Analisis Tingkat Penerimaan Pembelajaran berbasis MOOC dengan Pendekatan Extended UTAUT

Authors

  • Andika Isma Universitas Negeri Makassar
  • Sitti Hajerah Hasyim Universitas Negeri Makassar
  • Muhammad Fikri Aqil Universitas Negeri Makassar
  • Ananta Tri Mahardika Universitas Negeri Makassar
  • Dimas Prayoga Universitas Negeri Makassar

DOI:

https://doi.org/10.61255/jupiter.v2i2.208

Keywords:

MOOCs (Massive Open Online Courses), Penerimaan MOOCs, Model UTAUT

Abstract

Dalam konteks pendidikan, akses dan keterlibatan menjadi kendala signifikan. Solusi yang muncul adalah Massive Open Online Courses (MOOCs), platform kursus daring terbuka yang menawarkan akses gratis dan fleksibilitas melalui teknologi informasi dan komunikasi (TIK). Penelitian ini bertujuan untuk menyelidiki penerimaan MOOCs, berfokus pada model UTAUT yang dimodifikasi, serta memahami sikap dan persepsi pengguna terhadap MOOCs dalam konteks pendidikan. Dengan menggunakan desain penelitian cross-sectional, data dikumpulkan melalui penggunaan kuesioner. Hasil analisis statistik deskriptif menunjukkan preferensi pengguna terhadap pendekatan pembelajaran yang ditawarkan oleh MOOCs daripada fokus pada kemudahan teknis penggunaannya. Mayoritas responden cenderung melihat MOOCs sebagai alat efektif dalam pendidikan. Studi ini menyoroti kecenderungan positif terhadap penggunaan MOOCs dalam meningkatkan hasil akademis. Tujuan utama penelitian ini adalah untuk menyelidiki penerimaan dan persepsi pengguna terhadap MOOCs dalam konteks pendidikan, serta untuk memahami preferensi pengguna terhadap model pembelajaran yang ditawarkan oleh MOOCs.

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References

Al-azazi, F. A., & Ghurab, M. (2023). ANN-LSTM: A deep learning model for early student performance prediction in MOOC. Heliyon, 9(4), 15382. https://doi.org/10.1016/j.heliyon.2023.e15382

Alharbi, A. H. (2023). Investigating the acceptance and use of massive open online courses (MOOCs) for health informatics education. BMC Medical Education, 23(1), 656. https://doi.org/10.1186/s12909-023-04648-9

Asli, M. F., Hamzah, M., Ibrahim, A. A. A., & Ayub, E. (2020). Problem characterization for visual analytics in MOOC learner’s support monitoring: A case of Malaysian MOOC. Heliyon, 6(12), 5733. https://doi.org/10.1016/j.heliyon.2020.e05733

Bäuerle, A., Frewer, A. L., Rentrop, V., Schüren, L. C., Niedergethmann, M., Lortz, J., Skoda, E. M., & Teufel, M. (2022). Determinants of Acceptance of Weight Management Applications in Overweight and Obese Individuals: Using an Extended Unified Theory of

Acceptance and Use of Technology Model. Nutrients, 14(9). https://doi.org/10.3390/nu14091968

Bettiol, S., Psereckis, R., & MacIntyre, K. (2022). A perspective of massive open online courses (MOOCs) and public health. Frontiers in Public Health, 10, 1058383. https://doi.org/10.3389/fpubh.2022.1058383

Chen, B., Fan, Y., Zhang, G., Liu, M., & Wang, Q. (2020). Teachers’ networked professional learning with MOOCs. PLoS ONE, 15(7), 235170. https://doi.org/10.1371/journal.pone.0235170

Despujol, I., Castañeda, L., Marín, V. I., & Turró, C. (2022). What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review. International Journal of Educational Technology in Higher Education, 19(1), 53. https://doi.org/10.1186/s41239-022-00359-1

Dong, L., Ji, T., & Zhang, J. (2023). Motivational Understanding of MOOC Learning: The Impacts of Technology Fit and Subjective Norms. Behavioral Sciences, 13(2), 98. https://doi.org/10.3390/bs13020098

Fakhri, M. M., Ahmar, A. S., Isma, A., & Fadhilatunisa, D. (2024). Exploring Generative AI Tools Frequency: Impacts on Attitude, Satisfaction, and Competency in Achieving Higher Education Learning Goals. EduLine: Journal of Education and Learning Innovation, 4(1).

Fakhri, M. M., Fadhilatunisa, D., Rosidah, R., Fajar B, M., Satnur, M. A., & Fajrin, F. (2022). Pengaruh Media E-Learning Berbasis LMS Moodle dan Motivasi Belajar terhadap Hasil Belajar Mahasiswa di Masa Pandemi Covid-19. Chemistry Education Review (CER), 5(2), 157. https://doi.org/10.26858/cer.v5i2.32724

Fianu, E., Blewett, C., Ampong, G. O. A., & Ofori, K. S. (2018). Factors affecting MOOC usage by students in selected Ghanaian universities. Education Sciences, 8(2), 70. https://doi.org/10.3390/educsci8020070

Kumar, N., Hossain, M. Y., Jin, Y., Safeer, A. A., & Chen, T. (2021). Impact of Performance Lower Than Expectations on Work Behaviors: The Moderating Effect of Status Mutability and Mediating Role of Regulatory Focus. Psychology Research and Behavior Management, 14, 2257–2270. https://doi.org/10.2147/PRBM.S342562

Li, Y., & Zhao, M. (2021). A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect. Frontiers in Psychology, 12, 528259. https://doi.org/10.3389/fpsyg.2021.528259

Lund, B., & Wang, T. (2023). Chatting about chatgpt: how may ai and gpt impact academia and libraries? Library Hi Tech News, 40(3), 26–29. https://doi.org/10.1108/lhtn-01-2023-0009

Martín-Valero, R., Pastora-Bernal, J. M., Ortiz-Ortigosa, L., Casuso-Holgado, M. J., Pérez-Cabezas, V., & Ruiz-Párraga, G. T. (2021). The usefulness of a massive open online course about postural and technological adaptations to enhance academic performance and empathy in health sciences undergraduates. International Journal of Environmental Research and Public Health, 18(20), 10672. https://doi.org/10.3390/ijerph182010672

Patiño-Toro, O. N., Valencia-Arias, A., Fernández-Toro, A., Jiménez-Guzmán, A., & Puerta Gil, C. A. (2023). Proposed methodology for designing and developing MOOCs for the deaf community. Heliyon, 9(10), 20456. https://doi.org/10.1016/j.heliyon.2023.e20456

Ross, B., Penkunas, M. J., Maher, D., Certain, E., & Launois, P. (2022). Evaluating results of the implementation research MOOC using Kirkpatrick’s four-level model: a cross-sectional mixed-methods study. BMJ Open, 12(5), 54719. https://doi.org/10.1136/bmjopen-2021-054719

Tegos, S., Mavridis, A., & Demetriadis, S. (2021). Agent-Supported Peer Collaboration in MOOCs. Frontiers in Artificial Intelligence, 4,

https://doi.org/10.3389/frai.2021.710856

Tian, Y., Sun, Y., Zhang, L., & Qi, W. (2022). Research on MOOC Teaching Mode in Higher Education Based on Deep Learning. Computational Intelligence and Neuroscience, 2022, 1–10,. https://doi.org/10.1155/2022/8031602

Wu, C., Li, J., Zhang, Y., Lan, C., Zhou, K., Wang, Y., Lu, L., & Ding, X. (2021). Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs. Frontiers in Psychology, 12, 751492. https://doi.org/10.3389/fpsyg.2021.751492

Xu, D. (2022). Exploration on the Application Path of College English MOOCS Teaching under the Background of Internet of Things. Computational Intelligence and Neuroscience, 2022, 1–9,. https://doi.org/10.1155/2022/4572432

Yuni Kasmawati, Kastika Putri, P., & Kusumaningsih, D. (2022). Peran Integrasi Model UTAUT dan TFF untuk Kepuasan Pengguna E-Learning. In Jurnal Ekobistek (pp. 215–220). https://doi.org/10.35134/ekobistek.v11i3.352

Zeb, A., . H., Ali, M., Baig, R., & Rahman, S. (2019). Pre-Operative Anxiety in Patients at Tertiary Care Hospital Peshawar Pakistan. South Asian Research Journal of Nursing and Healthcare, 01(01), 26–30. https://doi.org/10.36346/sarjnhc.2019.v01i01.004

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Published

2024-05-15

How to Cite

Andika Isma, Sitti Hajerah Hasyim, Muhammad Fikri Aqil, Ananta Tri Mahardika, & Dimas Prayoga. (2024). Analisis Tingkat Penerimaan Pembelajaran berbasis MOOC dengan Pendekatan Extended UTAUT. Jurnal Pendidikan Terapan, 2(2), 117–132. https://doi.org/10.61255/jupiter.v2i2.208

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