Proceedings of the Sixth International Conference on Learning Analytics & Knowledge最新文献_第8页

A gaze-based learning analytics model: in-video visual feedback to improve learner's attention in MOOCs 基于注视的学习分析模型:视频中视觉反馈提高mooc学习者注意力

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883902

K. Sharma, Hamed S. Alavi, Patrick Jermann, P. Dillenbourg

{"title":"A gaze-based learning analytics model: in-video visual feedback to improve learner's attention in MOOCs","authors":"K. Sharma, Hamed S. Alavi, Patrick Jermann, P. Dillenbourg","doi":"10.1145/2883851.2883902","DOIUrl":"https://doi.org/10.1145/2883851.2883902","url":null,"abstract":"In the context of MOOCs, \"With-me-ness\" refers to the extent to which the learner succeeds in following the teacher, specifically in terms of looking at the area in the video that the teacher is explaining. In our previous works, we employed eye-tracking methods to quantify learners' With-me-ness and showed that it is positively correlated with their learning gains. In this contribution, we describe a tool that is designed to improve With-me-ness by providing a visual-aid superimposed on the video. The position of the visual-aid is suggested by the teachers' dialogue and deixis, and it is displayed when the learner's With-me-ness is under the average value, which is computed from the other students' gaze behavior. We report on a user-study that examines the effectiveness of the proposed tool. The results show that it significantly improves the learning gain and it significantly increases the extent to which the students follow the teacher. Finally, we demonstrate how With-me-ness can create a complete theoretical framework for conducting gaze-based learning analytics in the context of MOOCs.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"1 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":"131016166","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: 34

The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment 学习基础设施(ALI)的评估:自动化评估的理论、实践和可扩展性

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883872

Korinn S. Ostrow, Douglas Selent, Yan Wang, E. V. Inwegen, N. Heffernan, J. Williams

{"title":"The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment","authors":"Korinn S. Ostrow, Douglas Selent, Yan Wang, E. V. Inwegen, N. Heffernan, J. Williams","doi":"10.1145/2883851.2883872","DOIUrl":"https://doi.org/10.1145/2883851.2883872","url":null,"abstract":"Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"1 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":"129759273","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: 19

Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures 使用先决技能数据预测学生在后先决技能上的表现:一种改进先决技能结构的替代方法

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883867

Seth A. Adjei, Anthony F. Botelho, N. Heffernan

引用次数: 14

Learning analytics in practice: the effects of adaptive educational technology Snappet on students' arithmetic skills 实践中的学习分析:适应性教育技术snap对学生算术技能的影响

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883892

I. Molenaar, C. K. Campen

{"title":"Learning analytics in practice: the effects of adaptive educational technology Snappet on students' arithmetic skills","authors":"I. Molenaar, C. K. Campen","doi":"10.1145/2883851.2883892","DOIUrl":"https://doi.org/10.1145/2883851.2883892","url":null,"abstract":"Even though the recent influx of tablets in primary education goes together with the vision that educational technology empowered with learning analytics will revolutionize education, empirical results supporting this claim are scares. Adaptive educational technology Snappet combines extracted and embedded learning analytics daily in classrooms. While students make exercises on the tablet this technology displays real-time data of learner performance in a teacher dashboard (extracted analytics). At the same time, learner performance is used to adaptively adjust exercises to students' progress (embedded analytics). This quasiexperimental study compares the development of students' arithmetic skills over one schoolyear (grade 2 and 4) in a traditional paper based setting to learning with the adaptive educational technology Snappet. The results indicate that students in the Snappet condition make significantly more progress on arithmetic skills in grade 4. Moreover, in this grade students with a high ability level, benefit the most from working with this adaptive educational technology. Overall the development pattern of students with different abilities was more divergent in the AET condition compared to the control condition. These results indicate that adaptive educational technologies combining extracted and embedded learning analytics are indeed creating new education scenarios that contribute to personalized learning in primary education.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"38 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":"132015431","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

Enhancing the efficiency and reliability of group differentiation through partial credit 通过部分信用提高群体分化的效率和可靠性

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883910

Yan Wang, Korinn S. Ostrow, J. Beck, N. Heffernan

引用次数: 3

Automating assessment of collaborative writing quality in multiple stages: the case of wiki 多阶段协作写作质量的自动化评估:以wiki为例

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883963

Xiao Hu, Tzi-Dong Jeremy Ng, L. Tian, Chi-Un Lei

引用次数: 5

Analysing engagement in an online management programme and implications for course design 分析在线管理课程的参与情况及其对课程设计的影响

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883894

M. Wells, A. Wollenschlaeger, D. Lefevre, G. D. Magoulas, A. Poulovassilis

引用次数: 10

Modeling common misconceptions in learning process data 对学习过程数据中的常见误解进行建模

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883967

Ran Liu, Rony Patel, K. Koedinger

{"title":"Modeling common misconceptions in learning process data","authors":"Ran Liu, Rony Patel, K. Koedinger","doi":"10.1145/2883851.2883967","DOIUrl":"https://doi.org/10.1145/2883851.2883967","url":null,"abstract":"Student mistakes are often not random but, rather, reflect thoughtful yet incorrect strategies. In order for educational technologies to make full use of students' performance data to estimate the knowledge of a student, it is important to model not only the conceptions but also the misconceptions that a student's particular pattern of successes and errors may indicate. The student models that drive the \"outer loop\" of Intelligent Tutoring Systems typically do not represent or track misconceptions. Here, we present a method of representing misconceptions in the Knowledge Component models, or Q-Matrices, that are used by student models to estimate latent knowledge. We show, in a case study on a fraction arithmetic dataset, that incorporating a misconception into the Knowledge Component model dramatically improves the overall model's fit to data. We also derive qualitative insights from comparing predicted learning curves across models that incorporate varying misconception-related parameters. Finally, we show that the inclusion of a misconception in the Knowledge Component model can yield individual student estimates of misconception strength that are significantly correlated with out-of-tutor measures of student errors.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"42 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":"128606574","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: 26

Learning analytics for workplace and professional learning 工作场所和专业学习的学习分析

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883860

Tobias Ley, R. Klamma, Stefanie N. Lindstaedt, Fridolin Wild

引用次数: 9

Evaluation of an adaptive practice system for learning geography facts 地理事实学习适应性实践体系评价

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883884

Jan Papousek, V. Stanislav, Radek Pelánek

引用次数: 20