Using Data Analytic to Visualize Learning Style for Students TVET Polytechnic Malaysia

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50016632" target="_blank" >RIV/62690094:18450/19:50016632 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8986993" target="_blank" >http://dx.doi.org/10.1109/ICBDA47563.2019.8986993</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8986993" target="_blank" >10.1109/ICBDA47563.2019.8986993</a>

Result language
angličtina
Original language name
Using Data Analytic to Visualize Learning Style for Students TVET Polytechnic Malaysia
Original language description
Nowadays, with the emerging of rapid revolution industries, there are various educational learning styles evolve in solving the way of teaching and learning among the TVET polytechnic students. However, the surrounding changes of enormous data in learning styles have tangled the TVET polytechnic students to grab the opportunity to improve the performance of achievement in class. The study was conducted via a quantitative data survey with a total of 332 respondents gathered from several polytechnic Malaysia with engineering and non-engineering field of studies. The use of data analytics is considered as a relatively new area in TVET education in visualizing the pattern of learning styles that polytechnic students' required to use to, since TVET polytechnic students have a different background of academic level and courses. Thus, the findings of this study is to visualize the pattern of potential learning styles among TVET polytechnic students with different field studies. This result aims to give TVET polytechnic lecturers' promising data that lead to an improved student's learning achievement outcome, give students the competitive edge and empower the lecturers teaching style in relation to the industrial revolution 4.0 (IR4.0) learning analytics. © 2019 IEEE.
Czech name
—
Czech description
—

Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
ISBN
978-1-72813-308-9
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
29-33
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
US, Piscataway
Event location
Penang, Malaysia
Event date
Nov 19, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)