LAK21: 11th International Learning Analytics and Knowledge Conference最新文献_第4页

Charting the Design and Analytics Agenda of Learnersourcing Systems 绘制学习者资源系统的设计和分析议程

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448143

Hassan Khosravi, Gianluca Demartini, S. Sadiq, D. Gašević

{"title":"Charting the Design and Analytics Agenda of Learnersourcing Systems","authors":"Hassan Khosravi, Gianluca Demartini, S. Sadiq, D. Gašević","doi":"10.1145/3448139.3448143","DOIUrl":"https://doi.org/10.1145/3448139.3448143","url":null,"abstract":"Learnersourcing is emerging as a viable learner-centred and pedagogically justified approach for harnessing the creativity and evaluation power of learners as experts-in-training. Despite the increasing adoption of learnersourcing in higher education, understanding students’ behaviour while engaged in learnersourcing and best practices for the design and development of learnersourcing systems are still largely under-researched. This paper offers data-driven reflections and lessons learned from the development and deployment of a learnersourcing adaptive educational system called RiPPLE, which to date, has been used in more than 50-course offerings with over 12,000 students. Our reflections are categorised into examples and best practices on (1) assessing the quality of students’ contributions using accurate, explainable and fair approaches to data analysis, (2) incentivising students to develop high-quality contributions and (3) empowering instructors with actionable and explainable insights to guide student learning. We discuss the implications of these findings and how they may contribute to the growing literature on the development of effective learnersourcing systems and more broadly technological educational solutions that support learner-centred learning at scale.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123372521","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: 29

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments 在基于协作游戏的学习环境中检测学生聊天讨论中的破坏性谈话

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448178

Kyungjin Park, Hyunwoo Sohn, Bradford W. Mott, Wookhee Min, Asmalina Saleh, Krista D. Glazewski, C. Hmelo‐Silver, James C. Lester

{"title":"Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments","authors":"Kyungjin Park, Hyunwoo Sohn, Bradford W. Mott, Wookhee Min, Asmalina Saleh, Krista D. Glazewski, C. Hmelo‐Silver, James C. Lester","doi":"10.1145/3448139.3448178","DOIUrl":"https://doi.org/10.1145/3448139.3448178","url":null,"abstract":"Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students’ in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students’ learning outcomes and varies across gender and students’ prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128317241","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: 11

Detecting High Orders of Cognitive Complexity in Students’ Reasoning in Argumentative Writing About Ocean Acidification 发现学生在海洋酸化议论文推理中的高阶认知复杂性

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448202

Blair A. Winograd, Amber J. Dood, R. Moeller, A. Moon, A. Gere, G. Shultz

{"title":"Detecting High Orders of Cognitive Complexity in Students’ Reasoning in Argumentative Writing About Ocean Acidification","authors":"Blair A. Winograd, Amber J. Dood, R. Moeller, A. Moon, A. Gere, G. Shultz","doi":"10.1145/3448139.3448202","DOIUrl":"https://doi.org/10.1145/3448139.3448202","url":null,"abstract":"Providing students in STEM courses the opportunity to write about scientific content can be beneficial to the learning process. However, it is a logistical challenge to provide feedback to students’ written work in large-enrollment courses. Motivated by these reasons, the study presented herein considers a method to identify the depth of students’ scientific reasoning in their written work. A writing-to-learn (WTL) activity was implemented in a large undergraduate general chemistry class. An analytical framework of cognitive operations that characterizes students’ scientific reasoning evidenced in their writing was applied. Engagement in some of the more complex cognitive operations, such as causal reasoning and argumentation, was a sign that students were properly engaging in meaning making activities. This work considers a method to automate coaching of students in using more complex reasoning in their writing with the desired outcome that it may help students better engage with the science content. We consider a series of new natural language processing models to discern types of reasoning in student essays from the WTL activity.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127204475","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: 4

Early Alert Systems During a Pandemic: A Simulation Study on the Impact of Concept Drift 大流行期间的预警系统:概念漂移影响的模拟研究

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448190

Yang Xu, K. Wilson

引用次数: 8

The impact of automatic text translation on classification of online discussions for social and cognitive presences 自动文本翻译对社交和认知在场在线讨论分类的影响

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448147

A. Barbosa, Maverick Andre Dionisio Ferreira, R. F. Mello, R. Lins, D. Gašević

引用次数: 14

Modelling Learners in Adaptive Educational Systems: A Multivariate Glicko-based Approach 自适应教育系统中的学习者建模:基于多元glicko的方法

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448189

Solmaz Abdi, Hassan Khosravi, S. Sadiq

{"title":"Modelling Learners in Adaptive Educational Systems: A Multivariate Glicko-based Approach","authors":"Solmaz Abdi, Hassan Khosravi, S. Sadiq","doi":"10.1145/3448139.3448189","DOIUrl":"https://doi.org/10.1145/3448139.3448189","url":null,"abstract":"The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. A common characteristic across Elo-based learner models is that they are not sensitive to the lag time between two consecutive interactions of a student within the system. Implicitly, this characteristic assumes that students do not learn or forget between two consecutive interactions. However, this assumption seems insufficient in the context of adaptive learning systems where students could have improved their mastery through practising outside of the system or that their mastery may be declined due to forgetting. In this paper, we extend the existing works on the use of rating systems for modelling learners in adaptive educational systems by proposing a new learner model called MV-Glicko that builds on the Glicko rating system. MV-Glicko is sensitive to the lag time between two consecutive interactions of a student within the system and models it as a parameter that captures the confidence of the system in the current inferred rating. We apply MV-Glicko on three public data sets and three data sets obtained from an adaptive learning system and provide evidence that MV-Glicko outperforms other conventional models in estimating students’ knowledge mastery.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133860048","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: 7

Do Instrumentation Tools Capture Self-Regulated Learning? 仪表工具捕获自我调节学习吗?

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448181

J. Graaf, Lyn Lim, Yizhou Fan, J. Kilgour, Johanna D. Moore, M. Bannert, D. Gašević, I. Molenaar

{"title":"Do Instrumentation Tools Capture Self-Regulated Learning?","authors":"J. Graaf, Lyn Lim, Yizhou Fan, J. Kilgour, Johanna D. Moore, M. Bannert, D. Gašević, I. Molenaar","doi":"10.1145/3448139.3448181","DOIUrl":"https://doi.org/10.1145/3448139.3448181","url":null,"abstract":"Researchers have been struggling with the measurement of Self-Regulated Learning (SRL) for decades. Instrumentation tools have been proposed to help capture SRL processes that are difficult to capture. The aim of the present study was to improve measurement of SRL by embedding instrumentation tools in a learning environment and validating the measurement of SRL with these instrumentation tools using think aloud. Synchronizing log data and concurrent think aloud data helped identify which SRL processes were captured by particular instrumentation tools. One tool was associated with a single SRL process: the timer co-occurred with monitoring. Other tools co-occurred with a number of SRL processes, i.e., the highlighter and note taker captured superficial writing down, organizing, and monitoring, whereas the search and planner tools revealed planning and monitoring. When specific learner actions with the tool were analyzed, a clearer picture emerged of the relation between the highlighter and note taker and SRL processes. By aligning log data with think aloud data, we showed that instrumentation tool use indeed reflects SRL processes. The main contribution is that this paper is the first to show that SRL processes that are difficult to measure by trace data can indeed be captured by instrumentation tools such as high cognition and metacognition. Future challenges are to collect and process log data real time with learning analytic techniques to measure ongoing SRL processes and support learners during learning with personalized SRL scaffolds.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117164896","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: 14

Measuring Students’ Self-Regulatory Phases in LMS with Behavior and Real-Time Self Report 用行为和实时自我报告测量学生在LMS中的自我调节阶段

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448164

Fatemeh Salehian Kia, M. Hatala, R. Baker, Stephanie D. Teasley

{"title":"Measuring Students’ Self-Regulatory Phases in LMS with Behavior and Real-Time Self Report","authors":"Fatemeh Salehian Kia, M. Hatala, R. Baker, Stephanie D. Teasley","doi":"10.1145/3448139.3448164","DOIUrl":"https://doi.org/10.1145/3448139.3448164","url":null,"abstract":"Research has emphasized that self-regulated learning (SRL) is critically important for learning. However, students have different capabilities of regulating their learning processes and individual needs. To help students improve their SRL capabilities, we need to identify students’ current behaviors. Specifically, we applied instructional design to create visible and meaningful markers of student learning at different points in time in LMS logs. We adopted knowledge engineering to develop a framework of proximal indicators representing SRL phases and evaluated them in a quasi-experiment in two different learning activities. A comparison of two sources of collected students’ SRL data, self-reported and trace data, revealed a relatively high agreement between our classifications (weighted kappa, κ = .74 and κ = .68). However, our indicators did not always discriminate adjacent SRL phases, particularly for enactment and adapting phases, compared with students’ real-time self-reported behaviors. Our behavioral indicators also were comparably successful at classifying SRL phases for different self-regulatory engagement levels. This study demonstrated how the triangulation of various sources of students’ self-regulatory data could help to unravel the complex nature of metacognitive processes.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755669","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: 11

Footprints at School: Modelling In-class Social Dynamics from Students’ Physical Positioning Traces 足迹在学校:从学生的身体定位轨迹建模课堂社会动态

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448144

Lixiang Yan, Roberto Martínez Maldonado, Beatriz Gallo Cordoba, Joanne Deppeler, D. Corrigan, Gloria Fernández-Nieto, D. Gašević

{"title":"Footprints at School: Modelling In-class Social Dynamics from Students’ Physical Positioning Traces","authors":"Lixiang Yan, Roberto Martínez Maldonado, Beatriz Gallo Cordoba, Joanne Deppeler, D. Corrigan, Gloria Fernández-Nieto, D. Gašević","doi":"10.1145/3448139.3448144","DOIUrl":"https://doi.org/10.1145/3448139.3448144","url":null,"abstract":"Schools are increasingly becoming into complex learning spaces where students interact with various physical and digital resources, educators, and peers. Although the field of learning analytics has advanced in analysing logs captured from digital tools, less progress has been made in understanding the social dynamics that unfold in physical learning spaces. Among the various rapidly emerging sensing technologies, position tracking may hold promises to reveal salient aspects of activities in physical learning spaces such as the formation of interpersonal ties among students. This paper explores how granular x-y physical positioning data can be analysed to model social interactions among students and teachers. We conducted an 8-week longitudinal study in which positioning traces of 98 students and six teachers were automatically captured every day in an open-plan public primary school. Positioning traces were analysed using social network analytics (SNA) to extract a set of metrics to characterise students’ positioning behaviours and social ties at cohort and individual levels. Results illustrate how analysing positioning traces through the lens of SNA can enable the identification of certain pedagogical approaches that may be either promoting or discouraging in-class social interaction, and students who may be socially isolated.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400844","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

Student appreciation of data-driven feedback: A pilot study on OnTask 学生对数据驱动反馈的欣赏:OnTask的试点研究

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448212

Yi-Shan Tsai, R. F. Mello, J. Jovanović, D. Gašević

引用次数: 23