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1. Using Collaborative Interactivity Metrics to analyze students’ Problem-Solving Behaviors during STEM+C Computational Modeling Tasks

   Snyder Caitlin; Hutchins, Nicole; Cohn, Clayton; Fonteles, Joyce; Biswas, Gautam (September 2023, Learning and individual differences)

   Grieff, S. (Ed.)

   Recently there has been increased development of curriculum and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) learning environments. These environments serve as a catalyst for authentic collaborative problem-solving (CPS) and help students synergistically learn STEM+C content. In this work, we analyzed students’ collaborative problem-solving behaviors as they worked in pairs to construct computational models in kinematics. We leveraged social measures, such as equity and turn-taking, along with a domain-specific measure that quantifies the synergistic interleaving of science and computing concepts in the students’ dialogue to gain a deeper understanding of the relationship between students’ collaborative behaviors and their ability to complete a STEM+C computational modeling task. Our results extend past findings identifying the importance of synergistic dialogue and suggest that while equitable discourse is important for overall task success, fluctuations in equity and turn-taking at the segment level may not have an impact on segment-level task performance. To better understand students’ segment-level behaviors, we identified and characterized groups’ planning, enacting, and reflection behaviors along with monitoring processes they employed to check their progress as they constructed their models. Leveraging Markov Chain (MC) analysis, we identified differences in high- and low-performing groups’ transitions between these phases of students’ activities. We then compared the synergistic, turn-taking, and equity measures for these groups for each one of the MC model states to gain a deeper understanding of how these collaboration behaviors relate to their computational modeling performance. We believe that characterizing differences in collaborative problem-solving behaviors allows us to gain a better understanding of the difficulties students face as they work on their computational modeling tasks. 

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2. Assessing Students’ Knowledge Co-construction Behaviors in a Collaborative Computational Modeling Environment.

   https://doi.org/10.1007/978-3-031-11647-6_105  

   Snyder, C. (July 2022, AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.)

   Rodrigo, M.M. (Ed.)

   Successful knowledge co-construction during collaborative learning requires students to develop a shared conceptual understanding of the domain through effective social interactions. Developing and applying shared understanding of concepts and practices is directly impacted by the prior knowledge that students bring to their interactions. We present a systematic approach to analyze students’ knowledge co-construction processes as they work through a physics curriculum that includes inquiry activities, instructional tasks, and computational model-building activities. Utilizing a combination of students’ activity logs and discourse analysis, we assess how students’ knowledge impacts their knowledge co-construction processes. We hope a better understanding of how students’ co-construction processes develop and the difficulties they face will lead to better adaptive scaffolding of students’ learning and better support for collaborative learning. 

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3. HydroLearn: Improving Students’ Conceptual Understanding and Technical Skills in a Civil Engineering Senior Design Course

   Gallagher, Melissa Ann; Byrd, Jenny; Habib, Emad; Tarboton, David G; Willson, Clinton S (July 2021, 2021 ASEE Annual Conference)

   null (Ed.)

   Engineering graduates need a deep understanding of key concepts in addition to technical skills to be successful in the workforce. However, traditional methods of instruction (e.g., lecture) do not foster deep conceptual understanding and make it challenging for students to learn the technical skills, (e.g., professional modeling software), that they need to know. This study builds on prior work to assess engineering students’ conceptual and procedural knowledge. The results provide an insight into how the use of authentic online learning modules influence engineering students’ conceptual knowledge and procedural skills. We designed online active learning modules to support and deepen undergraduate students’ understanding of key concepts in hydrology and water resources engineering (e.g., watershed delineation, rainfall-runoff processes, design storms), as well as their technical skills (e.g., obtaining and interpreting relevant information for a watershed, proficiency using HEC-HMS and HEC-RAS modeling tools). These modules integrated instructional content, real data, and modeling resources to support students’ solving of complex, authentic problems. The purpose of our study was to examine changes in students’ self-reported understanding of concepts and skills after completing these modules. The participants in this study were 32 undergraduate students at a southern U.S. university in a civil engineering senior design course who were assigned four of these active learning modules over the course of one semester to be completed outside of class time. Participants completed the Student Assessment of Learning Gains (SALG) survey immediately before starting the first module (time 1) and after completing the last module (time 2). The SALG is a modifiable survey meant to be specific to the learning tasks that are the focus of instruction. We created versions of the SALG for each module, which asked students to self-report their understanding of concepts and ability to implement skills that are the focus of each module. We calculated learning gains by examining differences in students’ self-reported understanding of concepts and skills from time 1 to time 2. Responses were analyzed using eight paired samples t-tests (two for each module used, concepts and skills). The analyses suggested that students reported gains in both conceptual knowledge and procedural skills. The data also indicated that the students’ self-reported gain in skills was greater than their gain in concepts. This study provides support for enhancing student learning in undergraduate hydrology and water resources engineering courses by connecting conceptual knowledge and procedural skills to complex, real-world problems. 

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4. HydroLearn: Improving Students’ Conceptual Understanding and Technical Skills in a Civil Engineering Senior Design Course

   Gallagher, M. A. (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   Engineering graduates need a deep understanding of key concepts in addition to technical skills to be successful in the workforce. However, traditional methods of instruction (e.g., lecture) do not foster deep conceptual understanding and make it challenging for students to learn the technical skills, (e.g., professional modeling software), that they need to know. This study builds on prior work to assess engineering students’ conceptual and procedural knowledge. The results provide an insight into how the use of authentic online learning modules influence engineering students’ conceptual knowledge and procedural skills. We designed online active learning modules to support and deepen undergraduate students’ understanding of key concepts in hydrology and water resources engineering (e.g., watershed delineation, rainfall-runoff processes, design storms), as well as their technical skills (e.g., obtaining and interpreting relevant information for a watershed, proficiency using HEC-HMS and HEC-RAS modeling tools). These modules integrated instructional content, real data, and modeling resources to support students’ solving of complex, authentic problems. The purpose of our study was to examine changes in students’ self-reported understanding of concepts and skills after completing these modules. The participants in this study were 32 undergraduate students at a southern U.S. university in a civil engineering senior design course who were assigned four of these active learning modules over the course of one semester to be completed outside of class time. Participants completed the Student Assessment of Learning Gains (SALG) survey immediately before starting the first module (time 1) and after completing the last module (time 2). The SALG is a modifiable survey meant to be specific to the learning tasks that are the focus of instruction. We created versions of the SALG for each module, which asked students to self-report their understanding of concepts and ability to implement skills that are the focus of each module. We calculated learning gains by examining differences in students’ self-reported understanding of concepts and skills from time 1 to time 2. Responses were analyzed using eight paired samples t-tests (two for each module used, concepts and skills). The analyses suggested that students reported gains in both conceptual knowledge and procedural skills. The data also indicated that the students’ self-reported gain in skills was greater than their gain in concepts. This study provides support for enhancing student learning in undergraduate hydrology and water resources engineering courses by connecting conceptual knowledge and procedural skills to complex, real-world problems. 

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5. Using Epistemic Network Analysis and Sequential Pattern Mining to Explore the Impacts of Human Facilitation on Collaborative Mathematical Problem Solving

   https://doi.org/10.22318/cscl2025.352932  

   Jiang, Yang; Graf, Edith_Aurora; Andrews_Todd, Jessica (June 2025, International Society of the Learning Sciences Repository)

   Learning algebra concepts, particularly those involving functions, is crucial yet challenging in mathematics education. Research suggests that collaborative learning, where students work in small teams to solve math problems, can support conceptual learning. However, collaborative problem solving (CPS) is complex and requires students’ social and cognitive skills for success. This study investigates the impacts of guided facilitation on supporting CPS in mathematics tasks during small-team collaboration. Using epistemic network analysis and sequential pattern mining, we analyzed chat logs from student teams and human facilitators and compared the interactions in facilitated and unfacilitated teams. Results indicated that near-peer facilitation effectively promoted constructive behaviors like multiple turns of social negotiation, while reducing inappropriate communications. Different facilitation strategies, such as encouraging students to respond to each other or clarify their statements, elicited targeted CPS behaviors, possibly leading to improved team performance. Findings have practical implications for facilitating CPS in mathematics classrooms. 

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