Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

Abstract

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

LAK of Direction

“…A study that examined different methods for assessing middle school students' use of evidence and systems thinking was coded as measuring learning (Ahn et al, 2021). Two studies that examined children's dialogue during collaborative problem solving were both coded as measuring learning (Emara et al, 2021;Ma et al, 2022). For readers who are skeptical of our findings, rather than presume that we've mischaracterized a learning-oriented literature, we recommend reviewing the article list, and our associated codes, and drawing your own conclusions from the data, which we've made public at https://osf.io/tzhva/.…”

Implementation of Learning Analytics in Primary and Secondary School: A Systematic Literature Review

“…It has increased the potential for self-directed learning and social learning online (Martin et al, 2016). The C2STEM modules provide students with the opportunities to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation and productive collaboration (Emara et al, 2021). A study by showed that educational technologies that combine standalone and embedded learning analytics help support blended learning by balancing teacher-led instructions with class-paced and individually-paced practices.…”

Collaboration Analytics — Current State and Potential Futures

“…In the third article, Emara, Hutchins, Grover, Snyder, and Biswas (2021) present a framework for analyzing rich collaboration data. They suggest that collaboration analytics can provide a better understanding of socially shared regulation (SSR) processes by capturing collaborative dialogue, log files, and task scores.…”