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24th IEEE International Conference on Advanced learning Technologies (ICALT)

作者： Ouhaichi, Hamza Spikol, Daniel Vogel, Bahtijar Li, Zaibei Malmo Univ Dept Comp Sicence & Media Technol Malmo Sweden Univ Copenhagen Dept Sci Educ Copenhagen Denmark Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9798350362060;9798350362053

In the dynamic landscape of digital education, the Glocal Classroom (GC) stands out as a multifaceted smart learning environment. The integration of multimodal learning analytics (MMLA) comes as an intriguing proposition, promising insights into learning dynamics and enhancing educational outcomes. Encountering numerous interdependent considerations involved in the design and integration of MMLA systems, the MMLA design framework (MDF) addresses this challenge, providing a systematic approach. MDF consists of a phased and iterative method for designing MMLA systems. In this study, we delve into the details of the fifth phase, focusing on the development phase. Our primary objective is to assess and refine the applicability of MDF, by taking the integration of MMLA in GC as a use case. We analyze GC's technological infrastructure, evaluating existing hardware, network capabilities, and potential challenges. The central emphasis is on the technical architecture, specifically the hardware components supporting MMLA. By focusing on the technical complexities, the study provides insights into challenges and opportunities associated with MMLA implementation. The outcomes will deepen our understanding of technology in education and refine the MDF model, making it more effective for designing MMLA systems.

关键词： multimodal learning analytics Glocal Classroom Smart learning Environment learning Dynamics MMLA Design

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learning Swedish with AI: Exploring multimodal learning analytics in Spoken Language Acquisition 14th

Learning Swedish with AI: Exploring Multimodal Learning Anal...

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14th International Conference on Methodologies and Intelligent Systems for Technology Enhanced learning

作者： Ouhaichi, Hamza Spikol, Daniel Vogel, Bahtijar Malmo Univ Malmo Sweden Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031735370;9783031735387

This study investigates the application of multimodal learning analytics (MMLA) in language practice, specifically within the authentic and dynamic environment of language cafe settings. The MMLA Model for Design and Analysis (MAMDA), a design science approach, is utilized to systematically explore the requirements for designing the MMLA system. We identify and map three elements: 1) learning indicators, referring to spoken language learning signs, such as tone, amount and frequency of speech, and pronunciation. 2) Respective modalities and sensors, referring to the format of data to be collected and 3) analytics models, including NLP models, that can be employed to identify and process the modalities. We propose a conceptual system that utilizes AI voice assistant while simultaneously collecting audio data for MMLA to enhance language learning experiences. The system is meant for providing insights into learning patterns, participant engagement, and the overall effectiveness of language practice strategies. While presenting a novel system showcasing the use of AI and data analytics in a unique educational setting, the study's central focus is to test and critically reflect on MAMDA as a framework for designing and analyzing MMLA systems.

关键词： multimodal learning analytics Spoken Language Acquisition Design Science Methodology Generative AI

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Using multimodal learning analytics to Examine Learners' Responses to Different Types of Background Music during Reading Comprehension 24

Using Multimodal Learning Analytics to Examine Learners' Res...

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14th Annual International Conference on learning analytics and Knowledge (LAK) - learning analytics in the Age of Artificial Intelligence

作者： Que, Ying Ng, Jeremy Tzi Dong Hu, Xiao Mak, Mitchell Kam Fai Yip, Peony Tsz Yan Univ Hong Kong Fac Educ Hong Kong Peoples R China

ISBN: (纸本)9798400716188

Previous studies have evaluated the affordances and challenges of performing cognitively demanding learning tasks with background music (BGM), yet the effects of various types of BGM on learning still remain an open question. This study aimed to examine the impacts of different music genres and fine-grained music characteristics on learners' emotional, physiological, and pupillary responses during reading comprehension. Leveraging multimodal learning analytics (MmLA) methods of collecting data in multiple modalities from learners, a user experiment was conducted on 102 participants, with half of them reading with self-selected BGM (i.e., the experimental group), while the other half reading without BGM (i.e., the control group). Results of statistical analyses and interviews revealed significant differences between the two groups in their self-reported emotions and automatically measured physiological responses when the experimental group was exposed to classical, easy-listening, rebellious and rhythmic music. Finegrained music characteristics (e.g., instrumentation, tempo) could predict learners' emotions, pupillary, and physiological responses during reading comprehension. The expected contributions of this study include: 1) providing empirical evidence for understanding affective dimensions of learning with BGM, 2) applying MmLA methods for examining the impacts of BGM on learning, and 3) yielding practical implications on how to improve learning with BGM.

关键词： multimodal learning analytics Background Music Reading Comprehension Emotion Pupillary Responses Peripheral Physiological Signals

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Conceptual Design of multimodal learning analytics for Spoken Language Acquisition 19th

Conceptual Design of Multimodal Learning Analytics for Spoke...

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42nd Conference and Exposition on Structural Dynamics (IMAC)

作者： Ouhaichi, Hamza Spikol, Daniel Li, Zaibei Vogel, Bahtijar Malmo Univ Malmo Sweden Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031723117;9783031723124

This study details the technical design and evaluation of a multimodal learning analytics (MMLA) system designed to enhance spoken language acquisition in language cafe settings. Utilizing the MMLA Model for Design and Analysis(MAMDA) framework, we outline the development of a prototype system that integrates AI voice assistance with the collection and analysis of multimodal data, including audio and video. We provide details about the specific technologies and algorithms employed, such as the Arduino NiclaVision board for participant tracking and deep learning techniques for audio analysis. The implementation of the prototype for real-world language cafe sessions highlights its potential for providing valuable insights into learning patterns and interaction dynamics. We discuss the system's performance and limitations, paving the way for future refinements and broader applications in education.

关键词： multimodal learning analytics Language learning Language learning Conversational AI Natural Language Processing

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Toward multimodal learning analytics in simulation-based collaborative learning: A design ethnography of maritime training

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INTERNATIONAL JOURNAL OF COMPUTER-SUPPORTED COLLABORATIVE learning 2024年 1-21页

作者： Sellberg, Charlott Sharma, Amit Univ Gothenburg Dept Appl IT Gothenburg Sweden Univ Bergen Dept Psychosocial Sci Bergen Norway

Collaborative learning in high-fidelity simulators is an important part of how master mariner students are preparing for their future career at sea by becoming part of a ship's bridge team. This study aims to inform the design of multimodal learning analytics to be used for providing automated feedback to master mariner students engaged in collaborative learning activities in high-fidelity navigation simulators. Through a design ethnographic approach, we analyze video records of everyday training practices at a simulator center in Scandinavia, exploring (a) how feedback is delivered to students during collaborative activities in full-mission simulators and (b) which sensors are needed and why they are needed for capturing the multimodal nature of professional performance, communication, and collaboration in simulation-based collaborative learning. Our detailed analysis of two episodes from the data corpus shows how the delivery of feedback during simulations consists of recurring, multidimensional, and multimodal feedback cycles, comprising instructors' close monitoring of student's actions to continuously assess the fit between the learning objectives and the ongoing task. Through these embedded assessments, feedback that draws on the rich semiotic resources of the simulated environment, while considering aspects of realism and authenticity, is provided. Considering the multidimensional and multimodal nature of feedback in professional learning contexts, we identify technologies and sensors needed for capturing professional performance in simulated environments.

关键词： Design ethnography Simulation-based training multimodal learning analytics Maritime education and training (MET)

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Using multimodal learning analytics to understand effects of block-based and text-based modalities on computer programming

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JOURNAL OF COMPUTER ASSISTED learning 2024年第3期40卷 1123-1136页

作者： Sun, Dan Ouyang, Fan Li, Yan Zhu, Chengcong Zhou, Yang Hangzhou Normal Univ Zhejiang Prov Key Think Tank Chinese Educ Modernizat Res Inst Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Educ Yu Hang Tang Rd 866 Hangzhou 310058 Zhejiang Peoples R China Xiaoshan High Sch Hangzhou Zhejiang Peoples R China Hangzhou Dinghui Expt Middle Sch Hangzhou Zhejiang Peoples R China

BackgroundWith the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education *** further compare programming effects between and within text-based and block-based modalities, this research conducted a quasi-experimental research in China's secondary *** online programming platform, Code4all, was developed to allow learners to program in text-based and block-based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag-sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two *** and ConclusionsThe results indicated that learners in text-based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block-based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural *** major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process-oriented and summative analysis to reveal learners' programming learning quality. What is currently known about the subject matterThe implications of study findings

关键词： block-based programming computer programming multimodal learning analytics secondary education text-based programming

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Using multimodal learning analytics to model students' learning behavior in animated programming classroom

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第6期29卷 6947-6990页

作者： Yusuf, Abdullahi Noor, Norah Md Bello, Shamsudeen Sokoto State Univ Dept Sci Educ Sokoto Nigeria Univ Teknol Fac Arts & Social Sci Skudai Malaysia

Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In this research, we confirm the possibilities of classifying students' learning behavior using data obtained from multimodal distribution. We employed computer algorithms to classify students' learning behavior in animated programming classrooms and used information from this classification to predict learning outcomes. Specifically, our study indicates the presence of three clusters of students in the domain of "stay active", "stay passive", and "to-passive". We also found a relationship between these profiles and learning outcomes. We discussed our findings in accordance with the engagement and instructional quality models and believed that our statistical approach will support the ongoing refinement of the models in the context of behavioral profiling and classroom interaction. We recommend that further studies should identify different epistemological frames in diverse classroom settings to provide sufficient explanations of students' learning processes.

关键词： Hidden Markov Model multimodal learning analytics Computer programming Animations Behavioral profiles

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The role of learning theory in multimodal learning analytics

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BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY 2023年第5期54卷 1246-1267页

作者： Giannakos, Michail Cukurova, Mutlu Norwegian Univ Sci & Technol Dept Comp Sci Sem Saelandsvei 9 NO-7491 Trondheim Norway UCL UCL Knowledge Lab London England

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control-value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

关键词： learning theories multimodal data multimodal learning analytics

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Using multimodal learning analytics as a formative assessment tool: Exploring collaborative dynamics in mathematics teacher education

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JOURNAL OF COMPUTER ASSISTED learning 2024年第6期40卷 2753-2771页

作者： Moon, Jewoong Yeo, Sheunghyun Banihashem, Seyyed Kazem Noroozi, Omid Univ Alabama Tuscaloosa AL 35487 USA Daegu Natl Univ Educ Daegu South Korea Open Univ Heerlen Netherlands Wageningen Univ & Res Wageningen Netherlands

BackgroundTraditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of *** study aims to explore the potential of multimodal learning analytics as a formative assessment tool in math education. The focus is on discerning how collaborative discourse behaviours and emotional indicators interplay with lesson evaluation *** undergraduate students' multimodal data, which includes collaboration data, facial behaviour data, and emotional data, the study explored the patterns of collaboration and emotion. Through the lens of multimodal learning analytics, we conducted exploratory data analysis to identify meaningful relationships between specific types of collaborative discourse, facial expressions, and performance indicators. Moreover, the study evaluated a machine learning model's potential to predict target learning outcomes by integrating data from multiple *** analysis revealed key features from both discourse and emotion data as significant predictors. These findings underscore the potential of a multimodal analytical approach in understanding students' learning process and predicting *** study emphasizes the importance and feasibility of a multimodal learning analytic approach in the context of math education. It highlights the academic and practical implications of such an approach, along with its limitations, pointing towards future research directions in this area. What is currently known about this topic? learning analytics has emerged as a powerful tool that aligns with the purpose of formative assessment, enabling educators to monitor and understand students' learning. The primary focus of traditional learning analytics research has been on online learning environments, relying mostly on

关键词： collaborative dynamics formative assessment game-based learning math education multimodal learning analytics teacher education

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Rethinking MMLA: Design Considerations for multimodal learning analytics Systems 23

Rethinking MMLA: Design Considerations for Multimodal Learni...

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10th ACM Conference on learning at Scale (L at S)

作者： Ouhaichi, Hamza Spikol, Daniel Vogel, Bahtijar Malmo Univ Dept Comp Sci & Media Technol Malmo Sweden Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9798400700255

Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.

关键词： multimodal learning analytics System Design Internet of Things Scalability

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