Proceedings of the Sixth International Conference on Learning Analytics & Knowledge最新文献_第6页

Student perspectives on data provision and use: starting to unpack disciplinary differences 学生对数据提供和使用的看法:开始揭示学科差异

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883945

J. McPherson, H. L. Tong, S. Fatt, Danny Y. T. Liu

{"title":"Student perspectives on data provision and use: starting to unpack disciplinary differences","authors":"J. McPherson, H. L. Tong, S. Fatt, Danny Y. T. Liu","doi":"10.1145/2883851.2883945","DOIUrl":"https://doi.org/10.1145/2883851.2883945","url":null,"abstract":"How can we best align learning analytics practices with disciplinary knowledge practices in order to support student learning? Although learning analytics itself is an interdisciplinary field, it tends to take a 'one-size-fits-all' approach to the collection, measurement, and reporting of data, overlooking disciplinary knowledge practices. In line with a recent trend in higher education research, this paper considers the contribution of a realist sociology of education to the field of learning analytics, drawing on findings from recent student focus groups at an Australian university. It examines what learners say about their data needs with reference to organizing principles underlying knowledge practices within their disciplines. The key contribution of this paper is a framework that could be used as the basis for aligning the provision and/or use of data in relation to curriculum, pedagogy, and assessment with disciplinary knowledge practices. The framework extends recent research in Legitimation Code Theory, which understands disciplinary differences in terms of the principles that underpin knowledge-building. The preliminary analysis presented here both provides a tool for ensuring a fit between learning analytics practices and disciplinary practices and standards for achievement, and signals disciplinarity as an important consideration in learning analytics practices.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114445797","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}

引用次数: 18

Improving efficacy attribution in a self-directed learning environment using prior knowledge individualization 运用先验知识个性化改进自主学习环境下的效能归因

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883949

Z. Pardos, Yanbo Xu

{"title":"Improving efficacy attribution in a self-directed learning environment using prior knowledge individualization","authors":"Z. Pardos, Yanbo Xu","doi":"10.1145/2883851.2883949","DOIUrl":"https://doi.org/10.1145/2883851.2883949","url":null,"abstract":"Models of learning in EDM and LAK are pushing the boundaries of what can be measured from large quantities of historical data. When controlled randomization is present in the learning platform, such as randomized ordering of problems within a problem set, natural quasi-randomized controlled studies can be conducted, post-hoc. Difficulty and learning gain attribution are among factors of interest that can be studied with secondary analyses under these conditions. However, much of the content that we might like to evaluate for learning value is not administered as a random stimulus to students but instead is being self-selected, such as a student choosing to seek help in the discussion forums, wiki pages, or other pedagogically relevant material in online courseware. Help seekers, by virtue of their motivation to seek help, tend to be the ones who have the least knowledge. When presented with a cohort of students with a bi-modal or uniform knowledge distribution, this can present problems with model interpretability when a single point estimation is used to represent cohort prior knowledge. Since resource access is indicative of a low knowledge student, a model can tend towards attributing the resources with low or negative learning gain in order to better explain performance given the higher average prior point estimate. In this paper we present several individualized prior strategies and demonstrate how learning efficacy attribution validity and prediction accuracy improve as a result. Level of education attained, relative past assessment performance, and the prior per student cold start heuristic were employed and compared as prior knowledge individualization strategies.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077811","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}

引用次数: 6

The role of achievement goal orientations when studying effect of learning analytics visualizations 成就目标导向在学习分析可视化效果研究中的作用

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883904

Sanam Shirazi Beheshitha, M. Hatala, D. Gašević, Srécko Joksimovíc

{"title":"The role of achievement goal orientations when studying effect of learning analytics visualizations","authors":"Sanam Shirazi Beheshitha, M. Hatala, D. Gašević, Srécko Joksimovíc","doi":"10.1145/2883851.2883904","DOIUrl":"https://doi.org/10.1145/2883851.2883904","url":null,"abstract":"When designing learning analytics tools for use by learners we have an opportunity to provide tools that consider a particular learner's situation and the learner herself. To afford actual impact on learning, such tools have to be informed by theories of education. Particularly, educational research shows that individual differences play a significant role in explaining students' learning process. However, limited empirical research in learning analytics has investigated the role of theoretical constructs, such as motivational factors, that are underlying the observed differences between individuals. In this work, we conducted a field experiment to examine the effect of three designed learning analytics visualizations on students' participation in online discussions in authentic course settings. Using hierarchical linear mixed models, our results revealed that effects of visualizations on the quantity and quality of messages posted by students with differences in achievement goal orientations could either be positive or negative. Our findings highlight the methodological importance of considering individual differences and pose important implications for future design and research of learning analytics visualizations.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122579720","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}

引用次数: 57

Applying classification techniques on temporal trace data for shaping student behavior models 应用时间轨迹数据分类技术塑造学生行为模型

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883926

Z. Papamitsiou, E. Karapistoli, A. Economides

{"title":"Applying classification techniques on temporal trace data for shaping student behavior models","authors":"Z. Papamitsiou, E. Karapistoli, A. Economides","doi":"10.1145/2883851.2883926","DOIUrl":"https://doi.org/10.1145/2883851.2883926","url":null,"abstract":"Differences in learners' behavior have a deep impact on their educational performance. Consequently, there is a need to detect and identify these differences and build suitable learner models accordingly. In this paper, we report on the results from an alternative approach for dynamic student behavioral modeling based on the analysis of time-based student-generated trace data. The goal was to unobtrusively classify students according to their time-spent behavior. We applied 5 different supervised learning classification algorithms on these data, using as target values (class labels) the students' performance score classes during a Computer-Based Assessment (CBA) process, and compared the obtained results. The proposed approach has been explored in a study with 259 undergraduate university participant students. The analysis of the findings revealed that a) the low misclassification rates are indicative of the accuracy of the applied method and b) the ensemble learning (treeBagger) method provides better classification results compared to the others. These preliminary results are encouraging, indicating that a time-spent driven description of the students' behavior could have an added value towards dynamically reshaping the respective models.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218538","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}

引用次数: 15

Towards triggering higher-order thinking behaviors in MOOCs MOOCs中触发高阶思维行为的研究

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883964

Xu Wang, Miaomiao Wen, C. Rosé

引用次数: 72

Analyzing students' intentionality towards badges within a case study using Khan academy 在可汗学院的案例研究中分析学生对徽章的意向

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883947

José A. Ruipérez Valiente, P. Merino, C. D. Kloos

引用次数: 10

Sequencing educational content in classrooms using Bayesian knowledge tracing 利用贝叶斯知识追踪对课堂教学内容进行排序

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883885

Y. B. David, A. Segal, Y. Gal

{"title":"Sequencing educational content in classrooms using Bayesian knowledge tracing","authors":"Y. B. David, A. Segal, Y. Gal","doi":"10.1145/2883851.2883885","DOIUrl":"https://doi.org/10.1145/2883851.2883885","url":null,"abstract":"Despite the prevalence of e-learning systems in schools, most of today's systems do not personalize educational data to the individual needs of each student. This paper proposes a new algorithm for sequencing questions to students that is empirically shown to lead to better performance and engagement in real schools when compared to a baseline approach. It is based on using knowledge tracing to model students' skill acquisition over time, and to select questions that advance the student's learning within the range of the student's capabilities, as determined by the model. The algorithm is based on a Bayesian Knowledge Tracing (BKT) model that incorporates partial credit scores, reasoning about multiple attempts to solve problems, and integrating item difficulty. This model is shown to outperform other BKT models that do not reason about (or reason about some but not all) of these features. The model was incorporated into a sequencing algorithm and deployed in two classes in different schools where it was compared to a baseline sequencing algorithm that was designed by pedagogical experts. In both classes, students using the BKT sequencing approach solved more difficult questions and attributed higher performance than did students who used the expert-based approach. Students were also more engaged using the BKT approach, as determined by their interaction time and number of log-ins to the system, as well as their reported opinion. We expect our approach to inform the design of better methods for sequencing and personalizing educational content to students that will meet their individual learning needs.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323935","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}

引用次数: 38

Wikiglass: a learning analytic tool for visualizing collaborative wikis of secondary school students Wikiglass:一个用于中学生协作wiki可视化的学习分析工具

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883966

Xiao Hu, Jason Ip, Koossulraj Sadaful, George Lui, S. Chu

引用次数: 7

Introduction to data mining for educational researchers 教育研究人员数据挖掘导论

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883879

Christopher A. Brooks, Craig D. S. Thompson, Vitomir Kovanovíc

引用次数: 3

Using A/B testing in MOOC environments 在MOOC环境中使用A/B测试

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883876

Jan Renz, Daniel Hoffmann, T. Staubitz, C. Meinel

引用次数: 27