From Complexity to Parsimony: Integrating Latent Class Analysis to Uncover Multimodal Learning Patterns in Collaborative Learning

• URL: http://arxiv.org/abs/2411.15590v1
• Date: Sat, 23 Nov 2024 15:36:35 GMT
• Title: From Complexity to Parsimony: Integrating Latent Class Analysis to Uncover Multimodal Learning Patterns in Collaborative Learning
• Authors: Lixiang Yan, Dragan Gašević, Linxuan Zhao, Vanessa Echeverria, Yueqiao Jin, Roberto Martinez-Maldonado,
• Abstract summary: This study introduces a novel methodology for integrating latent class analysis (LCA) within Multimodal Learning Analytics (MMLA) LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious.
• Score: 0.0
• License:
• Abstract: Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicators into parsimonious multimodal ones. Using a high-fidelity healthcare simulation context, we collected positional, audio, and physiological data, deriving 17 monomodal indicators. LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement, each capturing unique monomodal patterns. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious while offering higher explanatory power regarding students' task and collaboration performances. The findings highlight the potential of LCA in simplifying the analysis of complex multimodal data while capturing nuanced, cross-modality behaviours, offering actionable insights for educators and enhancing the design of collaborative learning interventions. This study proposes a pathway for advancing MMLA, making it more parsimonious and manageable, and aligning with the principles of learner-centred education.

Related papers

• On the Comparison between Multi-modal and Single-modal Contrastive Learning [50.74988548106031]
  We introduce a theoretical foundation for understanding the differences between multi-modal and single-modal contrastive learning. We identify the critical factor, which is the signal-to-noise ratio (SNR), that impacts the generalizability in downstream tasks of both multi-modal and single-modal contrastive learning. Our analysis provides a unified framework that can characterize the optimization and generalization of both single-modal and multi-modal contrastive learning.
  arXiv  Detail & Related papers  (2024-11-05T06:21:17Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification [3.6616868775630587]
  We propose M3CoL, a Multimodal Mixup Contrastive Learning approach to capture nuanced shared relations inherent in multimodal data. Our work highlights the significance of learning shared relations for robust multimodal learning, opening up promising avenues for future research.
  arXiv  Detail & Related papers  (2024-09-26T12:15:13Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies. We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning [23.035725779568587]
  We study the role and interactions of multiple modalities in mitigating forgetting in deep neural networks (DNNs) Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. We propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality.
  arXiv  Detail & Related papers  (2024-05-04T22:02:58Z)
• Contrastive Learning on Multimodal Analysis of Electronic Health Records [15.392566551086782]
  We propose a novel feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation. Our theoretical analysis demonstrates the effectiveness of multimodal learning compared to single-modality learning. This connection paves the way for a privacy-preserving algorithm tailored for multimodal EHR feature representation learning.
  arXiv  Detail & Related papers  (2024-03-22T03:01:42Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications [90.6849884683226]
  We study the challenge of interaction quantification in a semi-supervised setting with only labeled unimodal data. Using a precise information-theoretic definition of interactions, our key contribution is the derivation of lower and upper bounds. We show how these theoretical results can be used to estimate multimodal model performance, guide data collection, and select appropriate multimodal models for various tasks.
  arXiv  Detail & Related papers  (2023-06-07T15:44:53Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease [19.86082635340699]
  We propose a taxonomy of all reasonable ways to organize self-supervised representation-learning algorithms. We first evaluate models on toy multimodal MNIST datasets and then apply them to a multimodal neuroimaging dataset with Alzheimer's disease patients. Results show that the proposed approach outperforms previous self-supervised encoder-decoder methods.
  arXiv  Detail & Related papers  (2020-12-25T20:28:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.