Intelligent Tutoring Systems Research Articles

ArgRewrite V.2: an annotated argumentative revisions corpus

Analyzing how humans revise their writings is an interesting research question, not only from an educational perspective but also in terms of artificial intelligence. Better understanding of this process could facilitate many NLP applications, from intelligent tutoring systems to supportive and collaborative writing environments. Developing these applications, however, requires revision corpora, which are not widely available. In this work, we present ArgRewrite V.2, a corpus of annotated argumentative revisions, collected from two cycles of revisions to argumentative essays about self-driving cars. Annotations are provided at different levels of purpose granularity (coarse and fine) and scope (sentential and subsentential). In addition, the corpus includes the revision goal given to each writer, essay scores, annotation verification, pre- and post-study surveys collected from participants as meta-data. The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis. We demonstrate some potential applications of ArgRewrite V.2 in the development of automatic revision purpose predictors, as a training source and benchmark.

Designing an intelligent tutoring system for computer programing in the Pacific.

Intelligent Tutoring Systems (ITSs) are educational systems that reflect knowledge using artificial intelligence implements. In this paper, we give an outline of the Programming-Tutor architectural design with the core implements on user interaction. This pilot proposal is for designing a model domain of a subset in the computer programming language. The completed project would be adequate to show the idea of a completely developed computing Intelligent Tutoring System in online programming courses to offer benefits to students in the Pacific. This proposed concept would also provide students with an immersive learning experience in an online course to assist in a formative assessment to enhance student learning. A smart tutoring system can provide prompt input of high quality which not only conveys to students about the consistency of the solution but also provides them with information on the precision of the key concerning their existing solutions expertise. This Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to be able to support the online tutoring system that can initiate the complex learning principles in computing science courses. It is also hypothesized that, based on the performance of other Intelligent Tutoring Systems, students would be able to learn to program more easily in regional campuses and acquire experiences more rapidly and efficiently than students who are taught using conventional methods in an online mode.

Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal

Massive Open Online Courses (MOOCs) are a type of Learning Management Systems (LMSs), but it seems that the influence of the instructor in these systems is minimal or simply lacking. These systems present the learning content and materials to all learners attending the course in the same way and fail to offer individualized instruction that recognizes the individual differences and needs of the learners. It is reported that such problems can be eliminated by making the new generation intelligent learning systems. However, there is still an ongoing search for making such systems intelligent and a conceptual discussion concerning them. Integrating an intelligent tutoring system (ITS) with learning analytics, this study seeks to design and present the framework of an ITS with open access that a) identifies the learning needs of learners through adaptive mastery testing and guides learners based on these needs, b) overcomes learning deficiencies, monitors learners' interactions with content through learning analytics and offers suggestions, c) supports learning with dynamic assessment processes and d) tests learners’ learning competencies. This article aims to explain the conceptual and system framework for the design of an adaptive, dynamic, intelligent tutoring system (SMIT), supported by learning analytics, which is a product of the project, which aims to integrate LMS and ITS, on the idea of how to make systems such as MOOCs smarter. In line with the findings obtained from the research, various suggestions were made for the design of smart Moocs.

Building an Intelligent Tutoring System for Learning Polysemous Words in Moore

This paper presents the results of our research carried out as part of the building of an Intelligent Tutoring System (ITS) to learn Moor´e, a tone language. A word in tone language may have many meanings according to the pitch. The system has an intelligent tutor to personalize and guide the learning of the transcription of polysemous words in Moor´e. This learning activity aims both to master the transcription and also to distinguish the lexical meaning of words according to the pitch used. A first step of this research has been the specification of the processes, inference and knowledge of the system. In this work we present the implementation and pedagogical assessment of the system. We designed the architecture of the ITS, the diagnosis of transcription errors and remediation approach. Then, we used the Petri net formalism to model the system dynamic in order to analyze its states and fix deadlocks. We developed the system in java and we evaluated its educational value by an experimentation with learners. This shows that the learning objectives can be achieved with this system.

Between AI and Learning Science: The Evolution and Commercialization of Intelligent Tutoring Systems

This article traces the history of a class of AI systems known as “intelligent tutoring systems” (ITSes). An ITS is structured like a tutor: A student engages with the system on some educational task (such as creating a geometry proof) and the system can calculate the student's learning and provide interventions that help the student develop a certain skill. The article shows that ITS researchers have been able to navigate the twists and turns in AI patronage as the field fell into its booms and busts. They did so by carefully crafting new identities—most notably as “learning scientists”—while also holding on to their AI credentials. To successfully commercialize ITSes for adoption in schools, they had to reconceive the systems themselves: from standalone aids for students to learning aids for both teachers and students.

The Analysis of Research Hot Spot and Trend on Artificial Intelligence in Education

In this work, a total of 871 papers that were published between 1999 and 2018were retrieved from the Web of ScienceTM (WOSTM) platform. The descriptive analysis considered the publication volume, and countries. A global network of authors’ keywords and content analysis of related scientific literature highlighted major techniques, including Virtual Reality (VR), Robotic, Machine learning, Artificial neural network, Artificial intelligence (AI), Natural language processing (NLP), most frequent applications in learning, teaching, and managing. AI technology applications in the field of education are summarized, mainly concentrated in self-adaptive learning, intelligent tutoring system and intelligent evaluation.

Objective Type Question Generation using Natural Language Processing

Automatic Question Generation (AQG) is a research trend that enables teachers to create assessments with greater efficiency in right set of questions from the study material. Today's educational institutions require a powerful tool to correctly assess learner’s mastery of concepts learned through study materials. Objective type questions are an excellent method of assessing a learner's topic understanding in learning process, based on Information and Communication Technology (ICT) and Intelligent Tutoring Systems (ITS).Creating a set of questions for assessment can take a significant amount of time for teachers, and obtaining questions from external sources such as assessment books or question banks may not be relevant to the content covered by students during their studies. This proposed system involves to generate the familiar objective type questions like True or False, ‘Wh’, Fill up with double blank space, match the following type question have generated using Natural Language Processing(NLP) techniquesfrom the given study material. Different rules are created to generate T/F and ‘Wh’ type questions. Dependence parser method has involved in ‘Wh’ questions. Proposed system is tested with Grade V Computer Science text book as an input. Experimental result shows that the proposed system is quite promising to generate the amount of objective type assessment questions.

AI-assisted knowledge assessment techniques for adaptive learning environments

The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instructions for students in an adaptive learning environment. There are increasing interests as well as many challenges in the application of Artificial Intelligence (AI) techniques in educational settings to provide adaptive learning content to learners. Knowledge assessment is necessary for providing an adaptive learning environment. A student model serves as a fundamental building block of knowledge assessment in an adaptive learning environment. This paper intends to review the development of dominant families of student models with psychometric theory in early educational research, recent adaptations, and advances with machine learning and deep learning techniques. Our review covers not only the important families of student models but also why they were invented from both theoretical and practical viewpoints with AI and educational perspectives. We believe that the discussion covered in this review will be a valuable reference of introductory insights to AI for educational researchers, as well as an endeavor of introducing basic psychometric perspectives to AI experts for knowledge assessment in the field of learning science. Finally, we provide recent challenges and some potential directions for developing efficient knowledge assessment techniques in future adaptive learning ecosystems.

Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review

Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.

Say What? Learner Reactions to Unexpected Agent Dialogue Moves

For maximally efficient and effective conversation-based intelligent tutoring systems, designers must understand the expectations carried by their intended learners. Strategies programmed into the agents may be interpreted differently by the learners. For example, conversational heuristics in these systems may be biased against false alarms in identifying wrong answers (potentially accepting more incorrect answers), or they may avoid directly answering learner-generated questions in an attempt to encourage more open-ended input. Regardless of pedagogical merit, the learner may view these agents’ dialogue moves as bugs rather than features and respond by disengaging or distrusting future interactions. We test this effect by orchestrating situations in agent-based instruction of electrical engineering topics (through an intelligent tutoring system called AutoTutor) where the pedagogical agent behaves in ways likely counter to learner expectations. To better understand the learning experience of the user, we then measure learner response via think-aloud protocol, eye-tracking, and direct interview. We find that, with few exceptions, learners do not reason that the actions are meant as instructional or technical strategies, but instead broadly understood as errors. This indicates a need for either alteration of agent dialogue strategies, or else additional (implicit or explicit) introduction of the strategies to productively shape learners’ interactions with the system.

Students’ Characteristics of Student Model in Intelligent Programming Tutor for Learning Programming: A Systematic Literature Review

This study describes preliminary results of a research related to Intelligent Programming Tutor (IPT) which is derived from Intelligent Tutoring System (ITS). The system architecture consists of four models. However, in this study student model mainly student characteristic was focused. From literature, 44 research articles were identified from a number of digital databases published between 1997 to 2022 base on systematic literature review (SLR) method. The findings show that the majority 48% of IPT implementation focuses on knowledge and skills. While 52% articles focused on a combination of two to three student characteristics where one of the combinations is knowledge and skill. When narrow down, 25% focused on knowledge and skills with errors or misconceptions; 4% focused on knowledge and skill with cognitive features; 5% focused focus on knowledge and skill with affective features; 2% focused on knowledge and skill with motivation; and 9% based on knowledge and skill with learning style and learning preferences as students’ characteristics to build their student model. Whereas 5% focused on a combination of three student characters which are knowledge and skill with cognitive and affective features and 2% focused on knowledge and skill with learning styles and learning preferences and motivation as students’ characteristics to construct the tutoring system student model. To provide an appropriate tutoring system for the students, students’ characteristic needs to decide for the student model before developing the tutoring system. From the findings, it can say that knowledge and skills is an essential students’ characteristic used to construct the tutoring system student model. Unfortunately, other students’ characteristic is less considered especially students’ motivation.

Thirty years of interactive learning environments: contributors, collaborations and research topics

Interactive Learning Environments (ILE) is an important and active international journal in the research field of interactive learning environments since 1990. Focusing on the major contributors, scientific collaborations, research topics and topic evolution, this paper analyses 784 ILE articles using topic modelling and bibliometrics to explore key milestones in ILE's history. Institutions from the USA and Taiwan have contributed the most to the community, as witnessed from their top ranks in both impact and productivity indicators. Scientific collaborations between institutions in the same regions are noteworthy, while cross-regional collaborations need to be enhanced. Diverse applications and technologies such as intelligent tutoring systems, concept maps, digital games, e-books, interactive response systems, digital storytelling and augmented reality are increasingly integrated into interactive learning contexts to facilitate flipped learning, personalised learning and project-based learning. Additionally, learners' emotions, cognitive load and acceptance of technologies are widely studied.

Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT

The Intelligent Tutoring Systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory.

Knowledge tracing for adaptive learning in a metacognitive tutor

Abstract Adaptive learning is provided in intelligent tutoring systems (ITS) to enable learners with varying abilities to meet their expected learning outcomes. Despite the personalized learning afforded by ITSes using adaptive learning, learners are still susceptible to shallow learning. Introducing metacognitive tutoring to teach learners how to be aware of their knowledge can enable deeper learning. However, metacognitive tutoring on top of cognitive tutoring can lead to unsustainable cognitive loads. Using metacognitive inputs for knowledge tracing was explored for managing cognitive loads. Hidden Markov models (HMM) and artificial neural networks were used to train models on a synthetic dataset created from predetermined learner personas. The models created with metacognitive inputs were compared with the models created without said inputs. The models using metacognitive inputs performed better than the standard models while still following learning intuitions. This indicates that combining knowledge tracing and metacognitive tutoring is a viable option for improving learning outcomes. This is an important finding since online learning, which demands metacognitive skills, is becoming popular for various topics, including those that are challenging even with immediate teacher assistance.

Bayesian Network Modelling for Improved Knowledge Management of the Expert Model in the Intelligent Tutoring System

Bayesian Network Modelling for Improved Knowledge Management of the Expert Model in the Intelligent Tutoring System

CQACD: A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology With Rich Semantics

In this study, a Concept Question Answering system applied to the Computer Domain (CQACD) for intelligent tutoring is proposed. This system is a dialogue-based Intelligent Tutoring System (ITS) that allows the tutor and student with mixed-initiative and natural language to ask each other questions concerning the basic computer knowledge in the <i>Computer Basics</i> course. CQACD is based on constructivist principles and encourages the learner to construct knowledge rather than merely receiving knowledge, which has the following characteristics: (a) this system employs a domain ontology with rich semantic relationships to model the basic computer knowledge and build up a concept-centric knowledge model, (b) uses a limited number of 80 input templates with description logics to acquire the intention of questions posed by students, (c) a textual entailment algorithm with semantic technologies is proposed to match the input template and assess the student&#x2019;s contribution to improve the flexibility of the system, and (d) an ontology-driven dialogue management mechanism is proposed, which can quickly form the conversational content and conversational sequence. The experimental results show that CQACD can replace the teachers&#x2019; tutoring in large classes and can promote the learning of poor students in large classes better than teachers can. The paper reveals that the domain ontology with rich semantic relationships plays an important role in the Concept Question Answer System (CQAS). It can model CQAS&#x2019;s discipline knowledge, provide structured domain knowledge for student model, template design and matching, and provide basic architectural architecture for dialogue management.

The Critical Feature Selection Approach using Ensemble Meta-Based Models to Predict Academic Performances

In this work, machine learning techniques are deemed to predict student academic performances in their historical performance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become a vital element to perceive the goal of academic quality. Research is improving and growing fast in Educational Data Mining (EDM) due to many students' information. Researchers urge to invent valuable patterns about students' learning behavior using their data that needs to be adequately processed to transform it into helpful information. This paper proposes a prediction model of students' academic performances with new data features, including student's behavioral features, Psychometric, family support, learning logs via e-learning management systems, and demographic information. In this paper, data collection and pre-processing are firstly conducted following the grouping of students with similar patterns of academic scores. Later, we selected the applicable supervised learning algorithms, and then the experimental work was implemented. The performance of the student's predictive model assessment is comprised of three steps: First, the critical Feature selection approach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models are improvised to boost the accuracy of the Classifier. Subsequently, the present study is associated with the solutions that help the students evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the results were produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students' academic performance.

New multi-agent approach for generating feedbacks base on Multiple Choice Questions

Most intelligent tutorial systems promote the learning and resolution of exercises based on feedback in the form of advice, remarks, explanation…To always remain in self-assessment, our approach offers a new type of feedback in the form of multiple-choice questions applied in the field of algorithms (language c, java ...) dedicated for beginners in programming. The approach is based on the multi-agent model to have interaction between learners without the help of the teacher. There are three types of agents in our work, there is the learner agent who represents the learner, there is also the feedback agent who sends the MCQs to the learners who made mistakes and finally, the controller agent feeds the base of multiple-choice questions based on feedback from learners. The controlling agent compares the instructions of the learners with the instructions of the correct model based on the AST abstract syntax tree and detects errors which will be proposed as erroneous items (distractors) for the learners. We can say that this type of feedback is not direct like other work (advice, comments, explanation ...) but we always remain in the evaluation: MCQ, exercise ... As perspectives, we will focus mainly on the classification of LO learning objects in the form of an ontology to facilitate the use of data and the generation of multiple-choice questions at different levels. And also, we aim to develop a suitable platform which allows to define the agents and the messages to be exchanged between them to set up our system.

Intelligent Tutoring System of Linear Programming

There is a growing technological development in intelligent teaching systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that helps students understand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems. It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities. The goal function and the limiting conditions represent a mathematical model of the observed problem. Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics. There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper. The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.

A simple model of smart learning environment

ABSTRACT In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances.