Automatic detection of learning style in adaptive online module system

2016 ◽  
Author(s):  
Arief Hidayat ◽  
Victor Gayuh Utomo
Keyword(s):  
Learning Style ◽  
Automatic Detection ◽  
Online Module ◽  
Module System

scholarly journals Adaptive Online Module Prototype for Learning Unified Modelling Language (UML)

2016 ◽  
Vol 6 (6) ◽  
pp. 2931 ◽  
Author(s):  
Arief Hidayat ◽  
Victor Gayuh Utomo
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Verbal Learning ◽  
Unified Modeling Language ◽  
Software Systems ◽  
Learning Material ◽  
Online Module ◽  
Industry Standard ◽  
Online Learning Resources

Every student has their own learning style. Some students may prefer to read the learning material while some other students may prefer to discuss with their colleagues to help them understanding the information more effectively. One of the learning materials is Unified Modeling Language or UML. UML is an industry standard language to specify, visualize, construct, and document the artifacts of software systems. Today, many UML online learning sites are designed to give added information to students other than face-to-face interaction in the classroom. However, most of UML materials provided by the online learning resources are limited to text-based material which is suitable only for students with verbal learning styles. This research aims to identify and analyze the needs before developing a prototype of an adaptive online module. The research is also driven by the need to develop a prototype of adaptive online module that is based on the student's learning style categories in order to help students understand UML better. The result of this research is a prototype of adaptive online module which will identify students' learning styles and lead the students to learning environment that suits their learning style.

scholarly journals Integrating Library Instruction into the Course Management System for a First-Year Engineering Class: An Evidence-Based Study Measuring the Effectiveness of Blended Learning on Students’ Information Literacy Levels

2015 ◽  
Vol 76 (7) ◽  
pp. 934-958 ◽  
Author(s):  
Qinqin Zhang ◽  
Maren Goodman ◽  
Shiyi Xie
Keyword(s):  
Learning Style ◽  
Information Literacy ◽  
Literacy Skills ◽  
First Year ◽  
Library Instruction ◽  
Blended Instruction ◽  
Online Module ◽  
Library Research ◽  
Post Test ◽  
Online Library

This research examines students in a first-year engineering course who receive library instruction by using a newly developed online module and attending optional in-person tutorials. It aims to evaluate the outcomes of library information literacy instruction using this module combined with in-person help. Results show a significant improvement in information literacy skills from a pre-test to a post-test. Focus group and survey data indicate that most students preferred the self-paced learning style of the online module and that the content of the module helped them to conduct library research for the course. This study also considers best practices for online library instruction. A blended instruction approach provides students with the flexibility to learn from a variety of formats at their own pace and also reduces library staff workload, especially for a large course.

Automatic Detection of Career Recommendation Using Fuzzy Approach

2018 ◽  
Vol 11 (4) ◽  
pp. 99-121 ◽  
Author(s):  
Rajalakshmi Krishnamurthi ◽  
Mukta Goyal
Keyword(s):  
Personality Traits ◽  
Learning Style ◽  
Research Work ◽  
Automatic Detection ◽  
Learning Material ◽  
Fuzzy Approach ◽  
Key Factor ◽  
Personality Score ◽  
Candidate Personality ◽  
Work Done

In today's era where personalization of the learning material is a key factor towards the success of learning goal for any student according to their personality traits, learning style and cognitive style, choosing a career according to personality traits is also become a key to success of job satisfaction. Hence, this article proposes the recommendation of career on the basis of candidate personality attributes. In this article, Holland's theory is used to detect candidate's personality. Weights are assigned to each personality traits to calculate the scores are calibrated manually with help of research work done in this domain before and the feedback. A survey is conducted to test the accuracy of proposed method on 217 responses. The result shows the 74.35% candidates are satisfied with the recommended career and average rating for personality traits score was 3.58/5. This article also suggests a fuzzy approach to automatic detection of career recommendation based on personality score.

Adaptive Online Module Prototype for Learning Unified Modelling Language (UML)

2016 ◽  
Vol 6 (6) ◽  
pp. 2931 ◽  
Author(s):  
Arief Hidayat ◽  
Victor Gayuh Utomo
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Verbal Learning ◽  
Unified Modeling Language ◽  
Software Systems ◽  
Learning Material ◽  
Online Module ◽  
Industry Standard ◽  
Online Learning Resources

<table width="562" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Every student has their own learning style. Some students may prefer to read the learning material while some other students may prefer to discuss with their colleagues to help them understanding the information more effectively. One of the learning materials is Unified Modeling Language or UML. UML is an industry standard language to specify, visualize, construct, and document the artifacts of software systems. Today, many UML online learning sites are designed to give added information to students other than face-to-face interaction in the classroom. However, most of UML materials provided by the online learning resources are limited to text-based material which is suitable only for students with verbal learning styles. This research aims to identify and analyze the needs before developing a prototype of an adaptive online module. The research is also driven by the need to develop a prototype of adaptive online module that is based on the student's learning style categories in order to help students understand UML better. The result of this research is a prototype of adaptive online module which will identify students' learning styles and lead the students to learning environment that suits their learning style.</p></td></tr></tbody></table>

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