This study aims to identify the conceptual structure and the thematic progress in Learning Analytics (evolution) and to elaborate on backbone/emerging topics in the field (maturity) from 2011 to September 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the Learning Analytics community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 459 papers from the proceedings of the Learning Analytics and Knowledge (LAK) conference and 168 articles published in the Journal of Learning Analytics (JLA), and the respective 3092 author-assigned keywords and 4051 machine-extracted key-phrases, were included in the analyses. The results indicate that the community has significantly focused in areas like Massive Open Online Courses and visualizations; Learning Management Systems, assessment and self-regulated learning are also basic topics, yet topics like natural language processing and orchestration are emerging. The analysis highlights the shift of the research interest throughout the past decade, and the rise of new topics, comprising evidence that the field is expanding. Limitations of the approach and future work plans conclude the paper.
{"title":"From childhood to maturity: Are we there yet? Mapping the intellectual progress in learning analytics during the past decade","authors":"Z. Papamitsiou, M. Giannakos, X. Ochoa","doi":"10.1145/3375462.3375519","DOIUrl":"https://doi.org/10.1145/3375462.3375519","url":null,"abstract":"This study aims to identify the conceptual structure and the thematic progress in Learning Analytics (evolution) and to elaborate on backbone/emerging topics in the field (maturity) from 2011 to September 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the Learning Analytics community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 459 papers from the proceedings of the Learning Analytics and Knowledge (LAK) conference and 168 articles published in the Journal of Learning Analytics (JLA), and the respective 3092 author-assigned keywords and 4051 machine-extracted key-phrases, were included in the analyses. The results indicate that the community has significantly focused in areas like Massive Open Online Courses and visualizations; Learning Management Systems, assessment and self-regulated learning are also basic topics, yet topics like natural language processing and orchestration are emerging. The analysis highlights the shift of the research interest throughout the past decade, and the rise of new topics, comprising evidence that the field is expanding. Limitations of the approach and future work plans conclude the paper.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"61 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":"126466635","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}
Nora'ayu Ahmad Uzir, D. Gašević, J. Jovanović, W. Matcha, Lisa-Angelique Lim, Anthea Fudge
This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N2017 = 250 and N2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning.
{"title":"Analytics of time management and learning strategies for effective online learning in blended environments","authors":"Nora'ayu Ahmad Uzir, D. Gašević, J. Jovanović, W. Matcha, Lisa-Angelique Lim, Anthea Fudge","doi":"10.1145/3375462.3375493","DOIUrl":"https://doi.org/10.1145/3375462.3375493","url":null,"abstract":"This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N2017 = 250 and N2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"33 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":"128189211","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}
Shay A. Geller, Nicholas Hoernle, Y. Gal, A. Segal, Amy X. Zhang, David R Karger, M. Facciotti, Michele Igo
Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses.
{"title":"#Confused and beyond: detecting confusion in course forums using students' hashtags","authors":"Shay A. Geller, Nicholas Hoernle, Y. Gal, A. Segal, Amy X. Zhang, David R Karger, M. Facciotti, Michele Igo","doi":"10.1145/3375462.3375485","DOIUrl":"https://doi.org/10.1145/3375462.3375485","url":null,"abstract":"Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"35 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":"125893818","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}
Shiva Shabaninejad, Hassan Khosravi, M. Indulska, Aneesha Bakharia, P. Isaías
The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation in analytical dashboards is a 'drill-down', which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.
{"title":"Automated insightful drill-down recommendations for learning analytics dashboards","authors":"Shiva Shabaninejad, Hassan Khosravi, M. Indulska, Aneesha Bakharia, P. Isaías","doi":"10.1145/3375462.3375539","DOIUrl":"https://doi.org/10.1145/3375462.3375539","url":null,"abstract":"The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation in analytical dashboards is a 'drill-down', which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"25 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":"115022968","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}
Luca Benedetto, Andrea Cappelli, R. Turrin, P. Cremonesi
The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation.
{"title":"R2DE: a NLP approach to estimating IRT parameters of newly generated questions","authors":"Luca Benedetto, Andrea Cappelli, R. Turrin, P. Cremonesi","doi":"10.1145/3375462.3375517","DOIUrl":"https://doi.org/10.1145/3375462.3375517","url":null,"abstract":"The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456834","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}
Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models. The result shows that our approach can achieve a much higher accuracy for student performance prediction in interactive online question pools than the traditional way of only using the statistical features (e.g., students' historical question scores) in various models. We further discuss the performance consistency of our approach across different prediction models and question classes, as well as the importance of the proposed interaction features in detail.
{"title":"Predicting student performance in interactive online question pools using mouse interaction features","authors":"Huan Wei, Haotian Li, Meng Xia, Yong Wang, Huamin Qu","doi":"10.1145/3375462.3375521","DOIUrl":"https://doi.org/10.1145/3375462.3375521","url":null,"abstract":"Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models. The result shows that our approach can achieve a much higher accuracy for student performance prediction in interactive online question pools than the traditional way of only using the statistical features (e.g., students' historical question scores) in various models. We further discuss the performance consistency of our approach across different prediction models and question classes, as well as the importance of the proposed interaction features in detail.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"36 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121164967","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 mission of learning analytics (LA) is to improve learner experiences using the insights from digitally collected learner data. While some areas of LA are maturing, this is not consistent across all LA specialisations. For instance, LA for social learning lack validated approaches to account for the effects of cross-course variability in learner behavior. Although the associations between network structure and learning outcomes have been examined in the context of online forums, it remains unclear whether such associations represent bona fide social effects, or merely reflect heterogeneity in individual posting behavior, leading to seemingly complex but artefactual social network structures. We argue that to start addressing this issue, posting activity should be explicitly included and modelled in forum network representations. To gain insight to what extent learner degree and edge weight are merely derivatives of learner activity, we construct random models that control for the level of posting and post properties, such as popularity and thread hierarchy level. Analysis of forum networks in twenty online courses presented in this paper demonstrates that individual posting behavior is highly predictive of both the breadth (degree) and frequency (strength) in forum communication networks. This implies that, in the context of forum-based modelling, degree and frequency may not reflect the social dynamics. However, results suggest that clustering of the network structure is not a derivative of individual posting behaviour. Hence, weighted local clustering coefficient may be a better proxy for social relationships. The empirical results are relevant to scientists interested in social interactions and learner networks in digital learning, and more generally to researchers interested in deriving informative social network models from online forums.
{"title":"Are forum networks social networks?: a methodological perspective","authors":"Oleksandra Poquet, L. Tupikina, Marc Santolini","doi":"10.1145/3375462.3375531","DOIUrl":"https://doi.org/10.1145/3375462.3375531","url":null,"abstract":"The mission of learning analytics (LA) is to improve learner experiences using the insights from digitally collected learner data. While some areas of LA are maturing, this is not consistent across all LA specialisations. For instance, LA for social learning lack validated approaches to account for the effects of cross-course variability in learner behavior. Although the associations between network structure and learning outcomes have been examined in the context of online forums, it remains unclear whether such associations represent bona fide social effects, or merely reflect heterogeneity in individual posting behavior, leading to seemingly complex but artefactual social network structures. We argue that to start addressing this issue, posting activity should be explicitly included and modelled in forum network representations. To gain insight to what extent learner degree and edge weight are merely derivatives of learner activity, we construct random models that control for the level of posting and post properties, such as popularity and thread hierarchy level. Analysis of forum networks in twenty online courses presented in this paper demonstrates that individual posting behavior is highly predictive of both the breadth (degree) and frequency (strength) in forum communication networks. This implies that, in the context of forum-based modelling, degree and frequency may not reflect the social dynamics. However, results suggest that clustering of the network structure is not a derivative of individual posting behaviour. Hence, weighted local clustering coefficient may be a better proxy for social relationships. The empirical results are relevant to scientists interested in social interactions and learner networks in digital learning, and more generally to researchers interested in deriving informative social network models from online forums.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131488073","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}
Giora Alexandron, Mary Ellen Wiltrout, Aviram Berg, José A. Ruipérez Valiente
Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior - the amount of cheating and learners' engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an 'analytically-verified' MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention.
{"title":"Assessment that matters: balancing reliability and learner-centered pedagogy in MOOC assessment","authors":"Giora Alexandron, Mary Ellen Wiltrout, Aviram Berg, José A. Ruipérez Valiente","doi":"10.1145/3375462.3375464","DOIUrl":"https://doi.org/10.1145/3375462.3375464","url":null,"abstract":"Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior - the amount of cheating and learners' engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an 'analytically-verified' MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127890966","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}
José A. Ruipérez Valiente, Matt Jenner, T. Staubitz, Xitong Li, Tobias Rohloff, Sherif A. Halawa, C. Turró, Yuan Cheng, Jiayin Zhang, Ignacio M. Despujol, J. Reich
Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Most of the research and spotlight has been concentrated on a handful of global, English-language providers, but there are a growing number of regional providers of MOOCS in languages other than English. In this work, we have partnered with thirteen MOOC providers from around the world. We apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners. This allows us to examine learning analytics trends at a macro level across various MOOC providers, with a goal of understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.
{"title":"Macro MOOC learning analytics: exploring trends across global and regional providers","authors":"José A. Ruipérez Valiente, Matt Jenner, T. Staubitz, Xitong Li, Tobias Rohloff, Sherif A. Halawa, C. Turró, Yuan Cheng, Jiayin Zhang, Ignacio M. Despujol, J. Reich","doi":"10.1145/3375462.3375482","DOIUrl":"https://doi.org/10.1145/3375462.3375482","url":null,"abstract":"Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Most of the research and spotlight has been concentrated on a handful of global, English-language providers, but there are a growing number of regional providers of MOOCS in languages other than English. In this work, we have partnered with thirteen MOOC providers from around the world. We apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners. This allows us to examine learning analytics trends at a macro level across various MOOC providers, with a goal of understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121768008","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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