Managing inference process in student modelling for intelligent tutoring systems

Probabilistic and Fuzzy Logic approaches have been used for developing Intelligent Tutoring Systems (ITS) for years to deal with uncertainties in ITS but without much attention to justification of the particular techniques, which we call the analysis of scientific rigor of the approach. In probabilistic approaches, there is a missing justification of the Markovian assumption of Bayesian networks along with others. In fuzzy logic approaches, there is a missing justification of fuzzy logic operations by the analysis of the specific ITS task. One of the fundamental and natural ways to provide a rigorously justified way to deal with the uncertainty in ITS is the systematic modeling the context of each ITS task. This paper proposes a methodology based on the comprehensive use of the uncertain contextual verbal and visual information.

This paper describes a knowledge-based architecture, called curriculum tree, for building intelligent tutoring systems. Primarily based on the subject domain knowledge structure, the architecture naturally incorporates the global curriculum planning and monitors the local learning activities. The curriculum tree can also be viewed as a structure of various teaching knowledge at different stages of learning. By adopting rule inheritance, the architecture allows additional additivity and flexibility for developing an intelligent tutoring system incrementally as well as efficiency for running rules in each learning episode. Thus, curriculum tree is an architecture towards building large scale intelligent tutoring systems. In this paper, we shall also discuss how the curriculum tree architecture is used in building Integration-Kid, a Learning Companion System which is a particularly complex type of intelligent tutoring system, in the domain of learning indefinite integration.KeywordsLeaf NodeIntelligent Tutoring SystemSchedule RuleCurriculum TreeSchedule NodeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cognitive and behavioral models of assertive behavior: Review, analysis and integration

This paper presents a new approach to the analysis and design of intelligent tutoring systems (ITS), based on reactive principles and cognitive models, this way leading to multiagent architecture. In these kinds of models, the analysis problem is treated bottom-up, as opposed to that of traditional artificial intelligence (AI), i.e., top down. We present one ITS example called Makatsina (meaning tutor in TOTONACA, a Mexican pre-Columbian language), constructed according to this approach, which teaches the skills necessary to solve the truss analysis problem by the method of joints. This learning domain is an integration skill. The classical ITS work is based on explicit goals and an internal representation of the environment. The new approach has reactive agents which have no representation of their environment and act using a stimulus response behavior type. In this way they can respond to the present state of the environment in which they are embedded. With these elements, errors, and teaching plans, each agent behaves as an expert assistant that is able to handle different teaching methods. Reactive agent programming is found to be simple because agents have simple behaviors. The difficulty lies in the interaction mechanism analysis and design between the environment and the intelligent reactive system.

The most important feature of the intelligent tutoring systems (ITS), one of the most popular study topics of recent years, is that it provides an opportunity for individual learning by taking students' individual differences into account. In order to be able to realize this feature, it is necessary that the system recognizes students well. The process of recognizing student is performed as a result of observations which ITS applies on students. A number of uncertainties arise during these observations. In order to minimize learning uncertainties and create a productive and effective ITS, type‐2 fuzzy logic, one of the artificial intelligence techniques, is used in the system developed in this study. In order to show the effectiveness of the developed web‐based ITS, it is applied to the teaching of a basic Control Course. The educational evaluation of the system is presented in the paper. © 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 561–571, 2013

This article aims at exploring the identified benefits and challenges when integrating Educational Artificial Intelligence (AIEd) into contemporary Constructive-Learning-Based Curriculum design. It is argued that the Intelligent Tutoring System, which lies at the centre of AIEd, could help enhancing learners’ constructive learning experience by providing personalised and engaging learning environment, and it could promoting learners’ twenty-first century skills during the learning process. However, biased data in the Intelligent Tutoring System may also give rise to learning recommendation bias and inequality. Also, the algorithms in Intelligent Tutoring System is uncapable of capturing unquantifiable learning information, and put its emphasize mainly on the quantifiable educational recommendations, that will deteriorate the construction of the Constructivist-Learning-Based Curriculum.KeywordsCurriculumConstructivist LearningIntelligent Tutoring System

In this chapter, a formal, adaptive tutoring process model for case-based Intelligent Tutoring Systems (ITSs) is described. Combining methods of Artificial Intelligence and Cognitive Science led to the development of ITSs more than 30 years ago. In contrast to the common agreement about the ITSs’ architecture, components of ITSs are rarely reusable. Reusability in ITSs is intimately connected with the application domain, that is, with the contents that should be learned and with the teaching and learning strategy. An example of a learning strategy is case-based learning, where the adaptation of the learning material to the learner plays a major role. Adaptation should take place automatically at runtime, and thus should be part of the ITS’s functionality. To support the development of ITSs with reusable components and the communication about and the evaluation of similar ITSs, a formal approach has been chosen. This approach is called the tutoring process model.

Progressive Mimic Learning: A new perspective to train lightweight CNN models

Cyberlearning is challenged by the lack of personal and assessment-driven learning, and students are often puzzled by the lack of instructor guidance and feedback, the huge volume and diversity of the learning materials, and thelack of the ability to zoom in from the general concepts to the more specific ones, or the opposite. Intelligent tutoring systems are needed to improve the cyberlearning quality. One of the major difficulties is knowledge representation. The current industry standard is to use Web Ontology Language (OWL) forrepresenting knowledge structure. But OWL only supports one first-class relation, is-a, between the concepts, and different knowledge areas usually need different custom relations to describe the relations among the concepts. For example part-of and time dependency are important relations torepresent most engineering knowledge bodies. OWL has to use object properties to emulate such custom relations, leading to awkward knowledge representation hard for domain experts to code, validate and use such knowledge bases. This research uses Pace University's extension to OWL, named Knowledge Graph (KG), to support knowledge representation with custom relations. The instructors can use Pace University extended Protege IDE to declare and apply custom relations in a single document. The instructor teaching experience is also coded in the KG to better support custom learning order by students with different backgrounds. The prototype of a knowledge-driven tutoring system was designed and implemented to illustrate how the KG supports integrated assessments, using assessment results to custom student learning order or material, and let the students freely navigate in the knowledge space from general to specific or the opposite, and following various custom relations. A web technology tutorial is used to validate the design and effectiveness of this approach.

A Neuro-Fuzzy Approach for student module of Physical Activity ITS

Current Intelligent Tutoring Systems (ITS) employ explicit and direct learning strategies when interacting with learners. Although these ITS use cognitive and logical models to analyze the conscious cognitive processes behind reasoning, we believe that in specific situations during knowledge acquisition, such as reasoning, unconscious cognitive processes are heavily solicited in the brain. In this paper, we will propose a complimentary and novel learning strategy to current ITS aimed at enhancing reasoning in a problem solving environment. This approach, called Cognitive Priming, is based on neural correlates of non-conscious perception. We will present two studies that have positively conditioned learners and enhanced different dimensions of their reasoning skills by employing a technique based on the science of subliminal perception. We will also present relevant cerebral data recorded throughout the studies and discuss the importance of such findings for the community.

As knowledge acquisition is an important component of health communication research, this study examines factors associated with Singaporean women’s breast cancer knowledge using an augmented cognitive mediation model. We conducted a nationally representative study that surveyed 802 women between the ages of 30 and 70 using random-digit dialing. The results supported the augmented cognitive mediation model, which proposes the inclusion of risk perception as a motivator of health information seeking and structural knowledge as an additional knowledge dimension. There was adequate support for the hypothesized paths in the model. Risk perception was positively associated with attention to newspaper, television, Internet, and interpersonal communication. Attention to the three media channels was associated with interpersonal communication, but only newspaper and television attention were associated with elaboration. Interpersonal communication was positively associated with structural knowledge, whereas elaboration was associated with both factual and structural knowledge. Differential indirect effects between media attention and knowledge dimensions via interpersonal communication and elaboration were found. Theoretical and practical implications are discussed.

Teams can be depicted in many organizations and is becoming increasingly used for effective and efficient productivity. Teams work as the collection of individuals that have different skills to complement each other rather than compete, their driving force is to achieve a common goal by inculcating strategical approaches of which they are held accountable. This, helps in maximizing general output, however the need of its incorporation in learning. This study presents an overview of Intelligent Tutoring System and its impact generally on learning. A review of previous works done on this area was also inculcated in this study highlighting their importance and relevance. Intelligent Tutoring System (ITS), been a machine intelligence learning paradigm. This research explores and unveils what an intelligent tutoring system is all about and its role in learning. The work also covers how intelligent tutoring system has enhanced learner’s skill and performance. In conclusion, the massive effects of intelligent tutoring system on the learning process are acknowledged.

Nowadays, the improvement of digital learning with Artificial Intelligence has attracted a lot of research, as it provides solutions for individualized education styles which are independent of place and time. This is particularly the case for computer science, as a tutoring domain, which is rapidly growing and changing and as such, learners need frequent update courses. In this paper, we present a thorough evaluation of a fuzzy-based intelligent tutoring system (ITS), that teaches computer programming. The evaluation concerns multiple aspects of the ITS. The evaluation criteria are: (i) context, (ii) effectiveness, (iii) efficiency, (iv) accuracy, (v) usability and satisfaction, and (vi) engagement and motivation. In the evaluation process students of an undergraduate program in Informatics of the University of Piraeus in Greece participated. The evaluation method that was used included questionnaires, analysis of log files and experiments. Also, t-tests were conducted to certify the validity of the evaluation results. Indeed, the evaluation results are very positive and show that the incorporated fuzzy mechanism to the presented ITS enhances the system with Artificial Intelligence and through this, it increases the learners’ satisfaction and new knowledge learning and mastering, improves the recommendation accuracy of the system, the efficacy of interactions, and contributes positively to the learners’ engagement in the learning process.

This study aims to develop and implement a tool called intelligent tutoring system in an online course to help a formative evaluation in order to improve student learning. According to Bloom et al. (1971,117) formative evaluation is a systematic evaluation to improve the process of teaching and learning. The intelligent tutoring system may provides a timely and high quality feedback that not only inform the correctness of the solution to the problem, but also informs the students about the accuracy of the response relative to their current knowledge about the solution. Constructive and supportive feedback should be given to the students to reveal the right and wrong answers immediately after taking the test. A feedback about the right answers is a form to reinforce positive behaviors. Identifying possible errors and relating them to the instruction material may help student to strengthen the content under consideration. The remedial suggestion should be given for each answer with detailed prescription with regards the materials and instructional procedures before taking next step. The main idea is to inform the students what he has learned and what he still has to learn. The open-source LMS called MoodleTM was extended to accomplish the formative evaluation, high-quality feedback, and communal knowledge presented here with a short online financial math course that is being offered at a large University in Brazil. The preliminary results shows that the intelligent tutoring system using high quality feedback helped the students to improve their knowledge about the solution to the problems based on the errors of their past cohorts. The results and suggestion for future work are presented and discussed.