Comprehensive mentoring in developing Deep Learning-based instructional materials for public junior high school mathematics teachers

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Teachers of the MGMP Mathematics at Public Junior High Schools in the Western Region of Jember Regency experience difficulties in developing Deep Learning-based instructional materials. This mentoring program aims to describe teachers’ challenges, formulate mentoring strategies, and explain the outcomes of the implemented strategies. The community service method involved preparation, planning, coordination, implementation, reflection, evaluation, and follow-up. Data were collected through interviews, observations, questionnaires, and documentation. Data analysis employed Miles and Huberman model, including comprising data reduction, data display, conclusion drawing and verification, assisted by NVivo 12 software. The primary challenges involved adapting to changes in Merdeka Curriculum instructional materials and the limited availability of continuous mentoring. Mentoring strategy based on participatory action research was implemented over three meetings. The results demonstrate improvements in teachers’ understanding and skills in developing in-depth learning plans, ability to utilize AI technology, strengthened collaboration and commitment among teachers to implement in-depth learning plans, and the establishment of mentor schools for further mentoring. Thus, this mentoring program plays a role in supporting SDG 4 (Quality Education) by enhancing teachers’ professional competencies and implementing a sustainable mentoring model to support the implementation of the Merdeka Curriculum through a Deep Learning approach.