Sandeep M. Jayaprakash, E. Lauría, Pritesh Gandhi, Dinesh Mendhe
This poster synthesizes the design features of a visualization layer applied on the Open Academic Analytics Initiative (OAAI), an open source academic early alert system based on predictive analytics. The poster explores ways to convey the predictive model outputs and benchmark student performances using visually intuitive radar plots.
{"title":"Benchmarking student performance and engagement in an early alert predictive system using interactive radar charts","authors":"Sandeep M. Jayaprakash, E. Lauría, Pritesh Gandhi, Dinesh Mendhe","doi":"10.1145/2883851.2883940","DOIUrl":"https://doi.org/10.1145/2883851.2883940","url":null,"abstract":"This poster synthesizes the design features of a visualization layer applied on the Open Academic Analytics Initiative (OAAI), an open source academic early alert system based on predictive analytics. The poster explores ways to convey the predictive model outputs and benchmark student performances using visually intuitive radar plots.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"112 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":"133963341","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}
John Dillon, G. Ambrose, N. Wanigasekara, Malolan Chetlur, Prasenjit Dey, Bikram Sengupta, S. D’Mello
This paper presents affect data collected from periodic emotion detection surveys throughout an introductory Statistics MOOC called "I Heart Stats." This is the first MOOC, to our knowledge, to capture valuable student affect data through self-reported surveys. To collect student affect, we used two self-reporting methods: (1) The Self-Assessment Manikin and (2) A discrete emotion list. We found that the most common reported MOOC emotion was Hope followed by Enjoyment and Contentment. There were substantial shifts in affective states over the course, notably with Anxiety and Pride. The most valuable result of our study is a preliminary description of the methods for collecting self-reported student affect at scale in a MOOC setting.
{"title":"Student affect during learning with a MOOC","authors":"John Dillon, G. Ambrose, N. Wanigasekara, Malolan Chetlur, Prasenjit Dey, Bikram Sengupta, S. D’Mello","doi":"10.1145/2883851.2883960","DOIUrl":"https://doi.org/10.1145/2883851.2883960","url":null,"abstract":"This paper presents affect data collected from periodic emotion detection surveys throughout an introductory Statistics MOOC called \"I Heart Stats.\" This is the first MOOC, to our knowledge, to capture valuable student affect data through self-reported surveys. To collect student affect, we used two self-reporting methods: (1) The Self-Assessment Manikin and (2) A discrete emotion list. We found that the most common reported MOOC emotion was Hope followed by Enjoyment and Contentment. There were substantial shifts in affective states over the course, notably with Anxiety and Pride. The most valuable result of our study is a preliminary description of the methods for collecting self-reported student affect at scale in a MOOC setting.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"53 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":"133001726","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. Koedinger, Elizabeth Mclaughlin, J. Z. Jia, Norman L. Bier
The "doer effect" is an association between the number of online interactive practice activities students' do and their learning outcomes that is not only statistically reliable but has much higher positive effects than other learning resources, such as watching videos or reading text. Such an association suggests a causal interpretation--more doing yields better learning--which requires randomized experimentation to most rigorously confirm. But such experiments are expensive, and any single experiment in a particular course context does not provide rigorous evidence that the causal link will generalize to other course content. We suggest that analytics of increasingly available online learning data sets can complement experimental efforts by facilitating more widespread evaluation of the generalizability of claims about what learning methods produce better student learning outcomes. We illustrate with analytics that narrow in on a causal interpretation of the doer effect by showing that doing within a course unit predicts learning of that unit content more than doing in units before or after. We also provide generalizability evidence across four different courses involving over 12,500 students that the learning effect of doing is about six times greater than that of reading.
{"title":"Is the doer effect a causal relationship?: how can we tell and why it's important","authors":"K. Koedinger, Elizabeth Mclaughlin, J. Z. Jia, Norman L. Bier","doi":"10.1145/2883851.2883957","DOIUrl":"https://doi.org/10.1145/2883851.2883957","url":null,"abstract":"The \"doer effect\" is an association between the number of online interactive practice activities students' do and their learning outcomes that is not only statistically reliable but has much higher positive effects than other learning resources, such as watching videos or reading text. Such an association suggests a causal interpretation--more doing yields better learning--which requires randomized experimentation to most rigorously confirm. But such experiments are expensive, and any single experiment in a particular course context does not provide rigorous evidence that the causal link will generalize to other course content. We suggest that analytics of increasingly available online learning data sets can complement experimental efforts by facilitating more widespread evaluation of the generalizability of claims about what learning methods produce better student learning outcomes. We illustrate with analytics that narrow in on a causal interpretation of the doer effect by showing that doing within a course unit predicts learning of that unit content more than doing in units before or after. We also provide generalizability evidence across four different courses involving over 12,500 students that the learning effect of doing is about six times greater than that of reading.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"02 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":"127241934","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}
I-Han Hsiao, Sesha Kumar Pandhalkudi Govindarajan, Yi-ling Lin
We designed and studied an innovative semantic visual learning analytics for orchestrating today's programming classes. The visual analytics integrates sources of learning activities by their content semantics. It automatically processs paper-based exams by associating sets of concepts to the exam questions. Results indicated the automatic concept extraction from exams were promising and could be a potential technological solution to address a real world issue. We also discovered that indexing effectiveness was especially prevalent for complex content by covering more comprehensive semantics. Subjective evaluation revealed that the dynamic concept indexing provided teachers with immediate feedback on producing more balanced exams.
{"title":"Semantic visual analytics for today's programming courses","authors":"I-Han Hsiao, Sesha Kumar Pandhalkudi Govindarajan, Yi-ling Lin","doi":"10.1145/2883851.2883915","DOIUrl":"https://doi.org/10.1145/2883851.2883915","url":null,"abstract":"We designed and studied an innovative semantic visual learning analytics for orchestrating today's programming classes. The visual analytics integrates sources of learning activities by their content semantics. It automatically processs paper-based exams by associating sets of concepts to the exam questions. Results indicated the automatic concept extraction from exams were promising and could be a potential technological solution to address a real world issue. We also discovered that indexing effectiveness was especially prevalent for complex content by covering more comprehensive semantics. Subjective evaluation revealed that the dynamic concept indexing provided teachers with immediate feedback on producing more balanced exams.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"9 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":"117062625","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}
S. Bull, B. Ginon, Clelia Boscolo, Matthew D. Johnson
This paper describes open learner models as visualisations of learning for learners, with a particular focus on how information about their learning can be used to help them reflect on their skills, identify gaps in their skills, and plan their future learning. We offer an approach that, in addition to providing visualisations of their learning, allows learners to propose changes to their learner model. This aims to help improve the accuracy of the learner model by taking into account student viewpoints on their learning, while also promoting learner reflection on their learning as part of a discussion of the content of their learner model. This aligns well with recent calls for learning analytics for learners. Building on previous research showing that learners will use open learner models, we here investigate their initial reactions to open learner model features to identify the likelihood of uptake in contexts where an open learner model is offered on an optional basis. We focus on university students' perceptions of a range of visualisations and their stated preferences for a facility to view evidence for the learner model data and to propose changes to the values.
{"title":"Introduction of learning visualisations and metacognitive support in a persuadable open learner model","authors":"S. Bull, B. Ginon, Clelia Boscolo, Matthew D. Johnson","doi":"10.1145/2883851.2883853","DOIUrl":"https://doi.org/10.1145/2883851.2883853","url":null,"abstract":"This paper describes open learner models as visualisations of learning for learners, with a particular focus on how information about their learning can be used to help them reflect on their skills, identify gaps in their skills, and plan their future learning. We offer an approach that, in addition to providing visualisations of their learning, allows learners to propose changes to their learner model. This aims to help improve the accuracy of the learner model by taking into account student viewpoints on their learning, while also promoting learner reflection on their learning as part of a discussion of the content of their learner model. This aligns well with recent calls for learning analytics for learners. Building on previous research showing that learners will use open learner models, we here investigate their initial reactions to open learner model features to identify the likelihood of uptake in contexts where an open learner model is offered on an optional basis. We focus on university students' perceptions of a range of visualisations and their stated preferences for a facility to view evidence for the learner model data and to propose changes to the values.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"40 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":"123483679","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}
H. Drachsler, T. Hoel, A. Cooper, G. Kismihók, Alan Berg, Maren Scheffel, Weiqin Chen, Rebecca Ferguson
Issues related to Ethics and Privacy have become a major stumbling block in application of Learning Analytics technologies on a large scale. Recently, the learning analytics community at large has more actively addressed the EP4LA issues, and we are now starting to see learning analytics solutions that are designed not only as an afterthought, but also with these issues in mind. The 2nd EP4LA@LAK16 workshop will bring the discussion on ethics and privacy for learning analytics to a the next level, helping to build an agenda for organizational and technical design of LA solutions, addressing the different processes of a learning analytics workflow.
{"title":"Ethical and privacy issues in the design of learning analytics applications","authors":"H. Drachsler, T. Hoel, A. Cooper, G. Kismihók, Alan Berg, Maren Scheffel, Weiqin Chen, Rebecca Ferguson","doi":"10.1145/2883851.2883933","DOIUrl":"https://doi.org/10.1145/2883851.2883933","url":null,"abstract":"Issues related to Ethics and Privacy have become a major stumbling block in application of Learning Analytics technologies on a large scale. Recently, the learning analytics community at large has more actively addressed the EP4LA issues, and we are now starting to see learning analytics solutions that are designed not only as an afterthought, but also with these issues in mind. The 2nd EP4LA@LAK16 workshop will bring the discussion on ethics and privacy for learning analytics to a the next level, helping to build an agenda for organizational and technical design of LA solutions, addressing the different processes of a learning analytics workflow.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"153 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":"123597873","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}
L. Agnihotri, Shirin Mojarad, N. Lewkow, Alfred Essa
Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.
{"title":"Educational data mining with Python and Apache spark: a hands-on tutorial","authors":"L. Agnihotri, Shirin Mojarad, N. Lewkow, Alfred Essa","doi":"10.1145/2883851.2883857","DOIUrl":"https://doi.org/10.1145/2883851.2883857","url":null,"abstract":"Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"38 5 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":"115637831","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}
Statway® is one of the Community College Pathways initiatives designed to promote students' success in their developmental math sequence and reduce the time required to earn college credit. A recent causal analysis confirmed that Statway dramatically increased students' success rates in half the time across two different cohorts. These impressive results were also obtained across gender and race/ethnicity groups. However, there is still room for improvement. Students who did not succeed in Statway often did not complete the first of the two-course sequence. Therefore, the objective of this study is to formulate a series of indicators from self-report and online learning system data, alerting instructors to students' progress during the first weeks of the first course in the Statway sequence.
{"title":"Real-time indicators and targeted supports: using online platform data to accelerate student learning","authors":"Ouajdi Manai, H. Yamada, Christopher A. Thorn","doi":"10.1145/2883851.2883942","DOIUrl":"https://doi.org/10.1145/2883851.2883942","url":null,"abstract":"Statway® is one of the Community College Pathways initiatives designed to promote students' success in their developmental math sequence and reduce the time required to earn college credit. A recent causal analysis confirmed that Statway dramatically increased students' success rates in half the time across two different cohorts. These impressive results were also obtained across gender and race/ethnicity groups. However, there is still room for improvement. Students who did not succeed in Statway often did not complete the first of the two-course sequence. Therefore, the objective of this study is to formulate a series of indicators from self-report and online learning system data, alerting instructors to students' progress during the first weeks of the first course in the Statway sequence.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120937448","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}
Shuchi Grover, M. Bienkowski, Amir Tamrakar, Behjat Siddiquie, David A. Salter, Ajay Divakaran
Collaborative problem solving (CPS) is seen as a key skill in K-12 education---in computer science as well as other subjects. Efforts to introduce children to computing rely on pair programming as a way of having young learners engage in CPS. Characteristics of quality collaboration are joint exploring or understanding, joint representation, and joint execution. We present a data driven approach to assessing and elucidating collaboration through modeling of multimodal student behavior and performance data.
{"title":"Multimodal analytics to study collaborative problem solving in pair programming","authors":"Shuchi Grover, M. Bienkowski, Amir Tamrakar, Behjat Siddiquie, David A. Salter, Ajay Divakaran","doi":"10.1145/2883851.2883877","DOIUrl":"https://doi.org/10.1145/2883851.2883877","url":null,"abstract":"Collaborative problem solving (CPS) is seen as a key skill in K-12 education---in computer science as well as other subjects. Efforts to introduce children to computing rely on pair programming as a way of having young learners engage in CPS. Characteristics of quality collaboration are joint exploring or understanding, joint representation, and joint execution. We present a data driven approach to assessing and elucidating collaboration through modeling of multimodal student behavior and performance data.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"5 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":"121247142","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}
Roberto Martínez Maldonado, Bertrand Schneider, Sven Charleer, S. B. Shum, J. Klerkx, E. Duval
The proliferation of varied types of multi-user interactive surfaces (such as digital whiteboards, tabletops and tangible interfaces) is opening a new range of applications in face-to-face (f2f) contexts. They offer unique opportunities for Learning Analytics (LA) by facilitating multi-user sensemaking of automatically captured digital footprints of students' f2f interactions. This paper presents an analysis of current research exploring learning analytics associated with the use of surface devices. We use a framework to analyse our first-hand experiences, and the small number of related deployments according to four dimensions: the orchestration aspects involved; the phases of the pedagogical practice that are supported; the target actors; and the levels of iteration of the LA process. The contribution of the paper is twofold: 1) a synthesis of conclusions that identify the degree of maturity, challenges and pedagogical opportunities of the existing applications of learning analytics and interactive surfaces; and 2) an analysis framework that can be used to characterise the design space of similar areas and LA applications.
{"title":"Interactive surfaces and learning analytics: data, orchestration aspects, pedagogical uses and challenges","authors":"Roberto Martínez Maldonado, Bertrand Schneider, Sven Charleer, S. B. Shum, J. Klerkx, E. Duval","doi":"10.1145/2883851.2883873","DOIUrl":"https://doi.org/10.1145/2883851.2883873","url":null,"abstract":"The proliferation of varied types of multi-user interactive surfaces (such as digital whiteboards, tabletops and tangible interfaces) is opening a new range of applications in face-to-face (f2f) contexts. They offer unique opportunities for Learning Analytics (LA) by facilitating multi-user sensemaking of automatically captured digital footprints of students' f2f interactions. This paper presents an analysis of current research exploring learning analytics associated with the use of surface devices. We use a framework to analyse our first-hand experiences, and the small number of related deployments according to four dimensions: the orchestration aspects involved; the phases of the pedagogical practice that are supported; the target actors; and the levels of iteration of the LA process. The contribution of the paper is twofold: 1) a synthesis of conclusions that identify the degree of maturity, challenges and pedagogical opportunities of the existing applications of learning analytics and interactive surfaces; and 2) an analysis framework that can be used to characterise the design space of similar areas and LA applications.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"46 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":"115006884","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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