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  1. Rodrigo, M.M. (Ed.)
    Formative assessments are an important component of instruction and pedagogy, as they provide students and teachers with insights on how students are progressing in their learning and problem-solving tasks. Most formative assessments are now coded and graded manually, impeding timely interventions that help students overcome difficulties. Automated evaluation of these assessments can facilitate more effective and timely interventions by teachers, allowing them to dynamically discern individual and class trends that they may otherwise miss. State-of-the-art BERT-based models dominate the NLP landscape but require large amounts of training data to attain sufficient classification accuracy and robustness. Unfortunately, educational data sets aremore »often small and unbalanced, limiting any benefits that BERT-like approaches might provide. In this paper, we examine methods for balancing and augmenting training data consisting of students’ textual answers from formative assessments, then analyze the impacts in order to improve the accuracy of BERT-based automated evaluations. Our empirical studies show that these techniques consistently outperform models trained on unbalanced and unaugmented data.« less
    Free, publicly-accessible full text available July 1, 2023
  2. 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-constructionmore »processes develop and the difficulties they face will lead to better adaptive scaffolding of students’ learning and better support for collaborative learning.« less
    Free, publicly-accessible full text available July 1, 2023
  3. Chinn, C. (Ed.)
    The integration of computational modeling into instruction in science classrooms is complex in that it requires the synergistic application of students’ developing science and computational thinking knowledge. This is not only difficult for students, but teachers often find it hard to parse the science content from the computational constructs to guide students when they have difficulties. Leveraging past literature that highlights the beneficial impact instructors can have when they immerse themselves in group problem-solving discussions, this paper examines the instructors’ role in facilitating students’ construction of and problem-solving with computational models. We utilize a case study approach to analyze instructor-facilitated,more »synchronous group discussions during applications of synergistic learning processes to understand how instructors may elicit students’ knowledge, misunderstanding, and difficulties to help guide, prompt, and engage groups in this complex task for more productive integration in K-12 science classrooms. We hope that this will lead to better scaffolding of students' learning, and better support for teachers when they use such curricula in classrooms.« less
    Free, publicly-accessible full text available June 1, 2023
  4. Student discussions have been shown to be beneficial to student learning (Chi & Wylie, 2014), however, the impact of prior knowledge on these discussions is not fully understood. In this research, we analyze students’ synchronous spoken discussions to study how prior knowledge impacted group discussions and knowledge construction while constructing computational models of 1D and 2D motion. We present a method for evaluating the impact of prior knowledge on student discussions and individual work. We illustrate this method through a case study analysis of two groups with students across a spectrum of prior knowledge. Our exploratory findings suggest that studentsmore »with low prior knowledge greatly benefit from group discussions followed by individual model construction.« less
    Free, publicly-accessible full text available April 1, 2023
  5. Strategies are an important component of self-regulated learning frameworks. However, the characterization of strategies in these frameworks is often incomplete: (1) they lack an operational definition of strategies; (2) there is limited understanding of how students develop and apply strategies; and (3) there is a dearth of systematic and generalizable approaches to measure and evaluate strategies when students’ work in open-ended learning environments (OELEs). This paper develops systematic methods for detecting, interpreting, and analyzing students’ use of strategies in OELEs, and demonstrates how students’ strategies evolve across tasks. We apply this framework in the context of tasks that students performmore »as they learn science topics by building conceptual and computational models in an OELE. Data from a classroom study, where sixth-grade students (N = 52) worked on science model-building activities in our Computational Thinking using Simulation and Modeling (CTSiM) environment demonstrates how we interpret students’ strategy use, and how strategy use relates to their learning performance. We also demonstrate how students’ strategies evolve as they work on multiple model-building tasks. The results demonstrate the effectiveness of our strategy framework in analyzing students’ behaviors and performance in CTSiM.« less
    Free, publicly-accessible full text available September 24, 2022
  6. de Vries, E. (Ed.)
    We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinementsmore »to our curriculum and scaffolding of students to further support the integrated learning of science and CT.« less
  7. Hmelo-Silver, C. E. (Ed.)
    This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness.
  8. Hmelo-Silver, C. E. (Ed.)
    This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness.
  9. de Vries, E. (Ed.)
    We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinementsmore »to our curriculum and scaffolding of students to further support the integrated learning of science and CT.« less