To statistically model large data sets of knowledge processes during asynchronous, online forums, we must address analytic difficulties involving the whole data set (missing data, nested data and the tree structure of online messages), dependent variables (multiple, infrequent, discrete outcomes and similar adjacent messages), and explanatory variables (sequences, indirect effects, false positives, and robustness). Statistical discourse analysis (SDA) addresses all of these issues, as shown in an analysis of 1,330 asynchronous messages written and self-coded by 17 students during a 13-week online educational technology course. The results showed how attributes at multiple levels (individual and message) affected knowledge creation processes. Men were more likely than women to theorize. Asynchronous messages created a micro-sequence context; opinions and asking about purpose preceded new information; anecdotes, opinions, different opinions, elaborating ideas, and asking about purpose or information preceded theorizing. These results show how informal thinking precedes formal thinking and how social metacognition affects knowledge creation.
{"title":"Statistical discourse analysis of online discussions: informal cognition, social metacognition and knowledge creation","authors":"M. Chiu, Nobuko Fujita","doi":"10.1145/2567574.2567580","DOIUrl":"https://doi.org/10.1145/2567574.2567580","url":null,"abstract":"To statistically model large data sets of knowledge processes during asynchronous, online forums, we must address analytic difficulties involving the whole data set (missing data, nested data and the tree structure of online messages), dependent variables (multiple, infrequent, discrete outcomes and similar adjacent messages), and explanatory variables (sequences, indirect effects, false positives, and robustness). Statistical discourse analysis (SDA) addresses all of these issues, as shown in an analysis of 1,330 asynchronous messages written and self-coded by 17 students during a 13-week online educational technology course. The results showed how attributes at multiple levels (individual and message) affected knowledge creation processes. Men were more likely than women to theorize. Asynchronous messages created a micro-sequence context; opinions and asking about purpose preceded new information; anecdotes, opinions, different opinions, elaborating ideas, and asking about purpose or information preceded theorizing. These results show how informal thinking precedes formal thinking and how social metacognition affects knowledge creation.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"60 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":"114347068","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}
MOOCs have the potential to help institutions and students needing remedial English language instruction in two ways. First, with their capacity to use a wide range of instructional approaches and to emphasize contextualized and visual learning, MOOCS can offer potentially more effective pedagogical approaches for remedial students. Second, if students increase success meeting college-level English competencies, MOOCS can help institutions and students conserve their limited resources. Similarly, MOOCs offer domestically and international employers opportunities to provide professional development to workers both in ways that are flexible, affordable and interactive.
{"title":"Patterns of persistence: what engages students in a remedial english writing MOOC?","authors":"John Whitmer, Eva Schiorring, Patrick James","doi":"10.1145/2567574.2567601","DOIUrl":"https://doi.org/10.1145/2567574.2567601","url":null,"abstract":"MOOCs have the potential to help institutions and students needing remedial English language instruction in two ways. First, with their capacity to use a wide range of instructional approaches and to emphasize contextualized and visual learning, MOOCS can offer potentially more effective pedagogical approaches for remedial students. Second, if students increase success meeting college-level English competencies, MOOCS can help institutions and students conserve their limited resources. Similarly, MOOCs offer domestically and international employers opportunities to provide professional development to workers both in ways that are flexible, affordable and interactive.","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":"121252121","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}
D. Gašević, C. Rosé, George Siemens, A. Wolff, Z. Zdráhal
Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.
{"title":"Learning analytics and machine learning","authors":"D. Gašević, C. Rosé, George Siemens, A. Wolff, Z. Zdráhal","doi":"10.1145/2567574.2567633","DOIUrl":"https://doi.org/10.1145/2567574.2567633","url":null,"abstract":"Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"137 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":"128064738","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 describe a process of constructing proxy variables that represent adult learners' time management strategies in an online course. Based upon previous research, three values were selected from a data set. According to the result of empirical validation, an (ir)regularity of the learning interval was proven to be correlative with and predict learning performance. As indicated in previous research, regularity of learning is a strong indicator to explain learners' consistent endeavors. This study demonstrates the possibility of using learning analytics to address a learner's specific competence on the basis of a theoretical background. Implications for the learning analytics field seeking a pedagogical theory-driven approach are discussed.
{"title":"Analyzing the log patterns of adult learners in LMS using learning analytics","authors":"I. Jo, Dongho Kim, Meehyun Yoon","doi":"10.1145/2567574.2567616","DOIUrl":"https://doi.org/10.1145/2567574.2567616","url":null,"abstract":"In this paper, we describe a process of constructing proxy variables that represent adult learners' time management strategies in an online course. Based upon previous research, three values were selected from a data set. According to the result of empirical validation, an (ir)regularity of the learning interval was proven to be correlative with and predict learning performance. As indicated in previous research, regularity of learning is a strong indicator to explain learners' consistent endeavors. This study demonstrates the possibility of using learning analytics to address a learner's specific competence on the basis of a theoretical background. Implications for the learning analytics field seeking a pedagogical theory-driven approach are discussed.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"65 14 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":"130536056","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 often think math is unrelated to their own interests. Instructional software has the potential to provide personalized instruction that responds to individuals' interests. Carnegie Learning's MATHia™ software for middle school mathematics asks learners to specify domains of their interest (e.g., sports & fitness, arts & music), as well as names of friends/classmates, and uses this information to both choose and personalize word problems for individual learners. Our analysis of MATHia's relatively coarse-grained personalization contrasts with more finegrained analysis in previous research on word problems in the Cognitive Tutor (e.g., finding effects on performance in parts of problems that depend on more difficult skills), and we explore associations of aggregate preference "honoring" with learner performance. To do so, we define a notion of "strong" learner interest area preferences and find that honoring such preferences has a small negative association with performance. However, learners that both merely express preferences (either interest area preferences or setting names of friends/classmates), and those that express strong preferences, tend to perform in ways that are associated with better learning compared to learners that do not express such preferences. We consider several explanations of these findings and suggest important topics for future research.
{"title":"Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics","authors":"Stephen E. Fancsali, Steven Ritter","doi":"10.1145/2567574.2567615","DOIUrl":"https://doi.org/10.1145/2567574.2567615","url":null,"abstract":"Learners often think math is unrelated to their own interests. Instructional software has the potential to provide personalized instruction that responds to individuals' interests. Carnegie Learning's MATHia™ software for middle school mathematics asks learners to specify domains of their interest (e.g., sports & fitness, arts & music), as well as names of friends/classmates, and uses this information to both choose and personalize word problems for individual learners. Our analysis of MATHia's relatively coarse-grained personalization contrasts with more finegrained analysis in previous research on word problems in the Cognitive Tutor (e.g., finding effects on performance in parts of problems that depend on more difficult skills), and we explore associations of aggregate preference \"honoring\" with learner performance. To do so, we define a notion of \"strong\" learner interest area preferences and find that honoring such preferences has a small negative association with performance. However, learners that both merely express preferences (either interest area preferences or setting names of friends/classmates), and those that express strong preferences, tend to perform in ways that are associated with better learning compared to learners that do not express such preferences. We consider several explanations of these findings and suggest important topics for future research.","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":"121513347","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}
Welcome to the third edition of the Learning Analytics and Knowledge conference. This year, the medieval and, at the same time, modern city of Leuven, Belgium is the venue where researchers and practitioners of this exciting field come together to discuss current status and future trends. Similar to Leuven, Learning Analytics is an old and new field at the same time. Old, because it deals with a problem that exists since Plato's times: how to improve the way students learn. New, because the tools used to achieve this goal, like Big Data and natural language processing, were not feasible merely 10 years ago. Leuven is also the home of beautiful centuries old buildings filled with young, smart and active students. In Learning Analytics, we can also find established researchers in the fields of Educational Research and Technology-Enhanced Learning, collaborating with a large contingent of new and promising researchers that could be called Learning Data Scientists.
{"title":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","authors":"D. Suthers, K. Verbert, E. Duval, X. Ochoa","doi":"10.1145/2567574","DOIUrl":"https://doi.org/10.1145/2567574","url":null,"abstract":"Welcome to the third edition of the Learning Analytics and Knowledge conference. This year, the medieval and, at the same time, modern city of Leuven, Belgium is the venue where researchers and practitioners of this exciting field come together to discuss current status and future trends. Similar to Leuven, Learning Analytics is an old and new field at the same time. Old, because it deals with a problem that exists since Plato's times: how to improve the way students learn. New, because the tools used to achieve this goal, like Big Data and natural language processing, were not feasible merely 10 years ago. Leuven is also the home of beautiful centuries old buildings filled with young, smart and active students. In Learning Analytics, we can also find established researchers in the fields of Educational Research and Technology-Enhanced Learning, collaborating with a large contingent of new and promising researchers that could be called Learning Data Scientists.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114225769","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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