Massive open online courses (MOOCs) provide a great opportunity to use multiple means of information representation through a mixture of various media such as text, graphics, and video, among others. However, most research on MOOCs focused on learning analytics and not much attention is given to content analysis. We gathered all text corpora and video transcripts of selected MOOCs using a web crawler and looked at word counts, clustered by distribution, and measured readability of the crawled data. Analyzing content distribution allows for a comparison of MOOCs regardless of topics, thus giving us an idea of what most course developers might think is ideal in terms of content distribution. This comparison along with readability analysis can be useful for course pre-run quality assessment and gauging content sufficiency.
{"title":"Content Type Distribution and Readability of MOOCs","authors":"M. Carlon, Nopphon Keerativoranan, J. Cross","doi":"10.1145/3386527.3405950","DOIUrl":"https://doi.org/10.1145/3386527.3405950","url":null,"abstract":"Massive open online courses (MOOCs) provide a great opportunity to use multiple means of information representation through a mixture of various media such as text, graphics, and video, among others. However, most research on MOOCs focused on learning analytics and not much attention is given to content analysis. We gathered all text corpora and video transcripts of selected MOOCs using a web crawler and looked at word counts, clustered by distribution, and measured readability of the crawled data. Analyzing content distribution allows for a comparison of MOOCs regardless of topics, thus giving us an idea of what most course developers might think is ideal in terms of content distribution. This comparison along with readability analysis can be useful for course pre-run quality assessment and gauging content sufficiency.","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":"84163759","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}
Rich models of students' learning and problem-solving behaviors can support tailored interventions by instructors and scaffolding of complex learning activities. Our goal in this paper is to identify students' reading behaviors as they engage with instructional texts in domain-specific activities. In this work, we apply theory and methodology from the learning sciences to a large-scale middle school dataset within a digital literacy platform, Actively Learn. We compare students' reading behaviors both within and across domains for 12,566 science and 16,240 social studies students. Our findings show that higher-performing students in science engaged in more metacognitively-rich reading activities, such as text annotation; whereas lower-performing students relied more on simple highlighting and took longer to respond to embedded questions. Higher-performing students in social studies, by contrast, engaged more with the vocabulary and took longer to read before attempting question responses. Our finding may be used as recommendations to help both teachers and students engage in and support more effective behaviors.
{"title":"Understanding Reading Behaviors of Middle School Students","authors":"Effat Farhana, Teomara Rutherford, Collin Lynch","doi":"10.1145/3386527.3405948","DOIUrl":"https://doi.org/10.1145/3386527.3405948","url":null,"abstract":"Rich models of students' learning and problem-solving behaviors can support tailored interventions by instructors and scaffolding of complex learning activities. Our goal in this paper is to identify students' reading behaviors as they engage with instructional texts in domain-specific activities. In this work, we apply theory and methodology from the learning sciences to a large-scale middle school dataset within a digital literacy platform, Actively Learn. We compare students' reading behaviors both within and across domains for 12,566 science and 16,240 social studies students. Our findings show that higher-performing students in science engaged in more metacognitively-rich reading activities, such as text annotation; whereas lower-performing students relied more on simple highlighting and took longer to respond to embedded questions. Higher-performing students in social studies, by contrast, engaged more with the vocabulary and took longer to read before attempting question responses. Our finding may be used as recommendations to help both teachers and students engage in and support more effective behaviors.","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":"75671970","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}
India Irish, Roy Finkelberg, Daniel K. Nkemelu, Swar Gujrania, Aadarsh Padiyath, Sumedha Raman, Chirag Tailor, Rosa I. Arriaga, Thad Starner
As enrollment numbers in online courses increase, students, instructors, and teaching assistants have difficulty finding needed information in online forums because of the number of posts, resulting in duplicate posts that exacerbate the problem. We introduce PARQR, a recommendation tool that suggests relevant contributions as participants compose their posts. We investigate the use of PARQR in five online degree-seeking courses. We survey 74 students and interview five teaching assistants to understand their experience with online forums and PARQR. We compare the differences between using and not using PARQR for an online course assignment. PARQR users found the tool to be useful for navigating online forums, and PARQR was effective in reducing the number of posts (0.291 vs. 0.506 posts per active student) and duplicate posts (17.8% vs. 25.6%) in an online course. These results suggest that PARQR makes on-line forums more efficient for users to find needed information.
{"title":"PARQR","authors":"India Irish, Roy Finkelberg, Daniel K. Nkemelu, Swar Gujrania, Aadarsh Padiyath, Sumedha Raman, Chirag Tailor, Rosa I. Arriaga, Thad Starner","doi":"10.1145/3386527.3405914","DOIUrl":"https://doi.org/10.1145/3386527.3405914","url":null,"abstract":"As enrollment numbers in online courses increase, students, instructors, and teaching assistants have difficulty finding needed information in online forums because of the number of posts, resulting in duplicate posts that exacerbate the problem. We introduce PARQR, a recommendation tool that suggests relevant contributions as participants compose their posts. We investigate the use of PARQR in five online degree-seeking courses. We survey 74 students and interview five teaching assistants to understand their experience with online forums and PARQR. We compare the differences between using and not using PARQR for an online course assignment. PARQR users found the tool to be useful for navigating online forums, and PARQR was effective in reducing the number of posts (0.291 vs. 0.506 posts per active student) and duplicate posts (17.8% vs. 25.6%) in an online course. These results suggest that PARQR makes on-line forums more efficient for users to find needed information.","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":"73428061","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}
Qiaosi Wang, Shan Jing, David A. Joyner, Lauren Wilcox, Hong Li, T. Plötz, Betsy Disalvo
Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a "scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.
{"title":"Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts","authors":"Qiaosi Wang, Shan Jing, David A. Joyner, Lauren Wilcox, Hong Li, T. Plötz, Betsy Disalvo","doi":"10.1145/3386527.3405917","DOIUrl":"https://doi.org/10.1145/3386527.3405917","url":null,"abstract":"Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a \"scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.","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":"76862946","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}
Students often experience confusion while learning, and if promptly resolved, it can promote engagement and deeper understanding. However, detecting student confusion and intervening in a timely and scalable manner challenges even seasoned instructors. To understand when and where students are most likely to be confused, we study the systematic occurrence of confusion in college classes among 29,511 students in twelve universities. We use a novel method for affect detection that allows students to self-report confusion on individual presentation slides during their classes. Across 1,366 class presentations, we find that confusion arises at different times during class and depends on class duration, class size, type of institution, and academic discipline. Confusion is most prevalent during short presentations, in small classes, low-tier institutions, and scientific disciplines.
{"title":"Examining Sources of Variation in Student Confusion in College Classes","authors":"Youjie Chen, René F. Kizilcec","doi":"10.1145/3386527.3405939","DOIUrl":"https://doi.org/10.1145/3386527.3405939","url":null,"abstract":"Students often experience confusion while learning, and if promptly resolved, it can promote engagement and deeper understanding. However, detecting student confusion and intervening in a timely and scalable manner challenges even seasoned instructors. To understand when and where students are most likely to be confused, we study the systematic occurrence of confusion in college classes among 29,511 students in twelve universities. We use a novel method for affect detection that allows students to self-report confusion on individual presentation slides during their classes. Across 1,366 class presentations, we find that confusion arises at different times during class and depends on class duration, class size, type of institution, and academic discipline. Confusion is most prevalent during short presentations, in small classes, low-tier institutions, and scientific disciplines.","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":"76349259","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, Neelesh Gattani, D. Zimmaro
Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with a new course or a new activity with little learning behavior data to provide personalized guidance, and the learners who interact with the course later. We propose a novel architecture, Attentive Neural Turing Machine (ANTM), to solve the cold start knowledge tracing problem. The proposed ANTM comprises an attentive controller module and differential reading and writing processes with extra memory bank. Accuracy (ACC) and Area Under Curve (AUC) measures are used for model performance comparison. Results show the proposed approach can learn fast and generalize well to unseen data. It achieves around 95% ACC trained with only 3 learners, while conventional deep learning based approaches achieve only 65% ACC with over prediction issues.
{"title":"Cold Start Knowledge Tracing with Attentive Neural Turing Machine","authors":"Jinjin Zhao, Shreyansh P. Bhatt, Candace Thille, Neelesh Gattani, D. Zimmaro","doi":"10.1145/3386527.3406741","DOIUrl":"https://doi.org/10.1145/3386527.3406741","url":null,"abstract":"Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with a new course or a new activity with little learning behavior data to provide personalized guidance, and the learners who interact with the course later. We propose a novel architecture, Attentive Neural Turing Machine (ANTM), to solve the cold start knowledge tracing problem. The proposed ANTM comprises an attentive controller module and differential reading and writing processes with extra memory bank. Accuracy (ACC) and Area Under Curve (AUC) measures are used for model performance comparison. Results show the proposed approach can learn fast and generalize well to unseen data. It achieves around 95% ACC trained with only 3 learners, while conventional deep learning based approaches achieve only 65% ACC with over prediction issues.","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":"87143202","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 its roots dating to popular television shows of the 1960s such as Star Trek, fanfiction has blossomed into an extremely widespread form of creative expression. In the past 20 years, amateur fanfiction writers, often young people between the ages of 13 and 25, have published over 61.5 billion words of fiction in online repositories, an amount that rivals the Google Books English fiction corpus of 80 billion words covering the past five centuries. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. Dr. Katie Davis will discuss insights from her book, Writers in the Secret Garden: Fanfiction, Youth, and New Forms of Mentoring (Aragon & Davis, 2019). Davis will describe how young people are utilizing new forms of technology to mentor each other in writing fanfiction, and developing their writing skills in the process. Over the course of five years, Davis and her co-author Dr. Cecilia Aragon conducted original mixed-methods research of online fanfiction repositories, combining their respective skills in data science and education. During the course of their research, they discovered a new kind of mentoring, which they call distributed mentoring, that is uniquely suited to networked communities, where people of all ages and experience levels engage with and support one another through a complex, interwoven tapestry of interactive, cumulatively sophisticated advice and informal instruction. Davis will use the insights from this research to reflect on what it is, exactly, about networked publics that can so effectively support interest-driven learning, and she will consider whether it's possible to apply these lessons to formal education environments.
{"title":"What My Little Pony Can Teach Us About Interest-Driven Learning","authors":"K. Davis","doi":"10.1145/3386527.3406207","DOIUrl":"https://doi.org/10.1145/3386527.3406207","url":null,"abstract":"With its roots dating to popular television shows of the 1960s such as Star Trek, fanfiction has blossomed into an extremely widespread form of creative expression. In the past 20 years, amateur fanfiction writers, often young people between the ages of 13 and 25, have published over 61.5 billion words of fiction in online repositories, an amount that rivals the Google Books English fiction corpus of 80 billion words covering the past five centuries. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. Dr. Katie Davis will discuss insights from her book, Writers in the Secret Garden: Fanfiction, Youth, and New Forms of Mentoring (Aragon & Davis, 2019). Davis will describe how young people are utilizing new forms of technology to mentor each other in writing fanfiction, and developing their writing skills in the process. Over the course of five years, Davis and her co-author Dr. Cecilia Aragon conducted original mixed-methods research of online fanfiction repositories, combining their respective skills in data science and education. During the course of their research, they discovered a new kind of mentoring, which they call distributed mentoring, that is uniquely suited to networked communities, where people of all ages and experience levels engage with and support one another through a complex, interwoven tapestry of interactive, cumulatively sophisticated advice and informal instruction. Davis will use the insights from this research to reflect on what it is, exactly, about networked publics that can so effectively support interest-driven learning, and she will consider whether it's possible to apply these lessons to formal education environments.","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":"88070543","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 paper, we propose a novel Transformer-based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where the exercise and response embedding sequences separately enter, respectively, the encoder and the decoder. The encoder applies self-attention layers to the sequence of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to the sequence of response embeddings. This separation of input allows us to stack attention layers multiple times, resulting in an improvement in area under receiver operating characteristic curve (AUC). To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately. We empirically evaluate SAINT on a large-scale knowledge tracing dataset, EdNet, collected by an active mobile education application, Santa, which has 627,347 users, 72,907,005 response data points as well as a set of 16,175 exercises gathered since 2016. The results show that SAINT achieves state-of-the-art performance in knowledge tracing with an improvement of 1.8% in AUC compared to the current state-of-the-art model.
{"title":"Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing","authors":"Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo","doi":"10.1145/3386527.3405945","DOIUrl":"https://doi.org/10.1145/3386527.3405945","url":null,"abstract":"In this paper, we propose a novel Transformer-based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where the exercise and response embedding sequences separately enter, respectively, the encoder and the decoder. The encoder applies self-attention layers to the sequence of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to the sequence of response embeddings. This separation of input allows us to stack attention layers multiple times, resulting in an improvement in area under receiver operating characteristic curve (AUC). To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately. We empirically evaluate SAINT on a large-scale knowledge tracing dataset, EdNet, collected by an active mobile education application, Santa, which has 627,347 users, 72,907,005 response data points as well as a set of 16,175 exercises gathered since 2016. The results show that SAINT achieves state-of-the-art performance in knowledge tracing with an improvement of 1.8% in AUC compared to the current state-of-the-art model.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78717489","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":"L@S'20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020","authors":"","doi":"10.1145/3386527","DOIUrl":"https://doi.org/10.1145/3386527","url":null,"abstract":"","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74566583","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}
Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end, we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.
{"title":"Human Languages in Source Code: Auto-Translation for Localized Instruction","authors":"C. Piech, Sami Abu-El-Haija","doi":"10.1145/3386527.3405916","DOIUrl":"https://doi.org/10.1145/3386527.3405916","url":null,"abstract":"Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end, we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73272217","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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