Implementasi Team-Based Learning Dalam Pengembangan Pembelajaran Online Berbasis Artificial Intelligence

Authors

  • Bens Pardamean Universitas Bina Nusantara Jakarta
  • Teddy Suparyanto Universitas Bina Nusantara Jakarta
  • Andri Anugrahana Universitas Sanata Dharma
  • Indri Anugraheni Universitas Kristen Satya Wacana
  • Digdo Sudigyo Universitas Bina Nusantara Jakarta

DOI:

https://doi.org/10.24246/j.js.2022.v12.i2.p118-126

Keywords:

Online Learning, Artificial Intelligence, Team Based Learning, MOODLE, Learning Management System, Personalize Learning

Abstract

Online learning is growing along with the development of today's technology. Online learning development will improve the quality of the learning process. The research goal is to improve an online learning portal based on Artificial Intelligence (AI) within the Team-Based Learning (TBL) framework. The benefits of AI in personalizing learning based on how students learn, and even the TBL usage as a learning method, are explored to improve student learning results. The method used in this research is a method for conducting research development. MOODLE as a Learning Management System is being used to expand the online learning portal. In addition, AI technology is developing using algorithms based on collaborative filtering to predict learning styles and evolutionary algorithms to analyze student group growth. There were 262 students from ten elementary schools (SD) in three provinces who participated in this study. The application of the R data analysis program used paired t-test to compare the pre-test and post-test. The findings of this study indicate that AI-based online learning within the TBL framework can significantly improve student learning outcomes.

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Published

2022-05-27

How to Cite

Pardamean, B., Suparyanto, T., Anugrahana, A., Anugraheni, I., & Sudigyo, D. (2022). Implementasi Team-Based Learning Dalam Pengembangan Pembelajaran Online Berbasis Artificial Intelligence. Scholaria: Jurnal Pendidikan Dan Kebudayaan, 12(2), 118–126. https://doi.org/10.24246/j.js.2022.v12.i2.p118-126

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