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  1. ; ; ; ; ; ( , Proceedings of the 25th International Conference on Multimodal Interaction)
    Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings. 
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    Free, publicly-accessible full text available October 9, 2024
  2. ; ; ; ( , Computer Science Education)
    Background and Context: Students’ self-efficacy toward computing affect their participation in related tasks and courses. Self- efficacy is likely influenced by students’ initial experiences and exposure to computer science (CS) activities. Moreover, student interest in a subject likely informs their ability to effectively regulate their learning in that domain. One way to enhance interest in CS is through using collaborative pair programming. Objective: We wanted to explore upper elementary students’ self- efficacy for and conceptual understanding of CS as manifest in collaborative and regulated discourse during pair programming. Method: We implemented a five-week CS intervention with 4th and 5th grade students and collected self-report data on students’ CS attitudes and conceptual understanding, as well as transcripts of dyads talking while problem solving on a pair programming task. Findings: The students’ self-report data, organized by dyad, fell into three categories based on the dyad’s CS self-efficacy and conceptual understanding scores. Findings from within- and cross-case analyses revealed a range of ways the dyads’ self-efficacy and CS conceptual understanding affected their collaborative and regulated discourse. Implications: Recommendations for practitioners and researchers are provided. We suggest that upper elementary students learn about productive disagreement and how to peer model. Additionally, our findings may help practitioners with varied ways to group their students. 
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  3. ; ; ; ( , International Journal of STEM Education)
    Abstract Background

    The Science Teaching Efficacy Belief Instrument A (STEBI-A; Riggs & Enochs, 1990 in Science Education, 74(6), 625-637) has been the dominant measurement tool of in-service science teacher self-efficacy and outcome expectancy for nearly 30 years. However, concerns about certain aspects of the STEBI-A have arisen, including the wording, validity, reliability, and dimensionality. In the present study, we revised the STEBI-A by addressing many concerns research has identified, and developed a new instrument called the T-STEM Science Scale. The T-STEM Science Scale was reviewed by expert panels and piloted first before it was administered to 727 elementary and secondary science teachers. The combination of classical test theory (CTT) and item response theory (IRT) approaches were used to validate the instrument. Multidimensional Rasch analysis and confirmatory factor analysis were run.

     Results

    Based on the results, the negatively worded items were found to be problematic and thus removed from the instrument. We also found that the three-dimensional model fit our data the best, in line with our theoretical conceptualization. Based on the literature review and analysis, although the personal science teaching efficacy beliefs (PTSEB) construct remained intact, the original outcome expectancy construct was renamed science teacher responsibility for learning outcomes beliefs (STRLOB) and was divided into two dimensions, above- and below-average student interest or performance. The T-STEM Science Scale had satisfactory reliability values as well.

     Conclusions

    Through the development and validation of the T-STEM Science Scale, we have addressed some critical concerns emergent from prior research concerning the STEBI-A. Psychometrically, the refinement of the wording, item removal, and the separation into three constructs have resulted in better reliability values compared to STEBI-A. While two distinct theoretical foundations are now used to explain the constructs of the new T-STEM instrument, prior literature and our empirical results note the important interrelationship of these constructs. The preservation of these constructs preserves a bridge, though imperfect, to the large body of legacy research using the STEBI-A.

     
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  4. ; ; ; ; ; ; ; ; ; ; et al ( , Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents)
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  5. ; ; ; ; ( , International Journal of Child-Computer Interaction)
    null (Ed.)
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  6. ; ; ; ; ; ; ; ; ( , ACM Technical Symposium on Computer Science Education)
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  7. ; ; ; ; ; ; ( , International Journal of Computer-Supported Collaborative Learning)
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  8. ; ; ; ; ( , The 26th ACM Conference on Innovation and Technology in Computer Science Education)
    null (Ed.)
    Pair programming is a popular strategy in computer science education to teach programming to novices. In this study, we examined the effect of three different pair programming conditions on up- per elementary school students’ CS conceptual understanding. The three conditions were one-computer with roles (1C with roles), two computers without roles (2C no roles), and two computers with roles (2C with roles). These students were engaged in four days of computer programming activities and took the CS concept assessment, CS attitudes, and collaboration perceptions before and after the activities. We used the validated E-CSCA (Elementary Computer Science Concepts Assessment) to measure elementary students’ understanding of CS concepts. We tested the relation- ship of different pair programming conditions on the students’ CS conceptual understanding and found that different conditions impacted students’ CS conceptual understanding, wherein students in 2C roles demonstrated better CS learning than the other two conditions. The results also showed no changes in students’ CS attitudes and perceptions of collaboration before and after the activities. Furthermore, the results indicated no significant impact of these attitudinal factors on students’ learning CS concepts in pair programming settings. Our study highlights the importance of the roles and number of computers in pair programming settings, especially for elementary students. 
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  9. ; ; ; ; ; ; ( , Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)
    null (Ed.)
    In successful collaborative paradigms such as pair programming, students engage in productive dialogue and work to resolve con- flicts as they arise. However, little is known about how elementary students engage in collaborative dialogue for computer science learning. Early findings indicate that these younger students may struggle to manage conflicts that arise during pair programming. To investigate collaborative dialogue that elementary learners use and the conflicts that they encounter, we analyzed videos of twelve pairs of fifth grade students completing pair programming activities. We developed a novel annotation scheme with a focus on collab- orative dialogue and conflicts. We found that student pairs used best-practice dialogue moves such as self-explanation, question generation, uptake, and praise in less than 23% of their dialogue. High-conflict pairs antagonized their partner, whereas this behav- ior was not observed with low-conflict pairs. We also observed more praise (e.g., “We did it!”) and uptake (e.g., “Yeah and. . . ”) in low-conflict pairs than high-conflict pairs. All pairs exhibited some conflicts about the task, but high-conflict pairs also engaged in conflicts about control of the computer and their partner’s con- tributions. The results presented here provide insights into the collaborative process of young learners in CS problem solving, and also hold implications for educators as we move toward building learning environments that support students in this context. 
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  10. ; ; ; ; ; ; ( , Computer Science Education)
    null (Ed.)
    Background and Context: Researchers and practitioners have begun to incorporate collaboration in programming because of its reported instructional and professional benefits. However, younger students need guidance on how to collaborate in environments that require substantial interpersonal interaction and negotiation. Previous research indicates that feedback fosters students’ productive collaboration. Objective: This study employs an intervention to explore the role instructor-directed feedback plays on elementary students’ dyadic collaboration during 2-computer pair programming. Method: We used a multi-study design, collecting video data on students’ dyadic collaboration. Study 1 qualitatively explored dyadic collaboration by coding video transcripts of four dyads which guided the design of Study 2 that examined conversation of six dyads using MANOVA and non-parametric tests. Findings: Result from Study 2 showed that students receiving feed- back used productive conversation categories significantly higher than the control condition in the sample group considered. Results are discussed in terms of group differences in specific conversation categories. Implications: Our study highlights ways to support students in pair programming contexts so that they can maximize the benefits afforded through these experiences. 
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