We study how the use of online learning systems stimulate cognitive activities, by conducting an experiment with the use of eye tracking technology to monitor eye fixations of 60 final year students engaging in online interactive tutorials at the start of their Final Year Project module. Our findings show that the students' visual scanning behaviours fall into three different types of eye fixation patterns, and the data corresponding to the different types relates to the performance of the students in other related academic modules. We conclude that this method of studying eye fixation patterns can identify different types of learners with respect to cognitive activities and academic potentials, allowing educators to understand how their instructional design using online learning environments can stimulate higher-order cognitive activities.
{"title":"A study on eye fixation patterns of students in higher education using an online learning system","authors":"Benedict The, M. Mavrikis","doi":"10.1145/2883851.2883871","DOIUrl":"https://doi.org/10.1145/2883851.2883871","url":null,"abstract":"We study how the use of online learning systems stimulate cognitive activities, by conducting an experiment with the use of eye tracking technology to monitor eye fixations of 60 final year students engaging in online interactive tutorials at the start of their Final Year Project module. Our findings show that the students' visual scanning behaviours fall into three different types of eye fixation patterns, and the data corresponding to the different types relates to the performance of the students in other related academic modules. We conclude that this method of studying eye fixation patterns can identify different types of learners with respect to cognitive activities and academic potentials, allowing educators to understand how their instructional design using online learning environments can stimulate higher-order cognitive activities.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121089566","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}
Srécko Joksimovíc, A. Manataki, D. Gašević, S. Dawson, Vitomir Kovanovíc, I. F. D. Kereki
As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both - descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.
{"title":"Translating network position into performance: importance of centrality in different network configurations","authors":"Srécko Joksimovíc, A. Manataki, D. Gašević, S. Dawson, Vitomir Kovanovíc, I. F. D. Kereki","doi":"10.1145/2883851.2883928","DOIUrl":"https://doi.org/10.1145/2883851.2883928","url":null,"abstract":"As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both - descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125668009","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}
Héctor J. Pijeira Díaz, H. Drachsler, Sanna Järvelä, P. Kirschner
Collaborative learning is considered a critical 21st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.
{"title":"Investigating collaborative learning success with physiological coupling indices based on electrodermal activity","authors":"Héctor J. Pijeira Díaz, H. Drachsler, Sanna Järvelä, P. Kirschner","doi":"10.1145/2883851.2883897","DOIUrl":"https://doi.org/10.1145/2883851.2883897","url":null,"abstract":"Collaborative learning is considered a critical 21st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126779645","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}
Arham Muslim, Mohamed Amine Chatti, T. Mahapatra, U. Schroeder
In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. Recently, the concept of open learning analytics (OLA) has received a great deal of attention from LA community, due to the growing demand for self-organized, networked, and lifelong learning opportunities. A key challenge in OLA is to follow a personalized and goal-oriented LA model that tailors the LA task to the needs and goals of multiple stakeholders. Current implementations of LA rely on a predefined set of questions and indicators. There is, however, a need to adopt a personalized LA approach that engages end users in the indicator definition process by supporting them in setting goals, posing questions, and self-defining the indicators that help them achieve their goals. In this paper, we address the challenge of personalized LA and present the conceptual, design, and implementation details of a rule-based indicator definition tool to support flexible definition and dynamic generation of indicators to meet the needs of different stakeholders with diverse goals and questions in the LA exercise.
{"title":"A rule-based indicator definition tool for personalized learning analytics","authors":"Arham Muslim, Mohamed Amine Chatti, T. Mahapatra, U. Schroeder","doi":"10.1145/2883851.2883921","DOIUrl":"https://doi.org/10.1145/2883851.2883921","url":null,"abstract":"In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. Recently, the concept of open learning analytics (OLA) has received a great deal of attention from LA community, due to the growing demand for self-organized, networked, and lifelong learning opportunities. A key challenge in OLA is to follow a personalized and goal-oriented LA model that tailors the LA task to the needs and goals of multiple stakeholders. Current implementations of LA rely on a predefined set of questions and indicators. There is, however, a need to adopt a personalized LA approach that engages end users in the indicator definition process by supporting them in setting goals, posing questions, and self-defining the indicators that help them achieve their goals. In this paper, we address the challenge of personalized LA and present the conceptual, design, and implementation details of a rule-based indicator definition tool to support flexible definition and dynamic generation of indicators to meet the needs of different stakeholders with diverse goals and questions in the LA exercise.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176167","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}
Danielle Hagood, Cynthia Carter Ching, S. Schaefer
To promote healthy awareness and activity learning, we gave 12-to 14-year-old youth activity monitors (Fitbits) to track their physical activity, which was then integrated into a videogame we created. The players' real-world steps transform into in-game resources needed for gameplay. In addition to requiring real-world steps for various in-game activities, a dashboard in this game presents visual representations of activity patterns, ostensibly informing students about patterns of their own activity. In this paper and poster, we discuss challenges in initial designs of our dashboard. We present findings and challenges in the process of creating a user-centered dashboard and conclude with our future design goals.
{"title":"Integrating physical activity data in videogames with user-centered dashboards","authors":"Danielle Hagood, Cynthia Carter Ching, S. Schaefer","doi":"10.1145/2883851.2883958","DOIUrl":"https://doi.org/10.1145/2883851.2883958","url":null,"abstract":"To promote healthy awareness and activity learning, we gave 12-to 14-year-old youth activity monitors (Fitbits) to track their physical activity, which was then integrated into a videogame we created. The players' real-world steps transform into in-game resources needed for gameplay. In addition to requiring real-world steps for various in-game activities, a dashboard in this game presents visual representations of activity patterns, ostensibly informing students about patterns of their own activity. In this paper and poster, we discuss challenges in initial designs of our dashboard. We present findings and challenges in the process of creating a user-centered dashboard and conclude with our future design goals.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116033406","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}
Many people who do not know English have moved to English-speaking countries to learn English. Once there, they learn English through formal and informal methods. While considerable work has studied the experiences of English language learners in different learning environments, we have yet to see analytics that detail the experiences of this population within formal and informal learning environments. This study used the experience sampling methodology to capture the information that is needed to detail the communication and affective experiences of advanced English language learners. The collected data reveals differences in how English language learners perceived their communication success based on their learning context, with higher levels of communicative success experienced in formal learning settings. No such differences were found for learners', highly negative, affect. The data suggest a need for additional emotional support within formal and informal learning environments as well as a need for oral communication support within informal contexts.
{"title":"English language learner experiences of formal and informal learning environments","authors":"Carrie Demmans Epp","doi":"10.1145/2883851.2883896","DOIUrl":"https://doi.org/10.1145/2883851.2883896","url":null,"abstract":"Many people who do not know English have moved to English-speaking countries to learn English. Once there, they learn English through formal and informal methods. While considerable work has studied the experiences of English language learners in different learning environments, we have yet to see analytics that detail the experiences of this population within formal and informal learning environments. This study used the experience sampling methodology to capture the information that is needed to detail the communication and affective experiences of advanced English language learners. The collected data reveals differences in how English language learners perceived their communication success based on their learning context, with higher levels of communicative success experienced in formal learning settings. No such differences were found for learners', highly negative, affect. The data suggest a need for additional emotional support within formal and informal learning environments as well as a need for oral communication support within informal contexts.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227824","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}
Vitomir Kovanovíc, Srécko Joksimovíc, Zak Waters, D. Gašević, Kirsty Kitto, M. Hatala, George Siemens
In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
{"title":"Towards automated content analysis of discussion transcripts: a cognitive presence case","authors":"Vitomir Kovanovíc, Srécko Joksimovíc, Zak Waters, D. Gašević, Kirsty Kitto, M. Hatala, George Siemens","doi":"10.1145/2883851.2883950","DOIUrl":"https://doi.org/10.1145/2883851.2883950","url":null,"abstract":"In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116459206","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 majority of applications and products that use learning analytics to understand and improve learning experiences assume the creation of actionable items that will affect students through an intermediary. Much less focus is devoted to exploring how to provide insight directly to students. Furthermore, student engagement has always been a relevant aspect to increase the quality of a learning experience. Learning analytics techniques can be used to provide real-time insight tightly integrated with the learning outcomes directly to the students. This paper describes a case study deployed in a first year engineering course using a flipped learning strategy to explore the behavior of students interacting with a dashboard updated in real time providing indicators of their engagement with the course activities. The results show different patterns of use and their evolution throughout the experience and shed some light on how students perceived this resource.
{"title":"Data2U: scalable real time student feedback in active learning environments","authors":"Imran Khan, A. Pardo","doi":"10.1145/2883851.2883911","DOIUrl":"https://doi.org/10.1145/2883851.2883911","url":null,"abstract":"The majority of applications and products that use learning analytics to understand and improve learning experiences assume the creation of actionable items that will affect students through an intermediary. Much less focus is devoted to exploring how to provide insight directly to students. Furthermore, student engagement has always been a relevant aspect to increase the quality of a learning experience. Learning analytics techniques can be used to provide real-time insight tightly integrated with the learning outcomes directly to the students. This paper describes a case study deployed in a first year engineering course using a flipped learning strategy to explore the behavior of students interacting with a dashboard updated in real time providing indicators of their engagement with the course activities. The results show different patterns of use and their evolution throughout the experience and shed some light on how students perceived this resource.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"5 16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116555997","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}
Francesc Martori, Jordi Cuadros, L. González-Sabaté
Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its interpretability and ability to infer student knowledge. A proper student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. Using four different datasets we study the relationship between the error coming from fitting the parameters and the difficulty index of the skills and the effect of the size of the dataset in this relationship. The relationship between the fitting error and the difficulty index can be very easy modeled and might be indicating some problems with BKTs performance. However, large datasets are required to clearly see this connection as there is an important sample size effect.
{"title":"Studying the relationship between BKT fitting error and the skill difficulty index","authors":"Francesc Martori, Jordi Cuadros, L. González-Sabaté","doi":"10.1145/2883851.2883901","DOIUrl":"https://doi.org/10.1145/2883851.2883901","url":null,"abstract":"Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its interpretability and ability to infer student knowledge. A proper student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. Using four different datasets we study the relationship between the error coming from fitting the parameters and the difficulty index of the skills and the effect of the size of the dataset in this relationship. The relationship between the fitting error and the difficulty index can be very easy modeled and might be indicating some problems with BKTs performance. However, large datasets are required to clearly see this connection as there is an important sample size effect.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127946875","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}
Research in formal education has repeatedly offered evidence of the importance of social interactions for student learning. However, it remains unclear whether the development of such interpersonal relationships has the same influence on learning in the context of large-scale open online learning. For instance, in MOOCs group members frequently change and the volume of interactions can quickly amass to chaos, therefore impeding an individual's propensity to foster meaningful relationships. This paper examined a MOOC for its potential to develop social processes. As it is exceedingly difficult to establish a relationship with somebody who seldom accesses a MOOC discussion, we singled out a cohort defined by its participants' regularity of forum presence. The study, analysed this 'cohort' and its development, in comparison to the entire MOOC learner network. Mixed methods of social network analysis (SNA), content analysis and statistical network modelling, revealed the potential for unfolding social processes among a more persistent group of learners in the MOOC setting.
{"title":"Untangling MOOC learner networks","authors":"Poquet Oleksandra, D. Shane","doi":"10.1145/2883851.2883919","DOIUrl":"https://doi.org/10.1145/2883851.2883919","url":null,"abstract":"Research in formal education has repeatedly offered evidence of the importance of social interactions for student learning. However, it remains unclear whether the development of such interpersonal relationships has the same influence on learning in the context of large-scale open online learning. For instance, in MOOCs group members frequently change and the volume of interactions can quickly amass to chaos, therefore impeding an individual's propensity to foster meaningful relationships. This paper examined a MOOC for its potential to develop social processes. As it is exceedingly difficult to establish a relationship with somebody who seldom accesses a MOOC discussion, we singled out a cohort defined by its participants' regularity of forum presence. The study, analysed this 'cohort' and its development, in comparison to the entire MOOC learner network. Mixed methods of social network analysis (SNA), content analysis and statistical network modelling, revealed the potential for unfolding social processes among a more persistent group of learners in the MOOC setting.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"70 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993786","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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