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Showing papers on "Intelligent tutoring system published in 2022"


Journal ArticleDOI
TL;DR: Seis Tutor as discussed by the authors is a face-to-face tutoring system that offers a learning environment that best suits the learner's preferences (learning styles) and grasping levels (learning levels).
Abstract: Face-to-face tutoring offers a learning environment that best suits the learner’s preferences (learning styles) and grasping levels (learning levels). This cognitive intelligence has been blended in our proposed intelligent tutoring system christened as “Seis Tutor”. In this paper, we have detailed the architecture of Seis Tutor system and compared it with other existing traditional tutoring systems. Further, the performance of Seis Tutor has been evaluated in terms of personalization and adaptation through a comparison with some existing tutoring systems, i.e., My Moodle, Course-Builder, and Teachable.

11 citations


Journal ArticleDOI
TL;DR: In this article , an Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to support the online tutoring system that can initiate the complex learning principles in computing science courses.
Abstract: 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.

5 citations


Journal ArticleDOI
TL;DR: In this paper , an adaptive, dynamic, intelligent tutoring system (SMIT) supported by learning analytics is proposed for the design of adaptive MOOCs, which is a product of the project, which aims to integrate LMS and ITS, on the idea of how to make systems such as massive open online courses smarter.
Abstract: 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.

5 citations


Journal ArticleDOI
TL;DR: A model of an intelligent tutoring system to control learning by accentuating the individual needs of a student is proposed and is oriented on the study of the English language, where each student receives a unique study plan, which is continuously adapted based on achieved results.
Abstract: The article is devoted to the issue of the construction of an intelligent tutoring system which was created by our university for implementing distance learning and combined forms of studies. Significantly higher demand for such tools occurred during the COVID-19 pandemic when distance learning was used by students in their full-time studies. Current Learning Management Systems (LMS) do not address students' individuality regarding their various levels of input knowledge and skills or their different learning styles, which, in our case, are based on sensory preferences. Therefore, this article proposes a model of an intelligent tutoring system to control learning by accentuating the individual needs of a student. The foundation stones of this system are an expert system and adaptation mechanisms. The expert system acts as a tool for the identification of students’ needs from the point of view of input knowledge and sensory preferences. Sensory preferences influence the student’s learning style. The implemented adaptation mechanisms control the progress of the student through a study unit. The model was implemented in the LMS Moodle environment. Regarding the focus of the research content, our model is oriented on the study of the English language, where each student receives a unique study plan, which is continuously adapted based on achieved results. We consider the focus on the individuality of the student to be an innovative approach that can be achieved automatically on a mass scale.

3 citations


Proceedings ArticleDOI
28 Mar 2022
TL;DR: In this paper , the authors investigate how students and tutors in an online learning program perceive the concept of conversational Intelligent Tutoring Systems, such as a chatbot, for online learning.
Abstract: A futuristic Technology Enhanced Learning concept, a conversational Intelligent Tutoring System (ITS) for deployment in an e-learning context, is gradually becoming a reality thanks to the continuous advancement in Artificial Intelligence and to the worldwide increasing demand for online learning, especially during the pandemic. However, we do need to consider whether such technology will support student learning or make learning more difficult. In the absence of a mature conversational Intelligent Tutoring System, this article aims to address this question indirectly through an investigation of how students and tutors in an online learning programme perceive the concept of conversational Intelligent Tutoring Systems, such as a chatbot, for online learning. This is achieved by surveying students who are currently enrolled in an online programme and interviewing the tutors on the same programme. The research concludes that ITS would very likely enhance online learning experience for both students and tutors, but there are various concerns that must be addressed.

3 citations



Proceedings ArticleDOI
06 Jan 2022
TL;DR: In this paper , the authors provide an overview of the fields of Intelligent Tutoring Systems, Computer-Supported Collaborative Learning, and Collaborative Intelligent Learning Systems (CITS) and identify the gaps for possible future research.
Abstract: This paper reviews recently published works in the emerging field of Collaborative Intelligent Tutoring Systems (CITS). The paper first provides an overview of the fields of Intelligent Tutoring Systems, Computer-Supported Collaborative Learning, and Collaborative Intelligent Tutoring Systems. We systematically search online bibliographic databases, code their research objectives, qualitatively analyze their methodology, and group papers into 3 categories according to our findings. Then we evaluate the associated systems, highlighting their main features and impacts on student learning. Finally, we identify the gaps for possible future research.

2 citations


Book ChapterDOI
01 Jan 2022
TL;DR: In this paper, a risk injury monitoring module was implemented using computer vision technologies to track user movements and evaluate risk, and a simulation with a virtual population of hundreds of men and women was performed, demonstrating that considering sexual dimorphism in the tutoring module is critical for injury prevention and for providing personalized sessions for women training.
Abstract: The development and the upkeep of psychomotor skills are fundamental for performing safely and efficiently daily life, professional, or leisure movements. Artificial intelligence offers the potential to develop adaptive systems that can support people's abilities to perform essential and specialized movements, by providing individualized and optimized learning sessions. Research on men is the building block of most theories and practices in the psychomotor development domain. However, it is assumed that women's inclusion will create potential interference due to their physiological variability (i.e., menstrual cycle and the effect of oral contraceptive). Thus, personalization of motor skills learning must consider significant differences between men's and women's physiology. Our study describes female-specific issues on psychomotor skills development (e.g., strength level, menstrual cycle, and female athlete triad). The sexual dimorphism-based individualization principles and parameters are afterward presented, together with their implementation in the Selfit intelligent tutoring system for psychomotor skills development. A risk injury monitoring module was implemented using computer vision technologies to track user movements and evaluate risk. A simulation with a virtual population of hundreds of men and women was performed, demonstrating that considering sexual dimorphism in the tutoring module is critical for injury prevention and for providing personalized sessions for women training.

1 citations


Book ChapterDOI
01 Jan 2022
TL;DR: In this article , the authors present a discussion on the research work done in the area, showing the work done on adaptive learning systems and intelligent tutoring systems, and the combination of adaptive learning system and intelligent learning system can be developed with advanced capabilities and can be more beneficial to the learners than traditional online learning.
Abstract: AbstractEducation is a vital part of everyone’s life. With the advancement of the internet, online learning has gained a wide scope. With the availability of a wide variety of courses and course content available online, learners usually struggle while choosing the course that will be most beneficial to them. It’s mainly because of the learners having different learning styles, different level of understanding, and different knowledge domains. Along with this, the learners also suffer from improper monitoring and evaluation. The adaptive learning system adapts itself as per the learning style of the learner and an intelligent tutoring system helps in the monitoring and evaluation of the learner’s performance. An intelligent tutoring system also provides an immediate and customized response to the learner. With the combination of Adaptive learning system and intelligent learning system, the online learning system can be developed with advanced capabilities and can be more beneficial to the learners than traditional online learning system. There are various learning styles in which learners can be categorized. Various learning style models are Felder Silverman model, VAK model, David Kolb learning style model etc. The paper presents a discussion on the research work done in the area, showing the work done on adaptive learning systems and intelligent tutoring systems.KeywordsAdaptive learning systemIntelligent tutoring systemLearning styleLearning style modelOnline learningPersonalized learning

1 citations


Proceedings ArticleDOI
20 Jul 2022
TL;DR: In this paper , a new model based on students' behaviors and skills is proposed to achieve intelligent tutoring; these models aim to predict the suitable teaching strategy and best learning style for each learner.
Abstract: Intelligent Tutoring defines the use of intelligent systems to personalize the tutoring and mimic the behavior of human tutors to improve the learning environment and enable learners to study and discuss topics in natural language, to have a deeper understanding of the topic. Various models are proposed to achieve intelligent tutoring; these models aim to predict the suitable teaching strategy and best learning style for each learner. However, these models have not covered all the student’s behaviors and preferences. Therefore, more analysis is needed to understand learners’ needs and examine the lack of existing systems to provide more efficient intelligent systems for effective one-to-one personalized learning. This research proposes a new model based on students’ behaviors and skills. A dataset of e-learning student reactions is used, which is a large-scale dataset for posts and reactions created by students on the e-learning platform. Three different classification methods namely Classifier Chains, Binary Relevance, and Label Powerset are applied to make a model for learning styles prediction and provide the best experience for each learner. Label Powerset achieves the best results of F1 Score (0.93) among all the binary classifiers that used the problem transformation method.

1 citations


Proceedings ArticleDOI
09 Oct 2022
TL;DR: In this article , the authors used an open source framework called Rasa to adapt the original button-based user interface of an algebraic/arithmetic word problem-solving ITS to one based primarily on the use of natural language.
Abstract: Conversational Intelligent Tutoring Systems (CITS) in learning environments are capable of providing personalized instruction to students in different domains, to improve the learning process. This interaction between the Intelligent Tutoring System (ITS) and the user is carried out through dialogues in natural language. In this study, we use an open source framework called Rasa to adapt the original button-based user interface of an algebraic/arithmetic word problem-solving ITS to one based primarily on the use of natural language. We conducted an empirical study showing that once properly trained, our conversational agent was able to recognize the intent related to the content of the student’s message with an average accuracy above 0.95.

Journal ArticleDOI
TL;DR: In this paper , a Concept Question Answering system applied to the Computer Domain (CQACD) for intelligent tutoring is proposed, which allows the tutor and student with mixed-initiative and natural language to ask each other questions concerning the basic computer knowledge in the course.
Abstract: 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 Computer Basics 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’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’ 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’s discipline knowledge, provide structured domain knowledge for student model, template design and matching, and provide basic architectural architecture for dialogue management.

Journal ArticleDOI
TL;DR: A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented that offers learners proper autonomy and learning might be facilitated using such a design.
Abstract: Purpose In intelligent tutoring systems (ITS), learners were often granted limited authority and are forced to obey the decision of the system which might not satisfy their needs. Failure to grant learners sufficient autonomy could yield unexpected effects that hinder learning, including undermining learners’ motivation, priming learners’ aversion to the algorithm. On the contrary, granting learners overwhelming autonomy could also be harmful as the absence of learning support would also have a negative impact on learning. As such, this study aims to design and implement an intelligent tutoring system that offers learners proper autonomy. Design/methodology/approach The main learning activity in the system is doing exercises, and by finishing exercises learners could earn virtual coins. Based on item response theory, exercises are administered to learners with proper difficulty. Based on a recommended difficulty parameter predicted by the system, learners could manually modify the difficulty of the exercises, they could earn more credits by finishing more challenging exercises. Meanwhile, a pedagogical agent is embedded. Learners could customize the agent’s personality jointly with the system to create the learning context they prefer. Findings A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented. Originality/value Few previous researches have noticed the potentially important role that LA plays in ITS. Learning might be facilitated using such a design.

Book ChapterDOI
TL;DR: In this paper , an intelligent approach to explanatory feedback generation for the task of function prototype creation training is proposed, where an approach to automatic teaching function design, a formal model of the subject domain based on OWL ontology and Jena rules to detect errors in the students answer using software reasoners, and intelligent tutor based on the developed formal model.
Abstract: Intelligent tutoring systems play an essential role in learning. In programming learning, the specificity of the learning process is related to creating code in a programming language and developing appropriate skills. One of the basic skills in code development is designing functions and their interfaces in a programming language. For these skills mastering using ITS, it is important to detect the student’s mistakes early and provide formative explanatory feedback for the student to help them find and fix the errors. In this paper, we propose the intelligent approach to explanatory feedback generation for the task of function prototype creation training. We developed an approach to automatic teaching function design, a formal model of the subject domain based on OWL ontology and Jena rules to detect errors in the students’ answers using software reasoners, and intelligent tutor based on the developed formal model.

Book ChapterDOI
01 Jan 2022
TL;DR: In this article , the authors describe a method for classifying items that are instructive, evaluative, motivational, versus potentially flawed based on analyses of items' psychometric properties.
Abstract: AbstractAutoTutor-ARC (adult reading comprehension) is an intelligent tutoring system that uses conversational agents to help adult learners improve their comprehension skills. However, in such a system, not all lessons and items optimally serve the same purposes. In this paper, we describe a method for classifying items that are instructive, evaluative, motivational, versus potentially flawed based on analyses of items’ psychometric properties. Further, there is no a priori way of determining which lessons are optimal given the learner’s reading profile needs. To address this, we evaluate how assessing learner component reading skills can inform various aspects of learner needs on AutoTutor lessons. More specifically, we compare learners who were classified as proficient, underengaged, conscientious, versus struggling readers based on their experiences with AutoTutor. Together, these analyses suggest the utility of integrating assessments with instruction: efficient, adaptive learning at the lesson level, more efficient and valid post-testing, and consequently, recommendations for more targeted, adaptive pathways through the instructional program/system.KeywordsIntelligent tutoring systemsReading skillsPsychometrics

Journal ArticleDOI
TL;DR: A collaborative ITS to teach UML is presented that is built to enable students to effectively communicate and share each other's mistakes and is capable of detecting and identifying student errors.
Abstract: Computer-based learning tools called Intelligent Tutoring Systems (ITS) assist students to become better learners by simulating human tutors using Artificial Intelligence (AI) approaches. Students can interact using collaborative ITSs from various locations to study, discuss, and articulate concepts relevant to a certain problem. This paper presents a collaborative ITS to teach UML that is built to enable students to effectively communicate and share each other's mistakes. The ITS is capable of detecting and identifying student errors and offers students suggestions during the problem-solving stage, giving them guidance on how to proceed. The ITS also determines a student's current level of thinking and intellect in order to assign them activities that need more attention. The evaluations conducted for this study revealed that the experimental group had considerably more learning gains (81% scores on the posttest) than the control group, where students only showed a very low significant change in their learning with posttest scores of 46%

Journal ArticleDOI
04 May 2022
TL;DR: This work advocates logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems and derives a dedicated inference scheme to speed up computations.
Abstract: Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of parameters might be important for multiple reasons. First, as these models are typically based on expert knowledge, a huge number of parameters to elicit might discourage practitioners from adopting them. Moreover, the number of model parameters affects the complexity of the inferences, while a fast computation of the queries is needed for real-time feedback. We advocate logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems. We discuss the semantics of the model parameters to elicit and the assumptions required to apply such approach in this domain. We also derive a dedicated inference scheme to speed up computations.

Journal ArticleDOI
TL;DR: An Intelligent Tutoring System (ITS) as discussed by the authors gives tailored teaching to students based on their learning preferences and/or skills to address the problems of learning, such as the difficulty of providing quality learning to students in remote areas, which may be hard to reach by students and teachers.
Abstract: an Intelligent Tutoring System (ITS) gives tailored teaching to students based on their learning preferences and/or skills to address the problems of learning. These include the difficulty of providing quality learning to students in remote areas, which may be hard to reach by students and/or teachers. Additionally, tracking the progress of each individual student and providing suitable learning material and exercises is not an easy task. One of the best candidates for such systems is Arabic grammar, which is a quite complex subject. Nevertheless, due to this complexity it hasn’t been adequately handled by researchers. Also, those systems have generally been developed for elementary-level grammar courses.

Book ChapterDOI
01 Jan 2022
TL;DR: In this article , the authors presented a study model for constructing an intelligent tutoring system for learner assessment based on bayesian network, which takes into account a client model and a learner model.
Abstract: The most crucial part of the educational system is the intelligent tutoring system. A computer system that intends to give learners quick and customised lessons or feedback, usually without the intervention of a professor, is known as an intelligent tutoring system. Artificial intelligence technology is employed in an intelligent tutoring system to provide a lot of help to learners in terms of acquiring skills and knowledge. Human professors are not required to contribute to the organisation in this process, and Bayesian Network has been employed to solve this problem. An intelligent tutoring system’s heart is the beginner learner model. Using a Bayesian network with high self-learning ability to build an intelligent tutoring system for the novice concept can considerably improve the level of comprehension of the intelligent tutoring system. The core philosophy of an intelligent tutoring system for the beginner concept will be the major focus. The rudiments of impact on the learners learning method are then studied at this level, starting with the perception of the beginner’s expertise in teaching, mutual with the state of learning, and the features of the beginner. Based on bayesian network, this work presented a study model for constructing an intelligent tutoring system for learner assessment. The tutoring system’s design model takes into account a client model and a learner model. The bayesian network was employed in an e-learning environment to assess the learner’s current level of knowledge so that the model may evolve and offer new knowledge to improve learner performance.

Posted ContentDOI
28 Jul 2022
TL;DR: In this paper , an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students, is presented. But the model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates.
Abstract: We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unlocking new opportunities for learning experience personalization.

Journal ArticleDOI
TL;DR: This work designed the architecture of the ITS, the diagnosis of transcription errors and remediation approach, and used the Petri net formalism to model the system dynamic in order to analyze its states and fix deadlocks and shows that the learning objectives can be achieved with this system.
Abstract: —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.

Book ChapterDOI
01 Jan 2022
TL;DR: In this article , personalization in the way questions are asked in an intelligent tutoring system (ITS) was investigated and it was shown that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains.
Abstract: This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student learning outcomes. Previous work on dialogue-based ITS personalization has yet to address question phrasing. We show that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains, using variants written by a domain expert and an experimental A/B test. This insight demonstrates that the linguistic realization of questions in an ITS affects the learning outcomes for students.

Proceedings ArticleDOI
03 Mar 2022
TL;DR: In this article , an adaptive learning exercises intended for mastery learning are performed until the learner reaches the intended mastery level, but how to grade them if all passing students reach the same mastery at the end?
Abstract: Adaptive learning exercises intended for mastery learning are performed until the learner reaches the intended mastery level. But how to grade them if all passing students reach the same mastery at the end? Our goal was to calculate grades from the number of wrong steps and correct steps while solving the last questions in the exercise. This approach is implemented in an intelligent tutoring system and its behavior was observed during the system's evaluation. The faster the students learned, the higher were their grades. This technique can be used to grade students directly using the results of solving problems in the training system without separate summative tests. This lets students receive feedback on how well they are learning without performing more exercises.

Proceedings ArticleDOI
18 Dec 2022
TL;DR: In this paper , an intelligent tutoring system (ITS) for math word problem solving (MathITS) is proposed, which automatically generates tutorial solutions for any user input problems and thus could be widely used in after-class tutoring.
Abstract: To provide the step by step tutoring service like a human tutor, an intelligent tutoring system (ITS) for math word problem solving (MathITS) is proposed in this paper. The proposed MathITS has an ability of automatically generate tutorial solutions for any user input problems and thus could be widely used in after-class tutoring. An improved math word problem solver is applied to generate the tutorial solution, which transforms expression solutions into logic sequences of arithmetic operations with illustrating texts. In stage of adaptive tutoring, hints and suggestions are generated and launched to students rather giving them explicit solutions. Finally, an evaluation module is provided which gives immediate feedback on the evaluation of the whole process of multi-turn tutoring interaction. A pioneer experiment is conducted and the results demonstrate the efficiency of the proposed system.

Posted ContentDOI
25 May 2022
TL;DR: In this paper , personalization in the way questions are asked in an intelligent tutoring system (ITS) was investigated and it was shown that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains.
Abstract: This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student learning outcomes. Previous work on dialogue-based ITS personalization has yet to address question phrasing. We show that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains, using variants written by a domain expert and an experimental A/B test. This insight demonstrates that the linguistic realization of questions in an ITS affects the learning outcomes for students.

Proceedings ArticleDOI
08 Oct 2022
TL;DR: In this paper , the application of EDM techniques, based on data generated by a web-based Intelligent Tutor System (ITS), developed for the teaching of algebra, which helps students to develop fundamental knowledge of mathematics, in addition to carrying out a comparative study between the level of difficulty of the proposed algebraic questions.
Abstract: This complete article of the research category presentes proposes the application of Educational Data Mining (EDM) techniques, based on data generated by a web-based Intelligent Tutor System (ITS), developed for the teaching of algebra, which helps students to develop fundamental knowledge of mathematics, in addition to carrying out a comparative study between the level of difficulty of the proposed algebraic questions. For this, we use the knowledge discovery methodology to perform the following steps from the data: cleaning, integration, selection, transformation, mining, evaluation and presentation of information. The practical results reveal that the proposed architecture can classify the academic performance of students in each evaluation period with an accuracy of around 80%. It was also possible to identify factors related to the resolution of questions, such as the rate of correct answers and the mathematical steps to solve an exercise, classifying the level of difficulty of the questions proposed by the system, acting on the student’s deficiencies, and the system. The results obtained from the data analysis can serve as a basis for decision-making, helping in the teaching process for teachers, tutors and managers to monitor academic performance, enabling the correction of problems and identifying the individual and collective difficulties present in each class. In summary, these results also provide a general roadmap on the performance of using EDM techniques in a given context.


Journal ArticleDOI
TL;DR: In this article, preliminary results of a research related to Intelligent Programming Tutor (IPT) which is derived from Intelligent Tutoring System (ITS) are presented, where student model mainly student characteristic was focused.
Abstract: —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.

Book ChapterDOI
TL;DR: The use of intelligent tutoring systems may be useful to support the teaching and learning process, especially in the area of Math, in order to contribute to improve the students' academic level as discussed by the authors .
Abstract: The use of intelligent tutoring systems may be useful to support the teaching and learning process, especially in the area of Math, in order to contribute to improve the students’ academic level. The selection strategies and evaluation criteria were defined in a previous work about Intelligent Tutoring in Teaching [11]. The present paper delves into the analysis of the selected intelligent tutoring systems taking their functional characteristics into consideration.

Journal ArticleDOI
TL;DR: In this paper , the authors present a thorough evaluation of a fuzzy-based intelligent tutoring system (ITS) that teaches computer programming. And 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.
Abstract: Abstract 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.