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1. Disconnected Development? The Importance of Specific Human Capital in the Transition From Student Teaching to the Classroom

   https://doi.org/10.3102/01623737211025306  

   Krieg, John M.; Goldhaber, Dan; Theobald, Roddy (March 2022, Educational Evaluation and Policy Analysis)

   We use a novel database of student teaching placements in Washington State to investigate teachers’ transitions from student teaching classrooms to first job classrooms and the implications for student achievement. We find first-year teachers are more effective when they teach in the same or an adjacent grade, in the same school type, or in a classroom with student demographics similar to their student teaching classroom. We document that only 27% of first-year teachers are teaching the same grade they student taught, and that first-year teachers tend to begin their careers in higher poverty classrooms than their student teaching placements. This suggests that better aligning student teacher placements with first-year teacher hiring could be a policy lever for improving early-career teacher effectiveness. 

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2. A Study of Class Meetings in VR: Student Experiences of Attending Lectures and of Giving a Project Presentation

   https://doi.org/10.3389/frvir.2021.648619  

   Yoshimura, Andrew; Borst, Christoph W. (May 2021, Frontiers in Virtual Reality)

   null (Ed.)

   We study student experiences of social VR for remote instruction, with students attending class from home. The study evaluates student experiences when: (1) viewing remote lectures with VR headsets, (2) viewing with desktop displays, (3) presenting with VR headsets, and (4) reflecting on several weeks of VR-based class attendance. Students rated factors such as presence, social presence, simulator sickness, communication methods, avatar and application features, and tradeoffs with other remote approaches. Headset-based viewing and presenting produced higher presence than desktop viewing, but had less-clear impact on overall experience and on most social presence measures. We observed higher attentional allocation scores for headset-based presenting than for both viewing methods. For headset VR, there were strong negative correlations between simulator sickness (primarily reported as general discomfort) and ratings of co-presence, overall experience, and some other factors. This suggests that comfortable users experienced substantial benefits of headset viewing and presenting, but others did not. Based on the type of virtual environment, student ratings, and comments, reported discomfort appears related to physical ergonomic factors or technical problems. Desktop VR appears to be a good alternative for uncomfortable students, and students report that they prefer a mix of headset and desktop viewing. We additionally provide insight from students and a teacher about possible improvements for VR class technology, and we summarize student opinions comparing viewing and presenting in VR to other remote class technologies. 

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3. Optimizing preservice teacher learning with digital teaching simulations: Comparing teachers’ self-assessment and scoring from trained raters

   Kinsey, Devon; Mikeksa, JN (March 2024, SITE)

   In this study, we explored how the use of an online digital teaching simulation impacts preservice teacher (PST) learning. We describe the overall implementation of an online practice suite of digital teaching simulations into five teacher education courses. Specifically, we detail the avatar-based simulation activity in which PSTs facilitate a discussion focused on argumentation with five student avatars controlled by a trained actor in the Mursion simulated classroom environment. The present study examines PSTs’ self-assessment of their performance facilitating a discussion in this simulated classroom compared to rubric-level scores assigned by trained raters. We share findings from our analysis of survey data regarding 47 PSTs’ perceptions about their experience with the simulated classroom, specifically how successful they thought they were across five dimensions of facilitating argumentation-focused discussions. Findings suggest that the PSTs’ self-assessment tended to align with the scores assigned from trained raters. However, when the PSTs’ self-assessment did not align with the raters’ scoring, PSTs tended to perceive their discussion facilitation more positively than the raters’ scores indicated, which suggests the need for additional support to help PSTs identify and attend to specific areas for improvement. Findings provide support for the use of both self-assessment and scoring from trained raters to optimize PST learning with digital teaching simulations. 

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4. Does Knowledge Distillation Really Work?

   Stanton, Samuel; Izmailov, Pavel; Kirichenko, Polina; Alemi, Alexander A.; Wilson, Andrew G. (January 2021, Advances in neural information processing systems)

   Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks. We show that while knowledge distillation can improve student generalization, it does not typically work as it is commonly understood: there often remains a surprisingly large discrepancy between the predictive distributions of the teacher and the student, even in cases when the student has the capacity to perfectly match the teacher. We identify difficulties in optimization as a key reason for why the student is unable to match the teacher. We also show how the details of the dataset used for distillation play a role in how closely the student matches the teacher --- and that more closely matching the teacher paradoxically does not always lead to better student generalization. 

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5. High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling

   Yin, Yuxuan; Wang, Yu; Li, Peng (July 2024, International Conference on Machine Learning (ICML) - Proceedings of Machine Learning Research)

   We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization TSBO, integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. TSBO incorporates a teacher model, an unlabeled data sampler, and a student model. The student is trained on unlabeled data locations generated by the sampler, with pseudo labels predicted by the teacher. The interplay between these three components implements a unique selective regularization to the teacher in the form of student feedback. This scheme enables the teacher to predict high-quality pseudo labels, enhancing the generalization of the GP surrogate model in the search space. To fully exploit TSBO, we propose two optimized unlabeled data samplers to construct effective student feedback that well aligns with the objective of Bayesian optimization. Furthermore, we quantify and leverage the uncertainty of the teacher-student model for the provision of reliable feedback to the teacher in the presence of risky pseudo-label predictions. TSBO demonstrates significantly improved sample-efficiency in several global optimization tasks under tight labeled data budgets. The implementation is available at https://github.com/reminiscenty/TSBO-Official. 

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