Showing papers on "Intelligent tutoring system published in 2019"

How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System

Abstract: The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

A Survey of Robot-Assisted Language Learning (RALL)

Abstract: Robot-assisted language learning (RALL) is becoming a more commonly studied area of human-robot interaction (HRI). This research draws on theories and methods from many different fields, with researchers utilizing different instructional methods, robots, and populations to evaluate the effectiveness of RALL. This survey details the characteristics of robots used—form, voice, immediacy, non-verbal cues, and personalization—along with study implementations, discussing research findings. It also analyzes robot effectiveness. While research clearly shows that robots can support native and foreign language acquisition, it has been unclear what benefits robots provide over computer-assisted language learning. This survey examines the results of relevant studies from 2004 (RALL's inception) to 2017. Results suggest that robots may be uniquely suited to aid in language production, with apparent benefits in comparison to other technology. As well, research consistently indicates that robots provide unique advantages in increasing learning motivation and in-task engagement, and decreasing anxiety, though long-term benefits are uncertain. Throughout this survey, future areas of exploration are suggested, with the hope that answers to these questions will allow for more robust design and implementation guidelines in RALL.

Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact

Abstract: Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students’ individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of personalization may be moderated by students’ depth of quantitative engagement with their out-of-school interests. We also examine whether math problems designed to draw upon students’ knowledge of their individual interests at a deep level (i.e., actual quantitative experiences) or surface level (i.e., superficial changes to problem topic) have differential effects. Results suggest that connecting math instruction to students’ out-of-school interests can be beneficial for learning in an ITS and reduces gaming the system. However, benefits may only be realized when students’ degree of quantitative engagement with their out-of-school interests matches the depth at which the personalized problems are written. Students whose quantitative engagement with their interests is minimal may benefit most when problems draw upon superficial aspects of their interest areas. Students who report significant quantitative engagement with their interests may benefit most when individual interests are deeply incorporated into the quantitative structure of math problems. We also find that problems with deeper personalization may spur positive affective states and ward off negative ones for all students. Findings suggest depth is a critical feature of personalized learning with implications for theory and AI instructional design.

Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents

Abstract: In this paper, we present the current state of the art of using conversational agents for educational purposes. These so-called pedagogical conversational agents are a specialized type of e-learning and intelligent tutoring systems. The main difference to traditional e-learning and intelligent tutoring systems is that they interact with learners using natural language dialogs, e.g. in the form of chatbots. For the sake of our research project, we analyzed current trends in the research stream as well as research gaps. Our results show for instance that (1) there is a trend towards using mobile conversational agents in education, (2) a proper generalization of existing research results (e.g. design knowledge) is missing, and (3) there is a need for comprehensive in-depth evaluation studies and corresponding process models. Based on our results, we outline a research agenda for future research studies.

A Meta-Analysis of the Effectiveness of ALEKS on Learning.

Abstract: Assessment and Learning in Knowledge Spaces (ALEKS) is one of the widely used online intelligent tutoring systems (ITS) in the USA, but it has rarely been included in meta-analyses of ITS e...

Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring.

Abstract: Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.

Intelligent Tutoring System for Negotiation Skills Training.

Abstract: Intelligent tutoring systems have proven very effective at teaching hard skills such as math and science, but less research has examined how to teach “soft” skills such as negotiation. In this paper, we introduce an effective approach to teaching negotiation tactics. Prior work showed that students can improve through practice with intelligent negotiation agents. We extend this work by proposing general methods of assessment and feedback that could be applied to a variety of such agents. We evaluate these techniques through a human subject study. Our study demonstrates that personalized feedback improves students’ use of several foundational tactics.

Intelligent tutoring systems and learning performance: Applying task-technology fit and IS success model

Abstract: Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.,Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.,Data analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers.,This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

Review on Development of Smart Education

Abstract: Smart education is leading the development direction of Chinese education informatization and becoming a main theme of education development in the era of which technology changes education. There are seven main branches of smart education, namely Intelligent Tutoring System (ITS), smart campus, Big Data in Education (BDE), knowledge graph, educational robots, virtual teachers, and personalized education. Based on literature survey and market research, this paper gives a comprehensive overview of the research and application of smart education from above seven aspects and proposes its development based on the status of smart education development.

Intelligent tutoring system model based on fuzzy logic and constraint-based student model

Abstract: A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.

It is not either or: An initial investigation into combining collaborative and individual learning using an ITS

Abstract: Research on Computer-Supported Collaborative Learning (CSCL) has provided significant insights into why collaborative learning is effective and how we can effectively provide support for it. Building on this knowledge, we can investigate when collaboration is beneficial to support learning. Specifically, collaborative and individual learning are often combined in the classroom, and it is important for the CSCL community to understand when a combination is beneficial compared to individual or collaborative learning alone. Before investing significant work into discovering these details, an initial investigation is needed to determine if there may be any value in a combination. In this study, we compared a combined condition to individual or collaborative-only learning conditions using an intelligent tutoring system for fractions. The study was conducted with 382 4th and 5th grade students. Students across all three conditions had significant learning gains, but the combined condition had higher learning gains than the other conditions. However, this difference was restricted to the 4th grade students. By analyzing the hints and errors of students over time from process data, we found that students in the combined condition tended to make fewer errors both when working collaboratively and individually, and asked for fewer hints than the students in the other conditions. Students who collaborated (collaborative and combined conditions) also reported having higher situational interest in the activity. By finding support for the effectiveness of combining collaborative and individual learning, this paper opens a broader line of inquiry into how they can effectively be combined to support learning.

Survey of Intelligent Tutoring Systems Up To the End of 2016

Abstract: The main goals of Intelligent Tutoring Systems (ITS) are: providing highly developed instructional guidance on a one-toone foundation that is improved than what is attained with traditional computer aided instruction and is analogous to that of a decent human tutor; and developing and testing models of intelligent processes associated with instruction. ITS is a subfield of artificial intelligence. ITS consists of four interacting components: the student model which embodies the student's present knowledge state, the pedagogical module which comprises appropriate instructional measures which are depending on the content of the student model, the knowledge model which contains the domain knowledge, and the user interface model which permits an effective dialog among ITS and the user. Usually, the knowledge model is the central part in the instructional process but there is a diversity of approaches that also put the stress on the other components. In this paper we have surveyed 55 existing intelligent tutoring system found in the literature. There might be other intelligent tutoring systems that we did not include in the current survey; but when found, we will make sure to be included in the next release of the intelligent tutoring systems survey.

Intelligent Tutoring System Trends 2006-2018: A Literature Review

Abstract: This research examined the longitudinal trends of intelligent tutoring systems (ITS) research using text mining techniques in a more comprehensive manner. Two hundred and thirty-one (231) refereed journal articles were retrieved and analyzed from the Web of Science database from the top six major educational technology-based journals, which are based on the Google Scholar metrics and Baidu Scholar in the period from January 2006 to December 2018. Content analysis was implemented for further analysis based on (a) category of research purpose, (b) disciplines domains, (c) sample group, (d) context utilization, (e) research design, (f) category of learning, (g) learning outcome, (h) periodic journal, (i) country, (j) publisher. This review research of ITS presented findings, which could be a layover platform and guidance for researchers, educators, policymakers or even journal publisher for the future research or reference in the realm of ITS regarding the latest trends.

Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System

Abstract: There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.

An Intelligent Tutoring System for Learning Java

Abstract: Java is one of the most widely used languages in Desktop developing, Web Development and Mobile Development, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language. Why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. In this paper, we describe the design of an Intelligent Tutoring System for teaching Java to help students learn Java easily and smoothly. Tutor provides beginner level in Java. Finally, we evaluated our tutor and the results were excellent by students and teachers.

The Reality of the Employees Performance in the Palestinian Cellular Telecommunications Company (Jawwal)

Abstract: The aim of this study was to identify the reality of the performance of the employees in The Palestinian Cellular Telecommunications Company (Jawwal), and to find the differences between the views of the study sample on the variables of the study according to the variables (age, scientific qualification, field of work and years of service). To achieve the objectives of the study, a questionnaire was designed and developed to measure the variables of the study applied to the company's 70 employees. The Complete Census method was used and 60 samples were recovered for analysis with a recovery rate (85.7%). The SPSS statistical package was adopted. The study reached several results, the most important of which is that the degree of approval for the job performance of the employees working in The Palestinian Cellular Telecommunications Company (Jawwal) is 81.56%. The results showed that there were no statistically significant differences at the level of α≤ 0.05 between the average of the respondents' opinions on the performance of the workers in the Palestinian Cellular Telecommunications Company (Jawwal) due to the following variables (age, scientific qualification, field of work, number of years of service). The most important recommendations were to increase the efficiency of the employees of the company using the equipment of their work, and the need to pay attention to the development of the skills of employees through specialized training programs to improve their performance. And focus on moral incentives because of their role in improving the performance of employees by spreading the spirit of cooperation between employees to work as a team seeking to achieve the objectives of the company.

Co-Designing an Intelligent Conversational History Tutor with Children

Abstract: In a world that demands independent and cooperative problem solving to address complex social, economic, ethical, and personal concerns, core social studies content is as basic for success as reading, writing, math, and computing. Nationally, only 20% of 4th grade students are at or above Proficient level in U.S. History, the lowest among the core disciplines of social studies. Reaching proficiency requires students to ask more profound questions of the past as well as construct deeper understandings of it. This research explores the intersection of natural language dialogue and intelligent tutoring systems to enhance history learning of upper elementary students in 3rd and 4th grade. Elementary and middle school students were participants in a participatory design study to extract their design needs for the development of an educational learning technology for social studies.

A Systematic Review of Domains, Techniques, Delivery Modes and Validation Methods for Intelligent Tutoring Systems

Abstract: An Intelligent Tutoring System (ITS) is a computer software that help students in learning educational or academics concepts in customized environment. ITSs are instructional systems that have capability to facilitate user by providing instantaneous feedback and instructions without any human intervention. The advancement of new technologies has integrated computer based learning with artificial intelligence methods with aim to develop better custom-made education systems that referred as ITS. One of the important factors that affect students learning process is self-learning; all students cannot have similar experience of learning scholastic concepts from same educational material. Because students have individual differences that make some topics difficult or easy to understand regarding taken subjects. These systems have capability to improve teaching and learning process in different educational domains while respecting individual learning needs. In this study an attempt is made to review the research in field of ITSs and highlight the educational areas or domains in which ITSs have been introduced. Techniques, delivering modes and evaluation methodologies that have been used in developed ITSs have also been discussed in this work. This work will be helpful for both academia and new comers in the field of ITSs to further strengthen basis of tutoring systems in educational domains.

The role of mastery learning in an intelligent tutoring system: Principal stratification on a latent variable

Abstract: Students in Algebra I classrooms typically learn at different rates and struggle at different points in the curriculum—a common challenge for math teachers. Cognitive Tutor Algebra I (CTA1), an educational computer program, addresses such student heterogeneity via what they term “mastery learning,” where students progress from one section of the curriculum to the next by demonstrating appropriate “mastery” at each stage. However, when students are unable to master a section’s skills even after trying many problems, they are automatically promoted to the next section anyway. Does promotion without mastery impair the program’s effectiveness? At least in certain domains, CTA1 was recently shown to improve student learning on average in a randomized effectiveness study. This paper uses student log data from that study in a continuous principal stratification model to estimate the relationship between students’ potential mastery and the CTA1 treatment effect. In contrast to extant principal stratification applications, a student’s propensity to master worked sections here is never directly observed. Consequently we embed an item-response model, which measures students’ potential mastery, within the larger principal stratification model. We find that the tutor may, in fact, be more effective for students who are more frequently promoted (despite unsuccessfully completing sections of the material). However, since these students are distinctive in their educational strength (as well as in other respects), it remains unclear whether this enhanced effectiveness can be directly attributed to aspects of the mastery learning program.

LANA-I: An Arabic Conversational Intelligent Tutoring System for Children with ASD

Abstract: Children with Autism Spectrum Disorder (ASD) share certain difficulties but being autistic will affect them in different ways in terms of their level of intellectual ability. Children with high functioning autism or Asperger syndrome are very intelligent academically but they still have difficulties in social and communication skills. Many of these children are taught within mainstream schools but there is a shortage of specialised teachers to deal with their specific needs. One solution is to use a virtual tutor to supplement the education of children with ASD in mainstream schools. This paper describes research to develop a novel Arabic Conversational Intelligent Tutoring System, called LANA-I, for children with ASD that adapts to the Visual, Auditory and Kinaesthetic learning styles model (VAK) to enhance learning. This paper also proposes an evaluation methodology and describes an experimental evaluation of LANA-I. The evaluation was conducted with neurotypical children and indicated promising results with a statistically significant difference between user’s scores with and without adapting to learning style. Moreover, the results show that LANA-I is effective as an Arabic Conversational Agent (CA) with the majority of conversations leading to the goal of completing the tutorial and the majority of the correct responses (89%).

Visualizing a collective student model for procedural training environments

Abstract: Visualization plays a relevant role for discovering patterns in big sets of data In fact, the most common way to help a human with a pattern interpretation is through a graphic In 2D/3D virtual environments for procedural training the student interaction is more varied and complex than in traditional e-learning environments Therefore, the visualization and interpretation of students’ behaviors becomes a challenge This motivated us to design the visualization of a collective student model built from student logs taken from 2D/3D virtual environments for procedural training This paper presents the design decisions that enable a suitable visualization of this model to instructors as well as a web tool that implements this visualization and is intended: to help instructors to improve their own teaching; and to enhance the tutoring strategy of an Intelligent Tutoring System Then, this paper illustrates, with three detailed examples, how this tool can be used to those educational purposes Next, the paper presents an experiment for validating the utility of the tool In this experiment we show how the tool can help to modify the tutoring strategy of a 3D virtual laboratory In this way, it is shown that the proposed visualization of the model can serve to improve the performance of students in 2D/3D virtual environments for procedural training

Developing an Intelligent Tutoring System for Robotic-assisted Surgery Instruction.

Abstract: Background Robotic-assisted laparoscopic surgery (RALS) is an operative innovation that has sparked global interest. Over the last decade, RALS cases have rapidly increased with over 750 000 robotic procedures completed in 2017. Until recently, Intuitive's da Vinci surgical system has been the only Food and Drug Administration (FDA)-approved robotic-assisted surgical device for human procedures. Robotic procedures with the da Vinci require a specific, dedicated training due to the introduction of the technological components and psychomotor skills needed to successfully utilize this system. When a surgeon becomes interested in learning robotics, there are limited avenues for training. Surgeons typically receive instruction on the necessary psychomotor and Operation Room (OR) communication skills in isolation from the cognitive and perceptual skills and may only perform these skills in an integrated manner during a 1- or 2-day course. Methods This paper discusses the development of a computer-based intelligent tutoring system (ITS) to train the cognitive and procedural skills needed to complete basic robotic suturing to novice robotic surgeons. The system was developed using the generalized intelligent framework for tutoring framework of tools. This information was captured as video, instruction sets, and flowcharts, which were reviewed for accuracy by surgeons and then encoded into an ITS using the generalized intelligent framework for tutoring tools. Conclusion The purpose of this paper was threefold-(a) explain the process used to obtain the critical data behind a basic robotic task, (b) develop an entry-level ITS to train the cognitive process and procedural steps behind multiple fundamental robotic surgery skills, and (c) provide future novice ITS developers lessons learned and future recommendations beyond the initial ITS prototype.

Intelligent tutoring system to improve learning outcomes

Abstract: This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019.

Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback

Abstract: We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.

Quantitizing Affective Data as Project Evaluation on the Use of a Mathematics Mobile Game and Intelligent Tutoring System

Abstract: Technology�enhanced learning generally focuses on the cognitive rather than the af� fective domain of learning. This multi�method evaluation of the INBECOM project (Integrating Behaviourism and Constructivism in Mathematics) was conducted from the point of view of af� fective learning levels of Krathwohl et al. (1964). The research questions of the study were: (i) to explore the affective learning experiences of the three groups of participants (researchers, teachers and students) during the use of a mobile game UFractions and an intelligent tutoring system ActiveMath to enhance the learning of fractions in mathematics; and (ii) to determine the significance of the relationships among the affective learning experiences of the three groups of participants (researchers, teachers and students) in the INBECOM project. This research followed a sequential, equal status, multi�mode research design and methodol� ogy where the qualitative data were derived from the interviews with researchers, teachers and students, as well as from learning diaries, feelings blogs, and observations (311 documents) across three contexts (South Africa, Finland, and Mozambique). The qualitative data was quantitized (Saldaña, 2009), i.e. analysed deductively in an objective and quantifiable way as instances on an ExcelTM spreadsheet for statistical analyses. All the data was explored from the affective per� spective by labelling the feelings participants experienced according to the affective levels of the Krathwohl et al. (1964) framework. The researchers concluded that: (i) the research participants not only received information, but actively participated in the learning process; responded to what they learned; associated value to their acquired knowledge; organised their values; elaborated on their learning; built abstract knowledge; and adopted a belief system and a personal worldview; and (ii) affirmation of affective learning at all five levels was recognised among the three groups of participants. The study raised a number of issues which could be addressed in future, like how affective levels of learning are in� tertwined with cognitive levels of learning while learning mathematics in a technology�enhanced learning environment; and how pedagogical models which take into account both cognitive and affective aspects of learning support deep learning.

Using Reinforcement Learning to Optimize the Policies of an Intelligent Tutoring System for Interpersonal Skills Training

Abstract: Reinforcement Learning (RL) has been applied successfully to Intelligent Tutoring Systems (ITSs) in a limited set of well-defined domains such as mathematics and physics. This work is unique in using a large state space and for applying RL to tutoring interpersonal skills. Interpersonal skills are increasingly recognized as critical to both social and economic development. In particular, this work enhances an ITS designed to teach basic counseling skills that can be applied to challenging issues such as sexual harassment and workplace conflict. An initial data collection was used to train RL policies for the ITS, and an evaluation with human participants compared a hand-crafted ITS which had been used for years with students (control) versus the new ITS guided by RL policies. The RL condition differed from the control condition most notably in the strikingly large quantity of guidance it provided to learners. Both systems were effective and there was an overall significant increase from pre- to post-test scores. Although learning gains did not differ significantly between conditions, learners had a significantly higher self-rating of confidence in the RL condition. Confidence and learning gains were both part of the reward function used to train the RL policies, and it could be the case that there was the most room for improvement in confidence, an important learner emotion. Thus, RL was successful in improving an ITS for teaching interpersonal skills without the need to prune the state space (as previously done).

The effect of emotional analysis on the improvement of experimental e‐learning systems

Abstract: E‐learning, also known as online learning or technology enhanced learning, needs to provide more heterogeneous learning including not only Web‐based courses and virtual classrooms but also remote labs with an affective recognition system. These latter allow students to interact with real experiments conducted from a distance with real‐time facial expression recognition using a webcam. However, multiple systems do not include this kind of learning. Thus, students' motivation, interest, and learning might be negatively affected since there can be no emotional interaction without “on demand” learning. This work analyzes the impact of students' emotions to enhance the learning experience in two aspects: (i) filling the existing gaps of students and (ii) improving the usability and readability of the platform. The overall goal of our research is to build a novel intelligent tutoring system, called “LabTutor,” adapted to the profile of every student. It aims to serve as an experienced teacher for students who access and perform experiments on real laboratory equipment in the field of engineering education.