This paper1 demonstrates how the application of item response theory yields useful item characteristics, which further can be the foundation of item pools and therefore adaptive educational software to come.
{"title":"Using item response theory to generate an item pool for an e-learning-system","authors":"M. Schweighart","doi":"10.1145/3027385.3029466","DOIUrl":"https://doi.org/10.1145/3027385.3029466","url":null,"abstract":"This paper1 demonstrates how the application of item response theory yields useful item characteristics, which further can be the foundation of item pools and therefore adaptive educational software to come.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126323318","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}
C. Aguerrebere, Cristóbal Cobo, Marcela Gomez, Matías Mateu
This work provides an overview of an education and technology monitoring system developed at Plan Ceibal, a nationwide initiative created to enable technology enhanced learning in Uruguay. Plan Ceibal currently offers one-to-one access to technology and connectivity to every student and teacher (from primary and secondary education) as well as a comprehensive set of educational software platforms. All these resources generate massive amounts of data about the progress and style of students learning. This work introduces the conceptual framework, design and preliminary results of the Big Data Center for learning analytics currently being developed at Plan Ceibal. This initiative is focused on exploiting these datasets and conducting advanced analytics to support the educational system. To this aim, a 360 degrees profile will be built including information characterizing the user's online behavior as well as a set of technology enhanced learning factors. These profiles will be studied both at user (e.g., student or teacher) and larger scale levels (e.g., per school or school system), addressing both the need of understanding how technology is being used for learning as well as to provide accurate feedback to support evidence based educational policies.
{"title":"Strategies for data and learning analytics informed national education policies: the case of Uruguay","authors":"C. Aguerrebere, Cristóbal Cobo, Marcela Gomez, Matías Mateu","doi":"10.1145/3027385.3027444","DOIUrl":"https://doi.org/10.1145/3027385.3027444","url":null,"abstract":"This work provides an overview of an education and technology monitoring system developed at Plan Ceibal, a nationwide initiative created to enable technology enhanced learning in Uruguay. Plan Ceibal currently offers one-to-one access to technology and connectivity to every student and teacher (from primary and secondary education) as well as a comprehensive set of educational software platforms. All these resources generate massive amounts of data about the progress and style of students learning. This work introduces the conceptual framework, design and preliminary results of the Big Data Center for learning analytics currently being developed at Plan Ceibal. This initiative is focused on exploiting these datasets and conducting advanced analytics to support the educational system. To this aim, a 360 degrees profile will be built including information characterizing the user's online behavior as well as a set of technology enhanced learning factors. These profiles will be studied both at user (e.g., student or teacher) and larger scale levels (e.g., per school or school system), addressing both the need of understanding how technology is being used for learning as well as to provide accurate feedback to support evidence based educational policies.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We conducted a literature review on systems that track learning analytics data (e.g., resource use, time spent, assessment data, etc.) and provide a report back to students in the form of visualizations, feedback, or recommendations. This review included a rigorous article search process; 945 articles were identified in the initial search. After filtering out articles that did not meet the inclusion criteria, 94 articles were included in the final analysis. Articles were coded on five categories chosen based on previous work done in this area: functionality, data sources, design analysis, perceived effects, and actual effects. The purpose of this review is to identify trends in the current student-facing learning analytics reporting system literature and provide recommendations for learning analytics researchers and practitioners for future work.
{"title":"Trends and issues in student-facing learning analytics reporting systems research","authors":"Robert G. Bodily, K. Verbert","doi":"10.1145/3027385.3027403","DOIUrl":"https://doi.org/10.1145/3027385.3027403","url":null,"abstract":"We conducted a literature review on systems that track learning analytics data (e.g., resource use, time spent, assessment data, etc.) and provide a report back to students in the form of visualizations, feedback, or recommendations. This review included a rigorous article search process; 945 articles were identified in the initial search. After filtering out articles that did not meet the inclusion criteria, 94 articles were included in the final analysis. Articles were coded on five categories chosen based on previous work done in this area: functionality, data sources, design analysis, perceived effects, and actual effects. The purpose of this review is to identify trends in the current student-facing learning analytics reporting system literature and provide recommendations for learning analytics researchers and practitioners for future work.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614167","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}
Ulla Ringtved, Sandra Milligan, L. Corrin, A. Littlejohn, N. Law
Notions of what constitutes quality in design in traditional on-campus or online teaching and learning may not always translate into scaled digital environments. The DesignLAK17 workshop builds on the DesignLAK16 workshop to explore one aspect of this theme, namely the opportunities arising from the use of analytics in scaled assessment design. New paradigms for learning design are exploiting the distinctive characteristics and potentials of analytics, trace data and newer kinds of sensory data usable on digital platforms to transform assessment. But, characteristics of quality assessment design need to be reconsidered, and new metrics for capturing quality are required. This symposium and workshop focuses on what might be appropriate quality metrics and indicators for assessment design in scaled learning. It aims to build a community of interest round the topic, to share perspectives, and to generate design and research ideas.
{"title":"DesignLAK17: quality metrics and indicators for analytics of assessment design at scale","authors":"Ulla Ringtved, Sandra Milligan, L. Corrin, A. Littlejohn, N. Law","doi":"10.1145/3027385.3029431","DOIUrl":"https://doi.org/10.1145/3027385.3029431","url":null,"abstract":"Notions of what constitutes quality in design in traditional on-campus or online teaching and learning may not always translate into scaled digital environments. The DesignLAK17 workshop builds on the DesignLAK16 workshop to explore one aspect of this theme, namely the opportunities arising from the use of analytics in scaled assessment design. New paradigms for learning design are exploiting the distinctive characteristics and potentials of analytics, trace data and newer kinds of sensory data usable on digital platforms to transform assessment. But, characteristics of quality assessment design need to be reconsidered, and new metrics for capturing quality are required. This symposium and workshop focuses on what might be appropriate quality metrics and indicators for assessment design in scaled learning. It aims to build a community of interest round the topic, to share perspectives, and to generate design and research ideas.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989141","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}
Customized content of e-textbook require teachers to spend greater efforts using authoring tools and planning activities before class, and teachers with various contexts have different demands on e-textbook. However, some teachers who lack ICT skills and dissatisfy with the features tend to give up using e-textbook. Thus we need to know the status of teachers' usage earlier before we decide to give them some technical supports. This paper describes an analysis method for predicting e-textbook adoption from usage records in early days, and an event segmentation method of teachers' usage is used in effort to provide features of predictive model.
{"title":"Predicting e-textbook adoption based on event segmentation of teachers' usage","authors":"Longwei Zheng, Wei Gong, X. Gu","doi":"10.1145/3027385.3029457","DOIUrl":"https://doi.org/10.1145/3027385.3029457","url":null,"abstract":"Customized content of e-textbook require teachers to spend greater efforts using authoring tools and planning activities before class, and teachers with various contexts have different demands on e-textbook. However, some teachers who lack ICT skills and dissatisfy with the features tend to give up using e-textbook. Thus we need to know the status of teachers' usage earlier before we decide to give them some technical supports. This paper describes an analysis method for predicting e-textbook adoption from usage records in early days, and an event segmentation method of teachers' usage is used in effort to provide features of predictive model.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129855886","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}
Tanja Käser, Nicole R. Hallinen, Daniel L. Schwartz
Modeling and predicting student learning is an important task in computer-based education. A large body of work has focused on representing and predicting student knowledge accurately. Existing techniques are mostly based on students' performance and on timing features. However, research in education, psychology and educational data mining has demonstrated that students' choices and strategies substantially influence learning. In this paper, we investigate the impact of students' exploration strategies on learning and propose the use of a probabilistic model jointly representing student knowledge and strategies. Our analyses are based on data collected from an interactive computer-based game. Our results show that exploration strategies are a significant predictor of the learning outcome. Furthermore, the joint models of performance and knowledge significantly improve the prediction accuracy within the game as well as on external post-test data, indicating that this combined representation provides a better proxy for learning.
{"title":"Modeling exploration strategies to predict student performance within a learning environment and beyond","authors":"Tanja Käser, Nicole R. Hallinen, Daniel L. Schwartz","doi":"10.1145/3027385.3027422","DOIUrl":"https://doi.org/10.1145/3027385.3027422","url":null,"abstract":"Modeling and predicting student learning is an important task in computer-based education. A large body of work has focused on representing and predicting student knowledge accurately. Existing techniques are mostly based on students' performance and on timing features. However, research in education, psychology and educational data mining has demonstrated that students' choices and strategies substantially influence learning. In this paper, we investigate the impact of students' exploration strategies on learning and propose the use of a probabilistic model jointly representing student knowledge and strategies. Our analyses are based on data collected from an interactive computer-based game. Our results show that exploration strategies are a significant predictor of the learning outcome. Furthermore, the joint models of performance and knowledge significantly improve the prediction accuracy within the game as well as on external post-test data, indicating that this combined representation provides a better proxy for learning.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125440510","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}
Sebastian Cross, Zak Waters, Kirsty Kitto, G. Zuccon
While help seeking has been extensively studied using self report survey data and models, there is a lack of content analysis techniques that can be directly applied to classify help seeking behaviour. In this preliminary work we propose a coding scheme which is then applied to an open dataset that we have created by carefully selecting sub groups from two popular discussion sites (Reddit and StackExchange). We then explore the possibility for automatically classifying help seeking behaviour using machine learning models. A preliminary model provides good initial results, suggesting that it may indeed be possible to construct student support systems that build off of an accurate classifier.
{"title":"Classifying help seeking behaviour in online communities","authors":"Sebastian Cross, Zak Waters, Kirsty Kitto, G. Zuccon","doi":"10.1145/3027385.3027442","DOIUrl":"https://doi.org/10.1145/3027385.3027442","url":null,"abstract":"While help seeking has been extensively studied using self report survey data and models, there is a lack of content analysis techniques that can be directly applied to classify help seeking behaviour. In this preliminary work we propose a coding scheme which is then applied to an open dataset that we have created by carefully selecting sub groups from two popular discussion sites (Reddit and StackExchange). We then explore the possibility for automatically classifying help seeking behaviour using machine learning models. A preliminary model provides good initial results, suggesting that it may indeed be possible to construct student support systems that build off of an accurate classifier.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552063","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}
Lecture videos are amongst the most commonly used instructional methods in present Massive Open Online Courses (MOOCs). As the main form of instruction, students' engagement behaviour with MOOC videos directly impacts the students' success or failure. This research focuses on an in-depth analysis of 1.5 million video interactions (e.g. pause, seek video) of a Programming MOOC. Our video-by-video analysis explores the rationale behind the time-wise variation of video interactions. We aim to analyse discourse features (e.g. syntactic simplicity of text, and speaking rate) and their correlation with the video interaction patterns. This paper presents preliminary results and educational video design implications.
{"title":"Discourse analysis to improve the effective engagement of MOOC videos","authors":"Thushari Atapattu, K. Falkner","doi":"10.1145/3027385.3029470","DOIUrl":"https://doi.org/10.1145/3027385.3029470","url":null,"abstract":"Lecture videos are amongst the most commonly used instructional methods in present Massive Open Online Courses (MOOCs). As the main form of instruction, students' engagement behaviour with MOOC videos directly impacts the students' success or failure. This research focuses on an in-depth analysis of 1.5 million video interactions (e.g. pause, seek video) of a Programming MOOC. Our video-by-video analysis explores the rationale behind the time-wise variation of video interactions. We aim to analyse discourse features (e.g. syntactic simplicity of text, and speaking rate) and their correlation with the video interaction patterns. This paper presents preliminary results and educational video design implications.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128654094","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}
Juliet Mutahi, Andrew Kinai, Nelson Bore, Abdigani Diriye, Komminist Weldemariam
In this paper, we study the engagement and performance of students in a classroom using a system the Cognitive Learning Companion (CLC). CLC is designed to keep track of the relationship between the student, content interaction and learning progression. It also provides evidence-based engagement-oriented actionable insights to teachers by assessing information from a sensor-rich instrumented learning environment in order to infer a learner's cognitive and affective states. Data captured from the instrumented environment is aggregated and analyzed to create interlinked insights helping teachers identify how students engage with learning content and view their performance records on selected assignments. We conducted a 1 month pilot with 27 learners in a primary school in Nairobi, Kenya during their maths and science instructional periods. We present our primary analysis of content-level interactions and engagement at the individual student and classroom level.
{"title":"Studying engagement and performance with learning technology in an African classroom","authors":"Juliet Mutahi, Andrew Kinai, Nelson Bore, Abdigani Diriye, Komminist Weldemariam","doi":"10.1145/3027385.3027395","DOIUrl":"https://doi.org/10.1145/3027385.3027395","url":null,"abstract":"In this paper, we study the engagement and performance of students in a classroom using a system the Cognitive Learning Companion (CLC). CLC is designed to keep track of the relationship between the student, content interaction and learning progression. It also provides evidence-based engagement-oriented actionable insights to teachers by assessing information from a sensor-rich instrumented learning environment in order to infer a learner's cognitive and affective states. Data captured from the instrumented environment is aggregated and analyzed to create interlinked insights helping teachers identify how students engage with learning content and view their performance records on selected assignments. We conducted a 1 month pilot with 27 learners in a primary school in Nairobi, Kenya during their maths and science instructional periods. We present our primary analysis of content-level interactions and engagement at the individual student and classroom level.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116714559","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 seamless learning analytics methods of VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs). VASCORLL is a system for visualizing and analyzing the learning logs collected by the seamless learning system, which supports language learning in the real-world. As far, several studies have been made in the seamless learning environments in order to bridge formal learning over informal learning. However, their focus was the implementation of the seamless learning environment in education. This study focuses on visualizing and analyzing learning logs collected in the seamless learning environment. This paper describes how our analytics could contribute to bridging the gap between formal and informal learning. An experiment was conducted to evaluate 1) whether our developed VASCORLL is effective in connecting the words learned in formal learning to the ones learned in informal learning, 2) which social network algorithm is effective to enhance learning in the seamless learning environment. Twenty international students participated in the evaluation experiment, and they were able to increase their learning opportunities by using VASCORLL. In addition, it was found that the betweenness centrality is useful in finding central words bridging formal and informal learning.1
{"title":"Learning analytics in a seamless learning environment","authors":"Kousuke Mouri, H. Ogata, Noriko Uosaki","doi":"10.1145/3027385.3027408","DOIUrl":"https://doi.org/10.1145/3027385.3027408","url":null,"abstract":"This paper describes seamless learning analytics methods of VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs). VASCORLL is a system for visualizing and analyzing the learning logs collected by the seamless learning system, which supports language learning in the real-world. As far, several studies have been made in the seamless learning environments in order to bridge formal learning over informal learning. However, their focus was the implementation of the seamless learning environment in education. This study focuses on visualizing and analyzing learning logs collected in the seamless learning environment. This paper describes how our analytics could contribute to bridging the gap between formal and informal learning. An experiment was conducted to evaluate 1) whether our developed VASCORLL is effective in connecting the words learned in formal learning to the ones learned in informal learning, 2) which social network algorithm is effective to enhance learning in the seamless learning environment. Twenty international students participated in the evaluation experiment, and they were able to increase their learning opportunities by using VASCORLL. In addition, it was found that the betweenness centrality is useful in finding central words bridging formal and informal learning.1","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328456","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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