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.  

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.

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.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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.

THE INFLUENCE OF STUDENTS LEARNING STYLE AND LEARNING INTEREST ON THE LEARNING OUTCOMES OF PHYSICS CLASS X IPA DURING THE COVID-19 PANDEMIC

2021 ◽  
Vol 9 (3) ◽  
pp. 132-143
Author(s):  
Albert Lumbu ◽  
Bonefasius Y Boy ◽  
Muhamad Akbar
Keyword(s):  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Style ◽  
Significant Influence ◽  
Pearson Correlation ◽  
Physics Learning ◽  
Significance Level ◽  
Pearson Correlation Test ◽  
Learning Interest ◽  
Learning Together

This study aims to determine whether there are: 1) the influence of learning styles on physics learning outcomes, 2) the influence of interest in learning on physics learning outcomes, 3) the influence of learning styles and interest in learning together on physics learning outcomes in class X SMA Negeri 1 Nimboran . This research was conducted at SMA Negeri 1 Nimboran in April - May 2021. The subjects in this study were all 52 students of class X majoring in science and the objects of research were learning styles, interest in learning and physics learning outcomes. The instruments used are questionnaires and documentation. Data analysis was carried out using the Pearson correlation test with a significance level of 0.05 and a regression test which resulted in conclusions as a result of the study. The results showed that, 1) there was a significant influence of learning style on physics learning outcomes with a contribution of 50.8%, 2) there was a significant influence of interest in learning on physics learning outcomes with a contribution of 60.9%, 3) there were significant influence of learning style and interest in learning, together on the learning outcomes of physics with a contribution of 64.3% influence

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 MODEL PEMBELAJARAN KOOPERATIF TSTS–NHT DAN GAYA BELAJAR TERHADAP HASIL BELAJAR PENGANTAR BISNIS

2019 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Mendarissan Aritonang ◽  
Abdul Hamid K ◽  
Julaga Situmorang
Keyword(s):  
Cooperative Learning ◽  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Style ◽  
Random Sampling ◽  
Business Students ◽  
Learning Model ◽  
Learning Models ◽  
Quasi Experimental ◽  
Cluster Random Sampling

Abstrak: Penelitian ini bertujuan :(1) Untuk mengetahui hasil belajar pengantar bisnis mahasiswa yang diajar dengan model pembelajaran kooperatif tipe TST lebih tinggi dari mahasiswa yang diajar dengan model pembelajaran kooperatif tipe NHT. (2) Untuk mengetahui hasil belajar  pengantar bisnis mahasiswa yang memiliki gaya belajar teoritis dan mahasiswa yang memiliki gaya belajar pragmatis. (3) Untuk mengetahui interaksi antara model pembelajaran kooperatif dengan gaya belajar  terhadap hasil belajar pengantar bisnis. Penelitian ini dilaksanakan di Universitas Methodist Indonesia. Populasi berjumlah 180 orang. Pengambilan sampel dilakukan dengan cluster random sampling. Metode penelitian kuasi eksperimen dengan desain faktorial 2 x 2 dilanjutkan dengan statistik inferensial dengan menggunakan ANAVA dua jalur dengan taraf signifikan α = 0,05 yang dilanjutkan dengan uji Scheffe. Hasil penelitian menunjukkan: (1) hasil belajar pengantar bisnis mahasiswa yang diajarkan dengan model pembelajaran kooperatif tipe TSTS lebih tinggi dari pada hasil belajar pengantar bisnis mahasiswa yang diajarkan dengan model pembelajaran kooperatif tipe NHT; (2) hasil belajar pengantar bisnis mahasiswa yang memiliki gaya belajar teoritis lebih tinggi dari pada hasil belajar pengantar bisnis mahasiswa yang memiliki gaya belajar pragmatis; (3) terdapat interaksi antara model pembelajaran kooperatif dengan gaya belajar  dalam mempengaruhi hasil belajar mahasiswa.  Kata Kunci: model pembelajaran kooperatif, TST, NHT, gaya belajar, pengantar bisnis.  Abstract: This study aims: (1) To find out the introductory learning outcomes of business students who are taught with the cooperative learning model of the TST type are higher than students who are taught with the cooperative learning model of the NHT type. (2) To find out the introductory learning outcomes of business students who have theoretical learning styles and students who have pragmatic learning styles. (3) To determine the interaction between cooperative learning models with learning styles on introductory business learning outcomes. This research was conducted at the Methodist University of Indonesia. The population is 180 people. Sampling was done by cluster random sampling. Quasi-experimental research method with 2 x 2 factorial design followed by inferential statistics using two-way ANAVA with a significant level α = 0.05 followed by the Scheffe test. The results showed: (1) the results of introductory business learning students who were taught with the cooperative learning model type TSTS were higher than the results of introductory business learning students who were taught with the NHT type cooperative learning model; (2) the results of introductory business learning students who have theoretical learning styles are higher than the results of introductory business learning students who have pragmatic learning styles; (3) there is an interaction between cooperative learning models and learning styles in influencing student learning outcomes. Keywords: cooperative learning model, TST, NHT, learning style, business introduction.

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

scholarly journals Komparasi Tingkat Hasil Belajar Berdasarkan Gaya Belajar Visual, Auditorial, dan Kinestetik

2021 ◽  
Vol 3 (1) ◽  
pp. 37-48
Author(s):  
Nastiti ◽  
Wahyu Rika Agustin
Keyword(s):  
Social Studies ◽  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Style ◽  
Visual Learning ◽  
Sampling Technique ◽  
Significance Level ◽  
Ex Post ◽  
Significant Difference ◽  
Good Learning

The purpose of the study was to determine whether there was a difference between learning outcomes based on VAK learning styles (visual, auditory, kinesthetic) in Integrated Social Studies subjects for seventh grade students of SMP Negeri 1 Patianrowo. Researchers used a comparative quantitative approach with a comparative causal design or ex post facto. The population in the study were all students of class VII, amounting to 295 students and the sample was 59 students taken from class VII H and I. The sample was taken using simple random sampling technique. The study used a questionnaire and documentation as data collection instruments. While the data analysis technique used kruskal wallis. The results showed that good learning outcomes based on visual learning styles were 20%, low learning outcomes were 60%, and moderate learning outcomes were 20%. Furthermore, good learning outcomes based on auditory learning styles are 68%, low learning outcomes are 3%, and moderate learning outcomes are 29%. Finally, good kinesthetic learning outcomes are 22%, low learning outcomes are 73%, and low learning outcomes are 5%. Based on these results, it can be concluded that there is a significant difference in learning outcomes based on learning styles using the VAK learning style in Integrated Social Studies subjects class VII SMP Negeri 1 Patianrowo. This can be seen from the sig value. namely 0.000 <from the significance level of 5% (0.05).

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.

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