Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.cervantes
Bianca Cervantes, G. Passante, Giaco Corsiglia, S. Pollock
In this paper, we analyze video recordings of students working on tutorials in Zoom breakout rooms in an upper-division quantum mechanics course. We investigate group behaviors in this virtual environment, including the effects of instructor presence. To this end, we modify the Color Frames coding scheme introduced by Scherr to suit the virtual nature of the interactions. By broadening the frames and allowing for multiple overlapping frames, we are able to describe some group behaviors not otherwise captured. For example, in some instances, students take on an authoritative role in the group, and in other instances, groups engage in overtly casual behavior while nonetheless having on-topic discussions. We observe significant variation in how much time each group spends in each frame, but find that all groups spend some time in all frames. Instructors can be present without dominating or eliminating discussion between students, and their presence need not significantly impact the time students spent in an “informal/friendly” frame. However, instructor presence significantly reduces time spent working individually. Our findings will support additional research into the dynamics of student discussions during tutorials and aid ongoing development of online tutorials that can, e.g., be assigned for use outside of class.
{"title":"Modified color frames for analyzing group interactions during an online quantum tutorial","authors":"Bianca Cervantes, G. Passante, Giaco Corsiglia, S. Pollock","doi":"10.1119/perc.2022.pr.cervantes","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.cervantes","url":null,"abstract":"In this paper, we analyze video recordings of students working on tutorials in Zoom breakout rooms in an upper-division quantum mechanics course. We investigate group behaviors in this virtual environment, including the effects of instructor presence. To this end, we modify the Color Frames coding scheme introduced by Scherr to suit the virtual nature of the interactions. By broadening the frames and allowing for multiple overlapping frames, we are able to describe some group behaviors not otherwise captured. For example, in some instances, students take on an authoritative role in the group, and in other instances, groups engage in overtly casual behavior while nonetheless having on-topic discussions. We observe significant variation in how much time each group spends in each frame, but find that all groups spend some time in all frames. Instructors can be present without dominating or eliminating discussion between students, and their presence need not significantly impact the time students spent in an “informal/friendly” frame. However, instructor presence significantly reduces time spent working individually. Our findings will support additional research into the dynamics of student discussions during tutorials and aid ongoing development of online tutorials that can, e.g., be assigned for use outside of class.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130603118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.sowles
Em Sowles, Drew J. Rosen, M. R. Stetzer
Previous research has shown that students who demonstrate sufficient skills and conceptual understanding to reason productively may perform inconsistently on analogous questions. Such inconsistencies can be explained via dual process theories of reasoning (DPToR). To gain insight into students’ reasoning trajectories, we developed an exploratory sequence of DPToR-aligned metacognitive prompts and administered the sequence immediately after students answered a physics question containing salient distracting features. The metacognitive prompts asked students to: describe their first ideas, reflect on any doubts they had with respect to those ideas, compare their first ideas with their submitted responses, and characterize their reasoning approaches. In this paper, we describe how we use student responses to these prompts along with timing data to investigate students’ reasoning trajectories. Students who self-reported that they revised their thinking before submitting an answer spent significantly longer answering the question than those who did not. In addition, students who retained a correct provisional response reported fewer doubts and the use of a process-first approach, whereas students who retained an incorrect provisional response reported more doubts and the use of an answer-first approach. We anticipate that a more detailed understanding of students’ reasoning trajectories arising from investigations like the one reported here will be an important step in the development of effective, research-based instructional materials that better support student reasoning in physics.
{"title":"Using metacognitive prompts to explore student reasoning trajectories","authors":"Em Sowles, Drew J. Rosen, M. R. Stetzer","doi":"10.1119/perc.2022.pr.sowles","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.sowles","url":null,"abstract":"Previous research has shown that students who demonstrate sufficient skills and conceptual understanding to reason productively may perform inconsistently on analogous questions. Such inconsistencies can be explained via dual process theories of reasoning (DPToR). To gain insight into students’ reasoning trajectories, we developed an exploratory sequence of DPToR-aligned metacognitive prompts and administered the sequence immediately after students answered a physics question containing salient distracting features. The metacognitive prompts asked students to: describe their first ideas, reflect on any doubts they had with respect to those ideas, compare their first ideas with their submitted responses, and characterize their reasoning approaches. In this paper, we describe how we use student responses to these prompts along with timing data to investigate students’ reasoning trajectories. Students who self-reported that they revised their thinking before submitting an answer spent significantly longer answering the question than those who did not. In addition, students who retained a correct provisional response reported fewer doubts and the use of a process-first approach, whereas students who retained an incorrect provisional response reported more doubts and the use of an answer-first approach. We anticipate that a more detailed understanding of students’ reasoning trajectories arising from investigations like the one reported here will be an important step in the development of effective, research-based instructional materials that better support student reasoning in physics.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.malespina
Alysa Malespina, C. Schunn, Chandralekha Singh
Intelligence mindset has been studied extensively in education research, but domain-specific intelligence mindset research is relatively new in the physics context. Additionally, recent mindset research has uncovered separable factors within the intelligence mindset construct. In this study, we test a model involving four factors (My Ability, My Growth, Others’ Ability, and Others’ Growth) to pre and post survey data from Physics 1 classes. In particular, we explore how these mindset factors change over time as well as their ability to predict course grade. We find that students are less likely to endorse a growth mindset for themselves and others at the end of their first calculus-based introductory physics course than at the beginning. We also find that decrease in mindset measures are more drastic for female students than male students. Finally, we find that the best predictor of course grades is the My Ability component of the mindset construct, which has implications both for creating equitable and inclusive learning environment and determining how educators implement mindset interventions.
{"title":"To whom do students believe a growth mindset applies?","authors":"Alysa Malespina, C. Schunn, Chandralekha Singh","doi":"10.1119/perc.2022.pr.malespina","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.malespina","url":null,"abstract":"Intelligence mindset has been studied extensively in education research, but domain-specific intelligence mindset research is relatively new in the physics context. Additionally, recent mindset research has uncovered separable factors within the intelligence mindset construct. In this study, we test a model involving four factors (My Ability, My Growth, Others’ Ability, and Others’ Growth) to pre and post survey data from Physics 1 classes. In particular, we explore how these mindset factors change over time as well as their ability to predict course grade. We find that students are less likely to endorse a growth mindset for themselves and others at the end of their first calculus-based introductory physics course than at the beginning. We also find that decrease in mindset measures are more drastic for female students than male students. Finally, we find that the best predictor of course grades is the My Ability component of the mindset construct, which has implications both for creating equitable and inclusive learning environment and determining how educators implement mindset interventions.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134020595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.garcia
Tyler Garcia, Caitlin Solis, Caleb L. Linville, B. Bridges, Wyatt Jones, J. Herington, Scott Tanona, James T. Laverty
Researchers across all scientific disciplines routinely face ethical decisions in their work, from addressing conflicts of interest to deciding whether and how to make data available for reproducibility. To help strengthen their ethical reasoning skills, they are encouraged to take online training programs like the CITI program. Ethics training is insufficient for improving ethical behavior. Better understanding of how scientists make decisions and reason about ethics is needed. To develop that understanding, we need expanded, asset-based measures of ethical reasoning that can be applied to open-ended responses and discussions. As part of a year-long intervention on a group of fifteen scientists’ value-based reasoning, we conducted pre/post interviews that included open-ended questions about ethical scenarios. For this paper, we explore an application of three theories of ethical and stakeholder reasoning to those answers, and determine that we can use them to examine quality, principles, and subjects of their reasoning in open responses.
{"title":"Examining Physicists' Ethical Reasoning: A New Methodology","authors":"Tyler Garcia, Caitlin Solis, Caleb L. Linville, B. Bridges, Wyatt Jones, J. Herington, Scott Tanona, James T. Laverty","doi":"10.1119/perc.2022.pr.garcia","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.garcia","url":null,"abstract":"Researchers across all scientific disciplines routinely face ethical decisions in their work, from addressing conflicts of interest to deciding whether and how to make data available for reproducibility. To help strengthen their ethical reasoning skills, they are encouraged to take online training programs like the CITI program. Ethics training is insufficient for improving ethical behavior. Better understanding of how scientists make decisions and reason about ethics is needed. To develop that understanding, we need expanded, asset-based measures of ethical reasoning that can be applied to open-ended responses and discussions. As part of a year-long intervention on a group of fifteen scientists’ value-based reasoning, we conducted pre/post interviews that included open-ended questions about ethical scenarios. For this paper, we explore an application of three theories of ethical and stakeholder reasoning to those answers, and determine that we can use them to examine quality, principles, and subjects of their reasoning in open responses.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.laverty
James T. Laverty, Amogh Sirnoorkar, Amali Priyanka Jambuge, Katherine Rainey, Joshua Weaver, Alexander Adamson, Bethany R. Wilcox
Research based assessments have a productive and storied history in PER. While useful for conducting research on student learning, their utility is limited for instructors interested in improving their own courses. We have developed a new assessment design process that leverages three-dimensional learning, evidence-centered design, and self-regulated learning to deliver actionable feedback to instructors about supporting their students’ learning. We are using this approach to design the Thermal and Statistical Physics Assessment (TaSPA), which also allows instructors to choose learning goals that align with their teaching. Perhaps more importantly, this system will be completely automated when it is completed, making the assessment scalable with minimal bur-den on instructors and researchers. This work represents an advancement in how we assess physics learning at a large scale and how the PER community can better support physics instructors and students.
{"title":"A New Paradigm for Research-Based Assessment Development","authors":"James T. Laverty, Amogh Sirnoorkar, Amali Priyanka Jambuge, Katherine Rainey, Joshua Weaver, Alexander Adamson, Bethany R. Wilcox","doi":"10.1119/perc.2022.pr.laverty","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.laverty","url":null,"abstract":"Research based assessments have a productive and storied history in PER. While useful for conducting research on student learning, their utility is limited for instructors interested in improving their own courses. We have developed a new assessment design process that leverages three-dimensional learning, evidence-centered design, and self-regulated learning to deliver actionable feedback to instructors about supporting their students’ learning. We are using this approach to design the Thermal and Statistical Physics Assessment (TaSPA), which also allows instructors to choose learning goals that align with their teaching. Perhaps more importantly, this system will be completely automated when it is completed, making the assessment scalable with minimal bur-den on instructors and researchers. This work represents an advancement in how we assess physics learning at a large scale and how the PER community can better support physics instructors and students.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132251909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.olsho
Alexis Olsho, Charlotte Zimmerman, A. Boudreaux, Trevor I. Smith, Philip Eaton, Suzanne White Brahmia
{"title":"Characterizing covariational reasoning in physics modeling","authors":"Alexis Olsho, Charlotte Zimmerman, A. Boudreaux, Trevor I. Smith, Philip Eaton, Suzanne White Brahmia","doi":"10.1119/perc.2022.pr.olsho","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.olsho","url":null,"abstract":"","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132588440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.indukuri
Sadhana Indukuri, Gina M. Quan
Learning assistants are undergraduate peer educators that help facilitate learning in a university classroom environment. Jardine (2019) found that learning assistant feedback to faculty roughly fell into three categories: course logistics, student behavior, and student understanding. We built from this previous work by further characterizing the feedback given to faculty by learning assistants and found the following categories: student experience, classroom content, classroom structure, accessibility, empathy, and broad feedback. Using interview data with learning assistants and faculty working with learning assistants, we created a preliminary framework for the types of feedback and examples by learning assistants. This framework may be useful for both learning assistants and faculty members as they provide and elicit feedback.
{"title":"Characterizing the feedback that learning assistants give to faculty","authors":"Sadhana Indukuri, Gina M. Quan","doi":"10.1119/perc.2022.pr.indukuri","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.indukuri","url":null,"abstract":"Learning assistants are undergraduate peer educators that help facilitate learning in a university classroom environment. Jardine (2019) found that learning assistant feedback to faculty roughly fell into three categories: course logistics, student behavior, and student understanding. We built from this previous work by further characterizing the feedback given to faculty by learning assistants and found the following categories: student experience, classroom content, classroom structure, accessibility, empathy, and broad feedback. Using interview data with learning assistants and faculty working with learning assistants, we created a preliminary framework for the types of feedback and examples by learning assistants. This framework may be useful for both learning assistants and faculty members as they provide and elicit feedback.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128614692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.burkholder
E. Burkholder, R. Strain
Instructors new to active learning classrooms frequently ask how they should best structure groups in the classroom to ensure optimum learning. Groups within classrooms are complex social systems with many variables, so unfortunately there is no easy answer. Existing group-formation algorithms do not specify how groups should be structured; they only provide a way for instructors to specify their own algorithm based on factors like GPA or Gender. There are many dimensions of student thinking, motivation, and experience that may be relevant, but here we focus on one measurement that is relatively easy to measure: prior preparation. There have been some studies investigating the role of preparation in cooperative grouping, but each study seems to come to a different conclusion. Here we provide some evidence as to why that might be the case by investigating outcomes based on different measures of preparation and investigating the effects of cooperative grouping for different groups of students. We find that groups that are heterogeneous with respect to physics preparation tend to perform better. Additionally, we find that this effect is particularly pronounced for women and under-represented students, but not for white men. This would seem to suggest that a reason for disagreements in the literature could be sensitive to how preparation is measured as well as the demographics of the study population.
{"title":"Investigating the role of student preparation on cooperative grouping in an active learning classroom","authors":"E. Burkholder, R. Strain","doi":"10.1119/perc.2022.pr.burkholder","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.burkholder","url":null,"abstract":"Instructors new to active learning classrooms frequently ask how they should best structure groups in the classroom to ensure optimum learning. Groups within classrooms are complex social systems with many variables, so unfortunately there is no easy answer. Existing group-formation algorithms do not specify how groups should be structured; they only provide a way for instructors to specify their own algorithm based on factors like GPA or Gender. There are many dimensions of student thinking, motivation, and experience that may be relevant, but here we focus on one measurement that is relatively easy to measure: prior preparation. There have been some studies investigating the role of preparation in cooperative grouping, but each study seems to come to a different conclusion. Here we provide some evidence as to why that might be the case by investigating outcomes based on different measures of preparation and investigating the effects of cooperative grouping for different groups of students. We find that groups that are heterogeneous with respect to physics preparation tend to perform better. Additionally, we find that this effect is particularly pronounced for women and under-represented students, but not for white men. This would seem to suggest that a reason for disagreements in the literature could be sensitive to how preparation is measured as well as the demographics of the study population.","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1119/perc.2022.pr.hull
M. M. Hull, H. Uematsu, Andrew Elby
, we aim to equip our pre-service teachers (PSTs) with “curricular knowledge” about instructional materials, knowledge about the “theory” underlying the curriculum and the reasons behind particular choices such as conceptual flow, use of individual vs. group work, and so on. This study presents two case studies grounded in of our attempts to teach nuanced curricular knowledge about differences between two fairly similar sets of curricular modules. Our analysis centers on two Masters of Science (MS) students who had various experiences involving Open Source Tutorials (OSTs), guided worksheets developed by the University of Maryland. A theoretically nuanced (and hence deep) component of curricular knowledge regarding OSTs is that they are based upon the “Knowledge in Pieces” (in contrast to a “Misconceptions” or unspecified) model of student ideas. The Pieces model maintains that student ideas are not always robustly intact and inherently incorrect cognitive structures, but rather, that student ideas are often temporary coherences of thought assembled from finer-grained pieces of knowledge that can productively be drawn upon and refined in instruction. In our courses, PSTs read research literature about OSTs, conduct mock lessons using existing OSTs, improve existing OSTs, design and teach their own OSTs to real students, and reflect upon the process to further improve the curriculum. Our analysis focuses upon case studies of Brock and Saki, MS students at our institutions. In addition to one-on-one interviews with these PSTs, we will draw upon data from in-class observations and written coursework to discuss how PSTs progressed in their understanding of nuanced curricular knowledge about OSTs and
{"title":"A progression of pre-service teachers towards deep curricular knowledge (the Pieces model in Open Source Tutorials)","authors":"M. M. Hull, H. Uematsu, Andrew Elby","doi":"10.1119/perc.2022.pr.hull","DOIUrl":"https://doi.org/10.1119/perc.2022.pr.hull","url":null,"abstract":", we aim to equip our pre-service teachers (PSTs) with “curricular knowledge” about instructional materials, knowledge about the “theory” underlying the curriculum and the reasons behind particular choices such as conceptual flow, use of individual vs. group work, and so on. This study presents two case studies grounded in of our attempts to teach nuanced curricular knowledge about differences between two fairly similar sets of curricular modules. Our analysis centers on two Masters of Science (MS) students who had various experiences involving Open Source Tutorials (OSTs), guided worksheets developed by the University of Maryland. A theoretically nuanced (and hence deep) component of curricular knowledge regarding OSTs is that they are based upon the “Knowledge in Pieces” (in contrast to a “Misconceptions” or unspecified) model of student ideas. The Pieces model maintains that student ideas are not always robustly intact and inherently incorrect cognitive structures, but rather, that student ideas are often temporary coherences of thought assembled from finer-grained pieces of knowledge that can productively be drawn upon and refined in instruction. In our courses, PSTs read research literature about OSTs, conduct mock lessons using existing OSTs, improve existing OSTs, design and teach their own OSTs to real students, and reflect upon the process to further improve the curriculum. Our analysis focuses upon case studies of Brock and Saki, MS students at our institutions. In addition to one-on-one interviews with these PSTs, we will draw upon data from in-class observations and written coursework to discuss how PSTs progressed in their understanding of nuanced curricular knowledge about OSTs and","PeriodicalId":253382,"journal":{"name":"2022 Physics Education Research Conference Proceedings","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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