Journal of Learning Analytics最新文献

Generative AI and Learning Analytics 生成式人工智能和学习分析

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-21 DOI: 10.18608/jla.2023.8333

Hassan Khosravi, Olga Viberg, Vitomir Kovanović, Rebecca Ferguson

引用次数: 0

Student Privacy and Learning Analytics 学生隐私与学习分析

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-12 DOI: 10.18608/jla.2023.7975

Mary Francis, M. Avoseh, Karen Card, Lisa Newland, Kevin Streff

引用次数: 0

Session-Based Time-Window Identification in Virtual Learning Environments 虚拟学习环境中基于会话的时间窗口识别

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-12 DOI: 10.18608/jla.2023.7911

D. Rotelli, Aleksandra Maslennikova, Anna Monreale

引用次数: 0

Bayesian Generative Modelling of Student Results in Course Networks 课程网络中学生成绩的贝叶斯生成模型

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-12 DOI: 10.18608/jla.2023.7957

Marcel Haas, Colin Caprani, Benji Van Beurden

引用次数: 0

NLP-Based Management of Large Multiple-Choice Test Item Repositories 基于 NLP 的大型多选题试题库管理

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-12 DOI: 10.18608/jla.2023.7897

Valentina Albano, D. Firmani, Luigi Laura, Jerin George Mathew, Anna Lucia Paoletti, Irene Torrente

{"title":"NLP-Based Management of Large Multiple-Choice Test Item Repositories","authors":"Valentina Albano, D. Firmani, Luigi Laura, Jerin George Mathew, Anna Lucia Paoletti, Irene Torrente","doi":"10.18608/jla.2023.7897","DOIUrl":"https://doi.org/10.18608/jla.2023.7897","url":null,"abstract":"Multiple-choice questions (MCQs) are widely used in educational assessments and professional certification exams. Managing large repositories of MCQs, however, poses several challenges due to the high volume of questions and the need to maintain their quality and relevance over time. One of these challenges is the presence of questions that duplicate concepts but are formulated differently. Such questions can indeed elude syntactic controls but provide no added value to the repository.In this paper, we focus on this specific challenge and propose a workflow for the discovery and management of potential duplicate questions in large MCQ repositories. Overall, the workflow comprises three main steps: MCQ preprocessing, similarity computation, and finally a graph-based exploration and analysis of the obtained similarity values. For the preprocessing phase, we consider three main strategies: (i) removing the list of candidate answers from each question, (ii) augmenting each question with the correct answer, or (iii) augmenting each question with all candidate answers. Then, we use deep learning–based natural language processing (NLP) techniques, based on the Transformers architecture, to compute similarities between MCQs based on semantics. Finally, we propose a new approach to graph exploration based on graph communities to analyze the similarities and relationships between MCQs in the graph. We illustrate the approach with a case study of the Competenze Digital program, a large-scale assessment project by the Italian government. ","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"34 14","pages":""},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007236","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

Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels 学习分析仪表板在提高学生参与度方面的效果

IF 3.9

Journal of Learning Analytics Pub Date : 2023-12-12 DOI: 10.18608/jla.2023.7935

Gomathy Ramaswami, Teo Susnjak, A. Mathrani

{"title":"Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels","authors":"Gomathy Ramaswami, Teo Susnjak, A. Mathrani","doi":"10.18608/jla.2023.7935","DOIUrl":"https://doi.org/10.18608/jla.2023.7935","url":null,"abstract":"Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students’ online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive components, while none possess the ability to explain how the predictive models work and how they have arrived at specific conclusions for a given student. A further gap exists within existing LADs with respect to prescriptive analytics that generate data-driven feedback to students on how to adjust their learning behaviour. The LAD in this study attempts to address this gap and integrates a full spectrum of current analytics technologies for sense-making while anchoring them within theoretical educational frameworks. This study’s LAD (SensEnablr) was evaluated for its effectiveness in impacting learning in a student cohort at a tertiary institution. Our findings demonstrate that student engagement with learning technologies and course resources increased significantly immediately following interactions with the dashboard. Meanwhile, results showed that the dashboard boosted the respondents’ learning motivation levels and that the novel analytics insights drawn from predictive and prescriptive analytics were beneficial to their learning. This study, therefore, has implications for future research when investigating student outcomes and optimizing student learning using LAD technologies.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"2 4","pages":""},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008502","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

Enriching Multimodal Data 丰富多模式数据

Journal of Learning Analytics Pub Date : 2023-11-04 DOI: 10.18608/jla.2023.7989

Yiqiu Zhou, Jina Kang

{"title":"Enriching Multimodal Data","authors":"Yiqiu Zhou, Jina Kang","doi":"10.18608/jla.2023.7989","DOIUrl":"https://doi.org/10.18608/jla.2023.7989","url":null,"abstract":"Collaboration is a complex, multidimensional process; however, details of how multimodal features intersect and mediate group interactions have not been fully unpacked. Characterizing and analyzing the temporal patterns based on multimodal features is a challenging yet important work to advance our understanding of computer-supported collaborative learning (CSCL). This paper highlights the affordances, as well as the limitations, of different temporal approaches in terms of analyzing multimodal data. To tackle the remaining challenges, we present an empirical example of multimodal temporal analysis that leverages multi-level vector autoregression (mlVAR) to identify temporal patterns of the collaborative problem-solving (CPS) process in an immersive astronomy simulation. We extend previous research on joint attention with a particular focus on the added value from a multimodal, temporal account of the CPS process. We incorporate verbal discussion to contextualize joint attention, examine the sequential and contemporaneous associations between them, and identify significant differences in temporal patterns between low- and high-achieving groups. Our paper does the following: 1) creates interpretable multimodal group interaction patterns, 2) advances understanding of CPS through examination of verbal and non-verbal interactions, and 3) demonstrates the added value of a complete account of temporality including both duration and sequential order.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"7 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773698","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

Network Analytics to Unveil Links of Learning Strategies, Time Management, and Academic Performance in a Flipped Classroom 网络分析揭示学习策略、时间管理和学习成绩在翻转课堂中的联系

Journal of Learning Analytics Pub Date : 2023-11-01 DOI: 10.18608/jla.2023.7843

Mladen Rakovic, Nora'ayu Ahmad Uzir, Wannisa Matcha, Dragan Gašević, Brendan Eagan, Jelena Jovanović, David Williamson Shaffer, Abelardo Pardo

{"title":"Network Analytics to Unveil Links of Learning Strategies, Time Management, and Academic Performance in a Flipped Classroom","authors":"Mladen Rakovic, Nora'ayu Ahmad Uzir, Wannisa Matcha, Dragan Gašević, Brendan Eagan, Jelena Jovanović, David Williamson Shaffer, Abelardo Pardo","doi":"10.18608/jla.2023.7843","DOIUrl":"https://doi.org/10.18608/jla.2023.7843","url":null,"abstract":"Preparatory learning tasks are considered critical for student success in flipped classroom courses. However, less isknown regarding which learning strategies students use and when they use those strategies in a flipped classroomcourse. In this study, we aimed to address this research gap. In particular, we investigated mutual connectionsbetween learning strategies and time management, and their combined effects on students’ performance in flippedclassrooms. To this end, we harnessed a network analytic approach based on epistemic network analysis (ENA) toanalyze student trace data collected in an undergraduate engineering course (N = 290) with a flipped classroomdesign. Our findings suggest that high-performing students effectively used their study time and enacted learningstrategies mainly linked to formative and summative assessment tasks. The students in the low-performing groupenacted less diverse learning strategies and typically focused on video watching. We discuss several implicationsfor research and instructional practice.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"189 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135371570","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

Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces: 协同学习空间中的社会空间学习分析

IF 3.9

Journal of Learning Analytics Pub Date : 2023-09-05 DOI: 10.18608/jla.2023.7991

Lixiang Yan, Linxuan Zhao, D. Gašević, Xinyu Li, Roberto Martínez-Maldonado

{"title":"Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:","authors":"Lixiang Yan, Linxuan Zhao, D. Gašević, Xinyu Li, Roberto Martínez-Maldonado","doi":"10.18608/jla.2023.7991","DOIUrl":"https://doi.org/10.18608/jla.2023.7991","url":null,"abstract":"Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to un-cover valuable educational insights from individuals’ social and spatial data traces. These traces are capturedautomatically through sensing technologies in physical learning spaces, and the research is commonly based onthe theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, asystematic literature review is timely in order to provide educational researchers and practitioners with a detailedsummary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyseswere conducted to identify the citation networks, essential components, opportunities, and challenges enabled bySSLA. The findings illustrated that SSLA provides the opportunity to (1) contribute unobtrusive and unsupervisedresearch methodologies, (2) support educators’ classroom orchestration through visualizations, (3) support learnerreflection with continuous and reliable evidence, (4) develop novel theories about social and collaborative learning,and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. Thesefindings could support learning analytics and educational technology scholars and practitioners to better understandand utilize SSLA to support future educational research and practice.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45739308","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

Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach 调整学习分析的目标与研究奖学金:一个开放的同行评论方法

IF 3.9

Journal of Learning Analytics Pub Date : 2023-08-30 DOI: 10.18608/jla.2023.8197

Rebecca Ferguson, Hassan Khosravi, Vitomir Kovanovíc, Olga Viberg, A. Aggarwal, Matthieu J. S. Brinkhuis, S. Buckingham Shum, Lujie Karen Chen, H. Drachsler, Valerie A. Guerrero, Michael Hanses, Caitlin Hayward, Bentley G. Hicks, I. Jivet, Kirsty Kitto, René F. Kizilcec, J. Lodge, Catherine A. Manly, Rebecca L. Matz, M. Meaney, X. Ochoa, Brendan A. Schuetze, Marco Spruit, Max van Haastrecht, Anouschka van Leeuwen, Lars Van Rijn, Yi-Shan Tsai, Joshua Weidlich, K. Williamson, Veronica X. Yan

引用次数: 0