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

Modeling Tutoring Knowledge

This is a preliminary version of the chapter, the final one can be accessed at http://www.springerlink.com/content/978-3-642-14362-5#section=784256&page=1&locus=29This chapter introduces the topic "modeling tutoring knowledge" in ITS research. Starting with its origin and with a characterization of tutoring, it proposes a general definition of tutoring, and a description of tutoring functions, variables, and interactions. The Interaction Hypothesis is presented and discussed, followed by the development of the tutorial component of ITSs, and their evaluation. New challenges are described, such as integrating the emotional states of the learner. Perspectives of opening the Tutoring Model and of equipping it with social intelligence are also presented

Relationships between Educational Participants in the Context of Problem Modeling

At present, the goal of education is not only to release a specialist who has received a high-level theoretical and practical training, but also to introduce him at the training stage to the development of new technologies, adapt them to the conditions of a particular production environment, make him a conductor of new technological solutions, therefore the educational process should develop under the conditions of a model-based approach. The purpose of this study is to present the developed structure of interaction between the subjects of the educational process in terms of the problem-model approach within preparation of elemetary education teachers. A methodical system of specialized training of students of pedagogical specialties has been developed and implemented in the context of problem modeling, which ensures that most students achieve a creative level of training in specialized academic disciplines and a high level of development of specialized and specialized competencies. The materials of the article can be useful: to the faculty and heads of universities, where future teachers are trained, to improve the quality of student training and more effective organization of the educational process; employers of future teachers; students of pedagogical areas of training and potential applicants

E-Learning

Technology development, mainly for telecommunications and computer systems, was a key factor for the interactivity and, thus, for the expansion of e-learning. This book is divided into two parts, presenting some proposals to deal with e-learning challenges, opening up a way of learning about and discussing new methodologies to increase the interaction level of classes and implementing technical tools for helping students to make better use of e-learning resources. In the first part, the reader may find chapters mentioning the required infrastructure for e-learning models and processes, organizational practices, suggestions, implementation of methods for assessing results, and case studies focused on pedagogical aspects that can be applied generically in different environments. The second part is related to tools that can be adopted by users such as graphical tools for engineering, mobile phone networks, and techniques to build robots, among others. Moreover, part two includes some chapters dedicated specifically to e-learning areas like engineering and architecture

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Membangun Kepercayaan terhadap Sistem Pendukung Pendidikan berbasis Kecerdasan Buatan: Sebuah Review Naratif

A primary challenge associated with the implementation of educational support systems is the establishment of student trust in the systems themselves. Trust is a critical factor in the acceptance and use of AI-enabled systems, as it reduces uncertainty and the perception of risk associated with new technology adoption. A literature review of existing studies on trust in AI-based systems is needed to provide a solid foundation for future studies. This research aims to identify gaps in the literature regarding the establishment of user trust in AI-based educational systems by exploring the criteria of trust and the challenges of building trust in AI systems. A narrative review of the literature is conducted to synthesize the findings of selected articles, covering (1) fundamental principles of trust and the process of establishing trust in non-human entities; (2) technical issues relating to explainable AI; (3) the utilization of explainable AI to facilitate decision-making; and (4) the use of AI systems in facilitating educational activities and its influence. This article summarizes trust criteria, including reliance, transparency, affectiveness, integrity, consistency, fairness, accountability, security, and usability. Building trust in AI systems involves addressing technical, ethical, and societal challenges to ensure the responsible and beneficial use of AI for individuals and society.Sistem pendukung pembelajaran berbasis kecerdasan buatan memiliki peranan penting dalam menyediakan dukungan yang optimal untuk meningkatkan kesuksesan belajar peserta didik, khususnya di lingkungan pendidikan terbuka dan pendidikan jarak jauh. Dalam implementasinya,masih terdapat isu kepercayaan terhadap strategi yang diberikan oleh sistem, sehingga pemanfaatannya masih belum berdampak signifikan pada peningkatan kesuksesan studi. Review naratif ini dilakukan untuk mengidentifikasi aspek filosofis, etika, dan teknis yang berpengaruh pada pembangunan kepercayaan peserta didik terhadap sistem pendukung pembelajaran berbasis kecerdasan buatan. Hasil yang diberikan memberikan pandangan dan arahan yang komprehensif untuk riset di bidang ini.    &nbsp

Affective e-learning approaches, technology and implementation model: a systematic review

A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling

Managing Learner’s Affective States in Intelligent Tutoring Systems

Abstract. Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner’s cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue con-cerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influ-ence of emotional events on learning it becomes important to induce specific emo-tions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotion-ally intelligent tutoring system able to recognize, interpret and influence learner’s emotions. We will talk about specific virtual agents that can influence learner’s emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which inter