Work place: Faculty of Education and Teacher Training, Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia
E-mail: lulu@mercubuana-yogya.ac.id
Website: https://orcid.org/0000-0002-7407-7751
Research Interests:
Biography
Lu’luil Maknun, M.Pd. is assistant professor at Universitas Mercu Buana Yogyakarta, Indonesia, and a doctoral student at Universitas Negeri Yogyakarta, Indonesia. Her research area includes English language teaching, language assessment (AaL, AoL, and AfL), technology-enhanced language learning, and student motivation.
By Maulana Mualim Margana Agus Widyantoro Luluil Maknun
DOI: https://doi.org/10.5815/ijmecs.2025.04.03, Pub. Date: 8 Aug. 2025
As the English language and information and communication technology (ICT) enhance global interconnection, demands on educating the young generation with English language skills and technological competence increase exponentially. As the successor of education, pre-service English teachers need to be trained with technological pedagogical content knowledge (TPACK). This study aims to develop an instructional model oriented to pre-service English teachers' (PST) TPACK. This is design-based research carried out in three stages: informed exploration, enactment, and evaluation. This study employed a multiphase mixed method. A qualitative design was done in the informed exploration stage, and an explanatory sequential mixed design was used for the evaluation stage. Nine PSTs, three lecturers, and 4 experts were invited as the participants of this study. The qualitative data were analyzed thematically on NVIVO software while the quantitative data were analyzed using descriptive statistic calculation. The results showed that the PSTs need an instructional model that facilitates student agency, learning agency, self-reliance, innovation, and cooperation. An instructional model called Inquiry-based, technology-saturated, and flipped instructional model (INSTALL) was developed. The expert validation result showed that the products of this development study were in the “Very Good” category. The results of the expert judgment indicated that INSTALL could be utilized to enhance the PSTs’ TPACK by blending inquiry-based learning and technology-saturated flipped instruction.
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