Wenbin Gan

Wenbin Gan

Researcher, National Institute of Information and Communications Technology (NICT)

Posts by Collection

portfolio

publications

Prerequisite-driven Q-matrix Refinement for Learner Knowledge Assessment: A Case Study in Online Learning Context (Best Paper Award Nomination)

Published in The 30th International Conference on Computers in Education (ICCE 2022), 2022

The ever growing abundance of learning traces in the online learning platforms promises unique insights into the learner knowledge assessment (LKA), a fundamental personalized-tutoring technique for enabling various further adaptive tutoring services in these platforms. Precise assessment of learner knowledge requires the fine-grained Q-matrix, which is generally designed by experts to map the items to skills in the domain. Due to the subjective tendency, some misspecifications may degrade the performance of LKA. Some efforts have been made to refine the small-scale Q-matrix, however, it is difficult to extend the scalability and apply these methods to the large-scale online learning context with numerous items and massive skills. Moreover, the existing LKA models employ flexible deep learning models that excel at this task, but the adequacy of LKA is still challenged by the representation capability of the models on the quite sparse item-skill graph and the learners’ exercise data. To overcome these issues, in this paper we propose a prerequisite-driven Q-matrix refinement framework for learner knowledge assessment (PQRLKA) in online context. We infer the prerequisites from learners’ response data and use it to refine the expert-defined Q-matrix, which enables the interpretability and the scalability to apply it to the large-scale online learning context. Based on the refined Q-matrix, we propose a Metapath2Vec enhanced convolutional representation method to obtain the comprehensive representations of the items with rich information, and feed them to the PQRLKA model to finally assess the learners’ knowledge. Experiments conducted on three real-world datasets demonstrate the capability of our model to infer the prerequisites for Q-matrix refinement, and also its superiority for the LKA task.

Recommended citation: Gan W, Sun Y. Prerequisite-driven Q-matrix Refinement for Learner Knowledge Assessment: A Case Study in Online Learning Context[C]. The 30th International Conference on Computers in Education (ICCE), 2022. https://arxiv.org/abs/2208.12642

An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios

Published in IEEE International Conference on Big Data (Big Data 2022), 2022

Driver reaction is of vital importance in risk scenarios. Drivers can take correct evasive maneuver at proper cushion time to avoid the potential traffic crashes, but this reaction process is highly experience-dependent and requires various levels of driving skills. To improve driving safety and avoid the traffic accidents, it is necessary to provide all road drivers with on-board driving assistance. This study explores the plausibility of case-based reasoning (CBR) as the inference paradigm underlying the choice of personalized crash evasive maneuvers and the cushion time, by leveraging the wealthy of human driving experience from the steady stream of traffic cases, which have been rarely explored in previous studies. To this end, in this paper, we propose an open evolving framework for generating personalized on-board driving assistance. In particular, we present the FFMTE model with high performance to model the traffic events and build the case database; A tailored CBR-based method is then proposed to retrieve, reuse and revise the existing cases to generate the assistance. We take the 100-Car Naturalistic Driving Study dataset as an example to build and test our framework; the experiments show reasonable results, providing the drivers with valuable evasive information to avoid the potential crashes in different scenarios.

Recommended citation: W. Gan, M. -S. Dao and K. Zettsu, "An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 1822-1829. https://ieeexplore.ieee.org/document/10020284

IoT-based Multimodal Analysis for Smart Education: Current Status, Challenges and Opportunities

Published in 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, in ACM International Conference on Multimedia Retrieval (ACM ICMR 2022), 2022

Download paper here

Recommended citation: Wenbin Gan, Minh Son Dao, Koji Zettsu, and Yuan Sun. Iot-based multimodal analysis for smart education: Current status, challenges and opportunities. In In the Workshop on Intelli- gent Cross-Data Analysis and Retrieval (ICDAR), ACM International Conference on Multimedia Retrieval (ACM ICMR 2022)., pages 1–9. ACM, 2022. https://dl.acm.org/doi/abs/10.1145/3512731.3534208

researches

Learner Knowledge Assesssment

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How to effectively infer and track the learning progress of a learner through his/her online interaction with learning materials?

Intelligent Tutoring Systems

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Improve the adaptive learning interaction of intelligent tutoring systems by tailoring learning content and adapting to learners’ status

Multimodal Big Data Analysis for Human Wellness

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Conduct research on and development of smart data analytics technology, to enable behavior support through real-world analysis and predictions that link data of various types from various fields.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.