Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments
- PDF / 1,650,327 Bytes
- 32 Pages / 439.642 x 666.49 pts Page_size
- 18 Downloads / 249 Views
Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments ¨ 1 Tanja Kaser
· Daniel L. Schwartz2
© International Artificial Intelligence in Education Society 2020
Abstract Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning behavior becomes especially important, when dealing with open-ended exploration environments, which do not dictate prescribed learning sequences and skills. In this paper, we work with data collected from an inquiry-based environment. We demonstrate that 1) students’ inquiry strategies indeed influence the learning outcome, and 2) students’ inquiry strategies also seem to be predictive for their academic achievement. Furthermore, we identified a new positive inquiry strategy, which has not yet been described in the literature. We propose the use of a probabilistic model jointly representing student knowledge and strategies and show that the inclusion of learning behavior into the model significantly improves prediction of external posttest results compared to only using accuracy data, a result that we validated on a second data set. Furthermore, we cluster the children into different groups with similar learning strategies to get a complete picture of students’ inquiry behavior. The obtained clusters can be semantically interpreted and are not only correlated to learning success in the game, but also to students’ science grades and standardized math assessments. We also validated the cluster solution on a second data set. The inquirybased environment together with the clustering solution has the potential to serve as an assessment tool for teachers and tutors. Keywords Learning · Strategies · Prediction · Simulation · Probabilistic models · Clustering Tanja K¨aser
[email protected] Daniel L. Schwartz [email protected] 1
EPFL, Lausanne, Switzerland
2
Graduate School of Education, Stanford University, Stanford, CA, USA
International Journal of Artificial Intelligence in Education
Introduction Over the last decade, there has been an increase in the use of open-ended learning environments such as discovery environments (Shute and Glaser 1990), narrativecentered learning environments (Rowe et al. 2009), or simulations (Wieman et al. 2008). Ideally, students explore different configurations of parameters to infer the underlying principles. One rationale is that students learn the principles more deeply through exploration than if they are simply told the principles and asked to practice applying them (Schwartz et al. 2011). However, not all students apply the inquiry skills necessary to effectively explore the environment (Kinnebrew et al. 2013; Sabourin et al. 2013; Mayer 2004). Modeling students’ learning as they try to benefit from relatively open-ended inquiry environments may be a useful next step in our abilities to support students’ development of indepe
Data Loading...
