higher education. Computers & Education. https://doi.org/10.1016/j.compedu.2022.104433

    Article  Google Scholar 

  • Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge University Press.

    Book  Google Scholar 

  • Baytelman, A., Iordanou, K., & Constantinou, C. P. (2020). Epistemic beliefs and prior knowledge as predictors of the construction of different types of arguments on socioscientific issues. Journal of Research in Science Teaching, 57, 1199–1227.

    Article  ADS  Google Scholar 

  • Brand-Gruwel, S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novices: Analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487–508.

    Article  Google Scholar 

  • Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using internet. Computers & Education, 53(4), 1207–1217.

    Article  Google Scholar 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.

    Google Scholar 

  • Cazan, A.-M. (2013). Validity of discipline focused epistemological beliefs questionnaire (DFEBQ) on a Romanian sample. Procedia: Social and Behavioral Sciences, 78, 713–717.

    Google Scholar 

  • Chen, C. H., Huang, K., & Liu, J. H. (2020). Inquiry-enhanced digital game-based learning: Effects on secondary students’ conceptual understanding in science, game performance, and behavioral patterns. The Asia-Pacific Education Researcher, 29(4), 319–330. https://doi.org/10.1007/s40299-019-00486-w

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152.

    Article  Google Scholar 

  • Creswell, J. W., & Clark, V. L. P. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publishing.

    Google Scholar 

  • Eichmann, B., Goldhammer, F., Greiff, S., Pucite, L., & Naumann, J. (2019). The role of planning in complex problem solving. Computers & Education, 128, 1–12.

    Article  Google Scholar 

  • Eichmann, B., Greiff, S., Naumann, J., Brandhuber, L., & Goldhammer, F. (2020). Exploring behavioural patterns during complex problem-solving. Journal of Computer Assisted Learning, 36(6), 933–956.

    Article  Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Rev). The MIT Press.

    Book  Google Scholar 

  • Ertmer, P. A., Stepich, D. A., York, C. S., Stickman, A., Wu, X. L., Zurek, S., & Goktas, Y. (2008). How instructional design experts use knowledge and experience to solve ill-structured problems. Performance Improvement Quarterly, 21(1), 17–42.

    Article  Google Scholar 

  • Frerejean, J., Velthorst, G. J., van Strien, J. L. H., Kirschner, P. A., & Brand-Gruwel, S. (2019). Embedded instruction to learn information problem solving: Effects of a whole task approach. Computers in Human Behavior, 90, 117–130.

    Article  Google Scholar 

  • Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding ill-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5–22. https://doi.org/10.1007/BF02504836

    Article  Google Scholar 

  • Ge, X., Law, V., & Huang, K. (2016). Detangling the interrelationships between self-regulation and ill-structured problem solving in PBL. Interdisciplinary Journal of Problem-based Learning, 10(2). https://doi.org/10.7771/1541-5015.1622

    Article  Google Scholar 

  • Greene, J. A., Cartiff, B. M., & Duke, R. F. (2018). A meta-analytic review of the relationship between epistemic cognition and academic achievement. Journal of Educational Psychology, 110(8), 1084–1111.

    Article  Google Scholar 

  • Hofer, B. (2000). Dimensionality and disciplinary differences in personal epistemology. Contemporary Educational Psychology, 25(4), 378–405.

    Article  CAS  PubMed  Google Scholar 

  • Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67(1), 88–140.

    Article  Google Scholar 

  • Huang, K., Ge, X., & Eseryel, D. (2017a). Metaconceptually-enhanced simulation-based inquiry learning: Effects on 8th grade students’ conceptual change and science epistemic beliefs. Educational Technology Research and Development, 65(1), 75–100. https://doi.org/10.1007/s11423-016-9462-5

    Article  Google Scholar 

  • Huang, K., Ge, X., & Law, V. (2017b). Deep and surface processing of instructor’s feedback in an online course. Educational Technology & Society, 20(4), 247–260.

    Google Scholar 

  • Huang, K., Ge, X., Law, V., Hu, L., & Chen, Y. (2020). Exploring patterns in undergraduate students’ information problem solving: A cross-case comparison study. Knowledge Management & E-Learning, 11(4), 153–169. https://doi.org/10.34105/j.kmel.2019.11.023

    Article  Google Scholar 

  • Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613

    Article  Google Scholar 

  • Kang, J., Liu, M., & Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757–770.

    Article  Google Scholar 

  • Kirschner, P. A., & van Merriënboer, J. J. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48, 169–183.

    Article  Google Scholar 

  • Lindfors, M., Winberg, M., & Bodin, M. (2019). The role of students’ scientific epistemic beliefs in computer-simulated problem solving. Scandinavian Journal of Educational Research, 63(1), 124–144.

    Article  Google Scholar 

  • Macedo-Rouet, M., Potocki, A., Scharrer, L., Ros, C., Stadtler, M., Salmerón, L., & Rouet, J. (2019). How good is this page? Benefits and limits of prompting on adolescents’ evaluation of web information quality. Reading Research Quarterly, 54(3), 299–321.

    Article  Google Scholar 

  • Mason, L. (2016). Psychological perspectives on measuring epistemic cognition. In J. A. Greene, W. A. Sandoval, & I. Braten (Eds.), Handbook of epistemic cognition (pp. 375–392). Routledge. https://doi.org/10.4324/9781315795225

    Chapter  Google Scholar 

  • Muis, K. R. (2007). The role of epistemic beliefs in self-regulated learning. Educational Psychologist, 42(3), 173–190.

    Article  Google Scholar 

  • Muis, K. R., Duffy, M. C., Trevors, G., Ranellucci, J., & Foy, M. (2014). What were they thinking? Using cognitive interviewing to examine the validity of self-reported epistemic beliefs. International Educational Research, 2(1), 17–32.

    Article  Google Scholar 

  • Noroozi, O. (2023). The role of students’ epistemic beliefs for their argumentation performance in higher education. Innovations in Education and Teaching International, 60(4), 501–512.

    Article  Google Scholar 

  • Partnership for 21st century skills. (2008). 21st century skills, education & competitiveness: A resource and policy guide. Washington: Author.

  • Schiefer, J., Edelsbrunner, A. A., Bernholt, A., Kampa, N., & Nehring, A. (2022). Epistemic beliefs in science—A systematic integration of evidence from multiple studies. Educational Psychology Review, 34, 1541–1575.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schommer, M., Crouse, A., & Rhodes, N. (1992). Epistemological beliefs and mathematical text comprehension: Believing it is simple does not make it so. Journal of Educational Psychology, 84(4), 435–443.

    Article  Google Scholar 

  • Schraw, G., Dunkle, M. E., & Bendixen, L. D. (1995). Cognitive processes in well-defined and ill-defined problem solving. Applied Cognitive Psychology, 9, 523–538.

    Article  Google Scholar 

  • Sinatra, G. M., & Mason, L. (2013). Beyond knowledge: Learner characteristics influencing conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (2nd ed., pp. 377–394). Springer.

    Google Scholar 

  • Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and application (pp. 72–99). Praeger.

    Google Scholar 

  • Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage.

    Google Scholar 

  • Tsai, M.-J., & Tsai, C.-C. (2003). Information searching strategies in web-based science learning: The role of Internet self-efficacy. Innovations in Education and Teaching International, 40, 43–50.

    Article  Google Scholar 

  • Tsai, M.-J., & Wu, A.-H. (2021). Visual search patterns, information selection strategies, and information anxiety for online information problem solving. Computers & Education, 172, 104236. https://doi.org/10.1016/j.compedu.2021.104236

    Article  Google Scholar 

  • Van Laar, E., van Deursen, A. J. A. M., van Dijk, J. A. G. M., & de Haan, J. (2017). The relationship between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577–588.

    Article  Google Scholar 

  • Van Merrienboer, J. J. G., & Kirschner, P. A. (2018). Ten steps to complex learning: A systematic approach to four-component instructional design (3rd ed.). Routledge.

    Google Scholar 

  • Vörös, Z., Kehl, D., & Rouet, J.-F. (2021). Task characteristics as source of difficulty and moderators of effect of time-on-task in digital problem-solving. Journal of Educational Computing Research, 58(8), 1494–1514.

    Article  Google Scholar 

  • Voss, J. F., & Post, T. A. (1988). On the solving of ill-structured problems. In M. T. H. Chi & R. Glaser (Eds.), The nature of expertise (pp. 261–285). Lawrence Erlbaum.

    Google Scholar 

  • Voss, J. F., Wolfe, C. R., Lawrence, J. A., & Engle, J. A. (1991). From representation to decision: An analysis of problem solving in international relations. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 119–158). Lawrence Erlbaum Associates.

    Google Scholar 

  • Walraven, A., Brand-Gruwel, S., & Boshuizen, H. P. A. (2008). Information-problem solving: A review of problems students encounter and instructional solutions. Computers in Human Behavior, 24, 623–648.

    Article  Google Scholar 

  • Walraven, A., Brand-Gruwel, S., & Boshuizen, H. P. A. (2009). How students evaluate information and sources when searching the World Wide Web for information. Computers & Education, 52, 234–246.

    Article  Google Scholar 

  • Whitelock-Wainwright, A., Laan, N., Wen, D., & Gasevic, D. (2020). Exploring student information problem solving behaviour using fine-grained concept map and search tool data. Computers & Education, 145, 103731. https://doi.org/10.1016/j.compedu.2019.103731

    Article  Google Scholar 

  • Zhou, M. (2013a). A systematic understanding of successful web searches in information-based tasks. Educational Technology & Society, 16(1), 321–331.

    Google Scholar 

  • Zhou, M. (2013b). Using traces to investigate self-regulatory activities: A study of self-regulation and achievement profiles in the context of web search for academic tasks. Journal of Cognitive Education and Psychology, 12(3), 287–305.

    Article  Google Scholar 

  • Zhou, M., & Lam, K. K. L. (2019). Metacognitive scaffolding for online information search in K-12 and higher education settings: A systematic review. Educational Technology Research & Development, 67, 1353–1384.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Curriculum and Instruction, University of Kentucky, Lexington, KY, USA

    Kun Huang

  2. Organization, Information & Learning Sciences Program, University of New Mexico, Albuquerque, NM, USA

    Victor Law

  3. Department of Learning Technologies, University of North Texas, Denton, TX, USA

    Xun Ge

  4. Department of Teaching and Learning, University of Nevada, Las Vegas, Las Vegas, NV, USA

    Yan Chen

  5. College of Foreign Language Education, Jilin University, Changchun, Jilin, China

    Ling Hu

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  1. Kun Huang
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  2. Victor Law
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  4. Yan Chen
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  5. Ling Hu
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Correspondence to Kun Huang.

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Huang, K., Law, V., Ge, X. et al. Exploring the relationship between students’ information problem solving patterns and epistemic beliefs: a mixed methods sequential analysis study. J Comput High Educ (2024). https://doi.org/10.1007/s12528-024-09396-3

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