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Field report for Platform mBox: Designing an Open MMLA Platform 平台 mBox 的实地报告：设计开放式 MMLA 平台
International Conference on Learning Analytics and Knowledge Pub Date : 2024-03-18 DOI: 10.1145/3636555.3636872
Zaibei Li, Martin Thoft Jensen, Alexander Nolte, Daniel Spikol
{"title":"Field report for Platform mBox: Designing an Open MMLA Platform","authors":"Zaibei Li, Martin Thoft Jensen, Alexander Nolte, Daniel Spikol","doi":"10.1145/3636555.3636872","DOIUrl":"https://doi.org/10.1145/3636555.3636872","url":null,"abstract":"","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":" 20","pages":"785-791"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390407","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
Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial 分析学生对数学学习对话式人工智能的关注度和接受度：随机对照试验的启示
International Conference on Learning Analytics and Knowledge Pub Date : 2024-03-18 DOI: 10.1145/3636555.3636895
Chenglu Li, Wangda Zhu, Wanli Xing, Rui Guo
{"title":"Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial","authors":"Chenglu Li, Wangda Zhu, Wanli Xing, Rui Guo","doi":"10.1145/3636555.3636895","DOIUrl":"https://doi.org/10.1145/3636555.3636895","url":null,"abstract":"","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":" 11","pages":"836-842"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390640","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
Effecti-Net: A Multimodal Framework and Database for Educational Content Effectiveness Analysis Effecti-Net：用于教育内容有效性分析的多模式框架和数据库
International Conference on Learning Analytics and Knowledge Pub Date : 2024-03-18 DOI: 10.1145/3636555.3636928
Deep Dwivedi, Ritik Garg, Shiva Baghel, Rushil Thareja, Ritvik Kulshrestha, Mukesh Mohania, Jainendra Shukla
{"title":"Effecti-Net: A Multimodal Framework and Database for Educational Content Effectiveness Analysis","authors":"Deep Dwivedi, Ritik Garg, Shiva Baghel, Rushil Thareja, Ritvik Kulshrestha, Mukesh Mohania, Jainendra Shukla","doi":"10.1145/3636555.3636928","DOIUrl":"https://doi.org/10.1145/3636555.3636928","url":null,"abstract":"","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":" 4","pages":"667-677"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390851","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
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning 大语言模型增强练习检索，促进个性化语言学习
International Conference on Learning Analytics and Knowledge Pub Date : 2024-02-08 DOI: 10.1145/3636555.3636883
Austin Xu, Will Monroe, K. Bicknell
{"title":"Large Language Model Augmented Exercise Retrieval for Personalized Language Learning","authors":"Austin Xu, Will Monroe, K. Bicknell","doi":"10.1145/3636555.3636883","DOIUrl":"https://doi.org/10.1145/3636555.3636883","url":null,"abstract":"We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natural language. Using real-world data collected from language learners, we observe that vector similarity approaches poorly capture the relationship between exercise content and the language that learners use to express what they want to learn. This semantic gap between queries and content dramatically reduces the effectiveness of general-purpose retrieval models pretrained on large scale information retrieval datasets like MS MARCO. We leverage the generative capabilities of large language models to bridge the gap by synthesizing hypothetical exercises based on the learner's input, which are then used to search for relevant exercises. Our approach, which we call mHyER, overcomes three challenges: (1) lack of relevance labels for training, (2) unrestricted learner input content, and (3) low semantic similarity between input and retrieval candidates. mHyER outperforms several strong baselines on two novel benchmarks created from crowdsourced data and publicly available data.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"108 ","pages":"284-294"},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140459968","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
Evidence-centered Assessment for Writing with Generative AI 使用生成式人工智能进行以证据为中心的写作评估
International Conference on Learning Analytics and Knowledge Pub Date : 2024-01-17 DOI: 10.48550/arXiv.2401.08964
Yixin Cheng, Kayley Lyons, Guanliang Chen, D. Gašević, Z. Swiecki
{"title":"Evidence-centered Assessment for Writing with Generative AI","authors":"Yixin Cheng, Kayley Lyons, Guanliang Chen, D. Gašević, Z. Swiecki","doi":"10.48550/arXiv.2401.08964","DOIUrl":"https://doi.org/10.48550/arXiv.2401.08964","url":null,"abstract":"We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"230 6","pages":"178-188"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504712","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
Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning 通过自动检测面对面协作学习中的参与情况，利用透明学习分析提供个性化支持
International Conference on Learning Analytics and Knowledge Pub Date : 2024-01-03 DOI: 10.1145/3636555.3636894
Qi Zhou, Wannapon Suraworachet, M. Cukurova
{"title":"Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning","authors":"Qi Zhou, Wannapon Suraworachet, M. Cukurova","doi":"10.1145/3636555.3636894","DOIUrl":"https://doi.org/10.1145/3636555.3636894","url":null,"abstract":"Using learning analytics to investigate and support collaborative learning has been explored for many years. Recently, automated approaches with various artificial intelligence approaches have provided promising results for modelling and predicting student engagement and performance in collaborative learning tasks. However, due to the lack of transparency and interpretability caused by the use of\"black box\"approaches in learning analytics design and implementation, guidance for teaching and learning practice may become a challenge. On the one hand, the black box created by machine learning algorithms and models prevents users from obtaining educationally meaningful learning and teaching suggestions. On the other hand, focusing on group and cohort level analysis only can make it difficult to provide specific support for individual students working in collaborative groups. This paper proposes a transparent approach to automatically detect student's individual engagement in the process of collaboration. The results show that the proposed approach can reflect student's individual engagement and can be used as an indicator to distinguish students with different collaborative learning challenges (cognitive, behavioural and emotional) and learning outcomes. The potential of the proposed collaboration analytics approach for scaffolding collaborative learning practice in face-to-face contexts is discussed and future research suggestions are provided.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"7 1","pages":"392-403"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140514783","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
LA-DECK: a card-based learning analytics co-design tool 一个基于卡片的学习分析协同设计工具
International Conference on Learning Analytics and Knowledge Pub Date : 2020-03-13 DOI: 10.1145/3375462.3375476
C. G. Prieto-Alvarez, Roberto Martínez Maldonado, S. B. Shum
{"title":"LA-DECK: a card-based learning analytics co-design tool","authors":"C. G. Prieto-Alvarez, Roberto Martínez Maldonado, S. B. Shum","doi":"10.1145/3375462.3375476","DOIUrl":"https://doi.org/10.1145/3375462.3375476","url":null,"abstract":"Human-centred software design gives all stakeholders an active voice in the design of the systems that they are expected to use. However, this is not yet commonplace in Learning Analytics (LA). Co-design techniques from other domains therefore have much to o ff er to LA, in principle, but there are few detailed accounts of exactly how such sessions unfold. Th is paper presents the rationale driving a card-based co-design tool speci fi cally tuned for LA, called LA-DECK. In the context of a pilot study with students, educators, LA researchers and developers, we provide qualitative and quantitative accounts of how participants used the cards. Using three different forms of analysis (transcript-centric design vignettes, card-graphs and time-on-topic), we characterise in what ways the sessions were “participatory” in nature, and argue that the cards succeeded in playing very similar roles to those documented in the literature on successful card-based design tools.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123561184","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
Learning from learning curves: discovering interpretable learning trajectories 从学习曲线学习:发现可解释的学习轨迹
International Conference on Learning Analytics and Knowledge Pub Date : 2017-03-13 DOI: 10.1145/3027385.3029449
Lujie Chen, A. Dubrawski
{"title":"Learning from learning curves: discovering interpretable learning trajectories","authors":"Lujie Chen, A. Dubrawski","doi":"10.1145/3027385.3029449","DOIUrl":"https://doi.org/10.1145/3027385.3029449","url":null,"abstract":"We propose a data driven method for decomposing population level learning curve models into mutually exclusive distinctive groups each consisting of similar learning trajectories. We validate this method on six knowledge components from the log data from an online tutoring system ASSIST-ment. Preliminary analysis reveals interpretable patterns of \"skill growth\" that correlate with students' performance in the subsequently administered state standardized tests.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125706698","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 in a flipped university course 在翻转式大学课程中学习分析学
International Conference on Learning Analytics and Knowledge Pub Date : 2016-04-25 DOI: 10.1145/2883851.2883874
A. V. Leeuwen
{"title":"Learning analytics in a flipped university course","authors":"A. V. Leeuwen","doi":"10.1145/2883851.2883874","DOIUrl":"https://doi.org/10.1145/2883851.2883874","url":null,"abstract":"In this poster, we describe the design of a university course with a blended learning character. Learning analytics were used both within the course to facilitate effective teacher-student interaction, as well as after the course to examine patterns between students' activities during the course and their performance on the test and the group assignment at the end of the course.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and 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":"131365511","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
Educational data scientists: a scarce breed 教育数据科学家:一个稀缺的品种
International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460355
S. B. Shum, Martin Hawksey, R. Baker, Naomi Jeffery, J. Behrens, R. Pea
{"title":"Educational data scientists: a scarce breed","authors":"S. B. Shum, Martin Hawksey, R. Baker, Naomi Jeffery, J. Behrens, R. Pea","doi":"10.1145/2460296.2460355","DOIUrl":"https://doi.org/10.1145/2460296.2460355","url":null,"abstract":"The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115709950","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: 30
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