Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school

Indriana Hidayah, Teguh Bharata Adji, Noor Akhmad Setiawan, Norliza Abd Rahman

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Adaptivity in learning systems depends on accuracy of learner modelling. Specifically, for generating cluster-based instructions, quality of student clustering is critical. Studies on student clustering are abundant; however, a system for clustering metacognitive that considers proper analysis technique of Likert-scaled dataset is unavailable. This article proposes a student clustering method which uses a new Likert scale analysis. It is performed on a dataset collected from 81 students of a vocational high school. The performance was compared to previous methods; enhancement is shown by higher silhouette-index and strong correlation with work readiness score. To evaluate the clustering effectiveness, it is implemented on an e-learning system to generate adaptive instructions. The e-learning system is a supplement for fundamental programming course in the school. The t-test result shows that learning gain of experiment group is significantly higher that of the control group. Therefore, the proposed method is effective in improving students' learning quality.

Original languageEnglish
Pages (from-to)419-436
Number of pages18
JournalInternational Journal of Innovation and Learning
Volume24
Issue number4
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Learning systems
Students
instruction
school
learning
student
electronic learning
Computer programming
supplement
Group
programming
Clustering
High school
experiment
performance
Experiments
Electronic learning

Keywords

  • Adaptive metacognitive instructions
  • Learning system
  • Likert scale analysis
  • Student clustering
  • Vocational high school

ASJC Scopus subject areas

  • Education
  • Management of Technology and Innovation

Cite this

Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school. / Hidayah, Indriana; Adji, Teguh Bharata; Setiawan, Noor Akhmad; Abd Rahman, Norliza.

In: International Journal of Innovation and Learning, Vol. 24, No. 4, 01.01.2018, p. 419-436.

Research output: Contribution to journalArticle

@article{0d47040721aa450ab36bba647cfc281f,
title = "Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school",
abstract = "Adaptivity in learning systems depends on accuracy of learner modelling. Specifically, for generating cluster-based instructions, quality of student clustering is critical. Studies on student clustering are abundant; however, a system for clustering metacognitive that considers proper analysis technique of Likert-scaled dataset is unavailable. This article proposes a student clustering method which uses a new Likert scale analysis. It is performed on a dataset collected from 81 students of a vocational high school. The performance was compared to previous methods; enhancement is shown by higher silhouette-index and strong correlation with work readiness score. To evaluate the clustering effectiveness, it is implemented on an e-learning system to generate adaptive instructions. The e-learning system is a supplement for fundamental programming course in the school. The t-test result shows that learning gain of experiment group is significantly higher that of the control group. Therefore, the proposed method is effective in improving students' learning quality.",
keywords = "Adaptive metacognitive instructions, Learning system, Likert scale analysis, Student clustering, Vocational high school",
author = "Indriana Hidayah and Adji, {Teguh Bharata} and Setiawan, {Noor Akhmad} and {Abd Rahman}, Norliza",
year = "2018",
month = "1",
day = "1",
doi = "10.1504/IJIL.2018.095367",
language = "English",
volume = "24",
pages = "419--436",
journal = "International Journal of Innovation and Learning",
issn = "1471-8197",
publisher = "Inderscience Enterprises Ltd",
number = "4",

}

TY - JOUR

T1 - Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school

AU - Hidayah, Indriana

AU - Adji, Teguh Bharata

AU - Setiawan, Noor Akhmad

AU - Abd Rahman, Norliza

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Adaptivity in learning systems depends on accuracy of learner modelling. Specifically, for generating cluster-based instructions, quality of student clustering is critical. Studies on student clustering are abundant; however, a system for clustering metacognitive that considers proper analysis technique of Likert-scaled dataset is unavailable. This article proposes a student clustering method which uses a new Likert scale analysis. It is performed on a dataset collected from 81 students of a vocational high school. The performance was compared to previous methods; enhancement is shown by higher silhouette-index and strong correlation with work readiness score. To evaluate the clustering effectiveness, it is implemented on an e-learning system to generate adaptive instructions. The e-learning system is a supplement for fundamental programming course in the school. The t-test result shows that learning gain of experiment group is significantly higher that of the control group. Therefore, the proposed method is effective in improving students' learning quality.

AB - Adaptivity in learning systems depends on accuracy of learner modelling. Specifically, for generating cluster-based instructions, quality of student clustering is critical. Studies on student clustering are abundant; however, a system for clustering metacognitive that considers proper analysis technique of Likert-scaled dataset is unavailable. This article proposes a student clustering method which uses a new Likert scale analysis. It is performed on a dataset collected from 81 students of a vocational high school. The performance was compared to previous methods; enhancement is shown by higher silhouette-index and strong correlation with work readiness score. To evaluate the clustering effectiveness, it is implemented on an e-learning system to generate adaptive instructions. The e-learning system is a supplement for fundamental programming course in the school. The t-test result shows that learning gain of experiment group is significantly higher that of the control group. Therefore, the proposed method is effective in improving students' learning quality.

KW - Adaptive metacognitive instructions

KW - Learning system

KW - Likert scale analysis

KW - Student clustering

KW - Vocational high school

UR - http://www.scopus.com/inward/record.url?scp=85054544008&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054544008&partnerID=8YFLogxK

U2 - 10.1504/IJIL.2018.095367

DO - 10.1504/IJIL.2018.095367

M3 - Article

AN - SCOPUS:85054544008

VL - 24

SP - 419

EP - 436

JO - International Journal of Innovation and Learning

JF - International Journal of Innovation and Learning

SN - 1471-8197

IS - 4

ER -