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Sign language can make possible effective communication between hearing and deaf-mute people. Despite years of extensive pedagogical research, learning sign language remains a formidable task, with the majority of the current systems relying extensively on online learning resources, presuming that users would regularly access them; yet, this approach can feel monotonous and repetitious. Recently, gamification has been proposed as a solution to the problem, however, the research focus is on game design, rather than user experience design. In this work, we present a system for user-defined interaction for learning static American Sign Language (ASL), supporting gesture recognition for user experience design, and enabling users to actively learn through involvement with user-defined gestures, rather than just passively absorbing knowledge. Early findings from a questionnaire-based survey show that users are more motivated to learn static ASL through user-defined interactions.
Online course reviews have been an essential way in which course providers could get insights into students' perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to get further interaction. Analyzing online course reviews is thus an inevitable part for course providers towards the improvement of course quality and the structuring of future courses. However, reading through the often-time thousands of comments and extracting key ideas is not efficient and will potentially incur non-coverage of some important ideas. In this work, we propose a key idea extractor that is based on fine-grained aspect-level semantic units from comments, powered by different variations of state-of-the-art pre-trained language models (PLMs). Our approach differs from both previous topic modeling and keyword extraction methods, which lies in: First, we aim to not only eliminate the heavy reliance on human intervention and statistical characteristics that traditional topic models like LDA are based on, but also to overcome the coarse granularity of stateof-the-art topic models like top2vec. Second, different from previous keyword extraction methods, we do not extract keywords to summarize each comment, which we argue is not necessarily helpful for human readers to grasp key ideas at the course level. Instead, we cluster the ideas and concerns that have been most expressed throughout the whole course, without relying on the verbatimness of students' wording. We show that this method provides high and stable coverage of students' ideas.
With the rapid development of Artificial Intelligence (AI), an increasing number of Machine Learning (ML) technologies have been widely applied in many aspects of life. In the field of education, Intelligence Tutoring Systems (ITS) have also made significant advancements using these technologies. Developing different teaching strategies automatically, according to mined student characteristics and learning styles, could significantly enhance students' learning efficiency and performance. This requires the ITS to recommend different learning strategies and trajectories for different individual students. However, one of the greatest challenges is the scarcity of data sets providing interactions between students and ITS, for training such ITS. One promising solution to this challenge is to train "sim students", which imitate real students' behaviour while using the ITS. The simulated interactions between these sim students and the ITS can then be generated and used to train the ITS to provide personalised learning strategies and trajectories to real students. In this paper, we thus propose SimStu, built upon a Decision Transformer, to generate learning behavioural data to improve the performance of the trained ITS models. The experimental results suggest that our SimStu could model real students well in terms of action frequency distribution. Moreover, we evaluate SimStu in an emerging ITS technology, Knowledge Tracing. The results indicate that SimStu could improve the efficiency of ITS training.
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