Showing papers on "Intelligent tutoring system published in 2018"
TL;DR: This study showed that perceived usefulness, self-efficacy, compatibility, and perceived support for enhancing social ties are important antecedents to continuance intention to use flipped classroom.
Abstract: Students nowadays are hard to be motivated to solve logical problems with traditional teaching methods. Computers, Smartphone's, tablets and other smart devices disturb their attention. But those smart devices can be used as auxiliary tools of modern teaching methods. The flipped classroom is one such innovative method that moves the solving problems outside the classroom via technology and reinforces solving problems inside the classroom via learning activities. In this paper, the authors implement flipped classroom as an element of Internet of Things (IOT) into learning process of mathematical logic course. In the flipped classroom, an Intelligent Tutoring System (ITS) was used to help students work with the problems in the course outside the classroom. This study showed that perceived usefulness, self-efficacy, compatibility, and perceived support for enhancing social ties are important antecedents to continuance intention to use flipped classroom.
90 citations
TL;DR: In this paper, the authors examined whether personalizing four units of algebra problems that high school students solve in an intelligent tutoring system could improve their performance in units (i.e., accuracy and learning efficiency) and on classroom exams.
Abstract: Context personalization—the incorporation of students’ out-of-school interests into learning tasks—has recently been shown to positively affect students’ situational interest and their performance and learning in mathematics. However, few studies have shown effects on both interest and achievement, drawing into question whether context personalization interventions can achieve both ends. The effects of personalization are theorized to result from activation of students’ prior knowledge of personal interests and generation of situational interest in math tasks, though theorists have begun to question whether situational interest serves as a mechanism by which learning outcomes are achieved. This experimental study examines whether personalizing 4 units of algebra problems that high school students (N = 150) solve in an intelligent tutoring system could improve their performance in units (i.e., accuracy and learning efficiency) and on classroom exams, whether adolescents who solved personalized problems would report greater situational interest in units (and later, individual interest in math) than peers who solved standard problems, and whether paths through situational interest would contribute to effects of personalization on outcomes. High school students in the personalization condition reported greater triggered situational interest in experimental units, and triggered interest predicted in-tutor outcomes (accuracy, learning efficiency). A total effect of personalization was also observed on classroom exam performance and individual interest in mathematics. Implications for theories of interest and context personalization are discussed, as are implications for math instruction and design of personalized learning environments. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
71 citations
TL;DR: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students and is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.
Abstract: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.
48 citations
24 Sep 2018
TL;DR: In this article, the authors proposed a deep knowledge tracing model for Intelligent Tutoring System (ITS), which captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and combines this information with a recurrent neural network architecture known as Deep Knowledge Tracing.
Abstract: In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling
48 citations
TL;DR: Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort.
Abstract: This study investigated learning outcomes and user perceptions from interactions with a hybrid intelligent tutoring system created by combining the AutoTutor conversational tutoring system with the Assessment and Learning in Knowledge Spaces (ALEKS) adaptive learning system for mathematics. This hybrid intelligent tutoring system (ITS) uses a service-oriented architecture to combine these two web-based systems. Self-explanation tutoring dialogs were used to talk students through step-by-step worked examples to algebra problems. These worked examples presented an isomorphic problem to the preceding algebra problem that the student could not solve in the adaptive learning system. Due to crossover issues between conditions, experimental versus control condition assignment did not show significant differences in learning gains. However, strong dose-dependent learning gains were observed that could not be otherwise explained by either initial mastery or time-on-task. User perceptions of the dialog-based tutoring were mixed, and survey results indicate that this may be due to the pacing of dialog-based tutoring using voice, students judging the agents based on their own performance (i.e., the quality of their answers to agent questions), and the students’ expectations about mathematics pedagogy (i.e., expecting to solving problems rather than talking about concepts). Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort. Integrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning. However, managing learner expectations during transitions between systems remains an open research area. Finally, while personalized adaptation can improve learning efficiency, effort and time-on-task for learning remains a dominant factor that must be considered by interventions.
43 citations
TL;DR: This survey reviews techniques that have been used to measure emotions and theories for modeling emotions, and investigates EEG-based Brain-Computer Interaction of general and learning emotion recognition.
39 citations
Posted Content•
TL;DR: A novel model for knowledge tracing is proposed that captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals and combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing.
Abstract: In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.
38 citations
Journal Article•
34 citations
TL;DR: A novel coding framework was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system, and demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time.
Abstract: Research on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co- and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners’ compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co- and socially-shared regulated learning theories to include learner-PA interactions are proposed.
31 citations
Journal Article•
TL;DR: An intelligent tutoring system for teaching Reciting Al-Quran "Tajweed" with Rewaya: Hafs from ‘Aasem by the way of Shatebiyyah was evaluated by teachers and students in reciting science and the outcome of the assessment was encouraging and promising.
Abstract: Undeniably, the greatest way for a Moslem to be closer to Allah, is recitation of Holy-Quran approves with the method conveyed from Messenger of Allah Mohammed from the feature of speech points of letters and the intrinsic and fleeting characteristics of the letters, So, there is a persistent need to teach all Moslems the science of Tajweed Al-Quran. ITS (Intelligent Tutoring System) is computer software that supplies direct and tailored training or response to students without human teacher interfering. The main target of ITS is smoothing the learning process using the wide-ranging facilities of computer. The proposed system will be implemented using the ITSB Authoring tool. In this thesis, the researcher presents an intelligent tutoring system for teaching Reciting Al-Quran \"Tajweed\" with Rewaya: Hafs from ‘Aasem by the way of Shatebiyyah. It was a novel idea that the researcher combined the science of Tajweed Al-Quran and the science of artificial intelligence in his thesis. The researcher arranged the material into chapters, lessons, examples then, added all these to the proposed system. He also added questions, right answers and the level of difficulty for each lesson. He prepared an exam for each chapter and a final exam to test the knowledge of the learner in the whole material. The system was evaluated by teachers and students in reciting science and the outcome of the assessment was encouraging and promising.
30 citations
Journal Article•
TL;DR: The design of an Intelligent Tutoring System for teaching ASP.net to help students learn ASP.
Abstract: ASP.net is one of the most widely used languages in web developing of its many advantages, 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. Therefore, in this paper, we describe the design of an Intelligent Tutoring System for teaching ASP.net to help students learn ASP.net easily and smoothly. Tutor provides beginner level in ASP.net. Finally, we evaluated our tutor and the results were excellent by students and teacher. Keywords— ASP; Net; Intelligent Tutoring System; Tutor
Journal Article•
TL;DR: An intelligent tutoring system for Learning Classical cryptography algorithms concentrate on the students registered in Advanced Topics in Information Security in the faculty of Engineering and Information Technology at Al-Azhar University in Gaza and it is suggested reasonable and suitable learning pedagogical for individual student to perform adaptive learning.
Journal Article•
TL;DR: The design of a desktop based intelligent tutoring system that functions as a special tutor who deals with trainees according to their levels and skills and is adaptive with the trainee’s individual progress is described.
Abstract: This paper aims at helping trainees to overcome the difficulties they face when dealing with Arduino platform by describing the design of a desktop based intelligent tutoring system. The main idea of this system is a systematic introduction into the concept of Arduino platform. The system shows the circuit boards of Arduino that can be purchased at low cost or assembled from freely-available plans; and an open-source development environment and library for writing code to control the board topic of Arduino platform. The system is adaptive with the trainee’s individual progress. The system functions as a special tutor who deals with trainees according to their levels and skills. Evaluation of the system has been applied on professional and unprofessional trainees in this field and the results were good.
TL;DR: The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described.
Abstract: An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of 'Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.
TL;DR: It is revealed that SITS is not only capable of boosting students’ learning interest, attitude and technology acceptance, but it also helps students achieve more in terms of problem-solving activities.
Abstract: In learning systems and environment research, intelligent tutoring and personalisation are considered the two most important factors. An Intelligent Tutoring System can serve as an effective tool to improve problem-solving skills by simulating a human tutor’s actions in implementing one-to-one adaptive and personalised teaching. Thus, in this research, a solution-based intelligent tutoring system (SITS) is proposed. It benefits from Bayesian networks in managing uncertainty based on the probability theory for the process of decision-making so as to aid students learn computer programming. Additionally, SITS benefits from a multi-agent system that employs an automatic text-to-flowchart conversion approach to engage novice programmers in flowchart development with the aim of improving their problem-solving skills. Finally, the performance of SITS is investigated through an experimental study. It is revealed that SITS is not only capable of boosting students’ learning interest, attitude and technolog...
TL;DR: This study identified the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS) to help foster their knowledge and talent to play a vital role in environmental development.
Abstract: Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.
Journal Article•
TL;DR: The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university and shows a positive impact on the evaluators.
Abstract: The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators.
Journal Article•
Journal Article•
TL;DR: As OGITS targets individual knowledge acquisition of computer programming and web-based problem-solving skills, it offers a suitable learning environment for students both as a stand-alone course and as a supplement to traditional classroom settings.
Abstract: Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas f...
Posted Content•
TL;DR: A web based intelligent tutoring system for teaching Android Applications Development to students to overcome the difficulties they face and is automatically adapted at run time to the student’s individual growth.
Abstract: The paper describes the design of a web based intelligent tutoring system for teaching Android Applications Development to students to overcome the difficulties they face. The basic idea of this system is a systematic introduction into the concept of Android Application Development. The system presents the topic of Android Application Development and administers automatically generated problems for the students to solve. The system is automatically adapted at run time to the student’s individual growth. The system provides obvious support for adaptive demonstration constructs. An initial assessment study was done to examine the effect of using the intelligent tutoring system on the performance of students enrolled in Smartphone Applications Development in the University College of Applied Sciences, Gaza. The results showed a positive impact on the evaluators.
Journal Article•
TL;DR: In this paper, a web-based intelligent tutoring system for teaching Android application development to students to overcome the difficulties they face is described, where the system is automatically adapted at run time to the student's individual growth.
Abstract: The paper describes the design of a web based intelligent tutoring system for teaching Android Applications Development to students to overcome the difficulties they face. The basic idea of this system is a systematic introduction into the concept of Android Application Development. The system presents the topic of Android Application Development and administers automatically generated problems for the students to solve. The system is automatically adapted at run time to the student's individual growth. The system provides obvious support for adaptive demonstration constructs. An initial assessment study was done to examine the effect of using the intelligent tutoring system on the performance of students enrolled in Smartphone Applications Development in the University College of Applied Sciences, Gaza. The results showed a positive impact on the evaluators.
TL;DR: A conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems is proposed and a mathematical evaluation is applied to determine the richness of the proposed model.
Abstract: Affectivity has influence in learning face-to-face environments and improves some aspects in students, such as motivation. For that reason, it is important to integrate affectivity elements into virtual environments. We propose a conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems. We design an ontology guided by methontology, and apply a mathematical evaluation (OntoQA) to determine the richness of the proposed model. The mathematical evaluation states that the proposed model has relationship richness and horizontal nature. We developed a software application implementing the conceptual model in order to prove its effectivity to generate students’ motivation. The findings suggest that the implemented affective learning ontology impacts positively the motivation in students with low academic performance, in female students and in engineering students.
26 Apr 2018
TL;DR: This work unpack the difficulties associated with data interpretation from those associated with warranting claims within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills.
Abstract: This chapter addresses students’ data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills. We conducted a systematic analysis of a subset of our data to address whether our scaffolding is supporting students in the acquisition and transfer of these inquiry skills. We also describe an additional study, which used Bayesian Knowledge Tracing (Corbett and Anderson. User Model User-Adapt Interact 4(4):253–278, 1995), a computational approach allowing for the analysis of the fine-grained sub-skills underlying our practices of data interpretation and warranting claims.
TL;DR: Case studies of application of social media, game-based learning and various technology-enhanced learning tools in different courses at several Serbian institutions show that educational processes must be modernized and enhanced by technological progress.
11 Jun 2018
TL;DR: AttentiveLearner2, a multimodal intelligent tutor running on unmodified smartphones, is proposed to supplement today’s clickstream-based learning analytics for MOOCs and detect 6 emotions in mobile MOOC learning reliably with high accuracy.
Abstract: Massive Open Online Courses (MOOCs) are a promising approach for scalable knowledge dissemination. However, they also face major challenges such as low engagement, low retention rate, and lack of personalization. We propose AttentiveLearner2, a multimodal intelligent tutor running on unmodified smartphones, to supplement today’s clickstream-based learning analytics for MOOCs. AttentiveLearner2 uses both the front and back cameras of a smartphone as two complementary and fine-grained feedback channels in real time: the back camera monitors learners’ photoplethysmography (PPG) signals and the front camera tracks their facial expressions during MOOC learning. AttentiveLearner2 implicitly infers learners’ affective and cognitive states during learning from their PPG signals and facial expressions. Through a 26-participant user study, we found that: (1) AttentiveLearner2 can detect 6 emotions in mobile MOOC learning reliably with high accuracy (average accuracy = 84.4%); (2) the detected emotions can predict learning outcomes (best R2 = 50.6%); and (3) it is feasible to track both PPG signals and facial expressions in real time in a scalable manner on today’s unmodified smartphones.
TL;DR: The study examined the effectiveness of the online writing tool for helping a group of EFL sophomores in Taiwan develop plagiarism avoidance knowledge, paraphrasing skills, and citation abilities through an online writing tutorial system entitled DWright.
Abstract: J Comput Assist Learn. 2018;1–12. Abstract With the increased use of digital materials, undergraduate writers in English as a foreign language (EFL) contexts have become more susceptible to plagiarism. In this study, the researchers designed a blended English writing course with an online writing tutorial system entitled DWright. The study examined the effectiveness of the online writing tool for helping a group of EFL sophomores in Taiwan develop plagiarism avoidance knowledge, paraphrasing skills, and citation abilities. Mixed‐method data included pretest, posttest, and delayed test, a feedback sheet, writing assignments, interviews, and a post‐course questionnaire, which reported increased awareness on plagiarism avoidance with evidence such as qualitative paraphrasing improvement and positive feedback on the use of DWright for developing plagiarism avoidance knowledge and citation competence. Pedagogical implications for researchers and instructors interested in blending online writing tutorials into their courses for plagiarism avoidance were also discussed.
Proceedings Article•
09 Jul 2018TL;DR: This paper investigates the real-time prediction of both students' achievement goals and affective valence while interacting with MetaTutor, an agent-based intelligent tutoring system, and shows that classifiers can outperform a majority-class baseline at predicting both achievement Goals and emotion valence.
Abstract: There is evidence that Pedagogical Agents (PA) can influence students' emotions while learning with Intelligent Tutoring Systems, and that this influence is modulated by the students' achievement goals for learning. This suggests that students may benefit from personalized PAs that could rectify episodes of negative affect depending on their achievement goals. To ascertain the possibility of devising such personalized PAs, this paper investigates the real-time prediction of both students' achievement goals and affective valence while interacting with MetaTutor, an agent-based intelligent tutoring system. We train classifiers using eye-tracking data to make such prediction, and show that these classifiers can outperform a majority-class baseline at predicting both achievement goals and emotion valence.
13 Jul 2018
TL;DR: This work gathered over 2,000 sketches from 20 novices and four experts for analysis and identified key metrics for quality assessment that were shown to significantly correlate with the quality of expert sketches and provide insight into providing intelligent user feedback in the future.
Abstract: Design sketching is an important skill for designers, engineers, and creative professionals, as it allows them to express their ideas and concepts in a visual medium. Being a critical and versatile skill for many different disciplines, courses on design sketching are often taught in universities. Courses today predominately rely on pen and paper; however, this traditional pedagogy is limited by the availability of human instructors, who can provide personalized feedback. Using a stylus-based intelligent tutoring system called SketchTivity, we aim to eventually mimic the feedback given by an instructor and assess student-drawn sketches to give students insight into areas for improvement. To provide effective feedback to users, it is important to identify what aspects of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with several classes of features that could potentially differentiate expert and novice sketches. Because improvement on one metric, such as speed, may result in a decrease in another metric, such as accuracy, the creation of a single score may not mean much to the user. We attempted to create a single internal score that represents overall drawing skill so that the system can track improvement over time and found that this score correlates highly with expert rankings. We gathered over 2,000 sketches from 20 novices and four experts for analysis. We identified key metrics for quality assessment that were shown to significantly correlate with the quality of expert sketches and provide insight into providing intelligent user feedback in the future.
27 Jun 2018
TL;DR: An intelligent tutoring system for learning English and French concepts is presented that incorporates a novel model for error diagnosis using machine learning and employs two algorithmic techniques.
Abstract: Intelligent computer-assisted language learning employs artificial intelligence techniques to create a more personalized and adaptive environment for language learning. Towards this direction, this paper presents an intelligent tutoring system for learning English and French concepts. The system incorporates a novel model for error diagnosis using machine learning. This model employs two algorithmic techniques and specifically Approximate String Matching and String Meaning Similarity in order to diagnose spelling mistakes, mistakes in the use of tenses, mistakes in the use of auxiliary verbs and mistakes originating from confusion in the simultaneous tutoring of languages. The model for error diagnosis is used by the fuzzy logic model which takes as input the results of the first or the knowledge dependencies existing among the different domain concepts of the learning material and decides dynamically about the learning content that is suitable to be delivered to the learner each time.