Development Of Intelligent Tutoring Systems Research Articles

Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems

Automation of teaching people new skills requires modeling of human reasoning because human cognition involves active reasoning over the new subject domain to acquire skills that will later become automatic. The article presents Thought Process Trees — a language for modeling human reasoning that was created to facilitate the development of intelligent tutoring systems, which can perform the same reasoning that is expected of a student and find deficiencies in their line of thinking, providing explanatory messages and allowing them to learn from performance errors. The methodology of building trees which better reflect human learning is discussed, with examples of design choices during the modeling process and their consequences. The characteristics of educational modeling that impact building subject-domain models for intelligent tutoring systems are discussed. The trees were formalized and served as a basis for developing a framework for constructing intelligent tutoring systems. This significantly lowered the time required to build and debug a constraint-based subject-domain model. The framework has already been used to develop five intelligent tutoring systems and their prototypes and is being used to develop more of them.

Bibliometric Analysis using Vos Viewer with Publish or Perish of Intelligent Tutoring System in Private Universities

The objective of this study is to analyze the development of intelligent tutoring systems in private universities. We conducted the analysis using bibliometric methods, utilizing the Publish or Perish and VOSviewer applications. Data was obtained by using the publish or perish application with the keyword "intelligent tutoring system in private university" from the Google Scholar database from 2019 to 2024. According to search results, the number of research papers has decreased from 117 to 23 from 2020 to 2024. Mapping using VOSviewer application produces three types of visualization, namely network, overlay, and density visualization. In its conclusion, this research notes a decrease in the number of studies discussing in private universities since 2020, but still shows great potential for development by other researchers.

An Intelligent Mathematics Problem-Solving Tutoring System Framework

This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.

Implications of Artificial Intelligence for Teaching and Learning

Artificial Intelligence (AI) has significantly transformed teaching and learning, facilitating a shift from teacher-centered to student-centered education. This review outlines the broad implications of AI for education and synthesizes both the opportunities and challenges associated with its implementation. Examining over 55 papers related to the impacts of AI on education, the review encompasses various educational contexts, avoiding a singular focus on specific types of education or the teaching of AI alone. According to the review, AI introduces new opportunities for creating intelligent content that enhances learning experiences, fostering interactivity and a student-centered approach. Smart content enables instructors to integrate multimedia, interactive tools, AI-related wearables, and information technologies, diversifying learning modes and engaging students more effectively. The creation of smart content aligns with smart education frameworks to ensure efficient content development. AI also contributes to the development of intelligent tutoring systems, which simulate human tutors to deliver personalized and adaptive educational experiences. These systems can host smart content, enabling independent learning. Additionally, AI improves virtual learning environments by analyzing student data to tailor content and delivery methods based on individual needs. It automates tasks such as grading and feedback, allowing teachers to concentrate on other essential responsibilities. While AI brings significant benefits, it is not without limitations. Challenges include infrastructure requirements, considerations of inclusion and equity, teacher readiness and preparation, data quality and inclusivity, profit orientation, data privacy and ethical concerns, and the potential for unequal access. Addressing these limitations is crucial for maximizing the positive impacts of AI in the realm of education.

A human-in-the-loop system for labeling knowledge components in Japanese mathematics exercises

Many modern learning systems rely on a data representation of the knowledge that is to be learned to estimate a learner’s mastery state and recommend appropriate learning tasks to further improve their acquisition of knowledge and skills. In particular, the rapid development of intelligent tutoring systems (ITS) and standardized curricula has increased the need for information on knowledge structures and their links to learning materials and tasks. However, manually labeling educational data has traditionally been a time-consuming, labor-intensive task, and thus has limited its use by time-constrained teachers and practitioners. In previous research, a range of machine-learning methods have been proposed to address this problem, with only a few of them focusing on Japanese educational datasets from secondary schools. In this paper, to support the labeling of Japanese mathematics exercises by teachers and other domain experts, we apply natural language processing techniques including word-embedding and key-phrase-based exercise-to-exercise similarity methods. We evaluated the proposed method by both the performance of the models when compared to several state-of-the-art methods, and also its effectiveness in supporting humans in the task of labeling educational materials. Through this two-phase evaluation, we found that the proposed method outperformed other methods, and when implemented in a human-in-the-loop system it achieved significantly more accuracy and consumed less time for the task of labeling mathematics exercises.

The Development of Intelligent Tutoring System in The Military Schools

The rapid and fast development of technology affects many aspects of life, especially in the field of education. The application of technology in education is needed to support a more effective and efficient learning process without any space and time constraints. An example is the implementation of the Intelligent Tutoring System (ITS) which is often known as an intelligent learning system. An ITS in the field of military education is needed to produce professional military graduates/HR. Therefore, researchers will examine trends in research development and updates on ITS technology in the military field. Efforts to achieve research objectives are carried out using the Systematic Literature Review method with bibliometric analysis or VOSviewer. The purpose of this study is to become a reference for determining research topics on ITS in the field of education, especially the military.

Integration of Brain Based Learning and Intelligent Tutoring System for Developing Pedagogical Software.(Dept.E)

The development of intelligent tutoring systems and learning environments has become an important issue in both computer science and education. This paper presents pedagogical software for a secondary school biology course. The proposed system integrates the concepts of Brain Based Learning (BBL) and intelligent tutoring system to adapt the learning interface according to the student thinking style. The system is formalized as finite automata with some attributes that allow lemur interaction (image, video, text, color, sound and contents) and some user's brain characteristics (right, left, and integrated thinking style). For the same contents and strategy the information of the learner thinking style enables the system to adapt a specific learning environment. The initial student model is formed through personal data and thinking style. The thinking style is obtained by a diagnostic activity, based on brain assessment test offered to the students. The system enables an adaptation in the output learning environment where the resulting user interface is highly personalized and dynamical.

The influence of context on information processing.

The main research question of this study is how the processing of information relates to different contextual characteristics. More specifically, how the context is associated with efficiency of information processing (success and speed), size of chunks, speed of chunk processing and the recall of a chunk. The research domain was the game of chess. The efficiency of information processing and the chunk characteristics were defined with the reconstruction of sequences of chess moves. Context variables were defined using a slightly adapted chess program. Variables on information dispersion, deviation, complexity and positivity were extracted in each chess position. Overall, the results showed that higher dispersion and complexity and lower positivity of information in a context lead to less efficient information processing. The results support the assumptions of the cognitive load theory about the negative effects of external factors burden on information processing and working memory. Our results also support the ACT-R theory, which suggests that more frequent information has a higher activation level and can therefore be retrieved more easily and quickly. The results are also congruent with the positivity effect, which proposes that it is easier to remember positive information than negative information. The findings of our study can be beneficial for the development of intelligent tutoring systems and the design of human-computer interaction systems.

The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an in-depth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends.

What’s Most Broken? A Tool to Assist Data-Driven Iterative Improvement of an Intelligent Tutoring System

Intelligent Tutoring Systems (ITS) have great potential to change the educational landscape by bringing scientifically tested one-to-one tutoring to remote and under-served areas. However, effective ITSs are too complex to perfect. Instead, a practical guiding principle for ITS development and improvement is to fix what’s most broken. In this paper we present SPOT (Statistical Probe of Tutoring): a tool that mines data logged by an Intelligent Tutoring System to identify the ‘hot spots’ most detrimental to its efficiency and effectiveness in terms of its software reliability, usability, task difficulty, student engagement, and other criteria. SPOT uses heuristics and machine learning to discover, characterize, and prioritize such hot spots in order to focus ITS refinement on what matters most. We applied SPOT to data logged by RoboTutor, an ITS that teaches children basic reading, writing and arithmetic.

Example-Tracing Tutors: Intelligent Tutor Development for Non-programmers

In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4–8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing tutors are an effective and mature ITS paradigm, CTAT-built tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.

Usability evaluation of intelligent tutoring system

Face-to-face and one-to-one human tutoring is the best tutoring field. Human tutors are not always and everywhere available and that’s why computer based tutoring is developed. Intelligent Tutoring System (ITS) is the software system developed for tutoring human subjects, when human tutor is not available. ITS performance is not as good as human tutors, but ITS domain is improving and recently gaining much popularity. An ITS development project can involve an interdisciplinary team of experts from the domains of learning sciences, cognitive sciences, software engineering, and Human-computer interaction (HCI). ITS is a software intended to be used by human user; any software development project involves designing of user interfaces. Although ITS development has improved, but most of the development has focused on learning sciences and mostly ignoring the user interfaces. This has resulted in current ITS with efficient learning modules, but less efficient interface design. Performance of a software can be measured by it usability, and one way to define quality of a software is by its “ease of use”. Software that is not easy to use by its user can be considered of lower quality. This paper reviews the literature of ITS and usability; with the intention of indicating the currant gap between ITS development and usability of the system. This article also presents a discussion which can provide future directions in ITS development with usability context.

DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS

Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.

РОЗВИТОК КОМП’ЮТЕРНИХ ТЕХНОЛОГІЙ ПІДТРИМКИ НАВЧАННЯ СТУДЕНТІВ ВИЩИХ НАВЧАЛЬНИХ ЗАКЛАДІВ УКРАЇНИ (ДРУГА ПОЛОВИНА 50-Х – ПОЧАТОК 90-Х РОКІВ XX СТ.)

У статті на основі широкого кола джерел і матеріалів здійснено спробу узагальнюючого дослідження еволюції комп’ютерних технологій підтримки навчання студентів вишів України, починаючи з другої половини 50-х років до початку 90-х років ХХ ст. Виявлено чотири історичних етапи: 1) поява алгоритмів програмованого навчання; 2) виникнення автоматизованих технологій для підтримки навчання; 3) поява перших систем комп’ютерного навчання і розвиток навчальних середовищ; 4) комплексний розвиток комп’ютерної техніки і технологій, розробка інтелектуальних навчальних систем і систем віртуальної реальності.

The MATHESIS meta-knowledge engineering framework: Ontology-driven development of intelligent tutoring systems

The effect of the knowledge acquisition bottleneck is still limiting the widespread use of knowledge-based systems (KBS), especially in the area of model-tracing tutors, as they demand the development of deep domain expertise, tutoring and student models. The MATHESIS meta-knowledge engineering framework for model-tracing tutors, presented in this article, aims at maximizing knowledge reuse. This is achieved through ontological representation of both the declarative and procedural knowledge of a KBS (model-tracing tutor), as well as of the declarative and procedural authoring knowledge of the process to develop a KBS. Declarative knowledge is represented in Ontology Web Language (OWL). Procedural knowledge is represented using the concepts of atomic and composite processes of OWL-S web services description ontology. The framework provides knowledge engineering tools, integrated into the Protege OWL ontology editor, for the development and management of the KBS's ontological representation. It also provides meta-knowledge engineering tools for the ontological representation of the knowledge engineering expertise as a set of composite knowledge engineering processes and atomic knowledge engineering statements. The latter constitute a language, ONTOMATH, for building executable knowledge engineering models that, when executed by the tools, guide non-expert knowledge engineers like domain experts to the creation of new knowledge-based systems (model-tracing tutors). The framework, being in an experimental stage, was used for the development of a monomial multiplication and division tutor. However, the overall design and implementation aimed at constituting the framework as a proof-of-concept system that can be used for the meta-knowledge engineering of more complex model-tracing tutors.

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data‐driven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

The 2011 Benjamin Franklin Medal in computer and cognitive science presented to John R. Anderson

John R. Anderson is an international leader in the computational representation and simulation of human cognition. He is credited as having developed the first “cognitive architecture”—a computational framework for specifying both the abilities and limitations of human behavior. Anderson has also made fundamental contributions in applying cognitive architectures to practical problems, most notably in the development of intelligent tutoring systems: computer-based tutors that continually infer the cognitive state of the student, striving to determine what the student knows and does not know, and targeting further instruction accordingly. For these achievements, Anderson was awarded the 2011 Benjamin Franklin Medal in Computer and Cognitive Science.

Model for designing Intelligent Tutorials Systems using Conceptual Maps and knowledge-based Systems

Intelligent Tutorials Systems have demonstrated their effectiveness in various applications of the teaching-learning processes. However; its construction involves a complex and intense work of knowledge engineering, which prevents a more general use and optimal use. In this work provides a model that integrates the knowledge-based systems, the Conceptual Maps and Intelligent Tutorials Systems that favors the design of these systems, taking into account the facilities and naturalness of the Conceptual Maps. The proposed model is the basis for the design and implementation of HEMCI computational tool, which is under development, and promotes the development of Intelligent Tutoring Systems to computer non-expert users. Finally, an example of the model described for the teaching-learning process of the basic theory of combinational logic approach.

Uma Linha de Produto de Software baseada na Web Semântica para Sistemas Tutores Inteligentes

This research describes an approach for design and development of Intelligent Tutoring Systems under the Semantic Web perspective, considering aspects of both effectiveness and large scale. It deals with a clearly interdisciplinary theme which combines characteristics of Artificial Intelligence, Semantic Web and Software Engineering, applied to this kind of educational system. Thus, this work proposes a study for the rapid development of Intelligent Tutoring Systems as part of Virtual Learning Environments for presencial or distance education, taking into account characteristics such as reuse and large scale development. In this sense, an assessment of the adequateness of current ITS approaches has been done, as well as the definition of an architecture containing the Intelligent Tutoring Systems essential components. The proposed architecture has been used to specify a product line that takes into consideration (i) the different user profiles, (ii) the nonpossession of advanced computing knowledge by the users (iii) the heterogeneity of knowledge domains that kind of systems will handle and (v) the constant change and evolution of the knowledge manipulated of this kind of systems. This way, satisfying the four mentioned considerations, a software product line based on the Semantic Web was developed and tested. The experiments carried out to evaluate the proposed approach, in two different domains, demonstrating its is effectivenees in terms of large scale, adaptivity and evolution.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems: A Cognitive Mapping Approach

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.