Science Problem-Solving Dynamics: Gender and Personality Type Analysis in Students

This study examines how problem-solving patterns in science among 9th-grade students correlate with different personality types based on Keirsey's classification: Idealist, Artisan, Guardian, and Rational. It employs the Investigation of Lived Experience methodology in Cognitive Psychology to delve into the internal experiences of 36 students from SMP Negeri 5 Bandung. The research explores gender distribution, school representation, and the prevalence of each personality type among the participants. Data collection methods include adapted questionnaires, written tests on earth and space science concepts, and follow-up interviews. These instruments are analyzed using the fixed comparison method to identify and understand distinct problem-solving tendencies associated with each personality type. Specifically, Idealists are found to prioritize understanding, Artisans employ intuitive-analogical approaches, Guardians rely on intuitive hypotheses, and rationalists utilize analogical reasoning. Overall, this study provides detailed insights into how personality types influence problem-solving strategies in science education. It suggests implications for personalized educational approaches tailored to individual student preferences and tendencies, aiming to enhance learning outcomes in 9th-grade science classrooms.

Authors

Institutions

Publication Details

Journal
Tarbiyah Jurnal Ilmiah Kependidikan
Published
2024-12-23
DOI
https://doi.org/10.18592/tarbiyah.v13i2.13513
Primary Topic
Education and Learning Interventions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Science Problem-Solving Dynamics: Gender and Personality Type Analysis in Students

Adi Rahmat, Nanang Winarno, Rizky Agassy Sihombing, Yanti Hamdiyati et al.
Tarbiyah Jurnal Ilmiah Kependidikan
Education and Learning Interventions
article

Science Problem-Solving Dynamics: Gender and Personality Type Analysis in Students

Adi Rahmat, Nanang Winarno, Rizky Agassy Sihombing, Yanti Hamdiyati, Amir Karimi, Naufal Rabah Wahidin
article en

Abstract

This study examines how problem-solving patterns in science among 9th-grade students correlate with different personality types based on Keirsey's classification: Idealist, Artisan, Guardian, and Rational. It employs the Investigation of Lived Experience methodology in Cognitive Psychology to delve into the internal experiences of 36 students from SMP Negeri 5 Bandung. The research explores gender distribution, school representation, and the prevalence of each personality type among the participants. Data collection methods include adapted questionnaires, written tests on earth and space science concepts, and follow-up interviews. These instruments are analyzed using the fixed comparison method to identify and understand distinct problem-solving tendencies associated with each personality type. Specifically, Idealists are found to prioritize understanding, Artisans employ intuitive-analogical approaches, Guardians rely on intuitive hypotheses, and rationalists utilize analogical reasoning. Overall, this study provides detailed insights into how personality types influence problem-solving strategies in science education. It suggests implications for personalized educational approaches tailored to individual student preferences and tendencies, aiming to enhance learning outcomes in 9th-grade science classrooms.

Tarbiyah Jurnal Ilmiah KependidikanVol. 13(2)
Indonesia University of Education (ID)
Openalex Percentile: Top 57%
Education and Learning Interventions
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.