John Saint, D. Gašević, W. Matcha, Nora'ayu Ahmad Uzir, A. Pardo
The temporal and sequential nature of learning is receiving increasing focus in Learning Analytics circles. The desire to embed studies in recognised theories of self-regulated learning (SRL) has led researchers to conceptualise learning as a process that unfolds and changes over time. To that end, a body of research knowledge is growing which states that traditional frequency-based correlational studies are limited in narrative impact. To further explore this, we analysed trace data collected from online activities of a sample of 239 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We employed SRL categorisation of micro-level processes based on a recognised model of learning, and then analysed the data using: 1) simple frequency measures; 2) epistemic network analysis; 3) temporal process mining; and 4) stochastic process mining. We found that a combination of analyses provided us with a richer insight into SRL behaviours than any one single method. We found that better performing learners employed more optimal behaviours in their navigation through the course's learning management system.
{"title":"Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning","authors":"John Saint, D. Gašević, W. Matcha, Nora'ayu Ahmad Uzir, A. Pardo","doi":"10.1145/3375462.3375487","DOIUrl":"https://doi.org/10.1145/3375462.3375487","url":null,"abstract":"The temporal and sequential nature of learning is receiving increasing focus in Learning Analytics circles. The desire to embed studies in recognised theories of self-regulated learning (SRL) has led researchers to conceptualise learning as a process that unfolds and changes over time. To that end, a body of research knowledge is growing which states that traditional frequency-based correlational studies are limited in narrative impact. To further explore this, we analysed trace data collected from online activities of a sample of 239 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We employed SRL categorisation of micro-level processes based on a recognised model of learning, and then analysed the data using: 1) simple frequency measures; 2) epistemic network analysis; 3) temporal process mining; and 4) stochastic process mining. We found that a combination of analyses provided us with a richer insight into SRL behaviours than any one single method. We found that better performing learners employed more optimal behaviours in their navigation through the course's learning management system.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":" 42","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113952087","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}
Modelling healthcare providers' knowledge while they are gaining new concepts is an important step towards supporting self-regulated personalised learning at scale. This is especially important if we are to address health workforce skills development and enhance the subsequent quality of care patients receive in the Global South, where a huge skills gap exists. Rich data about healthcare providers' learning can be captured by their responses to close-ended problems within conjunctive solution space -such as clinical training scenarios for emergency care delivery- on smartphone-based learning interventions which are being proposed as a solution for reducing the healthcare skills gap in this context. Together with sequential data detailing a learner's progress while they are solving a learning task, this provides useful insights into their learning behaviour. Predicting learning or forgetting curves from representations of healthcare providers knowledge is a difficult task, but recent promising machine learning advances have produced techniques capable of learning knowledge representations and overcoming this challenge. In this study, we train a Long Short-Term Memory neural network for predicting learners' future performance and forgetting curves by feeding it sequence embeddings of learning task attempts from healthcare providers from Global South. From this training, the model captures nuanced representations of a healthcare provider's clinical knowledge and their patterns of learning behaviours, predicting their future performance with high accuracy. More significantly, by differentiating reduced performance based on spaced learning, the model can help provide timely warning that helps support healthcare providers to reinforce their self-regulated learning while providing a basis for personalised instructional support to aid improved clinical outcomes from their professional practices.
{"title":"Learning to represent healthcare providers knowledge of neonatal emergency care: findings from a smartphone-based learning intervention targeting clinicians from LMICs","authors":"T. Tuti, C. Paton, N. Winters","doi":"10.1145/3375462.3375479","DOIUrl":"https://doi.org/10.1145/3375462.3375479","url":null,"abstract":"Modelling healthcare providers' knowledge while they are gaining new concepts is an important step towards supporting self-regulated personalised learning at scale. This is especially important if we are to address health workforce skills development and enhance the subsequent quality of care patients receive in the Global South, where a huge skills gap exists. Rich data about healthcare providers' learning can be captured by their responses to close-ended problems within conjunctive solution space -such as clinical training scenarios for emergency care delivery- on smartphone-based learning interventions which are being proposed as a solution for reducing the healthcare skills gap in this context. Together with sequential data detailing a learner's progress while they are solving a learning task, this provides useful insights into their learning behaviour. Predicting learning or forgetting curves from representations of healthcare providers knowledge is a difficult task, but recent promising machine learning advances have produced techniques capable of learning knowledge representations and overcoming this challenge. In this study, we train a Long Short-Term Memory neural network for predicting learners' future performance and forgetting curves by feeding it sequence embeddings of learning task attempts from healthcare providers from Global South. From this training, the model captures nuanced representations of a healthcare provider's clinical knowledge and their patterns of learning behaviours, predicting their future performance with high accuracy. More significantly, by differentiating reduced performance based on spaced learning, the model can help provide timely warning that helps support healthcare providers to reinforce their self-regulated learning while providing a basis for personalised instructional support to aid improved clinical outcomes from their professional practices.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122085065","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":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","authors":"","doi":"10.1145/3375462","DOIUrl":"https://doi.org/10.1145/3375462","url":null,"abstract":"","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245505","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}
This paper describes work in progress to answer the question of how we can identify and model the depth and quality of student participation in class discussion forums using the content of the discussion forum messages. We look at two widely-studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry (CoI) frameworks. Our goal is to discover where they agree and where they offer complementary perspectives on learning. In this study, we train predictive classifiers for both frameworks on the same data set in order to discover which attributes are most predictive and how those correlate with the framework labels. We find that greater depth and quality of participation is associated with longer and more complex messages in both frameworks, and that the threaded reply structure matters more than temporal order. We find some important differences as well, particularly in the treatment of messages of affirmation.
{"title":"Dialogue attributes that inform depth and quality of participation in course discussion forums","authors":"Elaine Farrow, Johanna D. Moore, D. Gašević","doi":"10.1145/3375462.3375481","DOIUrl":"https://doi.org/10.1145/3375462.3375481","url":null,"abstract":"This paper describes work in progress to answer the question of how we can identify and model the depth and quality of student participation in class discussion forums using the content of the discussion forum messages. We look at two widely-studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry (CoI) frameworks. Our goal is to discover where they agree and where they offer complementary perspectives on learning. In this study, we train predictive classifiers for both frameworks on the same data set in order to discover which attributes are most predictive and how those correlate with the framework labels. We find that greater depth and quality of participation is associated with longer and more complex messages in both frameworks, and that the threaded reply structure matters more than temporal order. We find some important differences as well, particularly in the treatment of messages of affirmation.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115982768","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}
Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turn-by-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.
{"title":"Socio-temporal dynamics in peer interaction events","authors":"Bodong Chen, Oleksandra Poquet","doi":"10.1145/3375462.3375535","DOIUrl":"https://doi.org/10.1145/3375462.3375535","url":null,"abstract":"Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turn-by-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114898030","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}
A. Whitelock-Wainwright, Yi-Shan Tsai, Kayley M. Lyons, Svetlana Kaliff, Mike Bryant, K. Ryan, D. Gašević
Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016--2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.
{"title":"Disciplinary differences in blended learning design: a network analytic study","authors":"A. Whitelock-Wainwright, Yi-Shan Tsai, Kayley M. Lyons, Svetlana Kaliff, Mike Bryant, K. Ryan, D. Gašević","doi":"10.1145/3375462.3375488","DOIUrl":"https://doi.org/10.1145/3375462.3375488","url":null,"abstract":"Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016--2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124536890","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}
Beata Beigman Klebanov, Anastassia Loukina, J. Lockwood, V. R. Liceralde, J. Sabatini, Nitin Madnani, Binod Gyawali, Zuowei Wang, J. Lentini
In a school context, learning is usually detected by repeated measurements of the skill of interest through a sequence of specially designed tests; in particular, this is the case with tracking improvement in oral reading fluency in elementary school children in the U.S. Results presented in this paper suggest that it is possible and feasible to detect improvement in oral reading fluency using data collected during children's independent reading of a book using the Relay Reader™ app. We are thus a step closer to the vision of having a child read for the story, not for a test, yet being able to unobtrusively assess their progress in oral reading fluency.
{"title":"Detecting learning in noisy data: the case of oral reading fluency","authors":"Beata Beigman Klebanov, Anastassia Loukina, J. Lockwood, V. R. Liceralde, J. Sabatini, Nitin Madnani, Binod Gyawali, Zuowei Wang, J. Lentini","doi":"10.1145/3375462.3375490","DOIUrl":"https://doi.org/10.1145/3375462.3375490","url":null,"abstract":"In a school context, learning is usually detected by repeated measurements of the skill of interest through a sequence of specially designed tests; in particular, this is the case with tracking improvement in oral reading fluency in elementary school children in the U.S. Results presented in this paper suggest that it is possible and feasible to detect improvement in oral reading fluency using data collected during children's independent reading of a book using the Relay Reader™ app. We are thus a step closer to the vision of having a child read for the story, not for a test, yet being able to unobtrusively assess their progress in oral reading fluency.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761541","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}
W. Matcha, D. Gašević, J. Jovanović, Nora'ayu Ahmad Uzir, C. Oliver, Andrew Murray, D. Gasevic
Studying online requires well-developed self-regulated learning skills to properly manage one's learning strategies. Learning analytics research has proposed novel methods for extracting theoretically meaningful learning strategies from trace data originating from formal learning settings (online, blended, or flipped classroom). Thus identified strategies proved to be associated with academic achievement. However, automated extraction of theoretically meaningful learning strategies from trace data in the context of massive open online courses (MOOCs) is still under-explored. Moreover, there is a lacuna in research on the relations between automatically detected strategies and the established psychological constructs. The paper reports on a study that (a) applied a state-of-the-art analytic method that combines process and sequence mining techniques to detect learning strategies from the trace data collected in a MOOC (N=1,397), and (b) explored associations of the detected strategies with academic performance and personality traits (Big Five). Four learning strategies detected with the adopted analytics method were shown to be theoretically interpretable as the well-known approaches to learning. The results also revealed that the four detected learning strategies were predicted by conscientiousness, emotional instability, and agreeableness and were associated with academic performance. Implications for theoretical validity and practical application of analytics-detected learning strategies are also provided.
{"title":"Analytics of learning strategies: the association with the personality traits","authors":"W. Matcha, D. Gašević, J. Jovanović, Nora'ayu Ahmad Uzir, C. Oliver, Andrew Murray, D. Gasevic","doi":"10.1145/3375462.3375534","DOIUrl":"https://doi.org/10.1145/3375462.3375534","url":null,"abstract":"Studying online requires well-developed self-regulated learning skills to properly manage one's learning strategies. Learning analytics research has proposed novel methods for extracting theoretically meaningful learning strategies from trace data originating from formal learning settings (online, blended, or flipped classroom). Thus identified strategies proved to be associated with academic achievement. However, automated extraction of theoretically meaningful learning strategies from trace data in the context of massive open online courses (MOOCs) is still under-explored. Moreover, there is a lacuna in research on the relations between automatically detected strategies and the established psychological constructs. The paper reports on a study that (a) applied a state-of-the-art analytic method that combines process and sequence mining techniques to detect learning strategies from the trace data collected in a MOOC (N=1,397), and (b) explored associations of the detected strategies with academic performance and personality traits (Big Five). Four learning strategies detected with the adopted analytics method were shown to be theoretically interpretable as the well-known approaches to learning. The results also revealed that the four detected learning strategies were predicted by conscientiousness, emotional instability, and agreeableness and were associated with academic performance. Implications for theoretical validity and practical application of analytics-detected learning strategies are also provided.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"14 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132748073","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}
Collaborative problem solving is defined as having cognitive and social dimensions. While network analytic techniques such as epistemic network analysis (ENA) and social network analysis (SNA) have been successfully used to investigate the patterns of cognitive and social connections that describe CPS, few attempts have been made to combine the two approaches. Building on prior work that used ENA and SNA metrics as independent predictors of collaborative learning, we propose and test the integrated social-epistemic network signature (iSENS), an approach that affords the simultaneous investigation of cognitive and social connections. We tested iSENS on data collected from military teams participating in training scenarios. Our results suggest that (1) these teams are defined by specific patterns of cognitive and social connections, (2) iSENS networks are able to capture these patterns, and (3) iSENS is a better predictor of team outcomes compared to ENA alone, SNA alone, and a non-integrated SENS approach.
{"title":"iSENS","authors":"Z. Swiecki, D. Shaffer","doi":"10.1145/3375462.3375505","DOIUrl":"https://doi.org/10.1145/3375462.3375505","url":null,"abstract":"Collaborative problem solving is defined as having cognitive and social dimensions. While network analytic techniques such as epistemic network analysis (ENA) and social network analysis (SNA) have been successfully used to investigate the patterns of cognitive and social connections that describe CPS, few attempts have been made to combine the two approaches. Building on prior work that used ENA and SNA metrics as independent predictors of collaborative learning, we propose and test the integrated social-epistemic network signature (iSENS), an approach that affords the simultaneous investigation of cognitive and social connections. We tested iSENS on data collected from military teams participating in training scenarios. Our results suggest that (1) these teams are defined by specific patterns of cognitive and social connections, (2) iSENS networks are able to capture these patterns, and (3) iSENS is a better predictor of team outcomes compared to ENA alone, SNA alone, and a non-integrated SENS approach.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114825795","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}
K. Michos, Charles Lang, Davinia Hernández Leo, Detra Price-Dennis
Involving teachers in the design of technology-enhanced learning environments is a useful method towards bridging the gap between research and practice. This is especially relevant for learning analytics tools, wherein the presentation of educational data to teachers or students requires meaningful sense-making to effectively support data-driven actions. In this paper, we present two case studies carried out in the context of two research projects in the USA and Spain which aimed to involve teachers in the co-design of learning analytics tools through professional development programs. The results of a cross-case analysis highlight lessons learned around challenges and principles regarding the meaningful involvement of teachers in learning analytics tooling design.
{"title":"Involving teachers in learning analytics design: lessons learned from two case studies","authors":"K. Michos, Charles Lang, Davinia Hernández Leo, Detra Price-Dennis","doi":"10.1145/3375462.3375507","DOIUrl":"https://doi.org/10.1145/3375462.3375507","url":null,"abstract":"Involving teachers in the design of technology-enhanced learning environments is a useful method towards bridging the gap between research and practice. This is especially relevant for learning analytics tools, wherein the presentation of educational data to teachers or students requires meaningful sense-making to effectively support data-driven actions. In this paper, we present two case studies carried out in the context of two research projects in the USA and Spain which aimed to involve teachers in the co-design of learning analytics tools through professional development programs. The results of a cross-case analysis highlight lessons learned around challenges and principles regarding the meaningful involvement of teachers in learning analytics tooling design.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140171","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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