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Modeling the Determinants of Students’ Satisfaction and Continuance-Intention Toward a Mathematics and Science Distance Education Program

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Listed:
  • Brandford Bervell
  • Paul Nyagorme
  • Justice K. Armah
  • Emmanuel Arthur-Nyarko
  • Valentina Arkorful
  • Benjamin Eduafo Arthur

Abstract

A distance education Diploma and Bachelor’s degree in Mathematics and Science programs are faced with low enrollment and graduation rate as well as high attrition of students. The aforementioned challenges are attributed to students’ satisfaction and continuance intention. However, students’ satisfaction and continuance intention toward distance education programs are as a result of certain antecedents. This modeled the determinants of satisfaction and program continuance intention by the distance education students on the math and science programs. Based on this, a quantitative approach based on a census survey was adopted and data collected from 113 students through a questionnaire and analyzed using a structural equation modeling approach based on partial least squares structural equation modeling (PLS-SEM). The results of the study showed that two key antecedents: student-instructor interaction, and study center physical facilities determine program satisfaction of mathematics and science distance students. However, administrative support of the program determined instructional modules; facilitation quality was predicted by both instructional modules and laboratory facilities. Finally, students program continuance intention was predicted by satisfaction of the program. Based on the findings, recommendations were made for policy and practice toward improving students’ satisfaction and promote positive intention in pursuant of mathematics and science education program by distance mode.

Suggested Citation

  • Brandford Bervell & Paul Nyagorme & Justice K. Armah & Emmanuel Arthur-Nyarko & Valentina Arkorful & Benjamin Eduafo Arthur, 2024. "Modeling the Determinants of Students’ Satisfaction and Continuance-Intention Toward a Mathematics and Science Distance Education Program," SAGE Open, , vol. 14(1), pages 21582440241, March.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:1:p:21582440241234743
    DOI: 10.1177/21582440241234743
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    References listed on IDEAS

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    1. Jian-Hong Ye & Yi-Sang Lee & Chiung-Ling Wang & Weiguaju Nong & Jhen-Ni Ye & Yu Sun, 2023. "The Continuous Use Intention for the Online Learning of Chinese Vocational Students in the Post-Epidemic Era: The Extended Technology Acceptance Model and Expectation Confirmation Theory," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    2. Yiwen Li & Norihiro Nishimura & Hisanori Yagami & Hye-Sook Park, 2021. "An Empirical Study on Online Learners’ Continuance Intentions in China," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    3. Brandford Bervell & Irfan Naufal Umar & Jeya Amantha Kumar & Beatrice Asante Somuah & Valentina Arkorful, 2021. "Blended Learning Acceptance Scale (BLAS) in Distance Higher Education: Toward an Initial Development and Validation," SAGE Open, , vol. 11(3), pages 21582440211, August.
    4. Clara Akuamoah- Boateng & Kankam Boadu, 2013. "Reducing Distance Learners¡¯ Attrition Rate at the University of Cape Coast: Tutors¡¯/Students¡¯ Perception," International Journal of Learning and Development, Macrothink Institute, vol. 3(3), pages 214-229, June.
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