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Register a new userLeveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
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Added by
Nataniel Ruiz
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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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%.
Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames. On the basis of these frames, a vehicle can be tracked by using radius growing method, and its speed can be calculated by calculating vehicle mask and its displacement in consecutive frames. The method uses pattern recognition, digital image processing and mathematical techniques for vehicle detection, tracking and speed calculation. The validity of the proposed model is proved by testing it on different highways.
As generation Zs big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting intelligent-like behavior, being capable to learn from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Zs addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.
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.
A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.
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