CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
- URL: http://arxiv.org/abs/2312.09066v2
- Date: Tue, 4 Jun 2024 01:27:35 GMT
- Title: CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
- Authors: Chi-hsuan Wu, Shih-yang Liu, Xijie Huang, Xingbo Wang, Rong Zhang, Luca Minciullo, Wong Kai Yiu, Kenny Kwan, Kwang-Ting Cheng,
- Abstract summary: We present the CMOSE dataset, which contains a large number of data from different engagement levels and high-quality labels annotated according to psychological advice.
We also propose a training mechanism MocoRank to handle the intra-class variety and the ordinal pattern of different degrees of engagement classes.
- Score: 26.537675109294234
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Online learning is a rapidly growing industry. However, a major doubt about online learning is whether students are as engaged as they are in face-to-face classes. An engagement recognition system can notify the instructors about the students condition and improve the learning experience. Current challenges in engagement detection involve poor label quality, extreme data imbalance, and intra-class variety - the variety of behaviors at a certain engagement level. To address these problems, we present the CMOSE dataset, which contains a large number of data from different engagement levels and high-quality labels annotated according to psychological advice. We also propose a training mechanism MocoRank to handle the intra-class variety and the ordinal pattern of different degrees of engagement classes. MocoRank outperforms prior engagement detection frameworks, achieving a 1.32% increase in overall accuracy and 5.05% improvement in average accuracy. Further, we demonstrate the effectiveness of multi-modality in engagement detection by combining video features with speech and audio features. The data transferability experiments also state that the proposed CMOSE dataset provides superior label quality and behavior diversity.
Related papers
- Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning [2.127312905562737]
This paper proposes EngageFormer, a transformer based architecture with sequence pooling using video modality for engagement classification.
The proposed architecture computes three views from the input video and processes them in parallel using transformer encoders.
A learning centered affective state dataset is curated from existing open source databases.
arXiv Detail & Related papers (2025-02-15T14:37:09Z) - DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning [54.35107462768146]
Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation.
Existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning.
This paper proposes a novel dual-diversity enhancing and uncertainty-aware framework for CSAL.
arXiv Detail & Related papers (2025-02-01T04:00:03Z) - CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework [15.538850922083652]
We propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS)
It employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels.
The CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity.
arXiv Detail & Related papers (2024-12-11T12:34:37Z) - BAL: Balancing Diversity and Novelty for Active Learning [53.289700543331925]
We introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data.
Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%.
arXiv Detail & Related papers (2023-12-26T08:14:46Z) - Leveraging Demonstrations to Improve Online Learning: Quality Matters [54.98983862640944]
We show that the degree of improvement must depend on the quality of the demonstration data.
We propose an informed TS algorithm that utilizes the demonstration data in a coherent way through Bayes' rule.
arXiv Detail & Related papers (2023-02-07T08:49:12Z) - Detecting Disengagement in Virtual Learning as an Anomaly [4.706263507340607]
Student engagement is an important factor in meeting the goals of virtual learning programs.
In this paper, we formulate detecting disengagement in virtual learning as an anomaly detection problem.
We design various autoencoders, including temporal convolutional network autoencoder, long-short-term memory autoencoder.
arXiv Detail & Related papers (2022-11-13T10:29:25Z) - Looking For A Match: Self-supervised Clustering For Automatic Doubt
Matching In e-learning Platforms [1.0705399532413613]
We develop a label-agnostic doubt matching paradigm based on the representations learnt via self-supervised technique.
We propose custom BYOL which combines domain-specific augmentation with contrastive objective over a varied set of appropriately constructed data views.
arXiv Detail & Related papers (2022-08-20T04:12:19Z) - LANA: Towards Personalized Deep Knowledge Tracing Through
Distinguishable Interactive Sequences [21.67751919579854]
We propose Leveled Attentive KNowledge TrAcing (LANA) to predict students' responses to future questions.
It uses a novel student-related features extractor (SRFE) to distill students' unique inherent properties from their respective interactive sequences.
With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved.
arXiv Detail & Related papers (2021-04-21T02:57:42Z) - On Deep Learning with Label Differential Privacy [54.45348348861426]
We study the multi-class classification setting where the labels are considered sensitive and ought to be protected.
We propose a new algorithm for training deep neural networks with label differential privacy, and run evaluations on several datasets.
arXiv Detail & Related papers (2021-02-11T15:09:06Z) - Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes.
Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
arXiv Detail & Related papers (2021-01-16T23:45:02Z) - CoMatch: Semi-supervised Learning with Contrastive Graph Regularization [86.84486065798735]
CoMatch is a new semi-supervised learning method that unifies dominant approaches.
It achieves state-of-the-art performance on multiple datasets.
arXiv Detail & Related papers (2020-11-23T02:54:57Z)
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.