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1. Computer-Based PTSD Assessment in VR Exposure Therapy

   https://doi.org/10.1007/978-3-030-59990-4_32  

   Tavabi, Leili; Poon, Anna; Rizzo, Albert Skip; Soleymani, Mohammad (October 2020, HCI International 2020 – Late Breaking Papers: Virtual and Augmented Reality)

   Stephanidis, Constantine; Chen, Jessie Y.; Fragomeni, Gino (Ed.)

   Post-traumatic stress disorder (PTSD) is a mental health condition affecting people who experienced a traumatic event. In addition to the clinical diagnostic criteria for PTSD, behavioral changes in voice, language, facial expression and head movement may occur. In this paper, we demonstrate how a machine learning model trained on a general population with self-reported PTSD scores can be used to provide behavioral metrics that could enhance the accuracy of the clinical diagnosis with patients. Both datasets were collected from a clinical interview conducted by a virtual agent (SimSensei) [10]. The clinical data was recorded from PTSD patients, who were victims of sexual assault, undergoing a VR exposure therapy. A recurrent neural network was trained on verbal, visual and vocal features to recognize PTSD, according to self-reported PCL-C scores [4]. We then performed decision fusion to fuse three modalities to recognize PTSD in patients with a clinical diagnosis, achieving an F1-score of 0.85. Our analysis demonstrates that machine-based PTSD assessment with self-reported PTSD scores can generalize across different groups and be deployed to assist diagnosis of PTSD. 

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2. Toward A Framework for Formative Assessment of Conceptual Learning in K-12 Computer Science Classrooms

   https://doi.org/10.1145/3408877.3432460  

   Grover, Shuchi (March 2021, 52nd ACM Technical Symposium on Computer Science Education (SIGCSE ’21))

   null (Ed.)

   Unlike summative assessment that is aimed at grading students at the end of a unit or academic term, formative assessment is assess- ment for learning, aimed at monitoring ongoing student learning to provide feedback to both student and teacher, so that learning gaps can be addressed during the learning process. Education research points to formative assessment as a crucial vehicle for improving student learning. Formative assessment in K-12 CS and program- ming classrooms remains a crucial unaddressed need. Given that assessment for learning is closely tied to teacher pedagogical con- tent knowledge, formative assessment literacy needs to also be a topic of CS teacher PD. This position paper addresses the broad need to understand formative assessment and build a framework to understand the what, why, and how of formative assessment of introductory programming in K-12 CS. It shares specific pro- gramming examples to articulate the cycle of formative assessment, diagnostic evaluation, feedback, and action. The design of formative assessment items is informed by CS research on assessment design, albeit related largely to summative assessment and in CS1 contexts, and learning of programming, especially student misconceptions. It describes what teacher formative assessment literacy PD should entail and how to catalyze assessment-focused collaboration among K-12 CS teachers through assessment platforms and repositories. 

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3. Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey

   https://doi.org/10.1016/j.iintel.2023.100031  

   Zhou, Kai; Wang, Zequn; Ni, Yi-Qing; Zhang, Yang; Tang, Jiong (June 2023, Journal of Infrastructure Intelligence and Resilience)
4. Collaborative Learning in Cloud-based Virtual Computer Labs

   https://doi.org/10.1109/FIE.2018.8659018  

   Hu, Xiaolin; Le, Hai; Bourgeois, Anu G.; Pan, Yi (October 2018, 2018 IEEE Frontiers in Education Conference (FIE))

   This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results. 

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5. Subtopic-specific heterogeneity in computer-based learning behaviors

   https://doi.org/10.1186/s40594-024-00519-x  

   Lee, HaeJin; Bosch, Nigel (December 2024, International Journal of STEM Education)

   Abstract BackgroundSelf-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning behaviors across four distinct subtopics in introductory statistics. Further, we explore how the time spent engaging in metacognitive strategies correlated with learning gain in those subtopics. ResultsBy employing two different analytical approaches that combine data-driven learning analytics (i.e., sequential pattern mining in this case), and theory-informed methods (i.e., coherence analysis), we discovered significant variability in the frequency of learning patterns that are potentially associated with SRL-relevant strategies across four subtopics. In a subtopic related to calculations, engagement in coherent quizzes (i.e., a type of metacognitive strategy) was found to be significantly less related to learning gains compared to other subtopics. Additionally, we found that students with different levels of prior knowledge and learning gains demonstrated varying degrees of engagement in learning patterns in an SRL context. ConclusionThe findings imply that the use—and the effectiveness—of learning patterns that are potentially associated with SRL-relevant strategies varies not only across contexts and domains, but even across different subtopics within a single subject. This underscores the importance of personalized, context-aware SRL training interventions in computer-based learning environments, which could significantly enhance learning outcomes by addressing the heterogeneous relationships between SRL activities and outcomes. Further, we suggest theoretical implications of subtopic-specific heterogeneity within the context of various SRL models. Understanding SRL heterogeneity enhances these theories, offering more nuanced insights into learners’ metacognitive strategies across different subtopics. 

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