Evaluating Bayesian networks’ precision for detecting students’ learning styles

García, Pablo Andrés; Amandi, Analía; Schiaffino, Silvia; Campo, Marcelo

doi:10.1016/j.compedu.2005.11.017

Cited by 357 publications

(271 citation statements)

References 11 publications

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“…Second, long questionnaires have a negative effect on a person's motivation [4], which may lead to abandonning the test, skipping questions or answering falsely. This can furthermore provoke an incorrect learning style assessment, which may have undesirable consequences in future interactions [5]. For example, in the case of an e-learning system, if a learner does not answer the questionnaire correctly, the ensuing interactions with the system may be done according to a wrong learning style, which may have detrimental effect on learning.…”

Section: Introductionmentioning

confidence: 99%

“…One major challenge in building a system that can adapt itself to a learner is giving it the capability of reducing the number of questions presented to the learner [4,5,11]. For instance, McSherry [9] reports that reducing the number of questions asked by an informal case-based reasoning system minimized frustration, made learning easier and increased efficiency.…”

Section: Myers-briggs Type Indicator (Mbti)mentioning

confidence: 99%

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An Adaptive Questionnaire for Automatic Identification of Learning Styles

Mwamikazi

Fournier‐Viger

Moghrabi

et al. 2014

Modern Advances in Applied Intelligence

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Abstract. Learning styles refer to how a person acquires and processes information. Identifying the learning styles of students is important because it allows more personnalized teaching. The most popular method for learning style recognition is through the use of a questionnaire. Although such an approach can correctly identify the learning style of a student, it suffers from three important limitations: (1) filling a questionnaire is time-consuming since questionnaires usually contain numerous questions, (2) learners may lack time and motivation to fill long questionnaires and (3) a specialist needs to analyse the answers. In this paper, we address these limitations by presenting an adaptative electronic questionnaire that dynamically selects subsequent questions based on previous answers, thus reducing the number of questions. Experimental results with 1,931 questionnaires for the Myers Briggs Type Indicators show that our approach (Q-SELECT) considerably reduces the number of questions asked (by a median of 30 %) while predicting learning styles with a low error rate.

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Section: Introductionmentioning

confidence: 99%

Section: Myers-briggs Type Indicator (Mbti)mentioning

confidence: 99%

An Adaptive Questionnaire for Automatic Identification of Learning Styles

Mwamikazi

Fournier‐Viger

Moghrabi

et al. 2014

Modern Advances in Applied Intelligence

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“…In on-line learning environment, the student's learning characteristics are changed accordingly when different tasks are provided. Due to these problems, several studies have been conducted in detecting student's learning style that are based on the student's browsing behavior [16,17,18,19]. This approach can be implemented successfully since the style of student's interaction with the system can be inferred accurately and can be used as attributes for adaptation purposes.…”

Section: Related Workmentioning

confidence: 99%

“…Various techniques have been used to represent student learning style such as statistics [17], Neural Network [19,13], Decision Tree [20], Bayesian Networks [16], Naïve Bayes [15] and Genetic Algorithm [18]. Previously, we have successfully classified students' learning style using Backpropagation Neural Network (BPNN) [21].…”

Section: Related Workmentioning

confidence: 99%

Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

Ahmad

Shamsuddin

Abraham

2010

Studies in Computational Intelligence

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Abstract. Pattern multiplicity of user interaction in learning management system can be intelligently examined to diagnose students' learning style. Such patterns include the way the user navigate, the choice of the link provided in the system, the preferences of type of learning material, and the usage of the tool provided in the system. In this study, we propose mapping development of student characteristics into Integrated Felder Silverman (IFS) learning style dimensions. Four learning dimensions in Felder Silverman model are incorporated to map the student characteristics into sixteen learning styles. Subsequently, by employing rough set technique, twenty attributes have been selected for mapping principle. However, rough set generates a large number of rules that might have redundancy and irrelevant. Hence, in this study, we assess and mining the most significant IFS rules for user behavior by filtering these irrelevant rules. The assessments of the rules are executed by evaluating the rules support, the rules length and the accuracy. The irrelevant rules are further filtered by measuring different rules support, rules length and rules accuracy. It is scrutinized that the rules with the length in between [4,8], and the rules support in the range of [6,43] succumb the highest accuracy with 96.62%.

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“…An example is [68], that presented a BN to model learning styles within a web-based education system. To this end, they consider three dimensions of Felder's framework [64], namely perception (sensory/intuitive), processing (active/reflective), and understanding (sequential/global) as unobservable nodes, and several evidential nodes, such as the use of mail, forum, chats, number of examples visited, and exam results.…”

Section: Context-rulementioning

confidence: 99%

User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

637

416

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Abstract. One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling. IntroductionAdaptive hypermedia and other adaptive Web systems (AWS) belong to the class of user-adaptive software systems [174]. One distinctive feature of an adaptive system is a user model. The user model is a representation of information about an individual user that is essential for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. For example, when the user searches for relevant information, the system can adaptively select and prioritize the most relevant items (see Chapter 6 of this book [125]). When the user navigates from one item to another, the system can manipulate the links (e.g., hide, sort, annotate) to provide adaptive navigation support (see Chapter 8 of this book [21]). When the user reaches a particular page, the system can present the content adaptively (see Chapter 13 of this book [28]). To create and maintain an up-to-date user model, an adaptive system collects data for the user model from various sources that may include implicitly observing user interaction and explicitly requesting direct input from the user. This process is known as user modeling. User modeling and adaptation are two sides of the same coin. The amount and the nature of the information represented in the user model depend to a large extent on the kind of adaptation effect that the system has to deliver. 4 P. Brusilovsky and E. Millán As mentioned in the introduction, Chapters 1 to 5 of this book are focused mostly on the modeling side of personalization, while the remaining chapters focus mostly on the adaptation side. Chapters 1 and 2 are specifically devoted to user models and user modeling. Beyond this, user modeling issues are discussed at different levels of detail in several other chapters. This chapter attempts to complement the remaining chapters in two ways. First, it provides an overview (a "big picture") of the user modeling side referring readers when necessary to additional information in chapters within this book. Second, it attempts to complement other chapters by presenting aspects that are either not covered in other chapters or covered insufficiently.To envision the big picture, this chapter follows Sleeman [175] who suggested classifying user models by the nature and form of information contained in the model as well as the methods of working with it. Following hi...

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Evaluating Bayesian networks’ precision for detecting students’ learning styles

Cited by 357 publications

References 11 publications

An Adaptive Questionnaire for Automatic Identification of Learning Styles

An Adaptive Questionnaire for Automatic Identification of Learning Styles

Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

User Models for Adaptive Hypermedia and Adaptive Educational Systems

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