Educational Technology - Artificial Intelligence
M. Abedin; E. Pazouki; R. Ebrahimpour
Abstract
Background and Objectives: Learning has consistently been one of the aspects of human development since the beginning of human existence on the Earth, encompassing all aspects of human life and holding a special place in human life plans. On the other hand, technological advancements in recent decades ...
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Background and Objectives: Learning has consistently been one of the aspects of human development since the beginning of human existence on the Earth, encompassing all aspects of human life and holding a special place in human life plans. On the other hand, technological advancements in recent decades have rapidly brought about significant changes in the realm of education and learning. One of the most prominent impacts of technological progress in the field of learning is the emergence of e-learning; a tool that enables learners to access educational resources at any time and place. Regarding the fact that every person has individual traits, preferences, and tendencies, traditional education's "one-size-fits-all" approach can be seen as one of its fundamental flaws. Learning styles, as one of the most important factors influencing learning, represent an individual's preferences and ways of processing and understanding information. Therefore, providing adaptive education based on learners' learning styles, with the aim of enhancing educational efficiency and reducing cognitive load during teaching, is both essential and inevitable. This research aimed to investigate the impact of adaptive education based on learners' learning styles. In order to achieve this, learners' learning styles were identified using an online platform and the Felder-Silverman Learning Style Indicator questionnaire, and educational content was automatically generated and presented to learners accordingly. Finally, the performance of the learners and cognitive load during instruction were examined.Methods: A total number of 37 male and female undergraduate computer science students with an average age of 20.3, participated in this study. Initially, the participants were divided into two groups, and their learning styles were determined using the Felder-Silverman Learning Style Indicator questionnaire. Subsequently, one group received educational content tailored to their learning styles, while the other group received content not aligned with their learning styles. After studying the provided material, the cognitive load and learning outcomes of the participants were assessed using the NASA Task Load Index questionnaire and a designed performance test, respectively. Finally, the significance level of the results obtained from the two groups was evaluated using an independent t-test.Findings: Based on the obtained results, no significant difference was observed in the test scores of the two groups' performance. However, when comparing the cognitive load between the two groups, the average cognitive load of the group that received content aligned with their learning style was significantly higher than the group that received incongruent content, with a value of 0.02 (p < 0.05).Conclusion: According to the research findings, providing educational content based on learners' learning styles significantly reduce cognitive load during learning. Therefore, offering personalized education based on learning styles plays a crucial role as one of the adaptive teaching methods in e-learning, enhancing performance, and reducing cognitive load for learners.
Education technology - Evaluation and testing
F. Taghiyar; M. Siadati; F. Oruji
Abstract
One of the most important parameters in personalization of adaptive learning web-based educational systems is learning style. Up to now, various learning styles proposed and this paper tries to evaluate the efficiency of using one of them, Jackson model. In this study, we categorized students ...
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One of the most important parameters in personalization of adaptive learning web-based educational systems is learning style. Up to now, various learning styles proposed and this paper tries to evaluate the efficiency of using one of them, Jackson model. In this study, we categorized students as model says and delivered learning content matched to each group learning style. Findings of the study indicate differences in performance between matched and non-matched students in one case of the study and in the other case, demonstrate no significant difference. Although these results pertain to an undergraduate educational session, however our proposed framework is general enough to be applied to effective and efficient pedagogy in any area at any level.
Modern Educational Approaches
J. Nasiri; A.M. Mir; S. Fatahi
Abstract
Background and Objective:Internet and computer access have created opportunities for e-learning. Easier access to resources and freedom of action for users is one of the benefits of e-learning. However, e-learning is not as attractive and dynamic as traditional or face-to-face instruction, and in these ...
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Background and Objective:Internet and computer access have created opportunities for e-learning. Easier access to resources and freedom of action for users is one of the benefits of e-learning. However, e-learning is not as attractive and dynamic as traditional or face-to-face instruction, and in these systems the user's condition, such as learning rate and motivation, is not taken into account. Therefore, the developers of e-learning systems can help to solve the problems mentioned in these systems by considering the learning style and design of interactive user relationships. Automated identification of learning style not only increases the attractiveness of e-learning, but also increases the efficiency and motivation of learners in e-learning environments. Research shows that people differ in decision making, problem solving, and learning. Learning style makes people understand a story differently. For example, people with good visual memory prefer to present topics visually rather than orally. Applying a proper teaching method improves the learner's performance in the learning environment. Lack of attention to students' learning style reduces their motivation and interest in studying and engagement in educational courses. Students’ success is one of the prominent goals in the learning environments. In order to achieve this goal, paying attention to students’ learning style is essential. Being aware of students’ learning style helps to design an appropriate education method which improves student’s performance in the learning environments. In this paper, the aim is to create a model for automatic prediction of learning styles. Methods: Therefore, two real datasets collected from an e-learning environment which consists of 202 electrical and computer engineering students. Behavioral features were extracted from users’ interaction with e-learning system and then learning styles were classified using twin support vector machine. Twin support vector machine is an extension of SVM which aims at generating two non-parallel hyperplanes. This classifier is not sensitive to imbalanced datasets and its training speed is fast. Findings: In this study, increasing the attractiveness of e-learning is emphasized and the issue of automatic recognition of students' learning style has been investigated by MBTI model. Two data sets from the interaction of 202 electrical and computer engineering students with the Moodle e-learning system have been collected. The collected data set is very unbalanced, which has a negative effect on the accuracy of the categories. With this in mind, the twin support vector machine uses the least squares as a binder. The distinctive feature of this category is the low sensitivity to data balance and very high speed. The results show that the proposed method, despite the inconsistency of the data, has performed very well in the classification of students' learning style and accurately recognizes 95% of learning styles.Conclusion: Due to the excellent performance of the proposed method, a new component can be added to e-learning systems such as Moodle by identifying the learning style, content and appropriate teaching method for the learner. Future research could also gather more data from an e-learning environment and categorize learning styles with cognitive characteristics from the learner.
Electronic learning- virtual
N. Saberi; Gh.A. Montazer
Abstract
Environmental monitoring for extraction of learner’s features and accurately learner modeling is a time and cost consuming task. This paper proposes a flexible framework using agent architecture capabilities in learners’ monitoring and also in optimization of Intelligent Tutoring subsystems ...
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Environmental monitoring for extraction of learner’s features and accurately learner modeling is a time and cost consuming task. This paper proposes a flexible framework using agent architecture capabilities in learners’ monitoring and also in optimization of Intelligent Tutoring subsystems interactions. In this research learner’s learning approaches have been recognized by learning style theory then their uncertainty has been reduced by Bayesian Network. This framework prepares recommendations by combination of learners’ learning style and learner’s abilities (that are computed by Item Response Theory (IRT)) for three groups: learners, tutors and system designer. The architecture of proposed tutoring system has been presented in three layers. In middle layer there are four agents that monitor learners, create learner’s model, update it and prepare some recommendations based on courseware features, learner’s abilities and his/her learning style. A study was conducted on 30 Computer Engineering students during one semester. The implementation of the proposed system on participants indicates an increase in all evaluation factors, for example it doubles educational success rate and learner’s satisfaction rate.
