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15th International Conference on learning analytics and Knowledge

作者： Liu, Yiming Ma, Zhengyang Ng, Jeremy Tzi Dong Hu, Xiao Univ Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Arizona Tucson AZ USA

ISBN: (纸本)9798400707018

Collaborative Problem Solving (CPS) has emerged as a key competence for the 21st century. In support of this, valid assessments of CPS skills have become critical. However, limited research has designed and developed CPS assessments for young students. Based on multimodal learning analytics, we aim to develop and validate a game-based assessment of CPS for primary school students. In this study, evidence centered design approach was used to design and develop the game-based CPS assessment. Specifically, we designed and developed a mobile multiplayer online 3D role-playing game on CPS and a coding scheme for coding students' gameplay data (i.e., game logs and voice chat) based on the ATC21S CPS framework. A total of 32 primary 5 students participated in this study to play the game in a group of four and complete a questionnaire of CPS skills. The gameplay data were coded based on our coding scheme. Correlation analysis between the coded results and the CPS questionnaire data supported the criterion validity of our game-based assessment measure. Additionally, the results of expert interview facilitated our understanding of assessment design and data use. This study will make methodological and practical contributions to the integration of MMLA into game-based CPS assessments.

关键词： Collaborative problem solving Game-based assessment multimodal learning analytics K-12 education

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multimodal learning analytics and Neurofeedback for Optimizing Online Learners' Self-Regulation

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TECHNOLOGY KNOWLEDGE AND learning 2023年第4期28卷 1937-1943页

作者： Han, Insook Obeid, Iyad Greco, Devon Korea Univ Dept Educ Seoul South Korea Temple Univ Dept Elect & Comp Engn Philadelphia PA USA Narbis Inc Ambler PA USA

This report describes the use of electroencephalography (EEG) to collect online learners' physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new technology for collecting accurate information on learners' concentration levels. When combined with other learner data, neural data can be used to analyze and predict self-regulated behaviors during online learning. We further suggest the use of machine learning algorithms to provide optimal live neurofeedback to train online learners' brains to improve their self-regulated learning behaviors. The challenges of EEG and neurofeedback in online educational settings are also discussed.

关键词： multimodal learning analytics Electroencephalogram Neurofeedback Online learners Self-regulated learning

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multimodal learning analytics-In-between student privacy and encroachment: A systematic review

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BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY 2023年第6期54卷 1566-1586页

作者： Prinsloo, Paul Slade, Sharon Khalil, Mohammad Univ South Africa Dept Business Management Pretoria South Africa Univ Bergen Ctr Sci Learning & Technol SLATE Bergen Norway POB 392 Unisa ZA-0003 Pretoria South Africa

Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML), and the provision of automated, mostly real-time feedback to students and instructors. Much of MMLA takes place in experimental and laboratory settings, researching students' learning in in-between spaces-between research and classroom application, in-between students' learning in private and public spaces as researchers track students' learning both in their use of social media and connected devices, and through the use of context-aware and adaptive devices;and lastly, in-between respecting students' privacy while increasingly using intrusive technologies. This study seeks to establish what is known about MMLA in terms of rationale for applications, the nature and scope of data collected, the study contexts, evidence of commercial interests and/or downstream uses of students' data, and consideration of ethics, privacy, and the protection of student data. This systematic review analysed 192 articles using a search string consisting of various combinations of multimodal (data) and learning analytics. The main findings include, inter alia, that though MMLA provide insights into learning and teaching, there is little evidence of MMLA findings successfully being applied to classroom settings, at scale. Given that the nature of MMLA research often necessitates the use of a range of (intrusive) sensors and recording technologies and can include children in its samples;the encroachment of students' right to privacy is a huge concern that is not addressed. There is also a need to reconsider the rationale for collecting multimodal data, the conditions under which such data collection will be ethical and in service of students' wellne

关键词： ethics multimodal learning analytics privacy student data

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multimodal learning analytics for assessing teachers' self-regulated learning in planning technology-integrated lessons in a computer-based environment

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EDUCATION AND INFORMATION TECHNOLOGIES 2023年第12期28卷 15823-15843页

作者： Huang, Lingyun Doleck, Tenzin Chen, Boyin Huang, Xiaoshan Tan, Chengyi Lajoie, Susanne P. Wang, Minhong Univ Hong Kong Fac Educ Hong Kong Peoples R China Simon Fraser Univ Canada Res Chair Vancouver BC Canada McGill Univ Dept Educ & Counselling Psychol Montreal PQ Canada

Teachers' self-regulated learning (SRL) plays a crucial role in developing technological pedagogical content knowledge (TPACK), a complex professional skill. It is crucial to identify teachers' SRL activities that may lead to favorable TPACK. Previous studies have focused on the analysis of individual data sources from self-reported surveys or log files, which are insufficient to capture all SRL activities in the TPACK context. While multimodal learning analytics (MMLA) has the potential to improve SRL measurement, it remains unknown how multimodal data collected from different sources can be combined to identify salient features of SRL activities and examine how TPACK outcomes can be predicted by SRL activities identified from multimodal data. This study combined multimodal data from computer logs and think-aloud data to analyze teachers' SRL activities in designing a technology-integrated lesson. We identified the salient features of SRL from the combined data and explored how identified SRL activities might predict TPACK outcomes reflected in teacher-generated lesson plans. The results of random forest regression analysis show that three SRL activities from the logs and two from the think-aloud data formed the best combination that explained a significant proportion of variances in TPACK performance. The impact of MMLA in SRL measurement and the implication of this study are discussed.

关键词： Self-regulated learning Logs Think-aloud multimodal learning analytics TPACK

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multimodal learning analytics of collaborative patterns during pair programming in higher education

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INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 2023年第1期20卷 8页

作者： Xu, Weiqi Wu, Yajuan Ouyang, Fan Zhejiang Univ Coll Educ 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Columbia Univ Teachers Coll New York NY USA

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs' multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

关键词： Collaborative problem solving Computer-supported collaborative learning Pair programming Computer programming education Collaborative pattern multimodal learning analytics

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multimodal learning analytics Research in the Wild: Challenges and their Potential Solutions 6

Multimodal Learning Analytics Research in the Wild: Challeng...

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6th Workshop on Leveraging multimodal Data for Generating Meaningful Feedback, CROSSMMLA 2023

作者： Chejara, Pankaj Kasepalu, Reet Prieto, Luis P. Rodríguez-Triana, María Jesús Ruiz-Calleja, Adolfo Shankar, Shashi Kant School of Digital Technologies Tallinn University Tallinn Estonia School of Education Science Tallinn University Tallinn Estonia Universidad de Valladolid Valladolid Spain

multimodal learning analytics (MMLA) has enabled researchers to address learning in physical settings which have long been either overlooked or studied using observational methods. With the use of sensors, researchers have been able to understand learning through an entirely new perspective (e.g., analyzing heart-rate variability to find collaboration indicators). Consequently, MMLA has grown significantly in the past few years, moving from a nascent stage towards a more mature field. It raises a question on how the MMLA researcher can move further, i.e., the transition towards practice which started getting researchers' attention. This paper discusses the challenges we faced while conducting MMLA studies in classroom settings over four years and potential solutions to realize the goal of transitioning MMLA research to educational practice. This paper aims to start a discussion in the field of MMLA over the transition of research to practice. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Collaboration analytics Collaboration intervention Machine learning MMLA multimodal learning analytics

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Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analytics

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BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY 2024年第4期55卷 1673-1702页

作者： Zhao, Linxuan Gasevic, Dragan Swiecki, Zachari Li, Yuheng Lin, Jionghao Sha, Lele Yan, Lixiang Alfredo, Riordan Li, Xinyu Martinez-Maldonado, Roberto Monash Univ Fac Informat Technol Ctr Learning Analyt Monash Clayton Vic 3168 Australia Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA USA

Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics;and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar *** notesWhat is currently known about this topic Immersive learning environments simulate real-world situations, helping students improve their teamwork skills. In these settings, students can have multiple simultaneous conversations while working together on tasks at different physical locations. The dynamic nature of these interactions makes it hard for teache

关键词： communication CSCL multimodal learning analytics teamwork

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Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning

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BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY 2024年第5期55卷 1900-1925页

作者： Yan, Lixiang Echeverria, Vanessa Jin, Yueqiao Fernandez-Nieto, Gloria Zhao, Linxuan Li, Xinyu Alfredo, Riordan Swiecki, Zachari Gasevic, Dragan Martinez-Maldonado, Roberto Monash Univ Fac Informat Technol Ctr Learning Analyt Monash Clayton Vic 3168 Australia Escuela Super Politecn Litoral ESPOL Guayas Ecuador

multimodal learning analytics (MMLA) offers the potential to provide evidence-based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real-world educational settings. This study evaluates the effectiveness of an MMLA solution in enhancing feedback and reflection within a complex and highly dynamic collaborative learning environment. A two-year longitudinal study was conducted with 399 students and 17 teachers, utilising an MMLA system in reflective debriefings in the context of healthcare education. We analysed the survey data of 74 students and 11 teachers regarding their perceptions of the MMLA system. We applied the Evaluation Framework for learning analytics, augmented by complexity, accuracy and trust measures, to assess both teachers' and students' perspectives. The findings illustrated that teachers and students both had generally positive perceptions of the MMLA solution. Teachers found the MMLA solution helpful in facilitating feedback provision and reflection during debriefing sessions. Similarly, students found the MMLA solution effective in providing clarity on the data collected, stimulating reflection on their learning behaviours, and prompting considerations for adaptation in their learning behaviours. However, the complexity of the MMLA solution and the need for qualitative measures of communication emerged as areas for improvement. Additionally, the study highlighted the importance of data accuracy, transparency, and privacy protection to maintain user trust. The findings provide valuable contributions to advancing our understanding of the use of MMLA in supporting feedback and reflection practices in intricate collaborative learning while identifying avenues for further research and improvement. We also provided several insights and practical recommendations for successful MMLA implementation in authentic learning *** is current

关键词： collaborative learning evaluation feedback learning analytics loop multimodal learning analytics reflection

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Design Framework for multimodal learning analytics Leveraging Human Observations 19th

Design Framework for Multimodal Learning Analytics Leveragin...

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42nd Conference and Exposition on Structural Dynamics (IMAC)

作者： Holm-Janas, Viktor Marshall, Oriel Caro Miya Li, Zaibei Bruun, Jesper Spikol, Daniel Univ Copenhagen Copenhagen Denmark Univ Antwerp Antwerp Belgium

ISBN: (纸本)9783031723117;9783031723124

Collecting and processing data from learning-teaching settings like classrooms is costly and time-consuming for human observers. multimodal learning analytics (MMLA) is an avenue to approach in-depth data from multiple streams of data and information. MMLA researchers are working towards more theory-driven development of these systems, emphasizing transparent and explainable data and the availability of these systems. This article presents a design framework for leveraging human observations to integrate learning theory when designing an MMLA system. Supported by a pilot study using indicators of participation in group work, this study shows promise in human-understandable measures and analysis in MMLA to make connections between sensor data and human observations. However, it also shows challenges in the rigid nature of automatic analysis of data accessible by sensors.

关键词： multimodal learning analytics Design Classroom Observation Human Centered Computing

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Exploring Collaboration Readiness with multimodal learning analytics: The Value of Generative Preparation Activities 19th

Exploring Collaboration Readiness with Multimodal Learning A...

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42nd Conference and Exposition on Structural Dynamics (IMAC)

作者： Zhou, Qi Suraworachet, Wannapon Cukurova, Mutlu UCL UCL Knowledge Lab London England

ISBN: (纸本)9783031723117;9783031723124

Productive and meaningful interactions among peers are considered as a key condition of effective collaborative learning. Although previous literature illustrated that the preparation for collaborative learning can foster the process of collaborative learning, most research focused on one specific type of preparation activity's (e.g., viewing prepared handouts) impact on collaboration outcomes rather than looking at a diverse set of preparation activities' impact on the collaboration process. This study applied multimodal learning analytics techniques in a real-world collaborative learning context to explore the effects of generative preparation activities (e.g., online debate contributions) and non-generative preparation activities (e.g., viewing readings, watching pre-recorded lectures, and other learning materials) on the interactions among learners during the collaborative learning process. The results showed that generative preparation activities have more significant impact on fostering group interactions during the collaborative learning process. This highlights the value of engaging students with generative preparation activities before their collaborative learning. We conclude the paper with a discussion on the implications of these results for teaching practice in real-world collaborative learning.

关键词： multimodal learning analytics Collaborative learning learning Preparation Generative vs. non-generative Activities

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