scholarly journals Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 735
Author(s):  
Christos Troussas ◽  
Akrivi Krouska ◽  
Cleo Sgouropoulou ◽  
Ioannis Voyiatzis
Keyword(s):  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Learning System ◽  
Majority Voting ◽  
Ensemble Classification ◽  
Automatic Identification ◽  
Instructional Routines ◽  
Fuzzy Weights ◽  
Selection Of

Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

scholarly journals Adaptyvios programavimo mokymosi sistemos modelis

2013 ◽  
Vol 66 ◽  
pp. 108-122
Author(s):  
Jūratė Urbonienė
Keyword(s):  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Style ◽  
Learning Difficulties ◽  
Learning Objects ◽  
Learning System ◽  
System Model ◽  
Brain Dominance ◽  
Adaptive Programming ◽  
Selection Of

Straipsnyje nagrinėjami programavimo mokymo ypatumai, apžvelgiama mokymosi stilių įvairovė bei mokymosi stiliaus įtaka mokymosi rezultatams. Plačiau aptariamas Herrmanno smegenų dominavimo instrumentas1 (HBDI), kuris leidžia besimokančiuosius suskirstyti pagal jų smegenų pusrutulio išsivystymą. Remiantis programavimo mokymo patirtimi ir fundamentaliais programavimo mokymo srities mokslininkų darbais, nagrinėjamos programavimo mokymosi sunkumų priežastys (programavimo srities specifiškumas, mokymosi būdų ir mokymosi metodų parinkimas), išskiriamos ir apibendrinamos esminės idėjos. Pagrindinis dėmesys skiriamas adaptyvios programavimo mokymosi sistemos (APMS) koncepciniam modeliui pristatyti. Modeliuojama APMS leis priderinti mokymosi objektus, išrenkant juos iš mokymosi objektų saugyklų, ir užtikrinti atitinkamų kompetencijų pasiekimo lygį atsižvelgiant į besimokančiojo mokymosi stilių pagal Herrmanno skirstymą bei ankstesnę jo mokymosi patirtį.Pagrindiniai žodžiai: programavimo mokymasis, adaptyvi mokymosi sistema, Herrmanno mokymosi stiliai.Model of an adaptive programming learning systemJūratė Urbonienė SummaryThe paper analyzes the characteristics of programming training, reviews students’ learning styles and learning style influence on learning outcomes, the Herrmann brain dominance Instrument (HBDI), which enables to classify learners according to their brain hemisphere development. Based on the programming learning experiences and fundamental programming training, scientists’ works, the programming learning difficulties (sych as the programming field specificity, selection of learning styles and learning methods selection) are analyzed, and the key ideas are identified and summarized. The main focus is on adaptive programming learning system. The proposed adaptive programming learning system model will improve students’ motivation and the level of achieved results according to the pre-defined rules. The motivation will be improved by selecting a student’s learning style and most appropriate learning objects. It is planned to create the system of introducing this model and testing it with real learners.  

scholarly journals Trial for E-Learning System on Information Security Incorporate with Learning Style and Consciousness Factors

2018 ◽  
Vol 8 (3) ◽  
pp. 120 ◽  
Author(s):  
Kiyoshi Nagata ◽  
Yutaka Kigawa ◽  
Tomoko Aoki
Keyword(s):  
Information Security ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Learning System ◽  
Psychological Aspects ◽  
Educational Institutions ◽  
Higher Educational Institutions ◽  
The Third ◽  
E Learning

In Japanese higher educational institutions as in universities, many studies and practices on e-learning have been reported, but the state of trial and errors are still continuing. Thus, we propose the construction of an e-learning system incorporating not only with learner's consciousness but also with their learning style. Our proposing system is consistent of three individual parts and one integration phase. The first part is composed of tools, methods, and facilities used to provide the e-learning environment. The second part is composed of theoretical or practical works on learning styles. The third part is composed of learner’s consciousness factors on information security which we have studied so far. In the integration phase, the components of e-learning discussed in the first part are evaluated from learning style related psychological aspects. In the paper, we propose a trial version of e-learning system which includes several types of content with proper links to information security factors and to learning styles.

Midwifery Students’ Learning Styles in Iran: A Review Study Based on Kolb’s Learning Theory

2021 ◽  
Vol 30 (1) ◽  
pp. 52-63
Author(s):  
Hadis Sourinejad ◽  
◽  
Fariba Haghani ◽  
Marjan Beigi ◽  
Elham Adibmoghaddam ◽  
...  
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Theory ◽  
Teaching Methods ◽  
Teaching Method ◽  
Important Research ◽  
Innovative Teaching ◽  
Review Study ◽  
Kolb's Learning Theory ◽  
Selection Of

Background: Identifying the factors that contribute to learning is one of the most important research goals. Learning style is one of these important and effective factors. The predominant learning styles of students in different universities of Iran are different. Objective: This study aimed to review the learning styles of midwifery students in Iran based on Kolb’s learning theory. Materials and Methods: In this review study, a search was conducted in Scopus, PubMed, Google Scholar, SID, and MagIran databases on articles published during 2000-2020 by using the keywords: Learning styles, midwifery students, and Kolb’s theory in Persian and English. Out of 28 eligible articles, 9 articles were finally reviewed. Results: Learning in midwifery students is usually done by using different styles, the most common of which was convergent style followed by assimilative style. Conclusion: The predominant learning styles of midwifery students in Iran are convergent and assimilative styles. While considering individual differences, educational planning and selection of teaching method should be done in a way that is appropriate for different learning styles of midwifery students and a variety of new and innovative teaching methods should be used.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals PENGARUH MOTIVASI BELAJAR, GAYA BELAJAR, DAN LINGKUNGAN BELAJAR PADA HASIL BELAJAR EKONOMI DI SMA NEGERI 1 WRINGINANOM GRESIK

2017 ◽  
Vol 2 (2) ◽  
pp. 142
Author(s):  
Muhammad Miftah Farid
Keyword(s):  
Linear Regression ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Environments ◽  
Learning Style ◽  
Total Sample ◽  
Learning Motivation ◽  
Analysis Method ◽  
Regression Techniques

This study aims to analyze the influence of learning motivation, learning styles and learning environments on learning outcomes economy partially or simultaneously. The population in this study is IPS students a total of 314 people and a total sample of 153 students in SMA Negeri 1 Wringinanom Gresik. The data collection techniques used were questionnaires and documentation. Analysis method using multiple linear regression techniques. The results showed that the learning motivation, learning style and learning environment partially positive significant effect on learning outcomes. Learning motivation, learning style and the learning environment simultaneously positive significant effect on learning outcomes

A Model for an Adaptive Hypermedia Learning System Based on Data Mining Technique

2017 ◽  
pp. 65-85
Author(s):  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Learning Performance ◽  
Adaptive Hypermedia ◽  
Data Mining Technique ◽  
Pedagogical Strategies ◽  
Hypermedia Learning

Since the learning style of each learner is different. Adaptive hypermedia learning system (AHLS) must fit different learner's needs. A number of AHLS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: less attention was paid to the relationship between learning styles and learning performance. This paper proposes an AHLS model based on learning styles and learning performance. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her learning style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

The Role of Learning Styles and Technology

2009 ◽  
Vol 4 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Royce Ann Collins
Keyword(s):  
Adult Learners ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Virtual Space ◽  
Classroom Teaching ◽  
Initial Learning ◽  
Education Today ◽  
Electronic Environments

Learning style research has informed effective classroom teaching strategies for decades. Technology has allowed faculty and students to move the learning environment from the four-walled classroom to a fluid global virtual space. Knowledge gained through the application of learning style research to online instruction has enhanced practice; however, research demonstrating the alignment of learning styles with current technological resources has been limited. Learning styles and their interrelationship with technology and adult learners is as important today as initial learning style research was in the six decades after its beginnings in the 1940s. Education today must meet the needs of students who are more comfortable in electronic environments, as well as those who need the four-walled classroom. The ability to use learning style research to accomplish both will lead to enhanced student learning and a more productive experience.

The Learning Style-Based Adaptive Learning System Architecture

2015 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

RunayaySoft

2019 ◽  
Vol 15 (1) ◽  
pp. 103-131
Author(s):  
Juan Camilo González Vargas ◽  
Angela Carrillo-Ramos ◽  
Ramon Fabregat ◽  
Lizzeth Camargo ◽  
Maria Caridad García Cepero ◽  
...  
Keyword(s):  
Learning Styles ◽  
Multiple Intelligences ◽  
Learning Style ◽  
Design Methodology ◽  
Educational Institutions ◽  
Content Type ◽  
Functional Prototype ◽  
The Relationship ◽  
Disciplinary Area ◽  
Selection Of

Purpose The purpose of this paper is to describe a support system to the selection of enrichment activities in educational environment called RunayaySoft, where Runayay comes from the word Quechua that means develop and Soft as it is an informatics tool that supports the educational institutions and their students, in the selection of activities that allow foster some of their skills based on their interests, learning styles, aptitudes, multiple intelligences, preferences and so on. Moreover, it suggests institutions about the activities that they should make in their building considering student´s characteristics and the agreements that they have. Design/methodology/approach It does a diagnostic for identifying which characteristics are going to be considered to students and institutions. Then, it generates adaptive profiles with the aim of generating suggestions of enrichment activities that allow to boost some of their skills. For the students were considered their preferences, learning style, aptitude, multiple intelligences and interests. In the case of institutions were the agreements, resources and activities that they develop. Based on this information, it defines the relations for the generation of suggestions of activities toward students, where it does the prioritization of which activities should be considered. Findings For validating the system, it was done as a functional prototype that generates suggestions to students, as well as educative institutions, through a satisfaction test student assess if they agree or disagree with the suggestions given. With that assessment, it is validated the relationship between student’s characteristics, activity and institution are related for generating activities suggestions. Research limitations/implications RunayaySoft generates adaptive profiles for the students, activity and institution. Each profile has information that allows adapt an advice toward students and institutions. Social implications RunayaySoft considers student’s characteristics, activities and educational institutions for generating suggestions for enrichment activities that allow to boost some of their skills. Many times, when activities are generated in educative institutions, they are not considered a learner’s needs and characteristics. For that reason, the system helps institutions to identify activities that should be done in their facilities or with those institutions which they have agreements when the institutions that students come from do not have the required resources. Originality/value RunayaySoft suggests enrichment activities to students as well as educative institutions. For students, it suggests disciplinary areas where they can boost their skills; for each disciplinary area are recommended activities based on their preferences. Once students select the disciplinary area and activities, the system suggests educative institutions activities that they can do. If the institutions do not have the necessary facilities, the system shows with which other institutions they can set agreements. Moreover, it supports educative institutions to identify enrichment clusters, where it clusters students based on similar interest, allowing institutions to identify the activities that they should focus on.

Learning Style and Students’ Achievement in Science

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
FRANCIS C. BUSILAOCO ◽  
SHARON R. LAO
Keyword(s):  
Student Achievement ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Science Learning ◽  
Survey Questionnaire ◽  
Visual Learners ◽  
Correlational Method ◽  
Significant Bearing ◽  
The Way

Educators assert that students have various learning styles. In most cases, whatis being taught has less impact on learners’ achievement than the way materialsare presented. In other words, learning style makes an important component inthe learning environment. The Learning Style Survey Questionnaire employed inthis study appears to be a viable tool to determine students’ learning style. Thisstudy investigated the learning styles and achievement in science of 288 students.Findings reveal that most of the students were visual learners. Results also showthat the students possessed average competencies in science. Furthermore, resultssuggest that learning style has no significant bearing on achievement in science.Keywords: Science, learning style, student achievement, descriptive correlational method,Philippines

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