No Arabic abstract
An Intelligent Tutoring System (ITS) has been shown to improve students learning outcomes by providing a personalized curriculum that addresses individual needs of every student. However, despite the effectiveness and efficiency that ITS brings to students learning process, most of the studies in ITS research have conducted less effort to design the interface of ITS that promotes students interest in learning, motivation and engagement by making better use of AI features. In this paper, we explore AI-driven design for the interface of ITS describing diagnostic feedback for students problem-solving process and investigate its impacts on their engagement. We propose several interface designs powered by different AI components and empirically evaluate their impacts on student engagement through Santa, an active mobile ITS. Controlled A/B tests conducted on more than 20K students in the wild show that AI-driven interface design improves the factors of engagement by up to 25.13%.
In the field of tutoring systems, investigations have shown that there are many tutoring systems specific to a specific domain that, because of their static architecture, cannot be adapted to other domains. As consequence, often neither methods nor knowledge can be reused. In addition, the knowledge engineer must have programming skills in order to enhance and evaluate the system. One particular challenge is to tackle these problems with the development of a generic tutoring system. AnITA, as a stand-alone application, has been developed and implemented particularly for this purpose. However, in the testing phase, we discovered that this architecture did not fully match the users intuitive understanding of the use of a learning tool. Therefore, AnITA has been redesigned to exclusively work as a client/server application and renamed to AnITA2. This paper discusses the evolvements made on the AnITA tutoring system, the goal of which is to use generic principles for system re-use in any domain. Two experiments were conducted, and the results are presented in this paper.
In most student dorms in developing countries, a large number of people live in single-function dorm units. The division of the dormitory is too fixed, resulting in the dormitory often lacking functional spaces such as entertainment, sports, meetings, etc. At the same time, a large number of people are likely to cause aggregation at a fixed time, which is not conducive to maintaining social distance under pandemic conditions such as COVID-19. This brings a lot of inconvenience to students life studies and management staff. In this paper, we present a smart dormitory system named Otaku using the Internet of Things technology to integrate facilities related to student dormitory life. By splitting the dormitory into several different categories according to their functionality by using smart door lock design, the system can achieve a more effective and flexible resource allocation, which not only helps the school management but also benefits students.
In the context of building an intelligent tutoring system (ITS), which improves student learning outcomes by intervention, we set out to improve prediction of student problem outcome. In essence, we want to predict the outcome of a student answering a problem in an ITS from a video feed by analyzing their face and gestures. For this, we present a novel transfer learning facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We model the temporal structure of video sequences of students solving math problems using a recurrent neural network architecture. Additionally, we extend the largest dataset of student interactions with an intelligent online math tutor by a factor of two. Our final model, coined ATL-BP (Affect Transfer Learning for Behavior Prediction) achieves an increase in mean F-score over state-of-the-art of 45% on this new dataset in the general case and 50% in a more challenging leave-users-out experimental setting when we use a user-personalized training scheme.
The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions. A verifiable record of achievements enables students to present academic accomplishments to employers, within a trusted framework. Furthermore, the availability of such a record system would enable students to review their learning throughout their career, giving them a platform on which to plan for their future accomplishments, both individually and with support from other parties (for example, academic advisors, supervisors, or potential employers). The proposed system will help students in universities to increase their extra-curricular activities and improve non-academic skills. Moreover, the system will facilitate communication between industry, students, and universities for employment purposes and simplify the search for the most appropriate potential employees for the job.
Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) with domain knowledge in physics, chemistry, and materials science. AI-driven materials design can be divided to mainly two stages; the first one is the modeling stage, where the goal is to build an accurate regression or classification model to predict material properties (e.g. glass transition temperature) or attributes (e.g. toxic/non-toxic). The next stage is design, where the goal is to assemble or tune material structures so that they can achieve user-demanded target property values based on a prediction model that is trained in the modeling stage. For maximum benefit, these two stages should be architected to form a coherent workflow. Today there are several emerging services and tools for AI-driven material design, however, most of them provide only partial technical components (e.g. data analyzer, regression model, structure generator, etc.), that are useful for specific purposes, but for comprehensive material design, those components need to be orchestrated appropriately. Our material design system provides an end-to-end solution to this problem, with a workflow that consists of data input, feature encoding, prediction modeling, solution search, and structure generation. The system builds a regression model to predict properties, solves an inverse problem on the trained model, and generates novel chemical structure candidates that satisfy the target properties. In this paper we will introduce the methodology of our system, and demonstrate a simple example of inverse design generating new chemical structures that satisfy targeted physical property values.