Social science education based on local wisdom in forming the character of students
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.22Keywords:
Project-based learning, Local wisdom, Social science subjects, Critical thinking, Self-efficacy.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This research was conducted to determine the impact of project-based learning with local wisdom in teaching social science subjects to increase critical thinking ability moderated by students’ self-efficacy. This experimental research employed a quantitative approach utilizing a probability sampling technique with a clustered sampling method to select particular groups within a population. Thus, as a sample, class XI IPS 2 was chosen as a control group and XI IPS 3 as an experimental one. The test results showed that each of the instruments was valid and reliable and met the classical assumptions. The indicated that project-based learning with local wisdom moderated with good self-efficacy can improve critical thinking ability. The integration of project-based learning with local wisdom into learning is necessary so that the methods applied by teachers not only focus on academic results but also inculcate the values of local wisdom. Therefore, it would be better if teachers at every level could integrate an approach to learning that incorporates local wisdomAbstract
How to Cite
Downloads
Similar Articles
- Shaik Abdulla P., Abdul Razak T., Retrieval-Based Inception V3-Net Algorithm and Invariant Data Classification using Enhanced Deep Belief Networks for Content-Based Image Retrieval , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- G. Deena, K. Raja, M. Azhagiri, W.A. Breen, S. Prema, Application of support vector classifier for mango leaf disease classification , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Shaik Khaleel Ahamed, Neerav Nishant, Ayyakkannu Selvaraj, Nisarg Gandhewar, Srithar A, K.K.Baseer, Investigating privacy-preserving machine learning for healthcare data sharing through federated learning , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Thangatharani T, M. Subalakshmi, Development of an adaptive machine learning framework for real-time anomaly detection in cybersecurity , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- S. TAMIL FATHIMA, K. FATHIMA BIBI, Early diagnosis of cardiac disease using Xgboost ensemble voting-based feature selection, based lightweight recurrent neural network approach , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- V Vijayaraj, M. Balamurugan, Monisha Oberai, Machine learning approaches to identify the data types in big data environment: An overview , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- V. Umadevi, S. Ranganathan, IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Indrani Sengupta, Merilyn Gomes, Unveiling the divide: Analyzing critical thinking skills in literature and commerce students , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- T. Ramyaveni, V. Maniraj, Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
