In inquiry-based learning tasks students are actively involved in learning knowledge and skills through experimentation. The success of these activities largely depends on student’s inquiry practices. While traditional assessment infers student competency from their responses and problem-solving steps, the pauses between these actions provide a valuable source of information. Pauses during inquiry tasks capture a wide range of productive and unproductive activities such as planning, reasoning and mind-wandering. We present efforts to characterize the pause behaviors during a science inquiry task using hidden Markov modeling. We explore how theory can inform data driven modeling approaches, describe initial evidence of meaningful pause states, and consider the limitations of this approach for supporting inferences about students’ science inquiry practices.

Tenison, C., & Arslan, B. (2020). Characterizing Pause Behaviors in a Science Inquiry Task. The 18th Annual Meeting of the International Conference on Cognitive Modeling.(Paper available here)

Tenison, C., & Arslan, B. (2022). Incorporating Pauses in Process Data Modeling with Heterogeneous Hidden Markov Models. Annual Meeting of the National Council for Measurement in Education.(Extended abstract here)

  • Hidden Markov modeling
  • Heterogeneous Hidden Markov modeling
  • Dr. Burcu Arslan