J. Learn. Anal.最新文献_第2页

Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education 六项实用建议，使预测学习分析在远程教育中的道德使用

J. Learn. Anal. Pub Date : 2023-02-28 DOI: 10.18608/jla.2023.7743

Irina Rets, C. Herodotou, Ann Gillespie

{"title":"Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education","authors":"Irina Rets, C. Herodotou, Ann Gillespie","doi":"10.18608/jla.2023.7743","DOIUrl":"https://doi.org/10.18608/jla.2023.7743","url":null,"abstract":"The progressive move of higher education institutions (HEIs) towards blended and online environments, accelerated by COVID-19, and their access to a greater variety of student data has heightened the need for ethical learning analytics (LA). This need is particularly salient in light of a lack of comprehensive, evidence-based guidelines on ethics that address gaps voiced in LA ethics research. Studies on the topic are predominantly conceptual, representing mainly institutional rather than stakeholder views, with some areas of ethics remaining underexplored. In this paper, we address this need by using a case of four years of interdisciplinary research in developing the award-winning Early Alerts Indicators (EAI) dashboard at a distance learning university. Through a lens focused on ethical considerations and informed by the practical approach to ethics, we conducted a case study review, using 10 relevant publications that report on the development and implementation of the tool. Our six practical recommendations on how to ethically engage with LA can inform an ethical development of LA that not only protects student privacy, but also ensures that LA tools are used in ways that effectively support student learning and development.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116899210","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: 1

Learning Analytics in Physics Education: Equity-Focused Decision-Making Lacks Guidance! 物理教育中的学习分析:以公平为中心的决策缺乏指导!

J. Learn. Anal. Pub Date : 2023-02-28 DOI: 10.18608/jla.2023.7793

Adrian Grimm, Anneke Steegh, M. Kubsch, K. Neumann

{"title":"Learning Analytics in Physics Education: Equity-Focused Decision-Making Lacks Guidance!","authors":"Adrian Grimm, Anneke Steegh, M. Kubsch, K. Neumann","doi":"10.18608/jla.2023.7793","DOIUrl":"https://doi.org/10.18608/jla.2023.7793","url":null,"abstract":"Learning Analytics are an academic field with promising usage scenarios for many educational domains. At the same time, learning analytics come with threats such as the amplification of historically grown inequalities. A range of general guidelines for more equity-focused learning analytics have been proposed but fail to provide sufficiently clear guidance for practitioners. With this paper, we attempt to address this theory–practice gap through domain-specific (physics education) refinement of the general guidelines We propose a process as a starting point for this domain-specific refinement that can be applied to other domains as well. Our point of departure is a domain-specific analysis of historically grown inequalities in order to identify the most relevant diversity categories and evaluation criteria. Through two focal points for normative decision-making, namely equity and bias, we analyze two edge cases and highlight where domain-specific refinement of general guidance is necessary. Our synthesis reveals a necessity to work towards domain-specific standards and regulations for bias analyses and to develop counter-measures against (intersectional) discrimination. Ultimately, this should lead to a stronger equity-focused practice in future.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129461313","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: 2

Learning Analytics: Practitioners, Take Note: Journal of Learning Analytics Editorial 2022 学习分析:实践者，请注意:学习分析杂志编辑2022

J. Learn. Anal. Pub Date : 2022-12-16 DOI: 10.18608/jla.2022.7937

Rebecca Ferguson, X. Ochoa, Vitomir Kovanovíc

引用次数: 0

Analytics for Game-Based Learning 基于游戏的学习分析

J. Learn. Anal. Pub Date : 2022-12-16 DOI: 10.18608/jla.2022.7929

Y. Kim, José A. Ruipérez Valiente, Dirk Ifenthaler, Erik Harpstead, Elizabeth Rowe

引用次数: 1

Causal Inference and Bias in Learning Analytics: A Primer on Pitfalls Using Directed Acyclic Graphs 学习分析中的因果推理和偏差:有向无环图陷阱入门

J. Learn. Anal. Pub Date : 2022-12-13 DOI: 10.18608/jla.2022.7577

Joshua Weidlich, D. Gašević, H. Drachsler

引用次数: 4

Privacy in LA Research: Understanding the Field to Improve the Practice 洛杉矶研究中的隐私:理解领域以改进实践

J. Learn. Anal. Pub Date : 2022-12-13 DOI: 10.18608/jla.2022.7751

Olga Viberg, Chantal Mutimukwe, Å. Grönlund

引用次数: 1

Understanding Student Navigation Patterns in Game-Based Learning 理解基于游戏的学习中的学生导航模式

J. Learn. Anal. Pub Date : 2022-12-13 DOI: 10.18608/jla.2022.7637

Min Liu, Ying Cai, Song-Ae Han, Peixia Shao

引用次数: 1

Game Learning Analytics:: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning 游戏学习分析:混合视觉和数据挖掘技术，以提高严肃游戏和更好地理解玩家的学习

J. Learn. Anal. Pub Date : 2022-12-09 DOI: 10.18608/jla.2022.7633

Cristina Alonso-Fernández, Antonio Calvo-Morata, M. Freire, I. Martínez-Ortiz, Baltasar Fernandez-Manjon

{"title":"Game Learning Analytics:: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning","authors":"Cristina Alonso-Fernández, Antonio Calvo-Morata, M. Freire, I. Martínez-Ortiz, Baltasar Fernandez-Manjon","doi":"10.18608/jla.2022.7633","DOIUrl":"https://doi.org/10.18608/jla.2022.7633","url":null,"abstract":"Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590208","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: 2

Developing a Stealth Assessment System Using a Continuous Conjunctive Model 基于连续连接模型的隐身评估系统开发

J. Learn. Anal. Pub Date : 2022-12-07 DOI: 10.18608/jla.2022.7639

Jiaqi Yu, Wenchao Ma, Jewoong Moon, André R. Denham

{"title":"Developing a Stealth Assessment System Using a Continuous Conjunctive Model","authors":"Jiaqi Yu, Wenchao Ma, Jewoong Moon, André R. Denham","doi":"10.18608/jla.2022.7639","DOIUrl":"https://doi.org/10.18608/jla.2022.7639","url":null,"abstract":"Integrating learning analytics in digital game-based learning has gained popularity in recent decades. The interactive nature of educational games creates an ideal environment for learning analytics data collection. However, past research has limited success in producing accessible and effective assessments using game learning analytics. In this study, a mathematics educational game called The Nomads was designed and developed to train learners’ adaptive expertise in rational number arithmetic. Players’ game log data were captured and fitted to a cognitive diagnostic model (CDM) — CCM (continuous conjunctive model). CCM lends itself well to the complex and dynamic nature of game learning analytics. Unlike traditional CDMs, CCM generates parameters at an attribute level and offers more parsimonious diagnoses using continuous variables. The findings suggest that learners’ attribute mastery improved during the gameplay and that learners benefit from using the scaffolds for three of the attributes instructed by the game. This study presents the application of a powerful new tool for game learning analytics. Future studies can benefit from more generalized analytics models and more specified learning attributes and game tasks.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132344722","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: 0

Multimodal Data Fusion to Track Students' Distress during Educational Gameplay 多模态数据融合跟踪学生在教育游戏中的苦恼

J. Learn. Anal. Pub Date : 2022-11-20 DOI: 10.18608/jla.2022.7631

Jewoong Moon, Fengfeng Ke, Zlatko Sokolikj, Ibrahim Dahlstrom‐Hakki

{"title":"Multimodal Data Fusion to Track Students' Distress during Educational Gameplay","authors":"Jewoong Moon, Fengfeng Ke, Zlatko Sokolikj, Ibrahim Dahlstrom‐Hakki","doi":"10.18608/jla.2022.7631","DOIUrl":"https://doi.org/10.18608/jla.2022.7631","url":null,"abstract":"Using multimodal data fusion techniques, we built and tested prediction models to track middle-school student distress states during educational gameplay. We collected and analyzed 1,145 data instances, sampled from a total of 31 middle-school students’ audio- and video-recorded gameplay sessions. We conducted data wrangling with student gameplay data from multiple data sources, such as individual facial expression recordings and gameplay logs. Using supervised machine learning, we built and tested candidate classifiers that yielded an estimated probability of distress states. We then conducted confidence-based data fusion that averaged the estimated probability scores from the unimodal classifiers with a single data source. The results of this study suggest that the classifier with multimodal data fusion improves the performance of tracking distress states during educational gameplay, compared to the performance of unimodal classifiers. The study finding suggests the feasibility of multimodal data fusion in developing game-based learning analytics. Also, this study proposes the benefits of optimizing several methodological means for multimodal data fusion in educational game research.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593997","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: 0