Proceedings of the 8th International Conference on Learning Analytics and Knowledge最新文献_第5页

Co-creation strategies for learning analytics 学习分析的共同创造策略

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170372

Mollie Dollinger, J. Lodge

引用次数: 70

Embracing imperfection in learning analytics 接受学习分析中的不完美

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170413

Kirsty Kitto, S. B. Shum, Andrew Gibson

引用次数: 62

The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors RAP系统:使用多模态分析和低成本传感器自动反馈口头陈述技巧

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170406

X. Ochoa, Federico Domínguez, B. Guamán, Ricardo Maya, Gabriel Falcones, Jaime Castells

引用次数: 53

Are MOOC forums changing? MOOC论坛在变化吗?

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170416

Oleksandra Poquet, Nia Dowell, Christopher A. Brooks, S. Dawson

引用次数: 15

What exactly do students learn when they practice equation solving?: refining knowledge components with the additive factors model 学生们在练习解方程的时候到底学到了什么?:用加性因子模型提炼知识成分

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170411

Yanjin Long, Kenneth Holstein, V. Aleven

{"title":"What exactly do students learn when they practice equation solving?: refining knowledge components with the additive factors model","authors":"Yanjin Long, Kenneth Holstein, V. Aleven","doi":"10.1145/3170358.3170411","DOIUrl":"https://doi.org/10.1145/3170358.3170411","url":null,"abstract":"Accurately modeling individual students' knowledge growth is important in many applications of learning analytics. A key step is to decompose the knowledge targeted in the instruction into detailed knowledge components (KCs). We search for an accurate KC model for basic equation solving skills, using data from an intelligent tutoring system (ITS), Lynnette. Key criteria are data fit and predictive accuracy based on a standard logistic model called the Additive Factors Model (AFM). We focus on three difficulty factors for equation solving: understanding of variables, the negative sign, and the complexity of the equation. Fine-grained KC models were found to have greater fit and predictive accuracy than an \"ideal,\" more abstract model, indicating that there is substantial under-generalization in students' equation-solving skill related to all three difficulty factors. The work enhances scientific understanding of the challenges students face in learning equation solving. It illustrates how learning analytics could inform the improvement of technology-enhanced learning environments.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"504 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123821440","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: 5

Explaining learning performance using response-time, self-regulation and satisfaction from content: an fsQCA approach 用反应时间、自我调节和内容满意度来解释学习表现:fsQCA方法

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170397

Z. Papamitsiou, A. Economides, I. Pappas, M. Giannakos

引用次数: 24

Unpacking the relationship between discussion forum participation and learning in MOOCs: content is key 解析mooc中论坛参与与学习之间的关系:内容是关键

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170403

A. Wise, Yi Cui

{"title":"Unpacking the relationship between discussion forum participation and learning in MOOCs: content is key","authors":"A. Wise, Yi Cui","doi":"10.1145/3170358.3170403","DOIUrl":"https://doi.org/10.1145/3170358.3170403","url":null,"abstract":"This study examined the relationship between discussion forum contributions and course assessment results in a statistics MOOC. An important feature of the study is that it distinguished between discussions that were related to the learning of course material (\"content-related\") and those which were not (\"non-content\"). Another contribution is that the study evaluated the additional usefulness of social centrality measures in predicting course grade after the quantity of forum contributions has been accounted for. Results showed that, overall, 15% of course learners contributed to the forums and these learners had a significantly higher rate of successfully passing the course than non-contributors (64% vs 32% passing). Learners who made posts to both content-related and non-content threads had a higher passing rate than those who only contributed to one type or the other. Among learners who successfully passed the course, there were no differences in course grade when comparing discussion contributors and non-contributors overall; however those who contributed to content-related threads performed slightly better than those who did not (course grade of 87% vs 85%). A predictive model based on the number of posts made to content-related threads explained a small proportion of variance in course grades; addition of social centrality measures did not significantly improve the variance explained by the model.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131576164","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: 42

(Dis)engagement matters: identifying efficacious learning practices with multimodal learning analytics (disengagement matters):用多模态学习分析识别有效的学习实践

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170420

M. Worsley

{"title":"(Dis)engagement matters: identifying efficacious learning practices with multimodal learning analytics","authors":"M. Worsley","doi":"10.1145/3170358.3170420","DOIUrl":"https://doi.org/10.1145/3170358.3170420","url":null,"abstract":"Video analysis is a staple of the education research community. For many contemporary education researchers, participation in the video coding process serves as a rite of passage. However, recent developments in multimodal learning analytics may help to accelerate and enhance this process by providing researchers with a more nuanced glimpse into a set of learning experiences. As an example of how to use multimodal learning analytics towards these ends, this paper includes a preliminary analysis from 54 college students, who completed two engineering design tasks in pairs. Gesture, speech and electro-dermal activation data were collected as students completed these tasks. The gesture data was used to learn a set of canonical clusters (N=4). A decision tree was trained based on individual students' cluster frequencies, and pre-post learning gains. The nodes in the decision tree were then used to identify a subset of video segments that were human coded based on prior work in learning analytics and engineering design. The combination of machine learning and human inference helps elucidate the practices that seem to correlate with student learning. In particular, both engagement and disengagement seem to correlate with student learning, albeit in a somewhat nuanced fashion.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124639638","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: 31

License to evaluate: preparing learning analytics dashboards for educational practice 评估许可:为教育实践准备学习分析仪表板

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170421

I. Jivet, Maren Scheffel, M. Specht, H. Drachsler

{"title":"License to evaluate: preparing learning analytics dashboards for educational practice","authors":"I. Jivet, Maren Scheffel, M. Specht, H. Drachsler","doi":"10.1145/3170358.3170421","DOIUrl":"https://doi.org/10.1145/3170358.3170421","url":null,"abstract":"Learning analytics can bridge the gap between learning sciences and data analytics, leveraging the expertise of both fields in exploring the vast amount of data generated in online learning environments. A typical learning analytics intervention is the learning dashboard, a visualisation tool built with the purpose of empowering teachers and learners to make informed decisions about the learning process. Related work has investigated learning dashboards, yet none have explored the theoretical foundation that should inform the design and evaluation of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our analysis revealed that very few dashboard evaluations take into account the educational concepts that were used as a theoretical foundation for their design. Furthermore, we report findings suggesting that comparison with peers, a common reference frame for contextualising information on learning analytics dashboards, was not perceived positively by all learners. We summarise the insights gathered through our literature review in a set of recommendations for the design and evaluation of learning analytics dashboards for learners.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036540","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: 210

Correlating affect and behavior in reasoning mind with state test achievement 推理思维中的情感和行为与状态测试成绩的关系

Proceedings of the 8th International Conference on Learning Analytics and Knowledge Pub Date : 2018-03-07 DOI: 10.1145/3170358.3170378

V. Kostyuk, M. Almeda, R. Baker

{"title":"Correlating affect and behavior in reasoning mind with state test achievement","authors":"V. Kostyuk, M. Almeda, R. Baker","doi":"10.1145/3170358.3170378","DOIUrl":"https://doi.org/10.1145/3170358.3170378","url":null,"abstract":"Previous studies have investigated the relationship between affect, behavior, and learning in blended learning systems. These articles have found that affect and behavior are closely linked with learning outcomes. In this paper, we attempt to replicate prior work on how affective states and behaviors relate to mathematics achievement, investigating these issues within the context of 5th-grade students in South Texas using a mathematics blended learning system, Reasoning Mind. We use automatic detectors of student behavior and affect, and correlate inferred rates of each behavior and affective state with the students' end-of-year standardized assessment score. A positive correlation between engaged concentration and test scores replicates previous studies, as does a negative correlation between boredom and test scores. However, our findings differ from previous findings relating to confusion, frustration, and off-task behavior, suggesting the importance of contextual factors for the relationship between behavior, affect, and learning. Our study represents a step in understanding how broadly findings on the relationships between affect/behavior and learning generalize across different learning platforms.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127628358","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