L’adoption des Nouvelles Technologies de l’Information et de la Communication (NTIC) a permis la modernisation des méthodes d’enseignement dans les systèmes d’apprentissage en ligne comme l’e-Learning, les systèmes tutoriels intelligents, etc. Ces derniers assurent une formation à distance qui répond aux besoins des apprenants. Un aspect très important à prendre en considération dans ces systèmes est l’évaluation précoce de l’apprenant en termes d’acquisition des connaissances. En général, trois types d’évaluation et leurs relations sont nécessaires durant le processus d’apprentissage, à savoir : (i) diagnostic qui est exécuté avant l’apprentissage pour estimer le niveau des élèves, (ii) évaluation formative qui est appliquée lors de l’apprentissage pour tester l’évolution des connaissances et (iii) évaluation sommative qui est considérée après l’apprentissage pour évaluer l’acquisition des connaissances. Ces méthodes peuvent être intégrées d’une manière semi-automatique, automatique ou adaptée aux différents contextes de formation, par exemple dans le domaine d’apprentissage des langues (français, anglais, etc.), des sciences fondamentales (mathématiques, physique, chimie, etc.) et langages de programmation (java, python, sql, etc.) Cependant, les méthodes d’évaluation usuelles sont statiques et se basent sur des fonctions linéaires qui ne prennent en considération que la réponse de l’apprenant. Elles ignorent, en effet, d’autres paramètres de son modèle de connaissances qui peuvent divulguer d’autres indicateurs de performance. Par exemple, le temps de résolution d’un problème, le nombre de tentatives, la qualité de la réponse, etc. Ces éléments servent à détecter les traits du profil, le comportement ainsi que les troubles d’apprentissage de l’apprenant. Ces paramètres additionnels sont vus dans nos travaux de recherche comme des traces d’apprentissage produites par l’apprenant durant une situation ou un contexte pédagogique donné. Dans ce cadre, nous proposons dans cette thèse une approche d’évaluation de l’apprenant à base des traces d’apprentissage qui peut être exploitée dans un système d’adaptation de la ressource et/ou de la situation pédagogique. Pour l’évaluation de l’apprenant, nous avons proposé trois modèles génériques d’évaluation qui prennent en considération la trace temporelle, le nombre de tentatives et leurs combinaisons. Ces modèles ont servi, par la suite, comme métrique de base à notre modèle d’adaptation de la ressource et/ou de la situation d’apprentissage. Le modèle d’adaptation est également fondé sur les trois traces susmentionnées et sur nos modèles d’évaluation. Notre modèle d’adaptation génère automatiquement des trajectoires d’apprentissage adaptées en utilisant un modèle d’état-transition. Les états présentent des situations d’apprentissage qui consomment des ressources et les transitions entre situations expriment les conditions nécessaires à remplir pour passer d’une situation à une autre. Ces concepts sont aussi implémentés dans une ontologie du domaine et un algorithme d’adaptation a été également proposé. L’algorithme assure deux types d’adaptation : (i) Adaptation de la situation et (ii) Adaptation des ressources dans une situation. Afin de collecter les traces d’apprentissage pour la mise en œuvre de notre approche d’évaluation de l’apprenant et d’adaptation de ressources et de situations d’apprentissage, nous avons effectué des expérimentations sur deux groupes d’étudiants en Licence Informatique (L2). Un groupe en apprentissage classique et un groupe en apprentissage adapté. Sur la base des traces obtenues des sessions de travail des étudiants, nous avons utilisé nos modèles d’évaluation dont les résultats ont été utilisés pour mettre en œuvre l’adaptation. Après comparaison des résultats de l’apprentissage adapté à ceux obtenus de l’apprentissage classique, nous avons constaté une amélioration des résultats en termes de moyenne générale et d’écart-type des moyennes des apprenants. / The adoption of new Information and Communication Technologies (ICT) has enabled the modernization of teaching methods in online learning systems such as e-Learning, intelligent tutorial systems (ITS), etc. These systems provide a remote training that which meets the learner needs. A very important aspect to consider in these systems is the early assessment of the learner in terms of knowledge acquisition. In general, three types of assessment and their relationships are needed during the learning process, namely : (i) diagnostic which is performed before learning to estimate the level of students, (ii) formative evaluation which is applied during learning to test the knowledge evolution and (iii) summative evaluation which is considered after learning to evaluate learner’s knowledge acquisition. These methods can be integrated into a semi-automatic, automatic or adapted way in different contexts of formation, for example in the field of languages literary learning such as French, English, etc., hard sciences (mathematics, physics, chemistry, etc.) and programming languages (java, python, sql, etc.). However, the usual evaluation methods are static and are based on linear functions that only take into account the learner’s response. They ignore other parameters of their knowledge model that may disclose other performance indicators. For example, the time to solve a problem, the number of attempts, the quality of the response, etc. These elements are used to detect the profile characteristics, behavior and learning disabilitiesof the learner. These additional parameters are seen in our research as learning traces produced by the learner during a given situation or pedagogical context. In this context, we propose in this thesis a learner evaluation approach based on learning traces that can be exploited in an adaptation system of the resource and/or the pedagogic situation. For the learner assessment, we have proposed three generic evaluation models that take into consideration the temporal trace, number of attempts and their combinations. These models are later used as a base metric for our resource adaptation model and/or learning situation. The adaptation model is also based on the three traces mentioned above and on our evaluation models. Our adaptation model automatically generates adapted paths using a state-transition model. The states represent learning situations that consume resources and the transitions between situations express the necessary conditions to pass from one situation to another. These concepts are implemented in a domain ontology and an algorithm that we have developed. The algorithm ensures two types of adaptation : (i) Adaptation of the situation and (ii) Adaptation of resources within a situation. In order to collect traces of training for the implementation of our approaches of learner evaluation and adaptation of resources and learning situations, we conducted experiments on two groups of students in Computer Science (L2). One group in classical training and the other group in adapted training. Based on the obtained traces from the students’ training sessions, we assessed merners based on our evaluation models. The results are then used to implement the adaptation in a domain ontology. The latter is implemented within oracle 11g which allows a rule-based semantic reasoning. After comparing the results of the adapted training with those obtained from the classical one, we found an improvement in the results in terms of general average and standard deviation of the learner averages.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 201-210).

Intelligent Tutoring Systems (ITS) have a significant educational impact on student's learning. However, researchers report time intensive interaction is needed between ITS developers and domain-experts to gather and represent domain knowledge. The challenge is augmented when the target domain is ill-defined. The primary problem resides in often using traditional approaches for gathering domain and tutoring experts' knowledge at design time and conventional methods for knowledge representation built for well-defined domains. Similar to evolving knowledge acquisition approaches used in other fields, we replace this restricted view of ITS knowledge learning merely at design time with an incremental approach that continues training the ITS during run time. We investigate a gradual knowledge learning approach through continuous instructor-student demonstrations. We present a Mixed-response Intelligent Tutoring System based on Learning from Demonstration that gathers and represents knowledge at run time. Furthermore, we implement two knowledge representation methods (Weighted Markov Models and Weighted Context Free Grammars) and corresponding algorithms for building domain and tutoring knowledge-bases at run time. We use students' solutions to cybersecurity exercises as the primary data source for our initial framework testing. Five experiments were conducted using various granularity levels for data representation, multiple datasets differing in content and size, and multiple experts to evaluate framework performance. Using our WCFG-based knowledge representation method in conjunction with a finer data representation granularity level, the implemented framework reached 97% effectiveness in providing correct feedback. The ITS demonstrated consistency when applied to multiple datasets and experts. Furthermore, on average, only 1.4 hours were needed by instructors to build the knowledge-base and required tutorial actions per exercise. Finally, the ITS framework showed suitable and consistent performance when applied to a second domain. These results imply that ITS domain models for ill-defined domains can be gradually constructed, yet generate successful results with minimal effort from instructors and framework developers. We demonstrate that, in addition to providing an effective tutoring performance, an ITS framework can offer: scalability in data magnitude, efficiency in reducing human effort required for building a confident knowledge-base, metacognition in inferring its current knowledge, robustness in handling different pedagogical and tutoring criteria, and portability for multiple domain use.

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

For many courses, mathematics included, there is an associated interactive e-learning system that provides assessment and tutoring. Some of these systems are classified as Intelligent Tutoring Systems. MyMathLab, Mathzone, and Assessment of LEarning in Knowledge Space (ALEKS) are just a few of the interactive e-learning systems in mathematics. In ALEKS, assessment and tutoring are based on the Knowledge Space Theory. Previous studies in a traditional learning environment have shown ALEKS users to perform equally or better in mathematics achievement than the group who did not use ALEKS. The purpose of this research was to investigate the effect of ALEKS on students’ achievement in mathematics in an online learning environment and to determine the cognitive complexity of mathematical tasks enacted by ALEKS’s initial (pretest) and final (posttest) assessments. The targeted population for this study was undergraduate students in College Mathematics I, in an online course at a private university in the southwestern United States. The study used a quasi-experimental One-Group non-randomized pretest and posttest design. Five methods of analysis and one model were used in analyzing data: t-test, correctional analysis, simple and multiple regression analysis, Cronbach’s Alpha reliability test and Webb’s depth of knowledge model. A t-test showed a difference between the pretest and posttest reports, meaning ALEKS had a significant effect on students’ mathematics achievement. The correlation analysis showed a significant positive linear relationship between the concept mastery reports and the formative and summative assessments reports meaning there is a direct relationship between the ALEKS concept mastery and the assessments. The regression equation showed a better model for predicting mathematics achievement with ALEKS when the time spent learning in ALEKS and the concept mastery scores are used as part of the model. According to Webb’s depth of knowledge model, the cognitive complexity of the pretest and posttest question items used by ALEKS were as follows: 50.5% required application of skills and concepts, 37.1% required recall of information, and 12.4% required strategic thinking: None of the questions items required extended thinking or complex reasoning, implying ALEKS is appropriate for skills and concepts building at this level of mathematics.

Emotions play a significant role in many human mental activities, including decision-making, motivation, and cognition. Various intelligent and expert systems can be empowered with emotionally intelligent capabilities, especially systems that interact with humans and mimic human behaviour. However, most current methods in affect recognition studies use intrusive, lab-based, and expensive tools which are unsuitable for real-world situations. Inspired by studies on keystrokes dynamics, this thesis investigates the effectiveness of diagnosing users’ affect through their typing behaviour in an educational context. To collect users’ typing patterns, a field study was conducted in which subjects used a dialogue-based tutoring system built by the researcher. Eighteen dialogue features associated with subjective and objective ratings for users’ emotions were collected. Several classification techniques were assessed in diagnosing users’ affect, including discrimination analysis, Bayesian analysis, decision trees, and neural networks. An artificial neural network approach was ultimately chosen as it yielded the highest accuracy compared with the other methods. To lower the error rate, a hierarchical classification was implemented to first classify user emotions based on their valence (positive or negative) and then perform a finer classification step to determining which emotions the user experienced (delighted, neutral, confused, bored, and frustrated). The hierarchical classifier was successfully able to diagnose users' emotional valence, while it was moderately able to classify users’ emotional states. The overall accuracy obtained from the hierarchical classifier significantly outperformed previous dialogue-based approaches and in line with some affective computing methods.

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). "January 2009." Includes bibliographical references (leaves 110-115). Also available via the World Wide Web.

Augmented Reality can visually convey abstract concepts and 3D spatial information in context with real-world objects, which makes it an ideal tool for training and educational purposes. This masters thesis investigates the use of Augmented Reality to assist with training for manual assembly and maintenance tasks. Improving on prior research, this approach combines Augmented Reality with a robust Intelligent Tutoring System to provide a more effective learning experience. After developing a modular software framework, a prototype was created that teaches the user to assemble hardware components on a computer motherboard. A thorough evaluation of the prototype found that the new intelligent approach significantly improves the learning outcome over traditional Augmented Reality training methods that do not employ Intelligent Tutoring Systems.

Intelligent Tutoring Systems (ITSs) are problem-solving environments that provide individualised instruction and are able to adapt to the abilities and needs of each individual student in order to maximise effective learning. They provide feedback on students' actions, but a problem arises when students do not always understand the feedback they receive. Therefore, it would be beneficial for students to be able to ask for additional clarifications at any time, and to receive feedback customised to their individual differences. This research focuses on providing an additional help channel in ITSs where students are able to ask free-form questions, as well as accounting for the students' psychometric measure of spatial ability. We describe ERM-Tutor, the test-bed ITS chosen for implementing our research framework. ERM-Tutor is a constraint-based tutoring system for teaching logical database design. Students practise this procedural task in ERM-Tutor by solving each step and receiving feedback on their solutions. We also present our approach to addressing the meta-cognitive skill of question-asking in ERM-Tutor. We added a question-asking module that enables students to ask free-form questions and receive the most appropriate answers stored in the system. In addition, we investigated the potential of tailoring the feedback messages towards the learners' psychometric measure of spatial ability. We modified ERM-Tutor to provide not only textual feedback messages, but also multimedia messages, containing a combination of text and pictures. We performed a series of evaluation studies in order to evaluate the effectiveness of our proposed solutions. All our studies were conducted with tertiary students enrolled in an introductory database course. The students had attended lectures on logical database design and were asked to use ERM-Tutor to develop and practise their mapping skills. The results show an overall improvement in performance and learning gain for all students using ERM-Tutor. Interactions with the question-asking module show that most questions asked by students were task-focused, directly requesting help on specific errors. The results confirm the need for addressing students' questions inside an ITS environment. Furthermore, there were no conclusive results to support a difference in effectiveness of the textual versus multimedia feedback presentation modes with respect to the students' spatial ability. However, we observed a number of trends indicating that matching the instruction presentation mode towards the students spatial ability influences their perception of the system and motivation to use it, more than their learning gain. Our results show promising indications for further explorations. We present our approaches, full analyses of the collected data from our evaluation studies, as well as our research contributions to the ITSs field. We also portray a number of future directions that will contribute towards maximising the effectiveness of learning in ITSs.