Abstract
Interactive simulations play a significant role in science education, serving as a platform for inquiry-based learning and fostering the development of scientific knowledge and skills. However, teaching and quantitatively measuring inquiry strategies has proven to be challenging due to their complex and inherently multidimensional nature. Our study goes beyond the prevalent focus on the Control of Variables Strategy (CVS) in prior work by incorporating additional relevant inquiry strategies in both teaching and measurement: exploring the variable range and conducting experiments under optimal conditions. We tested two different instructional approaches to jointly teach the three strategies by focusing either on data collection or on data interpretation. 161 chemistry apprentices were randomly assigned to one of the two instructional conditions or a control group without instruction and engaged in experimentation using an interactive simulation. In order to analyze joint strategy use, we applied a multi-step clustering method to students’ log data that helped identify multidimensional student profiles of inquiry strategies. We found four profiles that related differently to conceptual learning, suggesting that combining strategies is more effective for conceptual learning than utilizing them individually. We also found that students instructed on data collection increased the use of strategy combinations with an emphasis on CVS. This suggests a potential avenue for assessing instruction efficacy, indicating that the impact may be strategy-specific. Source code and materials are released at https://github.com/epfl-ml4ed/inquiry-skills.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
A Shapiro-Wilk test confirmed that the ANOVA assumptions were not satisfied.
- 2.
A Shapiro-Wilk test indicated a normal distribution of the data.
References
Bumbacher, E., Salehi, S., Wieman, C., Blikstein, P.: Tools for science inquiry learning: tool affordances, experimentation strategies, and conceptual understanding. J. Technol. Sci. Educ. (2018)
Chen, Z., Klahr, D.: All other things being equal: acquisition and transfer of the control of variables strategy. Child Dev. (1999)
Cock, J.M., Roll, I., Käser, T.: Consistency of inquiry strategies across subsequent activities in different domains. In: Proceedings of AIED (2023)
Cock, J.M., Marras, M., Giang, C., Käser, T.: Generalisable methods for early prediction in interactive simulations for education. In: Proceedings of EDM (2022)
Fratamico, L., Conati, C., Kardan, S., Roll, I.: Applying a framework for student modeling in exploratory learning environments: comparing data representation granularity to handle environment complexity. Int. J. Artif. Intell. Educ. (2017)
Fukuda, M., et al.: Scientific inquiry learning with a simulation: providing within-task guidance tailored to learners’ understanding and inquiry skill. Int. J. Sci. Educ. (2022)
Gholam, A.: Inquiry-based learning: student teachers’ challenges and perceptions. J. Inq, Action Educ. (2019)
Gobert, J., Pedro, M., Baker, R.: Assessing the learning and transfer of data collection inquiry skills using educational data mining on students’ log files. In: Proceedings of AERA (2012)
Gobert, J.D., Kim, Y.J., Sao Pedro, M.A., Kennedy, M., Betts, C.G.: Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Think. Skills Creat. (2015)
de Jong, T., Linn, M.C., Zacharia, Z.C.: Physical and virtual laboratories in science and engineering education. Science (2013)
Kalthoff, B., Theyssen, H., Schreiber, N.: Explicit promotion of experimental skills and what about the content-related skills? Int. J. Sci. Educ. (2018)
Kruit, P.M., Oostdam, R.J., van den Berg, E., Schuitema, J.A.: Assessing students’ ability in performing scientific inquiry: instruments for measuring science skills in primary education. Res. Sci. Technol. Educ. (2018)
Kuhn, D., Iordanou, K., Pease, M., Wirkala, C.: Beyond control of variables: what needs to develop to achieve skilled scientific thinking? Cogn. Dev. (2008)
Käser, T., Schwartz, D.L.: Modeling and analyzing inquiry strategies in open-ended learning environments. Int. J. Artif. Intell. Educ. (2020)
Lazonder, A.W., Harmsen, R.: Meta-analysis of inquiry-based learning: effects of guidance. Rev. Educ. Res. (2016)
Matlen, B.J., Klahr, D.: Sequential effects of high and low instructional guidance on children’s acquisition of experimentation skills: is it all in the timing? Instr. Sci. (2013)
Mejia-Domenzain, P., Marras, M., Giang, C., Käser, T.: Identifying and comparing multi-dimensional student profiles across flipped classrooms. In: Proceedings of AIED (2022)
Mulder, Y.G., Lazonder, A.W., de Jong, T.: Using heuristic worked examples to promote inquiry-based learning. Learn. Instrum. (2014)
Nicolay, B., et al.: Unsuccessful and successful complex problem solvers - a log file analysis of complex problem solving strategies across multiple tasks. Intelligence (2023)
Pedaste, M., et al.: Phases of inquiry-based learning: definitions and the inquiry cycle. Educ. Res. Rev. (2015)
Pedro, M.S., Gobert, J.D., Baker, R.: Assessing the learning and transfer of data collection inquiry skills using educational data mining on students’ log files. In: Proceedings of AERA (2012)
Peffer, M., Quigley, D., Mostowfi, M.: Clustering analysis reveals authentic science inquiry trajectories among undergraduates. In: Proceedings of LAK (2019)
Perez, S., Massey-Allard, J., Ives, J., Butler, D., Bonn, D., Bale, J., Roll, I.: Control of variables strategy across phases of inquiry in virtual labs. In: Proceedings of AIED (2018)
Pols, C.F.J., Dekkers, P.J.J.M., de Vries, M.J.: Defining and assessing understandings of evidence with the assessment rubric for physics inquiry: towards integration of argumentation and inquiry. Phys. Rev. Phys. Educ. Res. (2022)
Reiss, K., Renkl, A.: Learning to prove: the idea of heuristic examples. ZDM - Int. J. Math. Educ. (2002)
Roll, I., et al.: Understanding the impact of guiding inquiry: the relationship between directive support, student attributes, and transfer of knowledge, attitudes, and behaviours in inquiry learning. Instrum. Sci. (2018)
Saavedra, A.: Experiments in learning and transfer of inquiry strategies using short instructional videos. Ph.D. thesis, Stanford University (2022)
Saba, J., Kapur, M., Roll, I.: The development of multivariable causality strategy: instruction or simulation first? In: Proceedings of AIED (2023)
Sabourin, J., Mott, B., Lester, J.: Discovering behavior patterns of self-regulated learners in an inquiry-based learning environment. In: Proceedings of AIED (2013)
Scalise, K., Clarke-Midura, J.: The many faces of scientific inquiry: effectively measuring what students do and not only what they say. J. Res. Sci. Teach. (2018)
Schunn, C.D., Anderson, J.R.: The generality/specificity of expertise in scientific reasoning. Cogn. Sci. (1999)
Trautmann, N., MaKinster, J., Leanne, A.: What makes inquiry so hard? (and why is it worth it?). In: Proceedings of NARST (2004)
Vorholzer, A., von Aufschnaiter, C.: Guidance in inquiry-based instruction - an attempt to disentangle a manifold construct. Int. J. Sci. Educ. (2019)
Zacharia, Z.C., et al.: Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review. Educ. Technol. Res. Dev. (2015)
Acknowledgments
This project was substantially financed by the Swiss State Secretariat for Education, Research and Innovation SERI. Thanks to Hugues Saltini, Jade Maï Cock, Peter Bühlmann and Tanya Nazaretsky for their valuable input.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shved, E., Bumbacher, E., Mejia-Domenzain, P., Kapur, M., Käser, T. (2024). Teaching and Measuring Multidimensional Inquiry Skills Using Interactive Simulations. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science(), vol 14829. Springer, Cham. https://doi.org/10.1007/978-3-031-64302-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-64302-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64301-9
Online ISBN: 978-3-031-64302-6
eBook Packages: Computer ScienceComputer Science (R0)