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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

Intelligent Evaluation Model for Determining Multiple Intelligence Profiles of Junior High Students Through Digital Likert Scale Architecture

Intelligent Evaluation Model for Determining Multiple Intelligence Profiles of Junior High Students Through Digital Likert Scale Architecture

Authors

  • Dr. Nuwan Chamara Perera
    Department of Applied Sciences and Technology, Sri Lankan Institute of Technological Studies, Colombo, Sri Lanka
  • Ms. Tharushi Madushika Fernando
    Faculty of Technology and Applied Research, National Centre for Scientific Development, Kandy, Sri Lanka

Abstract

The identification of students’ multiple intelligence profiles has become an important component in developing personalized learning strategies, particularly at the junior high school level where students demonstrate diverse cognitive abilities, learning preferences, and academic potentials. Conventional evaluation approaches frequently emphasize limited academic indicators and provide insufficient representation of broader intelligence dimensions. This research proposes an Intelligent Evaluation Model for Determining Multiple Intelligence Profiles of Junior High Students Through Digital Likert Scale Architecture. The study conceptualizes a technology-supported assessment framework that integrates multiple intelligence indicators, digital questionnaire mechanisms, and structured evaluation processes using a Likert scale approach. The proposed model focuses on improving the accuracy, efficiency, and accessibility of intelligence profile identification through web-based assessment architecture.

The research adopts a conceptual development approach by analyzing previous studies related to multiple intelligence-based learning materials, digital learning systems, educational assessment models, and technology-supported evaluation methods. The model consists of several functional components, including intelligence indicator formulation, digital data acquisition, response weighting mechanisms, profile classification, and interpretation of evaluation outcomes. The framework is designed to support teachers in understanding student characteristics and enabling more adaptive instructional planning. Previous research regarding contextual learning approaches and guided discovery methods demonstrates the importance of aligning educational strategies with students’ cognitive and emotional characteristics (Agustyarini and Jailani, 2015). Similarly, studies on multiple intelligence-based educational resources highlight the potential of intelligence-oriented approaches for improving student engagement and learning effectiveness (Lestari and Nisa, 2018).

The proposed architecture contributes to educational technology research by providing a systematic approach for transforming qualitative intelligence assessment into a measurable digital evaluation process. The model also provides opportunities for data-driven educational decision-making while acknowledging limitations related to indicator selection, respondent subjectivity, and contextual differences among learners. This research provides a foundation for developing intelligent educational assessment systems that promote personalized learning environments in junior high schools.

References

A. V. D. Lestari and K. Nisa, “Pengembangan Lembar Kerja Siswa Berbasis Multiple Intelligence Pada Materi Enzim Siswa Sma,” Edubiotik J. Pendidikan, Biol. dan Terap., vol. 3, no. 02, pp. 48–57, 2018.

Y. Agustyarini and J. Jailani, “Pengembangan Bahan Ajar Matematika Dengan Pendekatan Kontekstual Dan Metode Penemuan Terbimbing Untuk Meningkatkan Eq Dan Sq Siswa Smp Akselerasi,” J. Ris. Pendidik. Mat., vol. 2, no. 1, p. 135, 2015.

H. Hajering, “Faktor-Faktor Yang Mempengaruhi Tingkat Pemahaman Mahasiswa Pada Mata Kuliah Auditing,” YUME J. Manag., vol. 4, no. 2, pp. 233–246, 2021.

S. G. Jabali, S. Supriyono, and P. Nugraheni, “Pengembangan Media Game Visual Novel Berbasis Etnomatematika Untuk Meningkatkan Pemahaman Konsep Pada Materi Aljabar,” Alifmatika J. Pendidik. dan Pembelajaran Mat., vol. 2, no. 2, pp. 185–198, 2020.

S. N. A. Mufidah and Mukhlisin, “Pembentukan Kecerdasan Linguistik dan Keterampilan Membaca Alquran Siswa Madrasah Tsanawiyah,” J. Pendidik. Islam, vol. 4, no. 1, pp. 59–78, 2020.

H. Purwatiningsih, “Pengaruh Blended Learning Dan Gaya Belajar Terhadap Hasil Belajar IPA,” JTP - J. Teknol. Pendidik., vol. 16, no. 2, pp. 94–104, 2014.

D. M. Putri, B. Putra, and Bahrum, “Analisis Tingkat Kepuasan Belajar Mahasiswa Jurusan Biologi di IAIN Kerinci Selama Pembelajaran Online,” JPDK, vol. 4, no. 1, pp. 1–7, 2022.

H. Rodiawati and K. Komarudin, “Pengembangan E-Learning Melalui Modul Interaktif Berbasis Learning Content Development System,” J. Tatsqif, vol. 16, no. 2, pp. 172–185, 2018.

D. S. Sari and M. N. Wulanda, “Pengembangan lembar kerja mahasiswa berbasis proyek dalam meningkatkan kemampuan berfikir kreatif mahasiswa,” Nat. J. Ilm. Pendidik. IPA, vol. 6, no. 1, p. 20, 2019.

B. Widiyaningsih et al., “Pengembangan Dan Pemanfaatan Multimedia Pembelajaran Matematika Dengan Powtoon Di Masa Pandemi Covid-19,” J. Ekon. dan Tek. Inform., vol. 9, no. 1, pp. 47–57, 2021.

B. Indriyani and R. Munajah, “The Development of Digital Students Worksheets for Students Elementary School Subtheme My Blood Circulation is Healthy,” Bull. Pedagog. Res., vol. 2, no. 2, pp. 1–15, 2022.