As is the case for many undergraduate STEM degree programs, computing degree programs are plagued by high attrition rates. This is especially true in early computing courses, in which failure and drop-out rates in the 35 to 50 percent range are common. By collecting learning process data as students engage in computer programming assignments, computing educators can place themselves in a position not only to better understand students' struggles, but also to better tailor instructional interventions to students' needs. We have developed OSBLE+, a learning management and analytics environment that interfaces with a computer programming environment to support the automatic collection of learners' programming process and social data as they work on programming assignments, while also providing an interactive environment for the analysis and visualization of those data. In ongoing work, we are using OSBLE+ to explore two possibilities: (a) leveraging learning and social data to strategically deliver automated learning interventions, and (b) presenting learners with visual representations of their learning data in order to prompt them to reflect on and discuss their learning processes.
{"title":"Supporting learning analytics in computing education","authors":"Daniel M. Olivares, C. Hundhausen","doi":"10.1145/3027385.3029472","DOIUrl":"https://doi.org/10.1145/3027385.3029472","url":null,"abstract":"As is the case for many undergraduate STEM degree programs, computing degree programs are plagued by high attrition rates. This is especially true in early computing courses, in which failure and drop-out rates in the 35 to 50 percent range are common. By collecting learning process data as students engage in computer programming assignments, computing educators can place themselves in a position not only to better understand students' struggles, but also to better tailor instructional interventions to students' needs. We have developed OSBLE+, a learning management and analytics environment that interfaces with a computer programming environment to support the automatic collection of learners' programming process and social data as they work on programming assignments, while also providing an interactive environment for the analysis and visualization of those data. In ongoing work, we are using OSBLE+ to explore two possibilities: (a) leveraging learning and social data to strategically deliver automated learning interventions, and (b) presenting learners with visual representations of their learning data in order to prompt them to reflect on and discuss their learning processes.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"12 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":"123826730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Wise, P. H. Winne, G. Lynch, X. Ochoa, I. Molenaar, S. Dawson, M. Hatala
The theme for LAK'17 purposely focused on the transdisciplinary nature of research in learning analytics. This theme extends the work of prior conferences that sought to bring together the diversity of disciplinary fields that now comprise learning analytics. The great diversity of papers submitted for LAK'17 demonstrates that LA research has very much embraced the benefits that can be leveraged from a truly transdisciplinary model of research. While there are inherent complexities in such an approach, the research presented for LAK'17 brings much excitement and promise to the field through the application of novel methods, cutting-edge learning technologies, and actual impact on the learning process. Following this theme, the aim of the conference is to provide a forum for presentation, exchange and discussion of research and practices regarding the transdisciplinary field of Learning Analytics.
{"title":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","authors":"A. Wise, P. H. Winne, G. Lynch, X. Ochoa, I. Molenaar, S. Dawson, M. Hatala","doi":"10.1145/3027385","DOIUrl":"https://doi.org/10.1145/3027385","url":null,"abstract":"The theme for LAK'17 purposely focused on the transdisciplinary nature of research in learning analytics. This theme extends the work of prior conferences that sought to bring together the diversity of disciplinary fields that now comprise learning analytics. The great diversity of papers submitted for LAK'17 demonstrates that LA research has very much embraced the benefits that can be leveraged from a truly transdisciplinary model of research. While there are inherent complexities in such an approach, the research presented for LAK'17 brings much excitement and promise to the field through the application of novel methods, cutting-edge learning technologies, and actual impact on the learning process. Following this theme, the aim of the conference is to provide a forum for presentation, exchange and discussion of research and practices regarding the transdisciplinary field of Learning Analytics.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"8 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":"122298268","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}
Chi-Un Lei, D. Gonda, X. Hou, Elizabeth Oh, Xinyu Qi, T. T. Kwok, Y. A. Yeung, Ray Lau
Since teachers are not physically present in an online class, instructional video is the major carrier of course contents in an online learning environment. This paper aims to investigate how course-level exploratory video retention analysis can be used for identifying moments with abnormal watching behaviors and revising videos for a higher video retention. We have empirically evaluated the effectiveness of video analysis and revisions, based on evaluating retentions of revised videos.
{"title":"Data-assisted instructional video revision via course-level exploratory video retention analysis","authors":"Chi-Un Lei, D. Gonda, X. Hou, Elizabeth Oh, Xinyu Qi, T. T. Kwok, Y. A. Yeung, Ray Lau","doi":"10.1145/3027385.3029454","DOIUrl":"https://doi.org/10.1145/3027385.3029454","url":null,"abstract":"Since teachers are not physically present in an online class, instructional video is the major carrier of course contents in an online learning environment. This paper aims to investigate how course-level exploratory video retention analysis can be used for identifying moments with abnormal watching behaviors and revising videos for a higher video retention. We have empirically evaluated the effectiveness of video analysis and revisions, based on evaluating retentions of revised videos.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"43 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":"129649914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Pardo, Roberto Martínez Maldonado, S. B. Shum, J. Schulte, Simon McIntyre, D. Gašević, Jing Gao, George Siemens
The amount of data extracted from learning experiences has grown at an astonishing pace both in depth due to the increasing variety of data sources, and in breath with courses now being offered to massive student cohorts. However, in this emerging scenario instructors are now facing the challenge of connecting the knowledge emerging from data analysis with the provision of meaningful support actions to students within the context of an instructional design. The objective of this tutorial is to give attendees a set of hypothetical scenarios in which the knowledge extracted from a learning experience needs to be used to provide frequent personalized feedback to students.
{"title":"Connecting data with student support actions in a course: a hands-on tutorial","authors":"A. Pardo, Roberto Martínez Maldonado, S. B. Shum, J. Schulte, Simon McIntyre, D. Gašević, Jing Gao, George Siemens","doi":"10.1145/3027385.3029441","DOIUrl":"https://doi.org/10.1145/3027385.3029441","url":null,"abstract":"The amount of data extracted from learning experiences has grown at an astonishing pace both in depth due to the increasing variety of data sources, and in breath with courses now being offered to massive student cohorts. However, in this emerging scenario instructors are now facing the challenge of connecting the knowledge emerging from data analysis with the provision of meaningful support actions to students within the context of an instructional design. The objective of this tutorial is to give attendees a set of hypothetical scenarios in which the knowledge extracted from a learning experience needs to be used to provide frequent personalized feedback to students.","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":"129786324","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}
M. Bannert, I. Molenaar, R. Azevedo, Sanna Järvelä, D. Gašević
In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) "Measuring and Supporting Student's Self-Regulated Learning in Adaptive Educational Technologies" which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.
{"title":"Relevance of learning analytics to measure and support students' learning in adaptive educational technologies","authors":"M. Bannert, I. Molenaar, R. Azevedo, Sanna Järvelä, D. Gašević","doi":"10.1145/3027385.3029463","DOIUrl":"https://doi.org/10.1145/3027385.3029463","url":null,"abstract":"In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) \"Measuring and Supporting Student's Self-Regulated Learning in Adaptive Educational Technologies\" which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"284 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":"124537601","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}
N. Elouazizi, G. Birol, Eric Jandciu, G. Öberg, Ashley J. Welsh, A. Han, Alice Campbell
In this paper, we report on a model that uses a mathematically and cognitively augmented Latent Semantic Analysis method to automatically assess aspects of written argumentation, produced by students in a science communication course.
{"title":"Automated analysis of aspects of written argumentation","authors":"N. Elouazizi, G. Birol, Eric Jandciu, G. Öberg, Ashley J. Welsh, A. Han, Alice Campbell","doi":"10.1145/3027385.3029484","DOIUrl":"https://doi.org/10.1145/3027385.3029484","url":null,"abstract":"In this paper, we report on a model that uses a mathematically and cognitively augmented Latent Semantic Analysis method to automatically assess aspects of written argumentation, produced by students in a science communication course.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"205 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":"124596836","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}
Learners' emotional state has proven to be a key factor for successful learning. Visualizing learners' emotions during synchronous on-line learning activities can help tutors in creating and maintaining socio-affective relationships with their learners. However, few dashboards offer emotional information on the learning activity. The current study focuses on synchronous interactions via a videoconferencing tool dedicated to foreign language training. We collected data on learners' emotions in real conditions during ten sessions (five sessions for two learners). We propose to adopt and combine different models of emotions (discrete and dimensional) and to use heterogeneous APIs for measuring learners' emotions from different data sources (audio, video, self-reporting and interaction traces). Based on a thorough data analysis, we propose an approach to combine different cues to infer information on learners' emotional states. We finally present the EMODA dashboard, an affective multimodal and contextual visual analytics dashboard, which allows the tutor to monitor learners' emotions and better understand their evolution during the synchronous learning activity.
{"title":"EMODA: a tutor oriented multimodal and contextual emotional dashboard","authors":"Mohamed Ez-zaouia, É. Lavoué","doi":"10.1145/3027385.3027434","DOIUrl":"https://doi.org/10.1145/3027385.3027434","url":null,"abstract":"Learners' emotional state has proven to be a key factor for successful learning. Visualizing learners' emotions during synchronous on-line learning activities can help tutors in creating and maintaining socio-affective relationships with their learners. However, few dashboards offer emotional information on the learning activity. The current study focuses on synchronous interactions via a videoconferencing tool dedicated to foreign language training. We collected data on learners' emotions in real conditions during ten sessions (five sessions for two learners). We propose to adopt and combine different models of emotions (discrete and dimensional) and to use heterogeneous APIs for measuring learners' emotions from different data sources (audio, video, self-reporting and interaction traces). Based on a thorough data analysis, we propose an approach to combine different cues to infer information on learners' emotional states. We finally present the EMODA dashboard, an affective multimodal and contextual visual analytics dashboard, which allows the tutor to monitor learners' emotions and better understand their evolution during the synchronous learning activity.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"30 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":"132541155","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 discuss Colorado State University Online's progress toward designing automated survey reports for student feedback data collected through our newly designed LTI survey tool. Using multiple R packages, including 'rmarkdown' and 'likert', the reporting tool imports student survey response data and generates reports for faculty and instructional designers. These reports focus on student perceptions of communication, course design, academic challenge, general satisfaction, and more. These reports display visual representations of the Likert-type response frequencies, basic descriptive statistics, and free-response comments. Surveys are administered just before half-way through the semester to provide formative feedback and just before the end of the semester to provide summative feedback. In this way, faculty and instructional designers can obtain a quick and easily digestible report to make changes and improvements to their classes with minimal effort in the back end production.
{"title":"Automating student survey reports in online education for faculty and instructional designers","authors":"Sean Burns, K. Corwin","doi":"10.1145/3027385.3029475","DOIUrl":"https://doi.org/10.1145/3027385.3029475","url":null,"abstract":"In this paper, we discuss Colorado State University Online's progress toward designing automated survey reports for student feedback data collected through our newly designed LTI survey tool. Using multiple R packages, including 'rmarkdown' and 'likert', the reporting tool imports student survey response data and generates reports for faculty and instructional designers. These reports focus on student perceptions of communication, course design, academic challenge, general satisfaction, and more. These reports display visual representations of the Likert-type response frequencies, basic descriptive statistics, and free-response comments. Surveys are administered just before half-way through the semester to provide formative feedback and just before the end of the semester to provide summative feedback. In this way, faculty and instructional designers can obtain a quick and easily digestible report to make changes and improvements to their classes with minimal effort in the back end production.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"87 8 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":"130941711","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}
MOOC research is typically limited to evaluations of learner behavior in the context of the learning environment. However, some research has begun to recognize that the impact of MOOCs may extend beyond the confines of the course platform or conclusion of the course time limit. This workshop aims to encourage our community of learning analytics researchers to examine the relationship between performance and engagement within the course and learner behavior and development beyond the course. This workshop intends to build awareness in the community regarding the importance of research measuring multi-platform activity and long-term success after taking a MOOC. We hope to build the community's understanding of what it takes to operationalize MOOC learner success in a novel context by employing data traces across the social web.
{"title":"Workshop on integrated learning analytics of MOOC post-course development","authors":"Y. Wang, Dan Davis, Guanliang Chen, L. Paquette","doi":"10.1145/3027385.3029430","DOIUrl":"https://doi.org/10.1145/3027385.3029430","url":null,"abstract":"MOOC research is typically limited to evaluations of learner behavior in the context of the learning environment. However, some research has begun to recognize that the impact of MOOCs may extend beyond the confines of the course platform or conclusion of the course time limit. This workshop aims to encourage our community of learning analytics researchers to examine the relationship between performance and engagement within the course and learner behavior and development beyond the course. This workshop intends to build awareness in the community regarding the importance of research measuring multi-platform activity and long-term success after taking a MOOC. We hope to build the community's understanding of what it takes to operationalize MOOC learner success in a novel context by employing data traces across the social web.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"9 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":"130899634","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}
Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics.
{"title":"Unravelling the dynamics of instructional practice: a longitudinal study on learning design and VLE activities","authors":"Quan Nguyen, B. Rienties, Lisette Toetenel","doi":"10.1145/3027385.3027409","DOIUrl":"https://doi.org/10.1145/3027385.3027409","url":null,"abstract":"Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"10 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":"128599723","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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