Learning Opportunities and Challenges of Sensor-enabled Intelligent Tutoring Systems on Mobile Platforms: Benchmarking the Reliability of Mobile Sensors to Track Human Physiological Signals and Behaviors to Enhance Tablet-Based Intelligent Tutoring Systems

Desktop-based intelligent tutoring systems have existed for many decades, but the advancement of mobile computing technologies has sparked interest in developing mobile intelligent tutoring systems (mITS). Personalized mITS are applicable to not only stand-alone and client-server systems but also cloud systems possibly leveraging big data. Device-based sensors enable even greater personalization through capture of physiological signals during periods of student study. However, personalizing mITS to individual students faces challenges. The Achilles heel of personalization is the feasibility and reliability of these sensors to accurately capture physiological signals and behavior measures. This research reviews feasibility and benchmarks reliability of basic mobile platform sensors in various student postures. The research software and methodology are generalizable to a range of platforms and sensors. Incorporating the tile-based puzzle game 2048 as a substitute for a knowledge domain also enables a broad spectrum of test populations. Baseline sensors include the on-board camera to detect eyes/faces and the Bluetooth Empatica E4 wristband to capture heart rate, electrodermal activity (EDA), and skin temperature. The test population involved 100 collegiate students randomly assigned to one of three different ergonomic positions in a classroom: sitting at a table, standing at a counter, or reclining on a sofa. Well received by the students, EDA proved to be more reliable than heart rate or face detection in the three different ergonomic positions. Additional insights are provided on advancing learning personalization through future sensor feasibility and reliability studies

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Comprehension based adaptive learning systems

Conversational Intelligent Tutoring Systems aim to mimic the adaptive behaviour of human tutors by delivering tutorial content as part of a dynamic exchange of information conducted using natural language. Deciding when it is beneficial to intervene in a student’s learning process is an important skill for tutoring. Human tutors use prior knowledge about the student, discourse content and learner non-verbal behaviour to choose when intervention will help learners overcome impasse. Experienced human tutors adapt discourse and pedagogy based on recognition of comprehension and non-comprehension indicative learner behaviour. In this research non-verbal behaviour is explored as a method of computationally analysing reading comprehension so as to equip an intelligent conversational agent with the human-like ability to estimate comprehension from non-verbal behaviour as a decision making trigger for feedback, prompts or hints. This thesis presents research that combines a conversational intelligent tutoring system (CITS) with near real-time comprehension classification based on modelling of e-learner non-verbal behaviour to estimate learner comprehension during on-screen conversational tutoring and to use comprehension classifications as a trigger for intervening with hints, prompts or feedback for the learner. To improve the effectiveness of tuition in e-learning, this research aims to design, develop and demonstrate novel computational methods for modelling e-learner comprehension of on-screen information in near real-time and for adapting CITS tutorial discourse and pedagogy in response to perception of comprehension indicative behaviour. The contribution of this research is to detail the motivation for, design of, and evaluation of a system which has the human-like ability to introduce micro-adaptive feedback into tutorial discourse in response to automatic perception of e-learner reading comprehension. This research evaluates empirically whether e-learner non-verbal behaviour can be modelled to classify comprehension in near real-time and presents a near real-time comprehension classification system which achieves normalised comprehension classification accuracy of 75%. Understanding e-learner comprehension creates exciting opportunities for advanced personalisation of materials, discourse, challenge and the digital environment itself. The research suggests a benefit is gained from comprehension based adaptation in conversational intelligent tutoring systems, with a controlled trial of a comprehension based adaptive CITS called Hendrix 2.0 showing increases in tutorial assessment scores of up to 17% when comprehension based discourse adaptation is deployed to scaffold the learning experience

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers