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1. Self-distancing improves interpersonal perceptions and behavior by decreasing medial prefrontal cortex activity during the provision of criticism

   https://doi.org/10.1093/scan/nsw168  

   Leitner, Jordan B.; Ayduk, Ozlem; Mendoza-Denton, Rodolfo; Magerman, Adam; Amey, Rachel; Kross, Ethan; Forbes, Chad E. (December 2016, Social Cognitive and Affective Neuroscience)

   Previous research suggests that people show increased self-referential processing when they provide criticism to others, and that this self-referential processing can have negative effects on interpersonal perceptions and behavior. The current research hypothesized that adopting a self-distanced perspective (i.e. thinking about a situation from a non-first person point of view), as compared with a typical self-immersed perspective (i.e. thinking about a situation from a first-person point of view), would reduce self-referential processing during the provision of criticism, and in turn improve interpersonal perceptions and behavior. We tested this hypothesis in an interracial context since research suggests that self-referential processing plays a role in damaging interracial relations. White participants prepared for mentorship from a self-immersed or self-distanced perspective. They then conveyed negative and positive evaluations to a Black mentee while electroencephalogram (EEG) was recorded. Source analysis revealed that priming a self-distanced (vs self-immersed) perspective predicted decreased activity in regions linked to self-referential processing (medial prefrontal cortex; MPFC) when providing negative evaluations. This decreased MPFC activity during negative evaluations, in turn, predicted verbal feedback that was perceived to be more positive, warm and helpful. Results suggest that self-distancing can improve interpersonal perceptions and behavior by decreasing self-referential processing during the provision of criticism. 

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2. Using anticipatory diagrammatic self-explanation to support learning and performance in early algebra.

   Nagashima, T.; Bartel, A. N.; Yadav, G.; Tseng, S.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. A. (January 2021, Annual Meeting of the International Society of the Learning Sciences–ISLS 2021)

   de Vries, E.; Ahn, J.; Hod, Y. (Ed.)

   Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

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3. Using anticipatory diagrammatic self-explanation to support learning and performance in early algebra

   https://doi.org/10.22318/icls2021.474  

   Nagashima, T.; Bartel, A. N.; Yadav, G.; Tseng, S.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. (January 2021, Annual Meeting of the International Society of the Learning Sciences–ISLS 2021.)

   de Vries, E.; Ahn, J.; Y. Hod, Y. (Ed.)

   Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

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4. All real projective measurements can be self-tested

   https://doi.org/10.1038/s41567-024-02584-z  

   Chen, Ranyiliu; Mančinska, Laura; Volčič, Jurij (October 2024, Nature Physics)

   Abstract Entangled quantum systems feature non-local correlations that are stronger than could be realized classically. This property makes it possible to perform self-testing, the strongest form of quantum functionality verification, which allows a classical user to deduce the quantum state and measurements used to produce a given set of measurement statistics. While self-testing of quantum states is well understood, self-testing of measurements, especially in high dimensions, remains relatively unexplored. Here we prove that every real projective measurement can be self-tested. Our approach employs the idea that existing self-tests can be extended to verify additional untrusted measurements, known as post-hoc self-testing. We formalize the method of post-hoc self-testing and establish the condition under which it can be applied. Using this condition, we construct self-tests for all real projective measurements. We build on this result to develop an iterative self-testing technique that provides a clear methodology for constructing new self-tests from pre-existing ones. 

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5. Why do CS1 Students Think They're Bad at Programming?: Investigating Self-Efficacy and Self-Assessments at Three Universities

   https://doi.org/10.1145/3372782.3406273  

   Gorson, Jamie; O'Rourke, Eleanor (August 2020, Proceedings of the 2020 ACM Conference on International Computing Education Research)

   null (Ed.)

   Undergraduate computer science (CS) programs often suffer from high dropout rates. Recent research suggests that self-efficacy -- an individual's belief in their ability to complete a task -- can influence whether students decide to persist in CS. Studies show that students' self-assessments affect their self-efficacy in many domains, and in CS, researchers have found that students frequently assess their programming ability based on their expectations about the programming process. However, we know little about the specific programming experiences that prompt the negative self-assessments that lead to lower self-efficacy. In this paper, we present findings from a survey study with 214 CS1 students from three universities. We used vignette-style questions to describe thirteen programming moments which may prompt negative self-assessments, such as getting syntax errors and spending time planning. We found that many students across all three universities reported that they negatively self-assess at each of the thirteen moments, despite the differences in curriculum and population. Furthermore, those who report more frequent negative self-assessments tend to have lower self-efficacy. Finally, our findings suggest that students' perceptions of professional programming practice may influence their expectations and negative self-assessments. By reducing the frequency that students self-assess negatively while programming, we may be able to improve self-efficacy and decrease dropout rates in CS. 

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