scholarly journals Nuotolinio mokymosi stilių personalizavimas

2011 ◽  
Vol 56 ◽  
pp. 42-49
Author(s):  
Saulius Preidys ◽  
Leonidas Sakalauskas
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Environments ◽  
Learning Style ◽  
Text Analysis ◽  
Virtual Learning Environments ◽  
Distance Learners ◽  
Using Data ◽  
Listening And Speaking ◽  
Article Analyse

Mokymosi stiliai yra skirtingi. Kai kurie teikia pirmenybę klausymui ir kalbėjimui, kiti teksto analizei arba mokosi pasitelkę vaizdines priemones. Tačiau daugumos studentų mokymosi stilius yra mišrus. Ypač tai aktualu žinoti kuratoriams ir kursų autoriams, dirbantiems su studentais, studijuojančiais nuotoliniu būdu: reikiamai parengta mokymosi medžiaga, suasmenintos užduotys ir asmeniški komentarai padės studentui greičiau pasiekti iškeltus kurso tikslus ir uždavinius. Šiame straipsnyjeautoriai, remdamiesi Honey ir Mumfordo (1992) sukurta tipologija, nagrinėja mokymosi stilių nustatymą ir vizualizavimą, analizuodami studentų veiklą virtualaus mokymosi aplinkose ir sukauptiems duomenims taikydami duomenų gavybos metodus.Personalization of Learning Styles in Distance EducationSaulius Preidys, Leonidas Sakalauskas SummaryDifferent learners have different styles of learning. Some of them give the priority to listening and speaking, others to text analysis or visual tools. Nevertheless, the learning style of many learners is mixed. Tutors and course creators working with distance learners should be aware of this fact. Appropriate learning materials, personalised assignments and personal comments would help the learner in achieving the aims and objectives of the course. Referring to Honey and Mumford (1992), the authors of the article analyse the determination and visualisation of learning styles, by tracking learners’ activities in virtual learning environments and using data mining methods.eight: 18px;"> 

scholarly journals INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ali Akhtar ◽  
Mohammad Serajuddin ◽  
Hasan Zafrul
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Style ◽  
Data Extraction ◽  
Verbal Learning ◽  
Educational Data Mining ◽  
Knowledge Presentation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0782/a.php" alt="Hit counter" /></p>

scholarly journals ANALYSIS OF STUDENTS’ STUDY ACTIVITIES IN VIRTUAL LEARNING ENVIRONMENTS USING DATA MINING METHODS / STUDENTŲ, BESIMOKANČIŲ VIRTUALAUS MOKYMO APLINKOJE, VEIKLOS ANALIZĖ TAIKANT DUOMENŲ GAVYBOS METODUS

2010 ◽  
Vol 16 (1) ◽  
pp. 94-108 ◽  
Author(s):  
Saulius Preidys ◽  
Leonidas Sakalauskas
Keyword(s):  
Data Mining ◽  
Distance Learning ◽  
Learning Environments ◽  
Virtual Learning ◽  
Virtual Learning Environments ◽  
Learning Materials ◽  
Learning Platform ◽  
Mining Methods ◽  
Using Data ◽  
Learning Factors

This article deals with application of data mining methods’ to analysis of learners’ behaviour using the distance learning platform BlackBoard Vista (BlackBoard 2008). Before planning a distance learning course, instructors have to pay attention to the fact that there exist different study methods: some students start reading learning materials from the very beginning to the end, some students look at unclear topics only, some start with the discussions, etc. Therefore after analyzing the learning factors and identifying learner's style, it is possible to prepare individualized learning materials and to choose a proper way of course presentation. Such a way of study organization would improve the quality of studies and make it possible to reach better results. The research was performed by observing the behaviour and results achieved by 528 students in 15 distance learning courses and, using the clustering method, 3 learner's styles using virtual learning environments (VLE) have been identified and work methods proposed for students with regard to those learners’ styles. Besides, the research aims to find out the factors that influence final evaluations of students’. Santrauka Prieš planuodami rengti ir teikti nuotolinio mokymosi kursą, rengėjai turi atsižvelgti į tai, kad žmonės studijuoja skirtingais metodais: vieni pradeda skaityti pateiktą medžiagą iš eiles, kiti peržiūri tik nesuprantamas vietas, treti persikelia į virtualias diskusijas ir pan. Todėl, išanalizavus mokymosi veiksmus ir nustačius studento stilių, vėliau galima pateikti suasmenintą mokymosi medžiagą, parinkti geresnius kurso pateikimo metodus. Toks mokymo organizavimas pagerintų studijų kokybę ir leistų pasiekti geresnių rezultatų. Šiame straipsnyje nagrinėjamas duomenų gavybos metodu taikymas, analizuojant studentų elgseną, naudojantis virtualaus mokymo terpe BlackBoard Vista (BlackBoard 2008).

EXPRESS: Testing Theories of Goal Progress in Online Learning

2021 ◽  
pp. 002224372199110
Author(s):  
Joy Lu ◽  
Eric T. Bradlow ◽  
J. Wesley Hutchinson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Detection System ◽  
Traditional Education ◽  
Performance Outcomes ◽  
Learner Engagement ◽  
Goal Progress ◽  
Business Courses ◽  
Using Data

Online educational platforms increasingly allow learners to consume content at their own pace with on-demand formats, in contrast to the synchronous content of traditional education. Thus, it is important to understand and model learner engagement within these environments. Using data from four business courses hosted on Coursera, we model learner behavior as a two-stage decision process, with the first stage determining across-day continuation versus quitting and the second stage determining within-day choices among lectures, quizzes, and breaks. By modeling the heterogeneity across learners pursuing lecture and quiz completion goals, we capture different patterns of consumption that correspond to extant theories of goal progress within an empirical field setting. We find that most individuals exhibit a learning style where lecture utility changes as an inverted-U-shaped function of current progress. Our model may also be used as an early detection system to anticipate changes in engagement and allows us to relate learning styles to final performance outcomes and enrollment in additional courses. Finally, we examine the role of quizzes in how consumption patterns vary across learners in different courses and between those who have paid or not paid for the option to earn a course certificate.

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

Students' Learning Styles and Learning Effects

2014 ◽  
pp. 197-235
Keyword(s):  
Data Mining ◽  
High Schools ◽  
Learning Styles ◽  
Learning Style ◽  
Negative Association ◽  
Survey Result ◽  
Learning Style Inventory ◽  
Open Source Data ◽  
Source Data ◽  
Exam Scores

This chapter introduces the concept of learning style and Memletics learning style inventory, and uses open-source data mining software WEKA to cluster the students of experiment classes in four high schools according to the values of seven dimensions in the Memletics learning style inventory that are calculated based on the survey result about their learning styles. The clustering result demonstrates that verbal and physical are always positively associated with exam scores, visual dimension usually has negative association with score exams; the association of learning style with exam scores remains almost static, and the high, medium, and low sum of dimension values of learning style corresponds to high schools in developed, developing, and undeveloped area in China, respectively. The findings are analyzed. The implication of learning style for intelligent instruction of English subject as a foreign language is suggested.

scholarly journals Trabajo colaborativo en ambientes virtuales de aprendizaje: Algunas reflexiones y perspectivas estudiantiles

2016 ◽  
Vol 20 (2) ◽  
pp. 1 ◽  
Author(s):  
Francisco Mora-Vicariol ◽  
Carlene Hooper-Simpson
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Quantitative Research ◽  
Collaborative Work ◽  
Virtual Learning ◽  
Virtual Learning Environments ◽  
Virtual Courses ◽  
Analysis Process ◽  
The Difference ◽  
Collaborative Activities

The collaborative work in virtual learning environments becomes more relevant at a time when technology is used intensively. This paper shows the results of research conducted at the Universidad Estatal a Distancia (UNED) in Costa Rica, about students’ perceptions around the concept and implications of collaborative work in two online courses provided by Dirección de Extensión Universitaria (Dirextu). The aim of this study is to establish the difference between collaborative work and group work as well as knowing the tools that can be used to promote this type of activity in virtual learning environments. The methodology used for this article applied a survey to two groups of students, each belonging to two different virtual courses of the Dirección de Extensión Universitaria (Dirextu). This study was based on quantitative research and applied an instrument with open and closed questions. The analysis process of the results was performed with the help of tables and figures (graphs). Finally, the study exposes a series of conclusions and recommendations among which we highlight the following: students do like collaborative activities because they diversify the teaching modes and improve the learning styles. 

scholarly journals Analyzing the effect of learning styles and study habits of distance learners on learning performances: A case of an introductory programming course

2014 ◽  
Vol 15 (4) ◽  
Author(s):  
Ünal Çakıroğlu
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Programming Language ◽  
Achievement Test ◽  
Study Habits ◽  
Distance Learners ◽  
Introductory Programming ◽  
Learning Style Inventory ◽  
Significant Relationships ◽  
Learning Performances

<p>This study examined the relationships among learning styles, study habits, and learning performances in an online programming language course. Sixty-two sophomore students who enrolled in an online introductory programming course participated in the study. Kolb’s Learning Style Inventory (LSI) was used to measure the students’ learning styles. Another inventory developed by the researcher was used to determine learners’ study habits. An achievement test was used to put forward their learning performances. As a result, significant relationships between learning styles, study habits, and learning performances were revealed. The results present some ideas about distance learners’ learning styles and study habits for instructors who wish to incorporate synchronous courses and support learners.</p>

scholarly journals Cluster Analysis for Learning Style of Vocational High School Student Using K-Means and FUZZY C-MEANS (FCM)

2017 ◽  
Vol 7 (2) ◽  
pp. 121
Author(s):  
REZA ANDREA ◽  
Shinta Palupi ◽  
Siti Qomariah
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Style ◽  
Absorptive Capacity ◽  
Data Cleaning ◽  
Mining Method ◽  
Vocational High School ◽  
Fuzzy C Means ◽  
Group Type ◽  
Cluster Group

The inability of students to absorb the various knowledge conveyed by the teacher is not due to the inability of his understanding and not because the teacher is not able to teach, but rather due to the incompatibility of learning styles (learning style) between students and teachers, so that students feel uncomfortable learning to certain teachers, it occurred also in SMKN 2 Penajam Paser Utara (PPU), research to analyze cluster (group) type of student learning by applying data mining method that is K-means and Fuzzy C-means (FCM). The goal to be achieved is to know the effectiveness of this type of learning clustering on the development of absorptive capacity and improvement of student achievement. In this research, the method used to cluster the learning type with data mining process starting from data cleaning, data selection, data transformation, data mining, pattern evolution, and knowledge (knowledge).

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