Rebecca Ferguson, D. Clow, Leah P. Macfadyen, Alfred Essa, S. Dawson, S. Alexander
Once learning analytics have been successfully developed and tested, the next step is to implement them at a larger scale -- across a faculty, an institution or an educational system. This introduces a new set of challenges, because education is a stable system, resistant to change. Implementing learning analytics at scale involves working with the entire technological complex that exists around technology-enhanced learning (TEL). This includes the different groups of people involved -- learners, educators, administrators and support staff -- the practices of those groups, their understandings of how teaching and learning take place, the technologies they use and the specific environments within which they operate. Each element of the TEL Complex requires explicit and careful consideration during the process of implementation, in order to avoid failure and maximise the chances of success. In order for learning analytics to be implemented successfully at scale, it is crucial to provide not only the analytics and their associated tools but also appropriate forms of support, training and community building.
{"title":"Setting learning analytics in context: overcoming the barriers to large-scale adoption","authors":"Rebecca Ferguson, D. Clow, Leah P. Macfadyen, Alfred Essa, S. Dawson, S. Alexander","doi":"10.1145/2567574.2567592","DOIUrl":"https://doi.org/10.1145/2567574.2567592","url":null,"abstract":"Once learning analytics have been successfully developed and tested, the next step is to implement them at a larger scale -- across a faculty, an institution or an educational system. This introduces a new set of challenges, because education is a stable system, resistant to change. Implementing learning analytics at scale involves working with the entire technological complex that exists around technology-enhanced learning (TEL). This includes the different groups of people involved -- learners, educators, administrators and support staff -- the practices of those groups, their understandings of how teaching and learning take place, the technologies they use and the specific environments within which they operate. Each element of the TEL Complex requires explicit and careful consideration during the process of implementation, in order to avoid failure and maximise the chances of success. In order for learning analytics to be implemented successfully at scale, it is crucial to provide not only the analytics and their associated tools but also appropriate forms of support, training and community building.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"159 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128901348","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}
Adult students often struggle to appreciate the relevance of their higher educational experiences to their careers. Capella University's competency map is a dashboard that visually indicates each student's status relative to specific assessed competencies. MBA students who utilize their competency map demonstrate competencies at slightly higher levels and persist in their program at greater rates, even after statistically controlling for powerful covariates, such as course engagement.
{"title":"Competency map: visualizing student learning to promote student success","authors":"Jeff Grann, Deborah Bushway","doi":"10.1145/2567574.2567622","DOIUrl":"https://doi.org/10.1145/2567574.2567622","url":null,"abstract":"Adult students often struggle to appreciate the relevance of their higher educational experiences to their careers. Capella University's competency map is a dashboard that visually indicates each student's status relative to specific assessed competencies. MBA students who utilize their competency map demonstrate competencies at slightly higher levels and persist in their program at greater rates, even after statistically controlling for powerful covariates, such as course engagement.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354832","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}
Carleton Coffrin, L. Corrin, P. D. Barba, G. Kennedy
In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.
在过去的五年里，人们对大规模在线开放课程(Massive Open Online Courses，简称MOOCs)产生了极大的兴趣。参与MOOC课程的大学传递出的一致信息是，它们渴望了解学生的在线学习过程。本文对两种不同课程和评估设计的mooc的学生学习过程进行了探索性调查。从普通MOOC学习分析的角度来看，学生最初的高水平兴趣，以及最终的高损耗率，使这两门课程看起来非常相似，而且与MOOC总体上非常相似。为了更好地理解学生在这些课程中的学习行为模式，我们研究了不同的学习分析方法和这些分析结果的可视化表示。使用这些方法，我们能够对学生类型进行有意义的分类，并将以前不清楚的学生参与模式可视化。本研究的发现有助于教育界理解学生在mooc中的参与度和表现，也为更广泛的学习分析界提供了学习分析数据分析和可视化的新方法建议。
{"title":"Visualizing patterns of student engagement and performance in MOOCs","authors":"Carleton Coffrin, L. Corrin, P. D. Barba, G. Kennedy","doi":"10.1145/2567574.2567586","DOIUrl":"https://doi.org/10.1145/2567574.2567586","url":null,"abstract":"In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663850","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}
Duygu Simsek, S. B. Shum, A. Liddo, Rebecca Ferguson, Ágnes Sándor
This paper describes a novel analytics dashboard which visualises the key features of scholarly documents. The Dashboard aggregates the salient sentences of scholarly papers, their rhetorical types and the key concepts mentioned within these sentences. These features are extracted from papers through a Natural Language Processing (NLP) technology, called Xerox Incremental Parser (XIP). The XIP Dashboard is a set of visual analytics modules based on the XIP output. In this paper, we briefly introduce the XIP technology and demonstrate an example visualisation of the XIP Dashboard.
{"title":"Visual analytics of academic writing","authors":"Duygu Simsek, S. B. Shum, A. Liddo, Rebecca Ferguson, Ágnes Sándor","doi":"10.1145/2567574.2567577","DOIUrl":"https://doi.org/10.1145/2567574.2567577","url":null,"abstract":"This paper describes a novel analytics dashboard which visualises the key features of scholarly documents. The Dashboard aggregates the salient sentences of scholarly papers, their rhetorical types and the key concepts mentioned within these sentences. These features are extracted from papers through a Natural Language Processing (NLP) technology, called Xerox Incremental Parser (XIP). The XIP Dashboard is a set of visual analytics modules based on the XIP output. In this paper, we briefly introduce the XIP technology and demonstrate an example visualisation of the XIP Dashboard.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134310980","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}
In this paper we propose an automated strategy to assess participation in a multi-mode math discourse environment called Virtual Math Teams with Geogrebra (VMTwG). A holistic participation clustering algorithm is applied through the lens of activity theory. Our activity theory-informed algorithm is a step toward accelerating heuristic approaches to assessing collaborative work in synchronous technology mediated environments like VMTwG. Our Exploratory findings provide an example of a novel, time-efficient, valid, and reliable participatory learning assessment tool for teachers in computer mediated learning environments. Scaling online learning with a combination of computation and theory is the overall goal of the work this paper is situated within.
{"title":"Learning analytics in CSCL with a focus on assessment: an exploratory study of activity theory-informed cluster analysis","authors":"Wanli Xing, Robert Wadholm, S. Goggins","doi":"10.1145/2567574.2567587","DOIUrl":"https://doi.org/10.1145/2567574.2567587","url":null,"abstract":"In this paper we propose an automated strategy to assess participation in a multi-mode math discourse environment called Virtual Math Teams with Geogrebra (VMTwG). A holistic participation clustering algorithm is applied through the lens of activity theory. Our activity theory-informed algorithm is a step toward accelerating heuristic approaches to assessing collaborative work in synchronous technology mediated environments like VMTwG. Our Exploratory findings provide an example of a novel, time-efficient, valid, and reliable participatory learning assessment tool for teachers in computer mediated learning environments. Scaling online learning with a combination of computation and theory is the overall goal of the work this paper is situated within.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114046375","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}
Early Warning Systems (EWSs) are being developed and used more frequently to aggregate multiple sources of data and provide timely information to stakeholders about students in need of academic support. As these systems grow more complex, there is an increasing need to incorporate relevant and real-time course-related information that could be predictors of a student's success or failure. This paper presents an investigation of how to incorporate students' use of course resources from a Learning Management System (LMS) into an existing EWS. Specifically, we focus our efforts on understanding the relationship between course resource use and a student's final course grade. Using ten semesters of LMS data from a requisite Chemistry course, we categorized course resources into four categories. We used a multinomial logistic regression model with semester fixed-effects to estimate the relationship between course resource use and the likelihood that a student receives an "A" or "B" in the course versus a "C." Results suggest that students who use Exam Preparation or Lecture resources to a greater degree than their peers are more likely to receive an "A" or "B" as a final grade. We discuss the implications of our results for the further development of this EWS and EWSs in general.
{"title":"Practice exams make perfect: incorporating course resource use into an early warning system","authors":"R. J. Waddington, Sungjin Nam","doi":"10.1145/2567574.2567623","DOIUrl":"https://doi.org/10.1145/2567574.2567623","url":null,"abstract":"Early Warning Systems (EWSs) are being developed and used more frequently to aggregate multiple sources of data and provide timely information to stakeholders about students in need of academic support. As these systems grow more complex, there is an increasing need to incorporate relevant and real-time course-related information that could be predictors of a student's success or failure. This paper presents an investigation of how to incorporate students' use of course resources from a Learning Management System (LMS) into an existing EWS. Specifically, we focus our efforts on understanding the relationship between course resource use and a student's final course grade. Using ten semesters of LMS data from a requisite Chemistry course, we categorized course resources into four categories. We used a multinomial logistic regression model with semester fixed-effects to estimate the relationship between course resource use and the likelihood that a student receives an \"A\" or \"B\" in the course versus a \"C.\" Results suggest that students who use Exam Preparation or Lecture resources to a greater degree than their peers are more likely to receive an \"A\" or \"B\" as a final grade. We discuss the implications of our results for the further development of this EWS and EWSs in general.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129709779","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}
Regression is the tool of choice for developing predictive models of student risk of failure. However, the forecasting literature has demonstrated the predictive equivalence of much simpler methods. We directly compare one simple tabulation technique, the index method, to a linear multiple regression approach for identifying students at risk. The broader purpose is to explore the plausibility of a flexible method that is conducive to adoption and diffusion. In this respect this paper fits within the ambit of the modest computing agenda, and suggests the possibility of a modest analytics. We built both regression and index method models on 2011 student data and applied these to 2012 student data. The index method was comparable in terms of predictive accuracy of student risk. We suggest that the context specificity of learning environments makes the index method a promising tool for educators who want a situated risk algorithm that is flexible and adaptable.
{"title":"Modest analytics: using the index method to identify students at risk of failure","authors":"Tim Rogers, C. Colvin, B. Chiera","doi":"10.1145/2567574.2567629","DOIUrl":"https://doi.org/10.1145/2567574.2567629","url":null,"abstract":"Regression is the tool of choice for developing predictive models of student risk of failure. However, the forecasting literature has demonstrated the predictive equivalence of much simpler methods. We directly compare one simple tabulation technique, the index method, to a linear multiple regression approach for identifying students at risk. The broader purpose is to explore the plausibility of a flexible method that is conducive to adoption and diffusion. In this respect this paper fits within the ambit of the modest computing agenda, and suggests the possibility of a modest analytics. We built both regression and index method models on 2011 student data and applied these to 2012 student data. The index method was comparable in terms of predictive accuracy of student risk. We suggest that the context specificity of learning environments makes the index method a promising tool for educators who want a situated risk algorithm that is flexible and adaptable.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056503","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}
In the foreseeable future it will be technically possible for instructors, advisors and other delegated representatives of a college or university to access student participation and performance data in near-real time. One potential benefit of this increased data flow could include an improved ability to identify students at risk of academic failure or withdrawal. The availability of these data could also lead to creation of new adaptive learning measures that can automatically provide students personalized guidance. This demonstration will describe how the student notes and questions are being mined to provide student study guides that automatically link to outside resources. The demonstration will also report on how these new study guides have been received by the students and how they are at least partially responsible for a significant increase in student outcomes.
{"title":"Analyzing student notes and questions to create personalized study guides","authors":"P. Samson","doi":"10.1145/2567574.2567576","DOIUrl":"https://doi.org/10.1145/2567574.2567576","url":null,"abstract":"In the foreseeable future it will be technically possible for instructors, advisors and other delegated representatives of a college or university to access student participation and performance data in near-real time. One potential benefit of this increased data flow could include an improved ability to identify students at risk of academic failure or withdrawal. The availability of these data could also lead to creation of new adaptive learning measures that can automatically provide students personalized guidance. This demonstration will describe how the student notes and questions are being mined to provide student study guides that automatically link to outside resources. The demonstration will also report on how these new study guides have been received by the students and how they are at least partially responsible for a significant increase in student outcomes.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486745","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 focus of this workshop is on the potential benefits and challenges of using specific computational methods to analyze interactions in networked learning environments, particularly with respect to integrating multiple analytic approaches towards understanding learning at multiple levels of agency, from individual to collective. The workshop is designed for researchers interested in analytical studies of collaborative and networked learning in socio-technical networks, using data-intensive computational methods of analysis (including social-network analysis, log-file analysis, information extraction and data mining). The workshop may also be of interest to pedagogical professionals and educational decision makers who want to evaluate the potential of learning analytics techniques to better inform their decisions regarding learning in technology-rich environments.
{"title":"Computational approaches to connecting levels of analysis in networked learning communities","authors":"H. Hoppe, D. Suthers","doi":"10.1145/2567574.2567632","DOIUrl":"https://doi.org/10.1145/2567574.2567632","url":null,"abstract":"The focus of this workshop is on the potential benefits and challenges of using specific computational methods to analyze interactions in networked learning environments, particularly with respect to integrating multiple analytic approaches towards understanding learning at multiple levels of agency, from individual to collective. The workshop is designed for researchers interested in analytical studies of collaborative and networked learning in socio-technical networks, using data-intensive computational methods of analysis (including social-network analysis, log-file analysis, information extraction and data mining). The workshop may also be of interest to pedagogical professionals and educational decision makers who want to evaluate the potential of learning analytics techniques to better inform their decisions regarding learning in technology-rich environments.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133767273","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 aim of this study is to suggest more meaningful components for learning analytics in order to help learners improving their learning achievement continuously through an educational technology approach. Multiple linear regression analysis is conducted to determine which factors influence student's academic achievement. 84 undergraduate students in a women's university in South Korea participated in this study. The six-predictor model was able to account for 33.5% of the variance in final grade, F(6, 77) = 6.457, p < .001, R2 = .335. Total studying time in LMS, interaction with peers, regularity of learning interval in LMS, and number of downloads were determined to be significant factors for students' academic achievement in online learning environment. These four controllable variables not only predict learning outcomes significantly but also can be changed if learners put more effort to improve their academic performance. The results provide a rationale for the treatment for student time management effort.
本研究的目的是为学习分析提供更有意义的组成部分，以帮助学习者通过教育技术方法不断提高他们的学习成绩。通过多元线性回归分析，确定影响学生学业成绩的因素。韩国某女子大学84名本科生参与了本研究。六个预测因子模型能够解释最终成绩方差的33.5%，F(6,77) = 6.457, p < .001, R2 = .335。在LMS中学习总时间、与同伴的互动、学习间隔的规律性和下载次数是影响学生在线学习环境中学习成绩的显著因素。这四个可控变量不仅可以显著预测学习结果，而且如果学习者更加努力地提高学习成绩，这四个变量可以被改变。研究结果为学生时间管理努力的治疗提供了理论依据。
{"title":"Educational technology approach toward learning analytics: relationship between student online behavior and learning performance in higher education","authors":"Taeho Yu, I. Jo","doi":"10.1145/2567574.2567594","DOIUrl":"https://doi.org/10.1145/2567574.2567594","url":null,"abstract":"The aim of this study is to suggest more meaningful components for learning analytics in order to help learners improving their learning achievement continuously through an educational technology approach. Multiple linear regression analysis is conducted to determine which factors influence student's academic achievement. 84 undergraduate students in a women's university in South Korea participated in this study. The six-predictor model was able to account for 33.5% of the variance in final grade, F(6, 77) = 6.457, p < .001, R2 = .335. Total studying time in LMS, interaction with peers, regularity of learning interval in LMS, and number of downloads were determined to be significant factors for students' academic achievement in online learning environment. These four controllable variables not only predict learning outcomes significantly but also can be changed if learners put more effort to improve their academic performance. The results provide a rationale for the treatment for student time management effort.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357126","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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