Quantum learning model based on differentiated learning in classical guidance service to improve understanding of social media ethics in students

  • Aditiono W
  • Nurajizah N
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Abstract

This research aims to compile a concept of a differentiated learning-based quantum learning model in classical guidance service to improve understanding of social media ethics. Social media use is now at an alarming threshold when looking at the consequences it has. This problem arises from social media, which often grabs public attention and even becomes a national issue and a hot discussion among all people in Indonesia. So, guidance and counseling services, especially classical guidance services with a differentiated learning-based quantum learning model, are expected to increase students' understanding and prevent negative impacts due to social media. The data collection technique used by researchers is a documentation technique in which researchers trace material about the variables raised in the form of notes, articles, journals, proceedings and other research results. The data analysis technique used in this study is content analysis. This technique is used to learn the nature of objects by describing books, texts, essays, articles and all types of discourse that can be analyzed (reduction process, presentation, and conclusion). The implementation of quantum learning based on differentiated learning in classical guidance service to increase the understanding of social media ethics in students is carried out through 6 stages Among them are: 1) enroll; 2) experience; 3) label; 4) demonstrate; 5) review; and 6) celebrate. This research is expected to be a recommendation for guidance teachers to carry out guidance and counseling services, especially classical guidance services and group guidance.

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APA

Aditiono, W., & Nurajizah, N. (2024). Quantum learning model based on differentiated learning in classical guidance service to improve understanding of social media ethics in students. Journal of Professional Teacher Education, 2(1), 38–47. https://doi.org/10.12928/jprotect.v2i1.778

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