David A. Joyner, Qiaosi Wang, Suyash Thakare, Shan Jing, Ashok K. Goel, B. MacIntyre
As online education proliferates, one concern that has been raised is that it may fail to capture desirable emergent phe-nomena from on-campus programs. Student community is one example of such a phenomenon: on-campus student communities thrive based on synchronous collocation. An online program might be designed to capture all deliberate constructs in an on-campus program, but there may be beneficial side effects of synchronous collocation that are not apparent. In this work, we examine the issue of social isolation in an online graduate program. By happenstance, three studies were conducted in relative isolation looking at social isolation from different angles. The first study exam-ined trajectories in social presence as a semester proceeded. The second study developed an understanding of students' needs with regard to community in an online program. The third study tested out an immersive virtual environment to try to improve students' sense of connectedness. Combin-ing their findings, we find compelling evidence of the exist-ence of a Synchronicity Paradox in online education: stu-dents desire synchronicity to form strong social communi-ties, and yet part of the chief appeal of these online pro-grams is their asynchronicity. In light of this finding, we provide design guidelines for how synchronicity may be reintroduced into asynchronous programs without sacrific-ing the benefits of asynchronicity. More specifically, we propose that scale itself may be the key to building emer-gent synchronicity.
{"title":"The Synchronicity Paradox in Online Education","authors":"David A. Joyner, Qiaosi Wang, Suyash Thakare, Shan Jing, Ashok K. Goel, B. MacIntyre","doi":"10.1145/3386527.3405922","DOIUrl":"https://doi.org/10.1145/3386527.3405922","url":null,"abstract":"As online education proliferates, one concern that has been raised is that it may fail to capture desirable emergent phe-nomena from on-campus programs. Student community is one example of such a phenomenon: on-campus student communities thrive based on synchronous collocation. An online program might be designed to capture all deliberate constructs in an on-campus program, but there may be beneficial side effects of synchronous collocation that are not apparent. In this work, we examine the issue of social isolation in an online graduate program. By happenstance, three studies were conducted in relative isolation looking at social isolation from different angles. The first study exam-ined trajectories in social presence as a semester proceeded. The second study developed an understanding of students' needs with regard to community in an online program. The third study tested out an immersive virtual environment to try to improve students' sense of connectedness. Combin-ing their findings, we find compelling evidence of the exist-ence of a Synchronicity Paradox in online education: stu-dents desire synchronicity to form strong social communi-ties, and yet part of the chief appeal of these online pro-grams is their asynchronicity. In light of this finding, we provide design guidelines for how synchronicity may be reintroduced into asynchronous programs without sacrific-ing the benefits of asynchronicity. More specifically, we propose that scale itself may be the key to building emer-gent synchronicity.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79944669","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}
Jinjin Zhao, Shreyansh P. Bhatt, Candace Thille, D. Zimmaro, Neelesh Gattani
Online learning systems that provide actionable and personalized guidance can help learners make better decisions during learning. Bayesian Knowledge Tracing (BKT) extensions and deep learning based approaches have demonstrated improved mastery prediction accuracy compared to the basic BKT model; however, neither set of models provides actionable guidance on learning activities beyond mastery prediction. We propose a novel framework for personalized knowledge tracing with attention mechanism. Our proposed framework incorporates auxiliary learner attributes into knowledge tracing and interprets mastery prediction with the learning attributes. The proposed approach can also provide personalized next best learning activity recommendations. We demonstrate that the accuracy of the proposed approach in mastery prediction is slightly higher compared to deep learning based approaches and that the proposed approach can provide personalized next best learning activity recommendation.
{"title":"Interpretable Personalized Knowledge Tracing and Next Learning Activity Recommendation","authors":"Jinjin Zhao, Shreyansh P. Bhatt, Candace Thille, D. Zimmaro, Neelesh Gattani","doi":"10.1145/3386527.3406739","DOIUrl":"https://doi.org/10.1145/3386527.3406739","url":null,"abstract":"Online learning systems that provide actionable and personalized guidance can help learners make better decisions during learning. Bayesian Knowledge Tracing (BKT) extensions and deep learning based approaches have demonstrated improved mastery prediction accuracy compared to the basic BKT model; however, neither set of models provides actionable guidance on learning activities beyond mastery prediction. We propose a novel framework for personalized knowledge tracing with attention mechanism. Our proposed framework incorporates auxiliary learner attributes into knowledge tracing and interprets mastery prediction with the learning attributes. The proposed approach can also provide personalized next best learning activity recommendations. We demonstrate that the accuracy of the proposed approach in mastery prediction is slightly higher compared to deep learning based approaches and that the proposed approach can provide personalized next best learning activity recommendation.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78502499","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}
Mobile learning apps such as Duolingo have allowed millions of students to study new subjects that they might not otherwise be able to. One study mode utilized in mobile learning is a digital "flashcard," a tool used by students for studying and memorizing content both in and out of the traditional classroom. Here I investigate whether a multiple-choice type flashcard, which asks the user to identify the picture from one of 4 options, performs better than a digital flashcard to help users identify pictures of fake, cartoon fish. The results of the study so far are inconclusive, not finding statistically significant differences in quiz scores between participants who use the standard flashcards and those who used the multiple-choice flashcard. However, the results may indicate that participants who studied using the multiple-choice flashcard achieved similar scores while studying less on average than those who studied using the standard flashcards.
{"title":"The Effectiveness of Multiple-Choice Type Flashcards for the Identification of Fish Species","authors":"I. Kerman","doi":"10.1145/3386527.3406753","DOIUrl":"https://doi.org/10.1145/3386527.3406753","url":null,"abstract":"Mobile learning apps such as Duolingo have allowed millions of students to study new subjects that they might not otherwise be able to. One study mode utilized in mobile learning is a digital \"flashcard,\" a tool used by students for studying and memorizing content both in and out of the traditional classroom. Here I investigate whether a multiple-choice type flashcard, which asks the user to identify the picture from one of 4 options, performs better than a digital flashcard to help users identify pictures of fake, cartoon fish. The results of the study so far are inconclusive, not finding statistically significant differences in quiz scores between participants who use the standard flashcards and those who used the multiple-choice flashcard. However, the results may indicate that participants who studied using the multiple-choice flashcard achieved similar scores while studying less on average than those who studied using the standard flashcards.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83332777","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}
The presence of "big data" in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In developing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Using administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable models such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place.
{"title":"Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success","authors":"C. Kung, Renzhe Yu","doi":"10.1145/3386527.3406755","DOIUrl":"https://doi.org/10.1145/3386527.3406755","url":null,"abstract":"The presence of \"big data\" in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In developing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Using administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable models such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82534676","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}
Andrew A. Mcreynolds, Sheba P. Naderzad, Mononito Goswami, Jack Mostow
Advances in education technology are enabling tremendous advances in learning at scale. However, they typically assume resources taken for granted in developed countries, including reliable electricity, high-bandwidth Internet access, fast WiFi, powerful computers, sophisticated sensors, and expert technical support to keep it all working. This paper examines these assumptions in the context of a massive test of learning at scale in a developing country. We examine each assumption, how it was broken, and some workarounds used in a 15-month-long independent controlled evaluation of pre- to posttest learning and social-emotional gains by over 2,000 children in 168 villages in Tanzania. We analyze those gains to characterize who gained how much, using test score data, social-emotional measures, and detailed logs from RoboTutor. We quantify the relative impact of pretest scores, literate aspirations, treatment, and usage on learning gains.
{"title":"Toward Learning at Scale in Developing Countries: Lessons from the Global Learning XPRIZE Field Study","authors":"Andrew A. Mcreynolds, Sheba P. Naderzad, Mononito Goswami, Jack Mostow","doi":"10.1145/3386527.3405920","DOIUrl":"https://doi.org/10.1145/3386527.3405920","url":null,"abstract":"Advances in education technology are enabling tremendous advances in learning at scale. However, they typically assume resources taken for granted in developed countries, including reliable electricity, high-bandwidth Internet access, fast WiFi, powerful computers, sophisticated sensors, and expert technical support to keep it all working. This paper examines these assumptions in the context of a massive test of learning at scale in a developing country. We examine each assumption, how it was broken, and some workarounds used in a 15-month-long independent controlled evaluation of pre- to posttest learning and social-emotional gains by over 2,000 children in 168 villages in Tanzania. We analyze those gains to characterize who gained how much, using test score data, social-emotional measures, and detailed logs from RoboTutor. We quantify the relative impact of pretest scores, literate aspirations, treatment, and usage on learning gains.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87560625","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}
Existing attempts to foster a greater sense of community in online education have largely focused on direct interactions among students in peer review, forums, and other mechanisms. In this paper, we pose a new design challenge for learning at scale: peripheral community. Peripheral community is the sense of community derived from peripheral interactions in which a student has visibility into others' behaviors without a direct, intentional interaction occurring between the students. We argue for the value of peripheral community by examining opportunities for such visibility in residential learning environments. We then explore possible ways to supply peripheral community, both in the form of new initiatives and in reinterpretations of existing interventions as fostering peripheral community.
{"title":"Peripheral and Semi-Peripheral Community: A New Design Challenge for Learning at Scale","authors":"David A. Joyner","doi":"10.1145/3386527.3406736","DOIUrl":"https://doi.org/10.1145/3386527.3406736","url":null,"abstract":"Existing attempts to foster a greater sense of community in online education have largely focused on direct interactions among students in peer review, forums, and other mechanisms. In this paper, we pose a new design challenge for learning at scale: peripheral community. Peripheral community is the sense of community derived from peripheral interactions in which a student has visibility into others' behaviors without a direct, intentional interaction occurring between the students. We argue for the value of peripheral community by examining opportunities for such visibility in residential learning environments. We then explore possible ways to supply peripheral community, both in the form of new initiatives and in reinterpretations of existing interventions as fostering peripheral community.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79511687","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}
Shreyansh P. Bhatt, Jinjin Zhao, Candace Thille, D. Zimmaro, Neelesh Gattani
Open navigation online learning systems allow learners to choose the next learning activity. These systems can be instrumented to provide learners with feedback to help them choose the next learning activity. One type of feedback is providing an estimate of the learner's current skill proficiency. A learner can then choose to skip the remaining learning activities for that skill after achieving proficiency in that skill. In this paper, we investigate whether predicting proficiency and communicating it to learners can save time for learners within a course. We evaluate the accuracy of the BKT based proficiency pre- diction framework for learner's proficiency prediction which considers one attempt per question. We extend the proficiency prediction framework to include multiple attempts at individual questions and show that it is more accurate in proficiency prediction than BKT based proficiency prediction framework. We discuss the potential implications of attempt enhanced framework on the learners' behavior for open navigation on- line learning systems.
{"title":"Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior","authors":"Shreyansh P. Bhatt, Jinjin Zhao, Candace Thille, D. Zimmaro, Neelesh Gattani","doi":"10.1145/3386527.3406746","DOIUrl":"https://doi.org/10.1145/3386527.3406746","url":null,"abstract":"Open navigation online learning systems allow learners to choose the next learning activity. These systems can be instrumented to provide learners with feedback to help them choose the next learning activity. One type of feedback is providing an estimate of the learner's current skill proficiency. A learner can then choose to skip the remaining learning activities for that skill after achieving proficiency in that skill. In this paper, we investigate whether predicting proficiency and communicating it to learners can save time for learners within a course. We evaluate the accuracy of the BKT based proficiency pre- diction framework for learner's proficiency prediction which considers one attempt per question. We extend the proficiency prediction framework to include multiple attempts at individual questions and show that it is more accurate in proficiency prediction than BKT based proficiency prediction framework. We discuss the potential implications of attempt enhanced framework on the learners' behavior for open navigation on- line learning systems.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72649810","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}
Kabir Abdulmajeed, David A. Joyner, Christine A. McManus
Education has traditionally been administered via physical interactions between teachers and students in classrooms. Through technological advancement in communications and digital devices, online education has been developed with the potential to scale education, making it affordable and accessible. With an internet connection and a laptop or mobile phone, learners can access massive open online courses (MOOCs) for free. Nonetheless, the opportunity to scale education and the advantages of online learning are not always fulfilled due to certain challenges. In this work, Socioeconomic, Sociocultural, and IT infrastructural factors are categorized as challenges hindering the adoption of online learning in Nigeria. Although some factors mitigating online learning have been identified in the past, there is relatively little empirical evidence indicating the reality and severity of these challenges. Since scaling education involves worldwide reach, local contexts such as found in Nigeria and other developing countries become critical. The objective of this work, therefore, is to understand these challenges, present empirical evidence through a questionnaire survey, rank these challenges in order of severity, and propose solutions.
{"title":"Challenges of Online Learning in Nigeria","authors":"Kabir Abdulmajeed, David A. Joyner, Christine A. McManus","doi":"10.1145/3386527.3405953","DOIUrl":"https://doi.org/10.1145/3386527.3405953","url":null,"abstract":"Education has traditionally been administered via physical interactions between teachers and students in classrooms. Through technological advancement in communications and digital devices, online education has been developed with the potential to scale education, making it affordable and accessible. With an internet connection and a laptop or mobile phone, learners can access massive open online courses (MOOCs) for free. Nonetheless, the opportunity to scale education and the advantages of online learning are not always fulfilled due to certain challenges. In this work, Socioeconomic, Sociocultural, and IT infrastructural factors are categorized as challenges hindering the adoption of online learning in Nigeria. Although some factors mitigating online learning have been identified in the past, there is relatively little empirical evidence indicating the reality and severity of these challenges. Since scaling education involves worldwide reach, local contexts such as found in Nigeria and other developing countries become critical. The objective of this work, therefore, is to understand these challenges, present empirical evidence through a questionnaire survey, rank these challenges in order of severity, and propose solutions.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76480694","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}
Vrinda Nandan, Andrew Spittlemeister, Federico Brubacher
This paper describes an educational tool developed to teach coding and computational thinking to children. We designed and implemented an adaptive, interactive learning game application (mobile and web) called "Pixasso". In this application, children will write a simple program to color the 'pixels' of an image. Through the game application, they will learn programming commands, sequencing and debugging. This educational application was built using prevailing research on child centered design knowledge regarding child user interface and experience and aims to help scale initiatives dedicated towards introducing children to computer science at an early age.
{"title":"Pixasso","authors":"Vrinda Nandan, Andrew Spittlemeister, Federico Brubacher","doi":"10.1145/3386527.3406747","DOIUrl":"https://doi.org/10.1145/3386527.3406747","url":null,"abstract":"This paper describes an educational tool developed to teach coding and computational thinking to children. We designed and implemented an adaptive, interactive learning game application (mobile and web) called \"Pixasso\". In this application, children will write a simple program to color the 'pixels' of an image. Through the game application, they will learn programming commands, sequencing and debugging. This educational application was built using prevailing research on child centered design knowledge regarding child user interface and experience and aims to help scale initiatives dedicated towards introducing children to computer science at an early age.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76189566","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}
We investigate student learning behaviors in a Massive Open Online Course with in-person components. Our goal is to improve the design of the course through learning analytics. The programming language taught, App Inventor, is a drag-and-drop language to create Android applications. We visualize and quantify student behaviors such as automatic and manual saving of code, video sections viewed, and the various forms of knowledge required to understand the course material. It appears students are less likely to go from course material that teaches procedures to other material that teaches procedures than we would expect, and rarely review previous topics covered in the course. We also find students tend to save marginally less at the beginning and end of sessions. However, since the data set is small, our conclusions are limited.
{"title":"Analyzing K-12 Blended MOOC Learning Behaviors","authors":"Robert S. Gold, Erik Hemberg, Una-May O’Reilly","doi":"10.1145/3386527.3406743","DOIUrl":"https://doi.org/10.1145/3386527.3406743","url":null,"abstract":"We investigate student learning behaviors in a Massive Open Online Course with in-person components. Our goal is to improve the design of the course through learning analytics. The programming language taught, App Inventor, is a drag-and-drop language to create Android applications. We visualize and quantify student behaviors such as automatic and manual saving of code, video sections viewed, and the various forms of knowledge required to understand the course material. It appears students are less likely to go from course material that teaches procedures to other material that teaches procedures than we would expect, and rarely review previous topics covered in the course. We also find students tend to save marginally less at the beginning and end of sessions. However, since the data set is small, our conclusions are limited.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74545682","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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