Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning

• URL: http://arxiv.org/abs/2401.10264v1
• Date: Wed, 3 Jan 2024 12:20:28 GMT
• Title: Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning
• Authors: Qi Zhou, Wannapon Suraworachet, Mutlu Cukurova
• Abstract summary: This paper proposes a transparent approach to automatically detect student's individual engagement in the process of collaboration. The proposed approach can reflect student's individual engagement and can be used as an indicator to distinguish students with different collaborative learning challenges.
• Score: 3.0184625301151833
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Exploring Engagement and Perceived Learning Outcomes in an Immersive Flipped Learning Context [0.195804735329484]
  The aim of this study was to explore the benefits and challenges of the immersive flipped learning approach in relation to students' online engagement and perceived learning outcomes. The study revealed high levels of student engagement and perceived learning outcomes, although it also identified areas needing improvement. The findings of this study can serve as a valuable resource for educators seeking to design engaging and effective remote learning experiences.
  arXiv  Detail & Related papers  (2024-09-19T11:38:48Z)
• Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving [3.392758494801288]
  This study addresses the challenge of forming effective groups in collaborative problem-solving environments. We propose a novel approach leveraging graph theory and reinforcement learning.
  arXiv  Detail & Related papers  (2024-03-15T04:04:40Z)
• Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics [0.0]
  This paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data to evaluate the effectiveness of these incentives on increasing student engagement. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives.
  arXiv  Detail & Related papers  (2024-03-13T16:39:38Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features [0.06999740786886534]
  Student diversity can be challenging as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning.
  arXiv  Detail & Related papers  (2022-04-13T21:14:58Z)
• What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? [59.51457877578138]
  We study contrastive learning on the wearable-based activity recognition task. This paper presents an open-source PyTorch library textttCL-HAR, which can serve as a practical tool for researchers.
  arXiv  Detail & Related papers  (2022-02-12T06:10:15Z)
• Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation [67.52229938775294]
  In past years, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, we propose to conduct further exploration to imitate the human learning process from different aspects.
  arXiv  Detail & Related papers  (2021-11-28T01:14:29Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Towards Explainable Student Group Collaboration Assessment Models Using Temporal Representations of Individual Student Roles [12.945344702592557]
  We propose using simple temporal-CNN deep-learning models to assess student group collaboration. We check the applicability of dynamically changing feature representations for student group collaboration assessment. We also use Grad-CAM visualizations to better understand and interpret the important temporal indices that led to the deep-learning model's decision.
  arXiv  Detail & Related papers  (2021-06-17T16:00:08Z)
• Dual Policy Distillation [58.43610940026261]
  Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning. In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment. The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-06-07T06:49:47Z)

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.