Wenbin Gan

Wenbin Gan

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

Intelligent Tutoring Systems

Intelligent tutoring systems (ITS) have long been verified by educational psychologists to have the great protential to improve the adaptive learning interaction of learners by tailoring learning contents and adapting to learners’ status.

I have conducted some research work in this direction to improve the ITSs with personalized learning support to learners.

AI-Tutor Framework


Introduction: Developing AI-powered intelligent tutoring systems to facilitate adaptive learning has been a hot research topic due to that these systems can provide personalized learning guidance by taking individual learner’s status and needs into consideration to improve their learning performance. Based on this perspective, this paper proposes a novel personalized adaptive tutoring system named AI-Tutor, which not only incorporates the basic functions of general tutoring systems that providing adaptive course learning to learners, but also can generate tailored remedial questions and answers based on cognitive diagnostic assessment. This unique and tailored tutoring service will potentially help learners master the deficient knowledge points in a much shorter time. This paper presents the preliminary research and development in implementing such an AI-powered tutor. First, it gives the system design with the function description of main modules and the operating procedure. Second, it identifies four main research problems behind this system and proposes approaches to solve these problems in developing the AI-Tutor.


[Keywords]: AI Tutor, remedial question generation, cognitive diagnostic assessment, automatic problem solving, adaptive learning.

IoT-based Multimodal Learning Analysis for Smart Education


Introduction: IoT-based multimodal learning analytics promises to obtain an in-depth understanding of the learning process. It provides the insights for not only the explicit learning indicators but also the implicit attributes of learners. Hence further potential learning support can be timely provided in both physical and cyber world accordingly. In this paper, we present a systematic review of the existing studies for examining the empirical evidences on the usage of IoT data in education and the capabilities of multimodal analysis to provide useful insights for smarter education. In particular, we classify the multimodal data into four categories based on the data sources (data from digital, physical, physiological and environmental spaces). Moreover, we propose a concept framework for better understanding the current state of the filed and summarize the insights into six main themes (learner behavior understanding, learner affection computing, smart learning environment, learning performance prediction, group collaboration modeling and intelligent feedback) based on the objectives for intelligent learning. The associations between different combinations of data modalities and various learning indicators are comprehensively discussed. Finally, the challenges and future directions are also presented from three aspects.


[Keywords]: Internet of things, IoT in Education, Multimodal Learning Analysis, Smart Education.

Online Course Discussion Forums Analysis (Co-author)

Introduction: Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.


[Keywords]: Discussion forums; sentiment and behaviour topic extraction; topic mode.