Eleanor O'Rourke, Yvonne Chen, K. Haimovitz, C. Dweck, Zoran Popovic
Video games have great potential to motivate students in environments for learning at scale. However, little is known about how to design in-game incentive structures to maximize learning and engagement. In this work, we expand on our previous research that introduced a new "brain points" incentive structure designed to promote the growth mindset, or the belief that intelligence is malleable. We replicate our original findings, showing that brain points increase student persistence and use of strategy. We also explore how brain points impact students from different demographic groups. We find that brain points are less engaging for low-income students, and discuss methods of improving our design in the future.
{"title":"Demographic Differences in a Growth Mindset Incentive Structure for Educational Games","authors":"Eleanor O'Rourke, Yvonne Chen, K. Haimovitz, C. Dweck, Zoran Popovic","doi":"10.1145/2724660.2728686","DOIUrl":"https://doi.org/10.1145/2724660.2728686","url":null,"abstract":"Video games have great potential to motivate students in environments for learning at scale. However, little is known about how to design in-game incentive structures to maximize learning and engagement. In this work, we expand on our previous research that introduced a new \"brain points\" incentive structure designed to promote the growth mindset, or the belief that intelligence is malleable. We replicate our original findings, showing that brain points increase student persistence and use of strategy. We also explore how brain points impact students from different demographic groups. We find that brain points are less engaging for low-income students, and discuss methods of improving our design in the future.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89498449","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}
Meredith M. Thompson, Eric J. Braude, Christopher D. Canfield, Jay Halfond, Aparaita Sengupta
The assessment of learning in large online courses such as Massive Online Open Courses, or MOOCs, requires tools that are valid, reliable, and can be automatically administered and scored. We have developed and assessed a tool called Knowledge Assembly for Learning and Assessment, or KNOWLA. The tool measures a student's knowledge in a particular subject by having her assemble a set of scrambled phrases into a logical order. Initial testing indicates that KNOWLA is reliable, and can be used to measure learning gains. KNOWLA also shows promise as a learning tool.
大规模在线开放课程(Massive online Open courses，简称MOOCs)等大型在线课程的学习评估需要有效、可靠、可以自动管理和评分的工具。我们开发并评估了一种名为“学习与评估知识汇编”(KNOWLA)的工具。该工具通过让学生将一组杂乱的短语按逻辑顺序组合起来，来衡量学生对某一特定学科的知识。初步测试表明KNOWLA是可靠的，可以用来衡量学习收益。KNOWLA也显示出作为学习工具的潜力。
{"title":"Assessment of KNOWLA: Knowledge Assembly for Learning and Assessment","authors":"Meredith M. Thompson, Eric J. Braude, Christopher D. Canfield, Jay Halfond, Aparaita Sengupta","doi":"10.1145/2724660.2728673","DOIUrl":"https://doi.org/10.1145/2724660.2728673","url":null,"abstract":"The assessment of learning in large online courses such as Massive Online Open Courses, or MOOCs, requires tools that are valid, reliable, and can be automatically administered and scored. We have developed and assessed a tool called Knowledge Assembly for Learning and Assessment, or KNOWLA. The tool measures a student's knowledge in a particular subject by having her assemble a set of scrambled phrases into a logical order. Initial testing indicates that KNOWLA is reliable, and can be used to measure learning gains. KNOWLA also shows promise as a learning tool.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78669497","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}
In this document, we describe the third-party authentication system we added to Open edX. With this system, Open edX administrators can allow their users to sign in with a large array of external authentication providers. We outline the features and advantages of the system, describe how it can be extended and customized, and highlight reusable design principles that can be applied to other authentication implementations in online education.
{"title":"Adding Third-Party Authentication to Open edX: A Case Study","authors":"John Cox, P. Simakov","doi":"10.1145/2724660.2728675","DOIUrl":"https://doi.org/10.1145/2724660.2728675","url":null,"abstract":"In this document, we describe the third-party authentication system we added to Open edX. With this system, Open edX administrators can allow their users to sign in with a large array of external authentication providers. We outline the features and advantages of the system, describe how it can be extended and customized, and highlight reusable design principles that can be applied to other authentication implementations in online education.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77669023","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}
Seungwon Yang, C. Domeniconi, Matt Revelle, Mack Sweeney, Ben U. Gelman, Chris Beckley, A. Johri
We analyzed informal learning in Scratch Online -- an online community with over 4.3 million users and 6.7 million user-generated content. Users develop projects, which are graphical interfaces involving manipulation of programming blocks. We investigated two fundamental questions: how can we model informal learning, and what patterns of informal learning emerge. We proceeded in two phases. First, we modeled learning as a trajectory of cumulative programming block usage by long-term users who created at least 50 projects. Second, we applied K-means++ clustering to uncover patterns of learning and corresponding subpopulations. We found four groups of users manifesting four different patterns of learning, ranging from the smallest to the largest improvement. At one end of the spectrum, users learned more and in a faster manner. At the opposite end, users did not show much learning, even after creating dozens of projects. The modeling and clustering of trajectory patterns that enabled us to quantitatively analyze informal learning may be applicable to other similar communities. The results can also support administrators of online communities in implementing customized interventions for specific subpopulations.
{"title":"Uncovering Trajectories of Informal Learning in Large Online Communities of Creators","authors":"Seungwon Yang, C. Domeniconi, Matt Revelle, Mack Sweeney, Ben U. Gelman, Chris Beckley, A. Johri","doi":"10.1145/2724660.2724674","DOIUrl":"https://doi.org/10.1145/2724660.2724674","url":null,"abstract":"We analyzed informal learning in Scratch Online -- an online community with over 4.3 million users and 6.7 million user-generated content. Users develop projects, which are graphical interfaces involving manipulation of programming blocks. We investigated two fundamental questions: how can we model informal learning, and what patterns of informal learning emerge. We proceeded in two phases. First, we modeled learning as a trajectory of cumulative programming block usage by long-term users who created at least 50 projects. Second, we applied K-means++ clustering to uncover patterns of learning and corresponding subpopulations. We found four groups of users manifesting four different patterns of learning, ranging from the smallest to the largest improvement. At one end of the spectrum, users learned more and in a faster manner. At the opposite end, users did not show much learning, even after creating dozens of projects. The modeling and clustering of trajectory patterns that enabled us to quantitatively analyze informal learning may be applicable to other similar communities. The results can also support administrators of online communities in implementing customized interventions for specific subpopulations.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"129 11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79598506","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}
With the increasing demand on knowledge sharing and problem solving, there is a growing participation on online Question & Answer (Q&A) forums in the recent past. We classify the online community participation on Stack Exchange into two different genres, one is technical and another is non-technical. Though several studies have measured community activity, studies that compare activity across forums within different topic areas are limited. In this work we examine the effect of incentives on contributions by exploring the differences between technical and non-technical communities in terms of user's participation. Given the increased attention on discussion forums as part of online learning, especially MOOCs, we believe that our findings can assist with providing better support for learners across different content areas.
{"title":"Does Online Q&A Activity Vary Based on Topic: A Comparison of Technical and Non-technical Stack Exchange Forums","authors":"Saif Ahmed, Seungwon Yang, A. Johri","doi":"10.1145/2724660.2728701","DOIUrl":"https://doi.org/10.1145/2724660.2728701","url":null,"abstract":"With the increasing demand on knowledge sharing and problem solving, there is a growing participation on online Question & Answer (Q&A) forums in the recent past. We classify the online community participation on Stack Exchange into two different genres, one is technical and another is non-technical. Though several studies have measured community activity, studies that compare activity across forums within different topic areas are limited. In this work we examine the effect of incentives on contributions by exploring the differences between technical and non-technical communities in terms of user's participation. Given the increased attention on discussion forums as part of online learning, especially MOOCs, we believe that our findings can assist with providing better support for learners across different content areas.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83865526","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}
Most of the current research on improving learning outcomes focuses on a small subset of variables of an immensely multi-dimensional space of the learning ecosystem. Most digital learning tools primarily focus on individual students, other research focuses only on teacher professional development, or only on curriculum improvement. In this talk I will describe our efforts on how to discover optimal parameters of the entire ecosystem that considers student factors (engagement and mastery), classroom factors (blended learning variations and group learning variations), curriculum factors (multidimensional variation of existing curricula), and teacher factors (in-class tools that mitigate weaknesses, and promote teacher development). I will describe our work on algorithms to discover optimal learning pathways in this high-dimensional space. I will conclude with the outcomes of deploying a portion of our platform on algebra challenges conducted on two US states and the country of Norway. Zoran Popovic is a Director of Center for Game Science at University of Washington and founder of Enlearn. Trained as a computer scientist his research focus is on creating interactive engaging environments for learning and scientific discovery. His laboratory created Foldit, a biochemistry game that produced three Nature publications in just two years, an award-winning math learning games played by over five million learners worldwide. He is currently focusing on engaging methods that can rapidly develop experts in arbitrary domains with particular focus on revolutionizing K-12 math education. His Algebra Challenges conducted in Washington, Minnesota, and Norway, have shown that 96% of children even in elementary school can learn key algebra concepts in 1.5 hours. He has recently founded Enlearn to apply his work on generative adaptation to any curricula towards the goal of achieving full mastery by 95% of students. His contributions to the field of interactive computer graphics have been recognized by a number of awards including the NSF CAREER Award, Alfred P. Sloan Fellowship and ACM SIGGRAPH Significant New Researcher Award.
目前大多数关于改善学习成果的研究都集中在学习生态系统中巨大多维空间的一小部分变量上。大多数数字学习工具主要关注单个学生，其他研究只关注教师的专业发展，或者只关注课程改进。在这次演讲中，我将描述我们如何发现整个生态系统的最佳参数的努力，考虑到学生因素(参与和掌握)，课堂因素(混合学习变化和小组学习变化)，课程因素(现有课程的多维变化)和教师因素(减轻弱点的课堂工具，促进教师发展)。我将描述我们在高维空间中发现最佳学习路径的算法方面的工作。我将以在美国两个州和挪威进行的代数挑战中部署我们平台的一部分的结果作为结束。Zoran Popovic是华盛顿大学游戏科学中心主任，也是Enlearn的创始人。作为一名计算机科学家，他的研究重点是为学习和科学发现创造互动的吸引人的环境。他的实验室创建了Foldit，这是一款生物化学游戏，在短短两年内就在《自然》杂志上发表了三篇文章，这是一款屡获殊荣的数学学习游戏，全世界有超过500万的学习者在玩。他目前专注于参与的方法，可以快速发展专家在任意领域，特别侧重于革命K-12数学教育。他在华盛顿、明尼苏达州和挪威进行的代数挑战表明，96%的小学生在1.5小时内就能学会关键的代数概念。他最近成立了Enlearn，将他在生成适应方面的研究应用于任何课程，以实现95%的学生完全掌握课程的目标。他对交互式计算机图形学领域的贡献得到了许多奖项的认可，包括NSF CAREER奖、Alfred P. Sloan奖学金和ACM SIGGRAPH重要新研究员奖。
{"title":"Achieving 96% Mastery at National Scale through Inspired Learning and Generative Adaptivity","authors":"Zoran Popovic","doi":"10.1145/2724660.2724684","DOIUrl":"https://doi.org/10.1145/2724660.2724684","url":null,"abstract":"Most of the current research on improving learning outcomes focuses on a small subset of variables of an immensely multi-dimensional space of the learning ecosystem. Most digital learning tools primarily focus on individual students, other research focuses only on teacher professional development, or only on curriculum improvement. In this talk I will describe our efforts on how to discover optimal parameters of the entire ecosystem that considers student factors (engagement and mastery), classroom factors (blended learning variations and group learning variations), curriculum factors (multidimensional variation of existing curricula), and teacher factors (in-class tools that mitigate weaknesses, and promote teacher development). I will describe our work on algorithms to discover optimal learning pathways in this high-dimensional space. I will conclude with the outcomes of deploying a portion of our platform on algebra challenges conducted on two US states and the country of Norway. Zoran Popovic is a Director of Center for Game Science at University of Washington and founder of Enlearn. Trained as a computer scientist his research focus is on creating interactive engaging environments for learning and scientific discovery. His laboratory created Foldit, a biochemistry game that produced three Nature publications in just two years, an award-winning math learning games played by over five million learners worldwide. He is currently focusing on engaging methods that can rapidly develop experts in arbitrary domains with particular focus on revolutionizing K-12 math education. His Algebra Challenges conducted in Washington, Minnesota, and Norway, have shown that 96% of children even in elementary school can learn key algebra concepts in 1.5 hours. He has recently founded Enlearn to apply his work on generative adaptation to any curricula towards the goal of achieving full mastery by 95% of students. His contributions to the field of interactive computer graphics have been recognized by a number of awards including the NSF CAREER Award, Alfred P. Sloan Fellowship and ACM SIGGRAPH Significant New Researcher Award.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81949192","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}
{"title":"Session details: Opening Keynote Address","authors":"G. Kiczales","doi":"10.1145/3077548.3257987","DOIUrl":"https://doi.org/10.1145/3077548.3257987","url":null,"abstract":"","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86904123","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}
Online discussion forums in a MOOC setting allow students to become aware of other students enrolled in the course. However, what is (usually) visible in the forums is the output of ``active'' students who engage in asking and answering questions. In addition to such active participants, there is (as always in online communities) a large group of ``passive'' users (so-called lurkers), who might find the forum useful to their learning, and read it regularly, despite remaining ``invisible''. Our analysis of a large MOOC online forum shows that for every active participant in the forum there are two passive ones. 30% of active participants complete the course, compared to only 6.6% of the passive participants. Vice-versa, 67% of students who complete the course are also active in the forum. However, ``invisible activity'' (e.g. reading or searching the forum) is something that both groups practice equally and more frequently, while only 3.3% of forum actions are visible.
{"title":"The Visible and Invisible in a MOOC Discussion Forum","authors":"Eni Mustafaraj, Jessie Bu","doi":"10.1145/2724660.2728691","DOIUrl":"https://doi.org/10.1145/2724660.2728691","url":null,"abstract":"Online discussion forums in a MOOC setting allow students to become aware of other students enrolled in the course. However, what is (usually) visible in the forums is the output of ``active'' students who engage in asking and answering questions. In addition to such active participants, there is (as always in online communities) a large group of ``passive'' users (so-called lurkers), who might find the forum useful to their learning, and read it regularly, despite remaining ``invisible''. Our analysis of a large MOOC online forum shows that for every active participant in the forum there are two passive ones. 30% of active participants complete the course, compared to only 6.6% of the passive participants. Vice-versa, 67% of students who complete the course are also active in the forum. However, ``invisible activity'' (e.g. reading or searching the forum) is something that both groups practice equally and more frequently, while only 3.3% of forum actions are visible.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75717201","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}
Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady
In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.
{"title":"Behavior Prediction in MOOCs using Higher Granularity Temporal Information","authors":"Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady","doi":"10.1145/2724660.2728687","DOIUrl":"https://doi.org/10.1145/2724660.2728687","url":null,"abstract":"In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75968106","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}
While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style:} the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naïve to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners for improving their code style based on the key stylistic differences between a given learner's submission and a submission that is stylistically slightly better. We are developing a methodology for analyzing and doing feature engineering on differences between submissions, and for learning from instructor-provided feedback as to which hints are most relevant. We describe the techniques used to do this in our prototype, which will be deployed in a residential software engineering course as an alpha test prior to deploying in a MOOC later this year.
{"title":"AutoStyle: Toward Coding Style Feedback at Scale","authors":"J. Moghadam, R. R. Choudhury, Hezheng Yin, A. Fox","doi":"10.1145/2724660.2728672","DOIUrl":"https://doi.org/10.1145/2724660.2728672","url":null,"abstract":"While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style:} the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naïve to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners for improving their code style based on the key stylistic differences between a given learner's submission and a submission that is stylistically slightly better. We are developing a methodology for analyzing and doing feature engineering on differences between submissions, and for learning from instructor-provided feedback as to which hints are most relevant. We describe the techniques used to do this in our prototype, which will be deployed in a residential software engineering course as an alpha test prior to deploying in a MOOC later this year.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77843587","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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