M. Almeda, P. Scupelli, R. Baker, Mimi Weber, A. Fisher
In this paper, we investigate the patterns of design choices made by classroom teachers for decorating their classroom walls, using cluster analysis to see which design decisions go together. Classroom visual design has been previously studied, but not in terms of the systematic patterns adopted by teachers in selecting what materials to place on classroom walls, or in terms of the actual semantic content of what is placed on walls. This is potentially important, as classroom walls are continuously seen by students, and form a continual off-task behavior option, available to students at all times. Using the k-means clustering algorithm, we find four types of visual classroom environments (one of them an outlier within our data set), representing teachers' strategies in classroom decoration. Our results indicate that the degree to which teachers place content-related decorations on the walls, is a feature of particular importance for distinguishing which approach teachers are using. Similarly, the type of school (e.g. whether private or charter) appeared to be another significant factor in determining teachers' design choices for classroom walls. The present findings begin the groundwork to better understand the impact of teacher decisions and choices in classroom design that lead to better outcomes in terms of engagement and learning, and finally towards developing classroom designs that are more effective and engaging for learners.
{"title":"Clustering of design decisions in classroom visual displays","authors":"M. Almeda, P. Scupelli, R. Baker, Mimi Weber, A. Fisher","doi":"10.1145/2567574.2567605","DOIUrl":"https://doi.org/10.1145/2567574.2567605","url":null,"abstract":"In this paper, we investigate the patterns of design choices made by classroom teachers for decorating their classroom walls, using cluster analysis to see which design decisions go together. Classroom visual design has been previously studied, but not in terms of the systematic patterns adopted by teachers in selecting what materials to place on classroom walls, or in terms of the actual semantic content of what is placed on walls. This is potentially important, as classroom walls are continuously seen by students, and form a continual off-task behavior option, available to students at all times. Using the k-means clustering algorithm, we find four types of visual classroom environments (one of them an outlier within our data set), representing teachers' strategies in classroom decoration. Our results indicate that the degree to which teachers place content-related decorations on the walls, is a feature of particular importance for distinguishing which approach teachers are using. Similarly, the type of school (e.g. whether private or charter) appeared to be another significant factor in determining teachers' design choices for classroom walls. The present findings begin the groundwork to better understand the impact of teacher decisions and choices in classroom design that lead to better outcomes in terms of engagement and learning, and finally towards developing classroom designs that are more effective and engaging for learners.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"42 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":"126732300","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}
Assessment of reading comprehension can be costly and obtrusive. In this paper, we use inexpensive EEG to detect reading comprehension of readers in a school environment. We use EEG signals to produce above-chance predictors of student performance on end-of-sentence cloze questions. We also attempt (unsuccessfully) to distinguish among student mental states evoked by distracters that violate either syntactic, semantic, or contextual constraints. In total, this work investigates the practicality of classroom use of inexpensive EEG devices as an unobtrusive measure of reading comprehension.
{"title":"Toward unobtrusive measurement of reading comprehension using low-cost EEG","authors":"Yueran Yuan, K. Chang, J. Taylor, Jack Mostow","doi":"10.1145/2567574.2567624","DOIUrl":"https://doi.org/10.1145/2567574.2567624","url":null,"abstract":"Assessment of reading comprehension can be costly and obtrusive. In this paper, we use inexpensive EEG to detect reading comprehension of readers in a school environment. We use EEG signals to produce above-chance predictors of student performance on end-of-sentence cloze questions. We also attempt (unsuccessfully) to distinguish among student mental states evoked by distracters that violate either syntactic, semantic, or contextual constraints. In total, this work investigates the practicality of classroom use of inexpensive EEG devices as an unobtrusive measure of reading comprehension.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"158 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":"126756314","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 virtual classrooms of open online courses include students from a vast array of individual, social, economic, and educational contexts. Detailed data were collected for the first course MIT ran on the edX platform, including student behavior, performance, and background information. In this paper, we estimate the systematic differences in average performance, distribution of performance, and performance conditional on behaviors for countries with different characteristics (e.g., language, income).
{"title":"National differences in an international classroom","authors":"Jennifer DeBoer, G. Stump","doi":"10.1145/2567574.2567602","DOIUrl":"https://doi.org/10.1145/2567574.2567602","url":null,"abstract":"The virtual classrooms of open online courses include students from a vast array of individual, social, economic, and educational contexts. Detailed data were collected for the first course MIT ran on the edX platform, including student behavior, performance, and background information. In this paper, we estimate the systematic differences in average performance, distribution of performance, and performance conditional on behaviors for countries with different characteristics (e.g., language, income).","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"253 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":"122532591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José Luís Santos, J. Klerkx, E. Duval, D. Gago, Luis Rodríguez
This paper presents an exploratory study about two language learning MOOCs deployed in the UNED COMA platform. The study identifies three research questions: a) How does activity evolve in these MOOCs? b) Are all learning activities relevant?, and c) Does the use of the target language influence?. We conclude that the MOOC activity drops not only due to the drop-outs. When students skips around 10% of the proposed activities, the percentage of passing the course decrease in a 25%. Forum activity is a useful indicator for success, however the participation in active threads is not. Finally, the use of the target language course is not an indicator to predict success.
{"title":"Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses","authors":"José Luís Santos, J. Klerkx, E. Duval, D. Gago, Luis Rodríguez","doi":"10.1145/2567574.2567627","DOIUrl":"https://doi.org/10.1145/2567574.2567627","url":null,"abstract":"This paper presents an exploratory study about two language learning MOOCs deployed in the UNED COMA platform. The study identifies three research questions: a) How does activity evolve in these MOOCs? b) Are all learning activities relevant?, and c) Does the use of the target language influence?. We conclude that the MOOC activity drops not only due to the drop-outs. When students skips around 10% of the proposed activities, the percentage of passing the course decrease in a 25%. Forum activity is a useful indicator for success, however the participation in active threads is not. Finally, the use of the target language course is not an indicator to predict success.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"13 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":"129697727","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}
Although video annotation software is no longer considered as a new innovation, its application in promoting student self-regulated learning and reflection skills has only begun to emerge in the research literature. Advances in text and video analytics provide the capability of investigating students' use of the tool and the psychometrics and linguistic processes evident in their written annotations. This paper reports on a study exploring students' use of a video annotation tool when two different instructional approaches were deployed -- graded and non-graded self-reflection annotations within two courses in the performing arts. In addition to counts and temporal locations of self-reflections, the Linguistic Inquiry and Word Counts (LIWC) framework was used for the extraction of variables indicative of the linguistic and psychological processes associated with self-reflection annotations of videos. The results indicate that students in the course with graded self-reflections adopted more linguistic and psychological related processes in comparison to the course with non-graded self-reflections. In general, the effect size of the graded reflections was lower for students who took both courses in parallel. Consistent with prior research, the study identified that students tend to make the majority of their self-reflection annotations early in the video time line. The paper also provides several suggestions for future research to better understand the application of video annotations in facilitating student learning.
{"title":"Analytics of the effects of video use and instruction to support reflective learning","authors":"D. Gašević, Negin Mirriahi, S. Dawson","doi":"10.1145/2567574.2567590","DOIUrl":"https://doi.org/10.1145/2567574.2567590","url":null,"abstract":"Although video annotation software is no longer considered as a new innovation, its application in promoting student self-regulated learning and reflection skills has only begun to emerge in the research literature. Advances in text and video analytics provide the capability of investigating students' use of the tool and the psychometrics and linguistic processes evident in their written annotations. This paper reports on a study exploring students' use of a video annotation tool when two different instructional approaches were deployed -- graded and non-graded self-reflection annotations within two courses in the performing arts. In addition to counts and temporal locations of self-reflections, the Linguistic Inquiry and Word Counts (LIWC) framework was used for the extraction of variables indicative of the linguistic and psychological processes associated with self-reflection annotations of videos. The results indicate that students in the course with graded self-reflections adopted more linguistic and psychological related processes in comparison to the course with non-graded self-reflections. In general, the effect size of the graded reflections was lower for students who took both courses in parallel. Consistent with prior research, the study identified that students tend to make the majority of their self-reflection annotations early in the video time line. The paper also provides several suggestions for future research to better understand the application of video annotations in facilitating student learning.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"26 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":"126799950","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 develop a conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement. While our approach is substantially informed by both higher education and K-12 settings, this framework is developed to apply across all educational contexts where digital data are used to inform learners and the management of learning. Although we can identify movements that are relatively independent of each other today, we believe they will in all cases expand from their current margins to encompass larger domains and increasingly overlap. The growth in these analytic activities leads to the need to find ways to synthesize understandings, find common language, and develop frames of reference to help these movements develop into a field.
{"title":"Educational data sciences: framing emergent practices for analytics of learning, organizations, and systems","authors":"Philip J. Piety, D. Hickey, Mj Bishop","doi":"10.1145/2567574.2567582","DOIUrl":"https://doi.org/10.1145/2567574.2567582","url":null,"abstract":"In this paper, we develop a conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement. While our approach is substantially informed by both higher education and K-12 settings, this framework is developed to apply across all educational contexts where digital data are used to inform learners and the management of learning. Although we can identify movements that are relatively independent of each other today, we believe they will in all cases expand from their current margins to encompass larger domains and increasingly overlap. The growth in these analytic activities leads to the need to find ways to synthesize understandings, find common language, and develop frames of reference to help these movements develop into a field.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"57 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":"124174581","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}
Interactive technologies have become an important part of teaching and learning. However, the data that these systems generate is increasingly unstructured, complex, and therefore difficult of which to make sense of. Current computationally driven methods (e.g., latent semantic analysis or learning based image classifiers) for classifying student contributions don't include the ability to function on multimodal artifacts (e.g., sketches, videos, or annotated images) that new technologies enable. We have developed and implemented a classifcation algorithm based on learners' interactions with the artifacts they create. This new form of semi-automated concept classification, coined Collaborative Spatial Classification, leverages the spatial arrangement of artifacts to provide a visualization that generates summary level data about about idea distribution. This approach has two benefits. First, students learn to identify and articulate patterns and connections among classmates ideas. Second, the teacher receives a high-level view of the distribution of ideas, enabling them to decide how to shift their instructional practices in real-time.
{"title":"Collaborative spatial classification","authors":"E. Coopey, R. Benjamin Shapiro, E. Danahy","doi":"10.1145/2567574.2567611","DOIUrl":"https://doi.org/10.1145/2567574.2567611","url":null,"abstract":"Interactive technologies have become an important part of teaching and learning. However, the data that these systems generate is increasingly unstructured, complex, and therefore difficult of which to make sense of. Current computationally driven methods (e.g., latent semantic analysis or learning based image classifiers) for classifying student contributions don't include the ability to function on multimodal artifacts (e.g., sketches, videos, or annotated images) that new technologies enable. We have developed and implemented a classifcation algorithm based on learners' interactions with the artifacts they create. This new form of semi-automated concept classification, coined Collaborative Spatial Classification, leverages the spatial arrangement of artifacts to provide a visualization that generates summary level data about about idea distribution. This approach has two benefits. First, students learn to identify and articulate patterns and connections among classmates ideas. Second, the teacher receives a high-level view of the distribution of ideas, enabling them to decide how to shift their instructional practices in real-time.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"63 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":"132572299","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 article reports the findings of a Group Concept Mapping study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by Learning Analytics.
{"title":"The impact of learning analytics on the dutch education system","authors":"H. Drachsler, S. Stoyanov, M. Specht","doi":"10.1145/2567574.2567617","DOIUrl":"https://doi.org/10.1145/2567574.2567617","url":null,"abstract":"The article reports the findings of a Group Concept Mapping study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by Learning Analytics.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"15 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":"115365249","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. Gruzd, C. Haythornthwaite, Drew Paulin, Rafa Absar, Michael Huggett
In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also beginning to affect how we teach and learn in this increasingly interconnected and information-rich world. The panelists will discuss their ongoing work that seeks to understand the affordances and potential roles of social media in learning, as well as to determine and provide methods that can help researchers and educators evaluate the use of social media for teaching and learning based on automated analyses of social media texts and networks. The panel will focus on the first phase of this five-year research initiative "Learning Analytics for the Social Media Age" funded by the Social Science and Humanites Research Council of Canada (2013--2018).
{"title":"Learning analytics for the social media age","authors":"A. Gruzd, C. Haythornthwaite, Drew Paulin, Rafa Absar, Michael Huggett","doi":"10.1145/2567574.2576773","DOIUrl":"https://doi.org/10.1145/2567574.2576773","url":null,"abstract":"In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also beginning to affect how we teach and learn in this increasingly interconnected and information-rich world. The panelists will discuss their ongoing work that seeks to understand the affordances and potential roles of social media in learning, as well as to determine and provide methods that can help researchers and educators evaluate the use of social media for teaching and learning based on automated analyses of social media texts and networks. The panel will focus on the first phase of this five-year research initiative \"Learning Analytics for the Social Media Age\" funded by the Social Science and Humanites Research Council of Canada (2013--2018).","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"100 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":"122046341","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}
Closing the feedback loop to improve learning is at the heart of good learning analytics practice. However, the quantity of data, and the range of different data sources, can make it difficult to take systematic action on that data. Previous work in the literature has emphasised the need for and value of human meaning-making in the process of interpretation of data to transform it in to actionable intelligence. This paper describes a programme of human Data Wranglers deployed at the Open University, UK, charged with making sense of a range of data sources related to learning, analysing that data in the light of their understanding of practice in individual faculties/departments, and producing reports that summarise the key points and make actionable recommendations. The evaluation of and experience in this programme of work strongly supports the value of human meaning-makers in the learning analytics process, and suggests that barriers to organisational change in this area can be mitigated by embedding learning analytics work within strategic contexts, and working at an appropriate level and granularity of analysis.
{"title":"Data wranglers: human interpreters to help close the feedback loop","authors":"D. Clow","doi":"10.1145/2567574.2567603","DOIUrl":"https://doi.org/10.1145/2567574.2567603","url":null,"abstract":"Closing the feedback loop to improve learning is at the heart of good learning analytics practice. However, the quantity of data, and the range of different data sources, can make it difficult to take systematic action on that data. Previous work in the literature has emphasised the need for and value of human meaning-making in the process of interpretation of data to transform it in to actionable intelligence. This paper describes a programme of human Data Wranglers deployed at the Open University, UK, charged with making sense of a range of data sources related to learning, analysing that data in the light of their understanding of practice in individual faculties/departments, and producing reports that summarise the key points and make actionable recommendations. The evaluation of and experience in this programme of work strongly supports the value of human meaning-makers in the learning analytics process, and suggests that barriers to organisational change in this area can be mitigated by embedding learning analytics work within strategic contexts, and working at an appropriate level and granularity of analysis.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"271 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":"121551977","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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