Este trabalho apresenta o processo de construção de um modelo de inferência de emoções que um aluno sente em relação a outros alunos durante interação síncrona em um contexto de jogo colaborativo de aprendizagem. A inferência de emoções está psicologicamente fundamentada na abordagem da avaliação cognitiva e foram investigadas relações entre objetivos e normas comportamentais do aluno e aspectos de sua personalidade. Especificamente, foram empregados o modelo OCC de emoções e o modelo Big-Five (Cinco Grandes Fatores) de traços de personalidade para a fundamentação teórica da modelagem. O modelo afetivo representa a vergonha e orgulho apresentados pelo aluno em resposta à avaliação cognitiva de suas próprias ações e a reprovação e admiração apresentadas pelo aluno em resposta a ações de seu parceiro de jogo, a partir da avaliação do comportamento observável dos parceiros representado por suas interações no jogo colaborativo, em relação a normas comportamentais do aluno. A fim de suportar a incerteza presente na informação afetiva e cognitiva do aluno, adotou-se uma representação deste conhecimento através de Rede Bayesiana. Um refinamento qualitativo parcial e a respectiva parametrização quantitativa do modelo probabilístico foram efetuados a partir da análise de uma base de casos obtida através da condução de experimentos. A fim de prover um ambiente experimental, foi concebido e prototipado um jogo colaborativo no qual dois indivíduos conjugam esforços a fim de resolver problemas lógicos comuns à dupla, através de ações coordenadas, negociação simples e comunicação estruturada, em competição com outras duplas. / This work presents the construction of a model to infer emotions a student feels towards other students during synchronous interaction in the context of a collaborative learning game. The emotions inference is psychologically based on cognitive appraisal theory. Some relations between students’ personality and their goals and behavioral standards were also investigated. This modeling was based on OCC emotion model and Big-Five personality model. The affective model represents the student’s proud and shame as an answer to the cognitive appraisal of her/his own attributed interactions, and the student’s admiration and reproach as an answer to the cognitive appraisal of her/his partner attributed interactions, both according to the student’s behavioral standards. Bayesian Network knowledge representation was employed to better stand for the uncertainty present in the student’s cognitive and affective information. Employing a data-driven procedure, the probabilistic model was partially refined in terms of qualitative relations and quantitative parameters. Experimental data were obtained by using a game prototype implemented in order to support a collaborative dynamics of coordinated action, simple negotiation and structured communication, through which students interacted in order to solve shared problems, during synchronous competition with other students.

Este trabalho apresenta o processo de construção de um modelo de inferência de emoções que um aluno sente em relação a outros alunos durante interação síncrona em um contexto de jogo colaborativo de aprendizagem. A inferência de emoções está psicologicamente fundamentada na abordagem da avaliação cognitiva e foram investigadas relações entre objetivos e normas comportamentais do aluno e aspectos de sua personalidade. Especificamente, foram empregados o modelo OCC de emoções e o modelo Big-Five (Cinco Grandes Fatores) de traços de personalidade para a fundamentação teórica da modelagem. O modelo afetivo representa a vergonha e orgulho apresentados pelo aluno em resposta à avaliação cognitiva de suas próprias ações e a reprovação e admiração apresentadas pelo aluno em resposta a ações de seu parceiro de jogo, a partir da avaliação do comportamento observável dos parceiros representado por suas interações no jogo colaborativo, em relação a normas comportamentais do aluno. A fim de suportar a incerteza presente na informação afetiva e cognitiva do aluno, adotou-se uma representação deste conhecimento através de Rede Bayesiana. Um refinamento qualitativo parcial e a respectiva parametrização quantitativa do modelo probabilístico foram efetuados a partir da análise de uma base de casos obtida através da condução de experimentos. A fim de prover um ambiente experimental, foi concebido e prototipado um jogo colaborativo no qual dois indivíduos conjugam esforços a fim de resolver problemas lógicos comuns à dupla, através de ações coordenadas, negociação simples e comunicação estruturada, em competição com outras duplas. / This work presents the construction of a model to infer emotions a student feels towards other students during synchronous interaction in the context of a collaborative learning game. The emotions inference is psychologically based on cognitive appraisal theory. Some relations between students’ personality and their goals and behavioral standards were also investigated. This modeling was based on OCC emotion model and Big-Five personality model. The affective model represents the student’s proud and shame as an answer to the cognitive appraisal of her/his own attributed interactions, and the student’s admiration and reproach as an answer to the cognitive appraisal of her/his partner attributed interactions, both according to the student’s behavioral standards. Bayesian Network knowledge representation was employed to better stand for the uncertainty present in the student’s cognitive and affective information. Employing a data-driven procedure, the probabilistic model was partially refined in terms of qualitative relations and quantitative parameters. Experimental data were obtained by using a game prototype implemented in order to support a collaborative dynamics of coordinated action, simple negotiation and structured communication, through which students interacted in order to solve shared problems, during synchronous competition with other students.

abstract: Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics. / Dissertation/Thesis / Masters Thesis Computer Science 2015

abstract: Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary, one of the most important elements in achieving fluency in a language. A well-balanced language curriculum must include both explicit vocabulary instruction and implicit vocabulary learning through interaction with authentic language materials. However, most language learning applications focus only on explicit instruction, providing little support for implicit learning. Students require support with implicit vocabulary learning because they need enough context to guess and acquire new words. Traditional techniques aim to teach students enough vocabulary to comprehend the text, thus enabling them to acquire new words. Despite the wide variety of support for vocabulary learning offered by learning applications today, few offer guidance on how to select an optimal vocabulary study set. This thesis proposes a novel method of student modeling which uses pre-trained masked language models to model a student's reading comprehension abilities and detect words which are required for comprehension of a text. It explores the efficacy of using pre-trained masked language models to model human reading comprehension and presents a vocabulary study set generation pipeline using this method. This pipeline creates vocabulary study sets for explicit language learning that enable comprehension while still leaving some words to be acquired implicitly. Promising results show that masked language modeling can be used to model human comprehension and that the pipeline produces reasonably sized vocabulary study sets. / Dissertation/Thesis / Masters Thesis Software Engineering 2020

The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student's interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems.

In this dissertation, several approaches I have taken to build upon the student learning model are described. There are two focuses of this dissertation. The first focus is on improving the accuracy with which future student knowledge and performance can be predicted by individualizing the model to each student. The second focus is to predict how different educational content and tutorial strategies will influence student learning. The two focuses are complimentary but are approached from slightly different directions. I have found that Bayesian Networks, based on belief propagation, are strong at achieving the goals of both focuses. In prediction, they excel at capturing the temporal nature of data produced where student knowledge is changing over time. This concept of state change over time is very difficult to capture with classical machine learning approaches. Interpretability is also hard to come by with classical machine learning approaches; however, it is one of the strengths of Bayesian models and aids in studying the direct influence of various factors on learning. The domain in which these models are being studied is the domain of computer tutoring systems, software which uses artificial intelligence to enhance computer based tutorial instruction. These systems are growing in relevance. At their best they have been shown to achieve the same educational gain as one on one human interaction. Computer tutors have also received the attention of White House, which mentioned an tutoring platform called ASSISTments in its National Educational Technology Plan. With the fast paced adoption of these data driven systems it is important to learn how to improve the educational effectiveness of these systems by making sense of the data that is being generated from them. The studies in this proposal use data from these educational systems which primarily teach topics of Geometry and Algebra but can be applied to any domain with clearly defined sub-skills and dichotomous student response data. One of the intended impacts of this work is for these knowledge modeling contributions to facilitate the move towards computer adaptive learning in much the same way that Item Response Theory models facilitated the move towards computer adaptive testing.

Τα τελευταία χρόνια η αυξανόμενη σημασία της πληροφορίας και της συνεχιζόμενης μάθησης έχει καταστήσει τη χρήση των τεχνολογιών πληροφορικής και επικοινωνιών (Τ.Π.Ε.) στην εκπαίδευση καθώς και την εξ αποστάσεως εκπαίδευση μέσω e-learning συστημάτων, ως επιτακτική ανάγκη. Το κυριότερο πλεονέκτημα των e-learning συστημάτων είναι ότι παρέχουν τη δυνατότητα στον κόσμο να παρακολουθήσει ένα μάθημα, ακόμα κι ένα ολόκληρο πρόγραμμα σπουδών εξ αποστάσεως, μέσω της σύγχρονης και παγκοσμίως πλέον διαδεδομένης μεθόδου των online courses. Τα περισσότερα μεγάλης εμβέλειας και αναγνωρισιμότητας, πανεπιστήμια του εξωτερικού προσφέρουν online μαθήματα μέσω τεχνολογιών του διαδικτύου, χρησιμοποιώντας διαφάνειες, βιντεο-διαλέξεις και online εκπαιδευτικές δραστηριότητες. Ένας τομέας όμως στον οποίο υστερεί η εκπαίδευση εξ αποστάσεως, ακόμα και στα πιο πρόσφατα ανεπτυγμένα e-learning συστήματα, είναι ο τομέας της προσωποποίησης του σπουδαστή και εξατομίκευσης του συστήματος στις ανάγκες του. Η παρούσα διπλωματική εργασία παρουσιάζει τις δυνατότητες που παρέχονται από τα ήδη υπάρχοντα συστήματα e-learning, την τωρινή κατάσταση με τα παρεχόμενα online μαθήματα από πανεπιστήμια του εξωτερικού και το ερευνητικό κενό που υπάρχει σχετικά με την «προσωποποίηση» (personalization) του σπουδαστή και την προσαρμογή (adaptation) του συστήματος στις ανάγκες του. Μελετάται η μοντελοποίηση του χρήστη (user modeling), τα μαθησιακά προφίλ, ο εμπλουτισμός του προφίλ του σπουδαστή ώστε να παρέχει περισσότερες και αξιόλογες πληροφορίες, καθώς και οι δυνατότητες εξατομίκευσης του συστήματος στον εκάστοτε σπουδαστή, ώστε να επιτευχθεί η επιτάχυνση και αποτελεσματικότητα της ηλεκτρονικής μάθησης. Στα πλαίσια της παρούσας διπλωματικής εργασίας σχεδιάστηκε και υλοποιήθηκε ένα σύστημα ηλεκτρονικής μάθησης, το οποίο στηρίζεται στο λογισμικό Moodle και έχουν αναπτυχθεί σε αυτό, νέες υπηρεσίες εξατομίκευσης και προσαρμοστικότητας του συστήματος στις ανάγκες και προτιμήσεις του εκπαιδευομένου. Το Moodle LMS (Modular Object Oriented Dynamic Learning Environment) είναι ελεύθερο λογισμικό διαχείρισης εκπαιδευτικού περιεχομένου και χρησιμοποιείται από πολλά πανεπιστήμια παγκοσμίως. Είναι ευέλικτο, εύκολο στην εκμάθηση και αρκετά ασφαλές για την ασύγχρονη εκπαίδευση από απόσταση. Ενσωματώνει πληθώρα λειτουργιών και δυνατοτήτων που επιτρέπουν στο διδάσκοντα να διαμορφώσει ένα καλά οργανωμένο και ευχάριστο μάθημα με ευρεία κλίμακα δραστηριοτήτων. Στο εξατομικευμένο και προσαρμοστικό σύστημα ηλεκτρονικής μάθησης που αναπτύχθηκε, διαμορφώθηκε ένα μάθημα με πλούσιο εκπαιδευτικό υλικό, το οποίο απαρτίζουν πολλά και διαφορετικού τύπου μαθησιακά αντικείμενα (πηγές πληροφόρησης, δραστηριότητες, κ.α.). Εμπλουτίστηκε το προφίλ χρήστη με περισσότερες προσωπικές πληροφορίες γι αυτόν και στη συνέχεια αναπτύχθηκαν μαθησιακά μονοπάτια που στηρίζονται είτε στα πεδία προφίλ χρήστη, είτε στο μαθησιακό στυλ του εκπαιδευομένου, είτε σε άλλους παράγοντες που παρουσιάζονται αναλυτικά στην εργασία. Για την εξατομίκευση του συστήματος αναπτύχθηκε μία ενότητα «Pre-course test» η οποία περιλαμβάνει δύο νέες υπηρεσίες για την άντληση πληροφοριών του χρήστη, μορφωτικό υπόβαθρο, μαθησιακό στυλ, προηγούμενη γνώση στο αντικείμενο του μαθήματος κ.α. Τα αποτελέσματα που προκύπτουν από εκεί, αξιοποιούνται κατάλληλα για τη διαμόρφωση ομάδων των εκπαιδευομένων και για τις υπηρεσίες προσαρμοστικότητας του συστήματος. Επίσης, στο διαδικτυακό αυτό σύστημα ηλεκτρονικής μάθησης έχουν αναπτυχθεί μαθησιακά αντικείμενα που ενισχύουν στη συνεργατική μάθηση, βελτιώνουν την ποιότητα του μαθήματος και έτσι αυξάνεται το κίνητρο του σπουδαστή. / Nowdays, e-learning environments have become increasingly popular in educational establishments. The rapid growth of e-learning has changed traditional learning behavior and presented a new situation to both educators (lecturers) and learners (students). The majority of current e-learning systems are based on Learning Management Systems (LMS), which allow students attending courses free from space and time limitations. They can attend the class anytime they are available and regardless of the place. LMS are web-based educational systems that offer students an active role in their own education through a variety of learning activities and different kinds of learning content, that the educator has produced. Although this fact, most Learning Management Systems lack of personalization and adaptivity features. The modern trend in education is the production and organization of Massive Open Online Courses (MOOCs) from large and internationally recognized Universities. So, they focus on gathering too many people in these courses (MOOCs) and not on personalization and adaptivity. Personalization in web-based systems means “attention to the user and his needs”. Web-based systems, such as e-commerce sites, focus on personalization by observing the user, his common actions and navigation paths, in order to understand his preferences and what he is looking for. So they differentiate each individual according to his interests, preferences, and generally his “profile”. Then, they adapt the content appropriately to his needs, and the user watches first the content that may interest him. Traditional and commonly used e-learning platforms offer to their users same educational content, same learning activities and possibilities, and generally exactly the same content, with no further personalization support. Some existing open source e-Learning systems may support, under certain circumstances adaptation and personalization features, but need extension and elaboration to acquire sufficiently these characteristics. Especially in the case of distance learning, that teacher and students have no face-to-face communication, learner’s personalization is essential in order the teacher to be aware of each learner’s characteristics. In e-learning systems, personalization could be achieved with an integrated user profile (student profile), which would include except from the classic information (name, surname, email) more specific and useful information. If the teacher had more information about each student, he could be able to guide him more effectively and evaluate him more correctly. In addition to that, if teacher was aware of each learner’s profile, meaning his learning style, interests, preferences and his previous knowledge of the course topics, he would be able to adapt the learning content to his needs. That would increase student’s satisfaction, motivation and consequently his participation in the course. In this master thesis, a personalized and adaptive e-learning system has been developed. The development of this integrated personalized e-learning system, is based on a very popular open source LMS, which in this case is used for further extension and development of new features. The most suitable LMS for implementing those new features is Moodle (Modular Object-oriented Dynamic Learning Environment), due to its modularity and extensibility as well as its vast community of users. There have been added new features in order to enrich student’s profile and adjust learning material to the learner, depending on his profile and learning progress. The teacher has the ability to organize suitable learning objects and learning paths for each student, depending on particular user profile fields, learning styles and student’s performance. This personalized and adaptive e-learning system increases student’s motivation and participation.

Αντικείμενο της συγκεκριμένης εργασίας αποτελεί η μελέτη των χαρακτηριστικών του μοντέλου εκπαιδευόμενου, ο καθορισμός των απαιτούμενων εξ' αυτών για ένα προσαρμοστικό σύστημα ηλεκτρονικής μάθησης καθώς και ο αποδοτικότερος τρόπος αναπαράστασής τους. Επίσης μελετώνται οι τρόποι συλλογής δεδομένων για την δημιουργία και συνεχή ανανέωση του μοντέλου (μοντελοποίηση) καθώς και οι αλγόριθμοι που χρησιμοποιούνται, με σκοπό την υλοποίηση του καταλληλότερου και αποδοτικότερου εξ' αυτών. Η διπλωματική στοχεύει στη δημιουργία ενός μοντέλου χρήστη-εκπαιδευόμενου συνοδευόμενο από τους αντίστοιχους αλγορίθμους μοντελοποίησης το οποίο θα συγκεντρώνει την απαραίτητη πληροφορία για την αυτοματοποιημένη και προσαρμοσμένη επιλογή κατάλληλου εκπαιδευτικού υλικού προς τον συγκεκριμένο χρήστη, με ευκολία και αφαιρετικότητα προς τον χρήστη. / In the present thesis we are studying the characteristics that can be represented in a user model, we define those that are essential in a student model and we propose an efficient way of representation. We also study the different ways of constructing and updating a student model and the algorithms that can be used in order to implement the most appropriate and efficient of them. The main purpose of the thesis is the development of a student model, with the suitable algorithms, that will support the automated process of adapted educational material provision in an easy and abstract way.

本研究以貝氏網路作為表示教學領域中各個學習觀念的關係的語言。教學領域中的學習觀念包含了基本觀念與綜合觀念，綜合觀念是由兩個以上的基本觀念所衍生出來的觀念，而綜合觀念的學習歷程即為學生在學習的過程中如何整合這些基本觀念的過程。了解綜合觀念的學習歷程可以幫助教師及出題者了解學生的學習路徑，並修改其教學或出題的方針，以期能提供適性化的教學及測驗。為了從考生答題資料中尋找出這個隱藏的綜合觀念學習歷程，我們提出一套以mutual information以及一套以chi-square test所發展出來的研究方法，希望能夠藉由一個模擬環境中模擬考生的答題資料來猜測考生學習綜合觀念的學習歷程。 初步的實驗結果顯示出，在一些特殊的條件假設下，我們的方法有不錯的機會找到暗藏在模擬系統中的學習歷程。因此我們進而嘗試提出一個策略來尋找較大規模結構中的學習歷程，利用搜尋的概念嘗試是否能較有效率的尋找出學生對於綜合觀念學習歷程。雖然在實驗中並沒有十分理想的結果，但是在實驗的過程中，我們除了發現學生答題資料的模糊程度為系統的正確率的主要挑戰之外，另外也發現了學生類別與觀念能力之間的關係也是影響實驗結果的主要因素。透過我們的方法，雖然不能完美的找出學生對於任何綜合觀念的綜合歷程，但是我們的實驗過程與結果也對隱藏的真實歷程結構提供了不少線索。 最後，我們探討如何藉由觀察學生接受測驗的結果來分類不同學習程度與狀況的學生之相關問題與技術。我們利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，探討是否能透過學生的答題資料有效的分辨學生能力的類別。實驗結果顯示出，在每個觀念擁有多道測驗試題的情況下，利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，藉由考生答題資料來進行學生能力類別的分類可以得到不錯的正確率。我們希望這些探討和結果能對適性化教學作出一些貢獻。 / In this thesis, I employ Bayesian networks to represent relations between concepts in pedagogical domains. We consider basic concepts, and composite concepts that are integrated from the basic ones. The learning processes of composite concepts are the ways how students integrate the basic concepts to form the composite concepts. Information about the learning processes can help teachers know the learning paths of students and revise their teaching methods so that teachers can provide adaptive course contents and assessments. In order to find out the latent learning processes based on students’ item response patterns, I propose two methods: a mutual information-based approach and a chi-square test-stimulated heuristics, and examine the ideas in a simulated environment. Results of some preliminary experiments showed that the proposed methods offered satisfactory performance under some particular conditions. Hence, I went a step further to propose a search method that tried to find out the learning process of larger structures in a more efficient way. Although the experimental results for the search method were not very satisfactory, we would find that both the uncertainty included by the students’ item response patterns and the relations between student groups and concepts substantially influenced the performance achieved by the proposed methods. Although the proposed methods did not find out the learning processes perfectly, the experimental processes and results indeed had the potential to provide information about the latent learning processes. Finally, I attempted to classify students’ competence according to their item response patterns. I used the nearest neighbor algorithm, the k-means algorithm, and some variations of these two algorithms to classify students’ competence patterns. Experimental results showed that the more the test items used in the assessment, the higher the accuracy of classification we could obtain. I hope that these experimental results can make contributions towards adaptive learning.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-06-13T15:22:17Z No. of bitstreams: 2 Dissertação - Alexandre Martins Ferreira Bueno - 2011.pdf: 2170812 bytes, checksum: ca9620fc2142f942080575c91a55e932 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-06-13T15:27:38Z (GMT) No. of bitstreams: 2 Dissertação - Alexandre Martins Ferreira Bueno - 2011.pdf: 2170812 bytes, checksum: ca9620fc2142f942080575c91a55e932 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-06-13T15:27:38Z (GMT). No. of bitstreams: 2 Dissertação - Alexandre Martins Ferreira Bueno - 2011.pdf: 2170812 bytes, checksum: ca9620fc2142f942080575c91a55e932 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2011-12-13 / Nowadays, e-learning has become an important source of knowledge, providing content without the limitation of time or space. In this context, Web-based education became an important sector. A large number of e-learning applications have utilized the Web to teach, mainly to provide distance learning. As a result of the substantial growth in elearning there has been an increase in the diversity of students that uses this environment to achieve their academic or professional backgrounds. In general, these students have di erent personal, social-cultural and cognitive characteristics. Adaptive Hypermedia Systems provide resources to assist this diverse public, they are able to consider the individual characteristics of students during the learning process. It is known that the representation of these features are often associated with uncertain and imprecise information. Given this context, this work proposes a Web-based Adaptive Educational Hypermedia System (with instructional content) that aims to personalize the process of distance learning. It has a student model that is able to represent the uncertainties related to the student's knowledge (characteristic of the students considered by the system) through the use of a Bayesian Network. This model is adjusted according to the answers provided by students in ability tests, which in turn are evaluated by a mathematical model of Item Response Theory. The experiment realized compared the results obtained by the proposed system, that is provided with adaptation (personalization) mechanisms, and other system that has no such mechanisms, in which the same content was presented to all the students. Statistical analysis of the collected data has shown a signi cant improvement in knowledge retention by the students who used the system provided with adaptation. However, it was expected, as a result of this improvement, that the levels of satisfaction reported by the students who used the proposed system were better than the levels reported by the students who used the system without adaptation, which was not evidenced by the gathered data. / Hoje em dia, o e-learning se tornou uma importante vertente na obtenção de conhecimento, fornecendo conteúdo sem a limitação de tempo ou espaço . Neste contexto, a educação baseada na Web tem recebido destaque. Inúmeras aplicações de e-learning tem se utilizado da Web para ensinar, principalmente para prover ensino a distancia. Como consequência do crescimento substancial do e-learning houve um aumento na diversidade dos alunos que fazem uso deste meio para a obtenção de suas formações acadêmicas ou prossionais. Em geral, estes alunos possuem diferentes características pessoais, sócio culturais e cognitivas. Os Sistemas Hiperm dia Adaptativos oferecem recursos para atender a este p ublico diverso, eles são capazes de considerar as características individuais dos alunos durante o processo de aprendizagem. Sabe-se que na representação destas características frequentemente estão associadas informações que são incertas e imprecisas. Diante do apresentado, este trabalho propõe um Sistema Hipermídia Adaptativo Educacional (com conteúdo instrucional) que tem como objetivo a personalização do processo de ensino a distância através da Web. Ele possui um modelo do aluno que e capaz de lidar com as incertezas relacionadas ao conhecimento dos alunos (caracteristica dos alunos considerada pelo sistema) através do uso de uma Rede Bayesiana. Este modelo e ajustado por respostas dadas pelos alunos em testes de habilidade, que são avaliadas por um modelo matemático da Teoria da Resposta ao Item. O experimento realizado comparou os resultados obtidos pelo sistema proposto, provido de mecanismos de adaptação (personalização), e outro sistema desprovido de adaptação, em que o mesmo conteúdo era apresentado a todos os alunos. A analise estatística dos dados coletados mostrou uma melhoria signicativa na retenção de conhecimento por parte dos alunos que zeram uso do sistema provido de adaptação. Entretanto, esperava-se, como consequência desta melhoria, que os níveis de satisfação informados pelos alunos que zeram uso do sistema proposto fossem superiores aos níveis informados pelos alunos que zeram uso do sistema desprovido de adaptação, fato não evidenciado pelos dados obtidos.