Loose Part Based STEAM Learning Using Local Wisdom of East Manggarai

Learning activities for early childhood that could be more optimal impact children's development, causing them to be less stimulated. Based on this, this research aims to analyze Loose Part Based STEAM Learning with East Manggarai Local Wisdom in early childhood. This type of research is qualitative research. The research method used is the case study method. The subjects in this research were 20 class B students. Data collection methods in this research are interviews, questionnaires, and documentation. The instruments used to collect data were observation guidelines and interviews. The research data analysis technique is a qualitative descriptive analysis using the Miles and Huberman Model data analysis. The research results show that first, loose part-based STEAM learning with East Manggarai local wisdom is simple, meaning that the activities implemented are how to introduce children to the surrounding culture and get them used to loving local culture. The two teachers design learning activities containing elements of local wisdom according to the theme, sub-theme of the activity, planning, facilitating, and evaluating. Third, the environment can be a learning resource using tools and materials from the child's environment. It was concluded that Loose Part Based STEAM Learning with East Manggarai Local Wisdom can stimulate young children.

Authors

Institutions

Publication Details

Journal
Journal of Education Action Research
Published
2024-09-20
DOI
https://doi.org/10.23887/jear.v8i3.84221
Citations
1
Primary Topic
Educational Curriculum and Learning Methods
Type
article
Field-Weighted Citation Impact
2.10
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Loose Part Based STEAM Learning Using Local Wisdom of East Manggarai

Ketut Indra Purnomo, Nice Maylani Asril, Mathilde Muhu
1 citations
Journal of Education Action Research
Educational Curriculum and Learning Methods
2.10
article

Loose Part Based STEAM Learning Using Local Wisdom of East Manggarai

Ketut Indra Purnomo, Nice Maylani Asril, Mathilde Muhu
article en
1 citations

Abstract

Learning activities for early childhood that could be more optimal impact children's development, causing them to be less stimulated. Based on this, this research aims to analyze Loose Part Based STEAM Learning with East Manggarai Local Wisdom in early childhood. This type of research is qualitative research. The research method used is the case study method. The subjects in this research were 20 class B students. Data collection methods in this research are interviews, questionnaires, and documentation. The instruments used to collect data were observation guidelines and interviews. The research data analysis technique is a qualitative descriptive analysis using the Miles and Huberman Model data analysis. The research results show that first, loose part-based STEAM learning with East Manggarai local wisdom is simple, meaning that the activities implemented are how to introduce children to the surrounding culture and get them used to loving local culture. The two teachers design learning activities containing elements of local wisdom according to the theme, sub-theme of the activity, planning, facilitating, and evaluating. Third, the environment can be a learning resource using tools and materials from the child's environment. It was concluded that Loose Part Based STEAM Learning with East Manggarai Local Wisdom can stimulate young children.

Journal of Education Action ResearchVol. 8(3)
Universitas Pendidikan Ganesha (ID)
Openalex Percentile: Top 9%
Educational Curriculum and Learning Methods
2.10
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.