State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Artificial Intelligence in Higher Education: A Bibliometric Study on its Impact in the Scientific Literature

Artificial intelligence has experienced major developments in recent years and represents an emerging technology that will revolutionize the ways in which human beings live. This technology is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of. Therefore, the purpose of this paper was to analyse the scientific production on artificial intelligence in higher education indexed in Web of Science and Scopus databases during 2007–2017. A bespoke methodology of bibliometric studies was used in the most relevant databases in social science. The sample was composed of 132 papers in total. From the results obtained, it was observed that there is a worldwide interest in the topic and that the literature on this subject is just at an incipient stage. We conclude that, although artificial intelligence is a reality, the scientific production about its application in higher education has not been consolidated

An adaptive hierarchical questionnaire based on the Index of Learning Styles

Proceedings of the Sixth International Workshop on Authoring of Adaptive and Adaptable HypermediaOne of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. With respect to learning styles, it is possible to decrease the number of versions taking into account the general tendency of the student and not the specific score obtained in each dimension. In this paper we present a new approach to reduce the number of questions of Index of Learning Styles (ILS) questionnaire based on Felder-Silverman’s Learning Style Model (FSLSM). The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning styles with high accuracy and only a few questions.This work is supported by the Spanish Ministry of Education and Science, TIN2007-6471

Ambientes personalizados de e-learning: considerando os contextos dos alunos

A personalização em sistemas de e-learning é fundamental, uma vez que esses são utilizados por uma grande variedade de alunos, com características diferentes. Há várias abordagens que visam personalizar ambientes e- learning. No entanto, esses se concentram principalmen- te na tecnologia e / ou em detalhes da rede, sem levar em consideração os aspectos contextuais. Eles consideram apenas uma versão limitada do contexto, proporcionando personalização. Em nosso trabalho, o objetivo é melhorar a personalização do ambiente de aprendizagem e-learning, fazendo uso de uma melhor compreensão e modelagem do contexto educacional e tecnológico do usuário, utilizando ontologias. Mostramos um exemplo do uso da nossa pro- posta no sistema AdaptWeb, na qual o conteúdo e as re- comendações de navegação fornecidas dependem do con- texto do aluno

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model

An adaptive educational system that caters for combination of two models of learning styles

This thesis aimed to explore the affect of combining two models of learning styles (VARK, and Honey and Mumford) in terms of students‘ learning gains and satisfaction. VARK focuses on how the students perceive learning, while Honey and Mumford examines how an individual would like to learn. A web-based educational system was built to test the combination of the two models of learning styles. A study to examine the feasibility of the system was carried out on 129 participants to explore whether the system presented tutorials according to their individual learning styles. A second study to investigate learning gains and user satisfaction was carried out on 149 participants. Satisfaction was divided into three main concepts: usability, preference and perception of learning. Learning gains were tested by giving participants a pre-test, a post-test and a confirmatory test. Participants were divided into four groups and had the lesson presented according to one learning style of either the VARK or Honey & Mumford model, both of the participants‘ learning styles or with no personal customization. The results found that participants who used the two models of learning styles showed higher learning gains and had higher levels of satisfaction across all three factors; compared to those using only one or no learning style. Furthermore, those using only one learning style showed higher learning gains and had higher levels of satisfaction than those with no learning style. The application of these findings would be of benefit to educational institutions‘ decision makers, educators, students and e-learning designers. Adaptation is a key feature of the system of research. It is intended for future work; preliminary research has shown that the users profile and learning item will change over time. This important finding is worth exploring in future research