scholarly journals Impact of Using Personalized E-Course in Computer Science Education

2020 ◽  
Vol 13 (4) ◽  
pp. 174-188
Author(s):  
Marián Mudrák ◽  
Milan Turčáni ◽  
Jaroslav Reichel
Keyword(s):  
Science Education ◽  
Secondary School ◽  
Learning Styles ◽  
Learning Style ◽  
Positive Impact ◽  
Minimal Impact ◽  
E Learning ◽  
Learning Platforms ◽  
The Impact ◽  
Initial Knowledge

At current e-learning platforms, is often seen non-efficient usage of their possibilities when creating educational content. This article deals with the possibilities of using adaptive tools that are offered by learning management system (LMS) Moodle when creating a personalised e-course. The methodology created by the authors of the article for personalised e-course adjusts the study content based on characteristics of each student stated by his or her initial knowledge, learning style, and motivation. The article is aimed at the presentation of the created methodology and its impact on the level of student's output knowledge as well as overall learning efficiency. By using the methodology, there was an opportunity to compare the impact of two different approaches – the personalised one and non-personalised. Statistical analysis revealed that the use of personalized e-course has a positive impact on students' activity, motivation, and their level of output knowledge. The results showed that the attended secondary school has no or only minimal impact on the output knowledge if the students studied through the personalized e-course. An interesting finding was that students in all surveys have a stronger tendency to prefer the same learning styles over the years.

scholarly journals Online Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic

2018 ◽  
Vol 7 (3.13) ◽  
pp. 76
Author(s):  
L Alfaro ◽  
C Rivera ◽  
J Luna-Urquizo ◽  
E Castañeda ◽  
F Fialho
Keyword(s):  
Fuzzy Logic ◽  
Learning Styles ◽  
Learning Style ◽  
User Behavior ◽  
Backpropagation Neural Network ◽  
User Interactions ◽  
Learning Platform ◽  
Proposed Model ◽  
E Learning ◽  
Learning Platforms

Individual Learning Style identification is an essential aspect in the development of intelligent or adaptive e-Learning platforms. Traditional methods are based on the application of questionnaires or psychological tests, which may not be the most appropriate in all cases. The proposed model is based on the analysis of user behavior through the study of their interactions within an e-Learning platform, using a multilayer Backpropagation Neural Network and Fuzzy Logic concepts, for the preprocessing of the inputs and the categorization of the outputs.                                                                              

scholarly journals Implementing basic e-learning tools into an undergraduate taxation curriculum

2016 ◽  
Vol 24 (3) ◽  
pp. 341-367 ◽  
Author(s):  
T. van Oordt ◽  
Ingrid Mulder
Keyword(s):  
Learning Styles ◽  
Student Performance ◽  
Positive Impact ◽  
Accounting Education ◽  
Learning Tools ◽  
Student Survey ◽  
Content Type ◽  
Large Classes ◽  
E Learning ◽  
The Impact

Purpose Educators in the accounting discipline are faced with the challenge of finding innovative ways to accommodate the flexible learning styles of Millennial students, using “in classroom/contact time” effectively and decreasing transactional distance between students and educators in large classes. In an attempt to address these challenges, this paper aims to describe the implementation of basic e-learning tools (podcasts, vodcasts and voice-over-PowerPoint) as supplementary and substitutional tools in an undergraduate taxation curriculum. The tools were implemented as part of a student-centred approach to the facilitation of learning, embedded in the Blended Learning Theory. The paper reports on students’ use and experience of various basic e-learning tools, as well as the impact of the use of these tools on student performance. Design/methodology/approach An action research methodology was followed, and data were collected by way of a voluntary, descriptive student survey and student class lists. A total of 387 students completed the survey. Findings Students appear to have access to devices and data to use e-learning tools. They perceive these tools as helpful study aids and prefer synchronous, substitutional tools. Use of the tools does not have a significant impact on performance; however, it does appear to have a positive impact on the learning environment and student engagement. Originality/value The results of the study may be of benefit to educators and curriculum designers who are responsible for reviewing and updating the content delivery methods of undergraduate taxation curricula in large classes with diverse student populations. These results add to the limited body of knowledge on the implementation of basic e-learning tools in a South African accounting education setting.

scholarly journals The Impact of Felder’s Learning Styles Index on Motivation and Adoption of Information Through E-Learning

2017 ◽  
Vol 12 (2) ◽  
Author(s):  
Željko Pekić ◽  
Srđan Jovanovski ◽  
Nađa Pekić
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Statistical Significance ◽  
Relevant Information ◽  
The Other ◽  
Independent Variables ◽  
E Learning ◽  
Initial Idea ◽  
The Individual ◽  
The Impact

In this paper, we examined the nature and distribution (direction and intensity) of motivation for using e-learning, focusing the connection between the independent variables on one side and the Felder’s learning style on the other. The most relevant information that we wanted to examine and present is the individual ways of the respondents in adopting the same material. We were also interested in the ways to technically adjust the information delivery. The results confirm the statistical significance of the initial idea.

An Adaptive E-Learning System based on Student's Learning Styles

2016 ◽  
Vol 14 (3) ◽  
pp. 34-51 ◽  
Author(s):  
Samia Drissi ◽  
Abdelkrim Amirat
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning System ◽  
Educational Materials ◽  
Learning Achievement ◽  
Web Based ◽  
Research Areas ◽  
E Learning ◽  
The Impact

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's learning achievement. The findings support the use of learning styles as guideline for adaptation into the adaptive e-learning hypermedia systems.

The Impact of Foreign Language Learning Style on Strategies Preferences in Art Colleges

2014 ◽  
Vol 971-973 ◽  
pp. 2677-2680
Author(s):  
Di Jiao
Keyword(s):  
Foreign Language ◽  
Learning Styles ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Style ◽  
Foreign Language Learning ◽  
English Learning ◽  
Factors Affecting ◽  
The Impact ◽  
The Relationship

Factors affecting students’ English learning performances are always debated among language researchers. This research is carried out in art colleges to figure out the students’ preferences in learning styles and learning strategies as well as the relationship between them. Questionnaires have been applied and data have been dealt with by SPSS. This research has shown that students in the art college tend to be visual and individual learners, and thus they prefer to adopt metacognitive, memory and affective strategies.

scholarly journals Exploration of Factors Affecting Learners' Motivation in E-learning

2019 ◽  
pp. 1269-1275
Author(s):  
Shubham Dubey ◽  
Biro Piroska ◽  
Manjulata Gautam
Keyword(s):  
Dropping Out ◽  
Individual Factor ◽  
Factors Affecting ◽  
E Learning ◽  
Learning Platforms ◽  
The Common ◽  
Khan Academy ◽  
Future Work ◽  
The Impact

The world is changing rapidly, so is academics. E-learning has altered the area of academics and education. ICT enabled learning has given ideal services to students by providing any type of content on demand which is proportional to the performance of students. The concentration of learner has been found instinctive; thus there is a need of engaging mind towards course progress with its entirety till the objectives of the course will be achieved. There are several e-learning platforms available as EdX, Udacity, Khan Academy, Alison those have a number of learners registered for various courses. Studies suggest that these platforms suffer from the common problem of learners’ dropping out. Investigations also claim early leaving rate is increasing due to lack of quality of content, distraction factors, learners’ mind change, outdated and succinct information, and some more detraction factors. These issues have been observed on the basis of early leaving rates in various MOOCs. Thus there is a mammoth scope for minimizing the impact of these reasons on the learners’ mind. It can be achieved by identifying these factors affecting learners’ motivation during the course. This study is aiming on identifying these factors. The approach is to explore some certain keywords on previous literature (total 41) and then calculating their frequencies and co-factors associated with them. Both grouped factors contribution and individual factor contribution have been taken care. The study gives a direction for future work towards overcoming these actor and engaging learners in ICT enabled learning.

The Use of Cloud-Computing to Promote Collaborative Learning in Higher Education

2019 ◽  
pp. 32-45
Author(s):  
Melissa S. Martin ◽  
Rachel E. Hugues ◽  
Alison Puliatte
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Collaborative Learning ◽  
Learning Styles ◽  
Instructional Strategy ◽  
Collaborative Work ◽  
Learning Preferences ◽  
Generation Z ◽  
E Learning ◽  
Learning Platforms

Generation Z students are inherently different than previous generations. These students may need adapted forms of instruction in order to match their learning styles. Collaborative learning can be adapted using cloud-computing, which helps students work together online and manage their interactions. These students may benefit from a technological twist to a common instructional strategy and are inclined to use online means of communication to complete coursework. Technology has dominated the educational experiences of these students and they are no strangers to collaborative work through e-learning platforms. Higher education institutions and instructors must develop the format of courses in order to meet the technological learning preferences of Generation Z.

Web Conferencing and Remote Laboratories as Part of Blended Learning in Engineering and Science

2012 ◽  
pp. 246-263
Author(s):  
Steve Mackay ◽  
Darrell Fisher
Keyword(s):  
Science Education ◽  
Blended Learning ◽  
New Technologies ◽  
Learning Experience ◽  
Web Conferencing ◽  
Science And Engineering ◽  
Remote Laboratories ◽  
Remote Labs ◽  
E Learning ◽  
The Impact

The purpose of this chapter is to provide an overview and subsequent application of research into the impact web conferencing and remote laboratories have on engineering and science education within the context of blended learning. The impact is examined especially in assessing the reaction and achievement of learners in using these new technologies compared to that of a traditional classroom or (the currently popular approach of) asynchronous e-learning. In recent years, there has been a significant increase in the level of remote or distance learning (or e-learning) using the Internet. One of the gaps in current research is the examination of the impact of web conferencing and remote science and engineering laboratories on the learning experience. The chapter is commenced by defining e-learning, synchronous, asynchronous and blended learning. Hereafter, web conferencing and remote laboratories are then defined and reviewed in detail. The impact that web conferencing and remote labs have on the e-learning experience as compared to the classroom and asynchronous approaches is then examined. The chapter is concluded by an examination of our research into what the perceived requirements are for web conferencing and remote laboratories and suggestions are given on how to apply web conferencing and remote labs to engineering and science education.

Design of an Adaptive Mobile Learning Management System

2008 ◽  
pp. 286-301 ◽  
Author(s):  
Hyungsung Park ◽  
Young Kyun Baek ◽  
David Gibson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Mobile Learning ◽  
Management System ◽  
Learning Management System ◽  
Analysis Tool ◽  
User Interactions ◽  
E Learning ◽  
Adaptive Environment ◽  
Learning Management

This chapter introduces the application of an artificial intelligence technique to a mobile educational device in order to provide a learning management system platform that is adaptive to students’ learning styles. The key concepts of the adaptive mobile learning management system (AM-LMS) platform are outlined and explained. The AM-LMS provides an adaptive environment that continually sets a mobile device’s use of remote learning resources to the needs and requirements of individual learners. The platform identifies a user’s learning style based on an analysis tool provided by Felder & Soloman (2005) and updates the profile as the learner engages with e-learning content. A novel computational mechanism continuously provides interfaces specific to the user’s learning style and supports unique user interactions. The platform’s interfaces include strategies for learning activities, contents, menus, and supporting functions for learning through a mobile device.

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