An Analisis Penerimaan Pengguna Quizizz pada SMPN 3 Susukan Banjarnegara dengan Menggunakan Pendekatan Technology Acceptance Model (TAM) yang diperluas
DOI:
https://doi.org/10.52436/1.jpti.1017Kata Kunci:
motivasi akademik, model penerimaan teknologi, Quizizz, SmartPLS, TAM, teknologi pembelajaranAbstrak
Pemanfaatan teknologi dalam pendidikan telah mengalami perkembangan pesat, terutama dalam metode evaluasi pembelajaran. Salah satu platform yang banyak digunakan adalah Quizizz, sebuah aplikasi berbasis gamifikasi yang memungkinkan kuis interaktif secara daring. Quizziz menawarkan berbagai keunggulan seperti fleksibilitas, umpan balik instan, dan pengalaman belajar yang lebih menarik, akan tetapi pengguna masih menghadapi berbagai tantangan adopsi terhadap teknologi. Penelitian ini bertujuan untuk menganalisis penerimaan penggunaan Quizizz dalam pembelajaran menggunakan pendekatan Technology Acceptance Model (TAM). Data dikumpulkan dari 222 responden, yang terdiri dari siswa dan guru yang aktif menggunakan Quizizz dalam pembelajaran. Analisis data dilakukan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan perangkat lunak SmartPLS untuk menguji validitas, reliabilitas, serta hubungan antar variabel dalam model penelitian ini. Hasil analisis kuantitatif menunjukkan bahwa seluruh konstruk dalam model memiliki reliabilitas dan validitas yang sangat baik, dengan nilai Cronbach’s Alpha dan Composite Reliability masing-masing berada di atas 0,70 dan 0,90, serta nilai AVE di atas 0,50, yang menandakan konsistensi internal dan validitas konvergen yang memadai. Hasil uji model struktural menunjukkan bahwa sikap terhadap penggunaan (Attitude Toward Using/AM) memiliki pengaruh paling kuat dan signifikan terhadap niat perilaku (Behavioral Intention/BI) dengan nilai koefisien ? = 0,744 dan p < 0,001. Selain itu, efikasi diri (Self-Efficacy/SE) dan kondisi yang memfasilitasi teknologi (Technology Facilitating Conditions/TF) berpengaruh signifikan terhadap persepsi kemudahan penggunaan (Perceived Ease of Use/PEU), masing-masing dengan ? = 0,340 dan ? = 0,586 (p < 0,001). Kualitas pengetahuan (Knowledge Quality/KQ) berpengaruh positif terhadap persepsi kegunaan (Perceived Usefulness/PU), sementara kualitas informasi (Information Quality/IQ) justru menunjukkan pengaruh negatif yang signifikan terhadap PU. Di sisi lain, hubungan antara PU dan BI, PEU dan BI, serta Social Influence (SI) terhadap BI tidak menunjukkan signifikansi statistik. Hasil ini menunjukkan bahwa penerimaan Quizizz lebih ditentukan oleh faktor personal pengguna, khususnya sikap dan kepercayaan diri, dibandingkan dengan aspek fungsional platform atau dorongan eksternal. Penelitian ini merekomendasikan pengembangan fitur yang lebih ramah pengguna serta optimalisasi pelatihan bagi guru dan siswa untuk memaksimalkan manfaat dari platform ini.
Unduhan
Referensi
E. P. J. KlEynhans, “The value of academic labour; [Die waarde van akademiese arbeid],” Tydskrif vir Geesteswetenskappe, vol. 60, no. 4, 2020.
A. Y?lmaz, “The effect of technology integration in education on prospective teachers’ critical and creative thinking, multidimensional 21st century skills and academic achievements,” Participatory Educational Research, vol. 8, no. 2, pp. 163–199, Apr. 2021, doi: 10.17275/per.21.35.8.2.
K. F. Hew and C. K. Lo, “Flipped classroom improves student learning in health professions education: A meta-analysis,” BMC Med Educ, vol. 18, no. 1, Mar. 2018, doi: 10.1186/s12909-018-1144-z.
B. Ayçiçek and T. Y. Yelken, “Classroom Life Perception Scale: A Scale Development Study1,” International Journal of Instruction, vol. 14, no. 1, pp. 253–264, Jan. 2020, doi: 10.29333/IJI.2021.14115A.
W. Wai Than, E. Mon Kyaw, and H. Zaw Htoo, “A Meta-Analytic Structural Equation Modelling on the Unified Theory of Acceptance and Use of Technology in Higher Education,” International Journal of Educational Management and Development Studies, vol. 2, no. 4, 2020.
P. Puspita Sari, J. Wijaya Kusuma, K. Kunci, M. Gamifikasi, K. Komunikasi Matematik, and M. Belajar, “Application Of Quizizz-Assisted Gamification Model to Students’ Mathematical Communication Skills and Learning Motivation,” Jurnal Derivat, vol. 11, no. 2, 2024.
H. Rokhaniyah, D. Ardiyanti, and N. Hidayat, “Quizizz-online gamification on learning engagement and outcomes in English lecturing process,” International Journal of Evaluation and Research in Education, vol. 14, no. 2, pp. 1408–1416, Apr. 2025, doi: 10.11591/ijere.v14i2.29992.
I. Buri? and L. E. Kim, “Teacher self-efficacy, instructional quality, and student motivational beliefs: An analysis using multilevel structural equation modeling,” Learn Instr, vol. 66, 2020, doi: 10.1016/j.learninstruc.2019.101302.
M. O. Alassafi, “E-learning intention material using TAM: A case study,” Mater Today Proc, vol. 61, pp. 873–877, Jan. 2022, doi: 10.1016/j.matpr.2021.09.457.
Y. Si, B. Wang, and A. Kawczy?ski, “Cooperation and competition enhance implicit sequence learning differently,” Learn Motiv, vol. 89, Feb. 2025, doi: 10.1016/j.lmot.2024.102090.
F. J. Rondan-Cataluña, J. Arenas-Gaitán, and P. E. Ramírez-Correa, “A comparison of the different versions of popular technology acceptance models a non-linear perspective,” Kybernetes, vol. 44, no. 5, pp. 788–805, May 2015, doi: 10.1108/K-09-2014-0184.
I. Maita and S. Majid, “Analisis Penerimaan terhadap Penggunaan E-Learning Menggunakan Metode Technology Acceptance Model (TAM),” Jurnal Sistim Informasi dan Teknologi, pp. 30–35, Mar. 2022, doi: 10.37034/jsisfotek.v4i1.120.
B. Chimbo and L. Motsi, “The Effects of Electronic Health Records on Medical Error Reduction: Extension of the DeLone and McLean Information System Success Model,” JMIR Med Inform, vol. 12, p. e54572, Oct. 2024, doi: 10.2196/54572.
M. Recinos, K. O’Hara, A. Tiwari, D. J. Whitaker, C. Wekerle, and S. Self-Brown, “The use of technology and mobile health apps in child maltreatment interventions: Perspectives of TF-CBT therapists and SafeCare providers,” Child Protection and Practice, vol. 3, p. 100075, Dec. 2024, doi: 10.1016/j.chipro.2024.100075.
W. Peng and K. Robinson-Tay, “Assessing the characteristics and outcomes of perceived usefulness and ease of use for autonomous vehicle adoption,” Transp Res Part F Traffic Psychol Behav, vol. 111, pp. 391–408, May 2025, doi: 10.1016/j.trf.2025.03.014.
R. Watson and T. J. H. Morgan, “An experimental test of epistemic vigilance: Competitive incentives increase dishonesty and reduce social influence,” Cognition, vol. 257, Apr. 2025, doi: 10.1016/j.cognition.2025.106066.
X. F. Reyes Trelles, P. I. Alvarado Cevallos, K. P. Calle Torres, and J. N. Galarza Parra, “Academic procrastination in Ecuadorian university students: An explanatory model based on academic motivation,” Heliyon, vol. 10, no. 24, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40787.
R. B. Ikhsan, Y. Fernando, H. Prabowo, Yuniarty, A. Gui, and E. A. Kuncoro, “An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust,” Digital Business, vol. 5, no. 1, Jun. 2025, doi: 10.1016/j.digbus.2024.100103.
T. Marta, Heri Mulyono, and Irsyadunas, “Analisis Penerimaan Siswa Terhadap Penggunaan Google Classroom Dengan Metode Technology Acceptance Model (TAM),” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 3, no. 1, pp. 30–37, Jan. 2023, doi: 10.51454/decode.v3i1.71.
A. Alsehaimi, A. Waqar, A. abd El Aal, S. Hayat, F. Ahmed Waris, and O. Benjeddou, “Optimising construction sector performance: A study of the rapidly growing global drone industry using smart PLS approach,” Journal of Engineering Research (Kuwait), 2024, doi: 10.1016/j.jer.2024.08.004.
K. James, N. Betty, M. J. Thaddeo, and J. B. Kirabira, “Blood production factors affecting transfusion sustainability: A study by using smart PLS-SEM approach,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 1, Mar. 2024, doi: 10.1016/j.joitmc.2024.100247.
I. Jahan, K. T. Kamal, P. Bhattacharjee, H. Md. Muhtasim Taqi, and Dr. S. Mithun Ali, “Improving Consumer Awareness for reducing Food Waste using Partial Least Squares Structural Equation Modelling (PLS-SEM) approach,” Cleaner and Responsible Consumption, p. 100282, May 2025, doi: 10.1016/j.clrc.2025.100282.
R. Yang and H. Yagi, “Evaluating occupational values in Japan’s urban farming: A comparison between the Likert scale and Best-Worst Scaling methods,” Cities, vol. 155, Dec. 2024, doi: 10.1016/j.cities.2024.105485.
S. Sukendro et al., “Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context,” Heliyon, vol. 6, no. 11, Nov. 2020, doi: 10.1016/j.heliyon.2020.e05410.
T. Teo, X. Fan, and J. Du, “Technology acceptance among pre-service teachers: Does gender matter? Background,” 2015.
W. J. Obidallah et al., “Beyond the hype: A TAM-based analysis of blockchain adoption drivers in construction industry,” Heliyon, vol. 10, no. 19, Oct. 2024, doi: 10.1016/j.heliyon.2024.e38522.
