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This article describes a cycle of teacher collaborative inquiry called the Student Experience Improvement Cycle (SEIC). The SEIC is a novel form of assessment: It focuses on supporting teachers in using evidence of the quality of student experience formatively to make the classroom more equitable. The SEIC begins by setting a goal for improvement in one of three aspects of student experience: coherence, relevance, and contribution. Then teachers review, adapt, and test research-based strategies for improving the quality of student experience overall and for students from systemically marginalized groups and communities. The article presents examples of improvement goals teachers set and the strat- egies they tried as part of one inquiry cycle. It also provides examples of survey items used to elicit student experience.
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REU: A Balancing Act
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We develop a hybrid control approach for robot learning based on combining learned predictive models with experience-based state-action policy mappings to improve the learning capabilities of robotic systems. Predictive models provide an understanding of the task and the physics (which improves sample-efficiency), while experience-based pol-icy mappings are treated as “muscle memory” that encode favorable actions as experiences that override planned actions. Hybrid control tools are used to create an algorithmic approach for combining learned predictive models with experience-based learning. Hybrid learning is presented as a method for efficiently learning motor skills by systematically combining and improving the performance of predictive models and experience-based policies. A deterministic variation of hybrid learning is derived and extended into a stochastic implementation that relaxes some of the key assumptions in the original derivation. Each variation is tested on experience-based learning methods (where the robot interacts with the environment to gain experience) as well as imitation learning methods(where experience is provided through demonstrations and tested in the environment). The results show that our method is capable of improving the performance and sample-efficiency of learning motor skills in a variety of experimental domains.more » « less
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In this paper we present an evaluation and lessons learned from a joint Research Experience for Undergraduates (REU) and Research Experience for Teachers (RET) program focused on energy and sustainability topics within a Materials Science and Engineering program at a public university. This program brought eleven undergraduate science and engineering students with diverse educational and institutional backgrounds and four local middle and high school teachers on campus for an 8-week research experience working in established lab groups at the university. Using the Qualtrics online survey software, we conducted pre-experience and post-experience surveys of the participants to assess the effects of participating in this summer research program. At the beginning of the summer, all participants provided their definition of technical research and described what they hoped to get out of their research experience, and the undergraduate students described their future career and educational plans. At the conclusion of the summer, a post-experience survey presented participants’ with their answers from the beginning of the summer and asked them to reflect on how their understanding of research and future plans involving research changed over the course of the summer experience. Many participants evolved a new understanding of research as a result of participating in the summer experience. In particular, they better recognized the collaborative nature of research and the challenges that can arise as part of the process of doing research. Participants acquired both technical and professional skills that they found useful, such as learning new programming languages, becoming proficient at using new pieces of equipment, reviewing technical literature, and improving presentation and communication skills. Undergraduates benefited from developing new relationships with their peers, while the teacher participants benefited from developing relationships with faculty and staff at the university. While most of the participants felt that they were better prepared for future studies or employment, they did not feel like the summer research experience had a significant impact on their future career or degree plans. Finally, while almost all of the participants described their summer research experience as positive, areas for improvement included better planning and access to mentors, as well as more structured activities for the teachers to adapt their research activities for the classroom.more » « less
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null (Ed.)People can relatively easily report summary properties for ensembles of objects, suggesting that this information can enrich visual experience and increase the efficiency of perceptual processing. Here, we ask whether the ability to judge diversity within object arrays improves with experience. We surmised that ensemble judgments would be more accurate for commonly experienced objects, and perhaps even more for objects of expertise like faces. We also expected improvements in ensemble processing with practice with a novel category, and perhaps even more with repeated experience with specific exemplars. We compared the effect of experience on diversity judgments for arrays of objects, with participants being tested with either a small number of repeated exemplars or with a large number of exemplars from the same object category. To explore the role of more prolonged experience, we tested participants with completely novel objects (random-blobs), with objects familiar at the category level (cars), and with objects with which observers are experts at subordinate-level recognition (faces). For objects that are novel, participants showed evidence of improved ability to distribute attention. In contrast, for object categories with long-term experience, i.e., faces and cars, performance improved during the experiment but not necessarily due to improved ensemble processing. Practice with specific exemplars did not result in better diversity judgments for all object categories. Considered together, these results suggest that ensemble processing improves with experience. However, the role of experience is rapid, does not rely on exemplar-level knowledge and may not benefit from subordinate-level expertise.more » « less
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Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states.more » « less
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