INVESTIGATING UNIVERSITY STUDENT’S ACCEPTANCE OF VIRTUAL AND REMOTE LABS IN THEIR LEARNING

  • Nanda Eska Anugrah Nasution IAIN Jember, Indonesia. SCOPUS ID: 57205353128
    (ID)
  • Chairany Rizka UIN Kiai Haji Achmad Siddiq Jember
    (ID)
Keywords: Flow theory, Pre-Service Teachers, Technology Acceptance Model, University Student, Virtual and Remote Laboratories

Abstract

With the advancement of information and technology, virtual and remote laboratories have become supplementary or extra tools for hands-on biology laboratories. In this study, we modified the technology acceptance model to incorporate three additional external variables derived from flow theory in predicting students' acceptance and use of virtual and remote laboratories. This research included 145 college students. These students used virtual and remote laboratories for at least three months. The learning subjects in this research are deoxyribonucleic acid extraction, polymerase chain reaction, gel electrophoresis, deoxyribonucleic acid microarray, and flow cytometry. Using SPSS 25.0, a multiple regression analysis was performed to test the structural model hypothesis. This study validated the association between the basic variables used in the technology acceptance model: perceived ease of use, perceived usefulness, attitudes toward using, behavioral intention, and actual use. There were no surprising discoveries for the technology acceptance model's primary variables. Concentration and perceived enjoyment in the flow theory variables have an extensive relationship with the technology acceptance model variables, perceived usefulness, and perceived ease of use. Meanwhile, one flow theory variable, time distortion, exhibits no significant relationship with perceived usefulness or ease of use.

Abstrak:

Laboratorium virtual dan jarak jauh menjadi tren yang dimanfaatkan sebagai alat bantu praktikum biologi. Kami memodifikasi model penerimaan teknologi dalam penelitian ini dengan memasukkan tiga variabel eksternal tambahan yang berasal dari teori flow dalam memprediksi bagaimana mahasiswa menerima dan menggunakan laboratorium virtual dan jarak jauh. Penelitian melibatkan 145 mahasiswa. Para mahasiswa ini telah menggunakan laboratorium virtual dan jarak jauh setidaknya tiga bulan. Materi pembelajaran penelitian ini adalah ekstraksi asam deoksiribonukleat (DNA), polymerase chain reaction (PCR), gel electrophoresis, deoxyribonucleic acid microarray, dan flow cytometry. Hubungan antara variabel dasar yang digunakan dalam technology acceptance model yaitu kemudahan penggunaan yang dirasakan (perceived ease of use), kebergunaan yang dirasakan (perceived usefulness), sikap (attitudes toward using), niat perilaku (behavioral intention), dan penggunaan sebenarnya (actual use) divalidasi dalam penelitian ini. Data yang terkumpul dianalisis regresi berganda dengan bantuan SPSS 25. Tidak ada penemuan mengejutkan untuk variabel utama technology acceptance model. Variabel konsentrasi (concentration) dan kesenangan yang dirasakan (perceived enjoyment) pada teori flow memiliki hubungan yang signifikan dengan variabel technology acceptance model, kebergunaan yang dirasakan dan kemudahan penggunaan yang dirasakan. Sedangkan satu variabel teori flow, distorsi waktu (time distortion) tidak menunjukkan hubungan yang signifikan dengan kebergunaan yang dirasakan atau kemudahan penggunaan yang dirasakan.

Downloads

Download data is not yet available.

References

Abdulwahed, M., & Nagy, Z. K. (2009). The impact of the virtual laboratory on the hands-on laboratory learning outcomes, a two years’ empirical study. 20th Australasian Association for Engineering Education Conference. https://aaee.net.au/wp-content/uploads/2018/10/AAEE2009-Abdulwahed_Nagy-Virtual_lab_impact_on_the_hands_on_lab_learning_outcomes.pdf.

Adwan, A. A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. International Journal of Education and Development Using Information and Communication Technology, 9(2), 4–18. https://files.eric.ed.gov/fulltext/EJ1071365.pdf.

Al-Assaf, N., Almarabeh, T., & Eddin, L. (2015). A study on the impact of learning management system on students of the university of Jordan. Journal of Sosftware Engineering and Applications, 8(590–601). https://doi.org/10.4236/jsea.2015.811056.

Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155. https://doi.org/10.14569/IJACSA.2014.050120.

Almarabeh, T., Mohammad, H., Yousef., & Majdalawi, Y, K. (2014). The University of Jordan E-Learning Platform: State, Students’ Acceptance and Challenges. Journal of Software Engineering and Applications, 999–1007. https://doi.org/10.4236/jsea.2014.712087.

Ambuisaidi, A., Musawi, A. A., Al-Balushi, S., & Al-Balushi, K. (2018). The Impact of Virtual Lab Learning Experiences on 9th Grade Students’ Achievement and Their Attitudes Towards Science and Learning by Virtual Lab. Journal of Turkish Science Education, 15(2), 13–29. https://doi.org/10.12973/tused.10227a.

Babateen, H. M. (2011). The role of virtual laboratories in science education. IACSIT press.

Barbeau, M. L., Johnson, M., Gibson, C., & Rogers, K. A. (2013). The development and assessment of an online microscopic anatomy laboratory course. Anatomical Sciences Education, 6(4), 246–256.

https://doi.org/10.1002/ase.1347.

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921.

Birt, J., Stromberga, Z., Cowling, M., & Moro, C. (2018). Mobile mixed reality for experiential learning and simulation in medical and health sciences education. Information (Switzerland), 9(2), 1–14. https://doi.org/10.3390/info9020031.

Buil, I., Catalán, S., & Martínez, E. (2018). Exploring students’ flow experiences in business simulation games. Journal of Computer Assisted Learning, 34(2), 183–192. https://doi.org/10.1111/jcal.12237.

C, T. (2010). The effect of the virtual laboratory on students’ achievement and attitude in Chemistry. International Online Journal of Educational Sciences, 2(1), 37–53. https://iojes.net/?mod=tammetin&makaleadi=&makaleurl=IOJES_167.pdf&key=41381.

Chang, C. C., Warden, C. A., Liang, C., & Lin, G. Y. (2018). Effects of digital game-based learning on achievement, flow and overall cognitive load. Australasian Journal of Educational Technology, 34(4), 155–167. https://doi.org/10.14742/ajet.2961.

Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28, 809–826. https://doi.org/10.14742/ajet.818.

Chao, J., Chiu, J. L., DeJaegher, C. J., & Pan, E. A. (2016). Sensor-augmented virtual labs: Using physical interactions with science simulations to promote understanding of gas behavior. Journal of Science Education and Technology, 25(1), 16–33. https://doi.org/10.1007/s10956-015-9574-4.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. M.I.S. Quarterly, 19(2). https://doi.org/10.2307/249688.

Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life. Basic Books.

Csikszentmihalyi, M. (2014). Applications of flow in human development and education: The collected work of Mihaly Csikszentmihalyi. Springer.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.

Davis, F. D., Bagozzi, R. ., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x.

Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology, 14(5), 1–16. https://doi.org/10.1186/s41239-017-0045-2.

Essel, D. D., & Wilson, O. A. (2017). Factors Affecting University Students’ Use of Moodle: An Empirical Study Based on TAM. International Journal of Information and Communication Technology Education, 13(1), 14–26. https://doi.org/10.4018/IJICTE.2017010102.

Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students’ flow experiences in an online learning environmentModelling students’ flow experiences in an online learning environment. Computers & Education, 71. https://doi.org/10.1016/j.compedu.2013.09.012.

Falode, O. C. (2018). Pre-service Teachers’ Perceived Ease of Use, Perceived Usefulness, Attitude and Intentions Towards Virtual Laboratory Package Utilization in Teaching and Learning of Physics. Malaysian Online Journal of Educational Technology, 6(3). https://files.eric.ed.gov/fulltext/EJ1184206.pdf.

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (L.M.S.s) In Higher Education Institutions. Journal of Online Learning and Teaching, 11(2), 210–232. https://jolt.merlot.org/Vol11no2/Fathema_0615.pdf.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA, Addison Wesley.

Freina, L., & Ott, M. (2015). A literature review on immersive virtual reality in education: state of the art and perspectives. In The International Scientific Conference Elearning and Software for Education, 1(333). https://doi.org/10.12753/2066-026X-15-020.

Ghani, J., Supnick, R., & Rooney, P. (1991). The Experience of Flow in Computer-Mediated and in Face-to-Face Groups. Proceedings of the Twelfth International Conference on Information Systems. https://core.ac.uk/download/pdf/301364008.pdf.

Gunstone, R. F. (1991). Reconstructing theory from practical experience. Practical Science.

Harahap, F., Nasution, N. E. A., & Manurung, B. (2019). The Effect of Blended Learning on Student’s Learning Achievement and Science Process Skills in Plant Tissue Culture Course. International Journal of Instruction, 12(1), 521–538. https://doi.org/10.29333/iji.2019.12134a.

Hasanah, N. U., Farihah, U., & Nasution, N. E. A. (2022). The Effect of Interactive Multimedia Adobe Flash Professional CS6 on Student Learning Outcomes of Excretion System Material Based on The Revised Bloom Taxonomy. Proceeding Cgant Unej. https://proceedingcgantunej.or.id/index.php/proceedingcgant/article/view/10.

Hawkins, I., & Phelps, A. J. (2013). Virtual laboratory vs. traditional laboratory: Which is more effective for teaching electrochemistry?. Chemistry Education Research and Practice, 14(4). https://doi.org/10.1039/C3RP00070B.

Kirschner, P. (1988). The Laboratory in Higher Science Education, Problems, Premises, and Objectives. High Educ, 17, 81–98. https://doi.org/10.1007/BF00130901.

Koretsky, M., Kelly, C., & Gummer, E. (2011). Students’ perceptions of learning in the laboratory: comparison of industrially situated virtual laboratories to capstone physical laboratories. Journal of Engineering Education, 100(540–573). https://doi.org/10.1002/j.2168-9830.2011.tb00026.x.

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83.

Lang, J. (2012). Comparative study of hands-on and remote physics labs for first year university level physics students. Transformative Dialogues: Teaching & Learning Journal, 6(1), 1–25. https://journals.kpu.ca/index.php/td/article/view/1363/817.

Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 501–516. https://doi.org/10.1016/j.compedu.2009.09.002.

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(2), 1095–1104. https://doi.org/10.1016/j.im.2003.10.007.

Li, D., & Browne, G. J. (2006). The role of need for cognition and mood in online flow experience. Journal of Computer Information Systems, 46(1), 11–17. https://doi.org/10.1080/08874417.2006.11645894.

Liao, S., Hong, J.-C., Wen, M.-H., Pan, Y.-C., & Wu, Y. (2018). Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production. Eurasia Journal of Mathematics, Science and Technology Education, 14(10), 1–12. https://doi.org/10.29333/ejmste/93575.

Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. A. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers and Education, 54, 600–610. https://doi.org/10.1016/j.compedu.2009.09.009.

Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers and Education, 54. https://doi.org/10.1016/j.compedu.2008.11.002.

Mahfouz, A. Y., Joonas, K., & Opara, E. U. (2020). An overview of and factor analytic approach to flow theory in online contexts. Technology in Society, 61. https://doi.org/10.1016/j.techsoc.2020.101228.

Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60(225–236). https://doi.org/10.1016/j.learninstruc.2017.12.007.

Moakofhi, K. M., Phiri, T. V., Leteane, O., & Bangomwa, E. (2019). Using Technology Acceptance Model to Predict Lecturers’ Acceptance of Moodle: Case of Botswana University of Agriculture and Natural Resources. Literacy Information and Computer Education Journal, 10(1). https://doi.org/10.20533/licej.2040.2589.2019.0407.

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6.

Moro, C., Stromberga, Z., & Stirling, A. (2017). Virtualisation devices for student learning: Comparison between desktop-based (Oculus Rift) and mobile-based (Gear VR) virtual reality in medical and health science education. Australasian Journal of Educational Technology, 33(6), 1–10. https://doi.org/10.14742/ajet.3840.

Nasution, N. E. A. (2023). Using artificial intelligence to create biology multiple choice questions for higher education. Agricultural and Environmental Education, 2(1), 1–11. https://doi.org/10.29333/agrenvedu/13071.

Pramono, S. E., Prajanti, S. D. W., & Wibawanto, W. (2019). Virtual Laboratory for Elementary Students. International Conference on Education. Science and Technology. https://doi.org/10.1088/1742-6596/1387/1/012113.

Pyatt, K., & Sims, R. (2012). Virtual and physical experimentation in inquiry-based science labs: Attitudes, performance and access. Journal of Science Education and Technology, 21(1), 133–147. https://doi.org/10.1007/s10956-011-9291-6Q.

Rai, R. S., & Selnes, F. (2019). Conceptualizing task-technology fit and the effect on adoption – A casestudy of a digital textbook service. Information & Management, 56(8), 1–10. https://doi.org/10.1016/j.im.2019.04.004.

Rauniar, R., Rawski, Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30. https://doi.org/10.1108/JEIM-04-2012-0011.

Romdaniyah, S., Nasution, N. E. A., & Rizka, C. (2023). Analysis of Biology Learning Planning on Plant Tissue Course in the Independent Learning Activity Unit (UKBM) based on Scientific Approach Class XI MIPA 5 at MAN Sumenep. META: Journal of Science and Technological Education, 2(2). https://meta.amiin.or.id/index.php/meta/article/view/57.

Sun, K., Lin, Y., & Yu, C. (2008). A study on learning effect among different learning styles in a web-based lab of science for elementary school students. Computers & Education, 50. https://doi.org/10.1016/j.compedu.2007.01.003.

Syahfitri, F. D., Manurung, B., & Sudibyo, M. (2019). The Development of Problem Based Virtual Laboratory Media to Improve Science Process Skills of Students in Biology. International Journal of Research & Review, 6(6). https://www.ijrrjournal.com/IJRR_Vol.6_Issue.6_June2019/IJRR0012.pdf.

Tatli, Z., & Ayas, A. (2013). Effect of a virtual chemistry laboratory on students’ achievement. Educational Technology & Society, 16(1), 159–170. http://www.ifets.info/journals/16_1/14.pdf.

Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: a structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(253–262). https://doi.org/10.1007/s12564-009-9066-4.

Teo, T. Sang, G., Mei, B., & Hoi, C. K. W. (2018). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4). https://doi.org/10.1080/10494820.2018.1489290.

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that infuence university students’ intention to use Moodle: a study in MacAU. Education Tech Research Dev, 67, 749–766. https://doi.org/10.1007/s11423-019-09650-x.

Yasa, N. N., Ratnaningrum, L. P., & Sukaatmaja, P. G. (2014). The Aplication of Technology Acceptance Model on Internet Banking Users in the City of Denpasar. Manajemen Dan Kewirausahaan, 16(2), 93–102. https://doi.org/10.9744/jmk.16.2.93-102.

Zain, F. M., Hanafi, E., Don, Y., Yaakob, M. F. M., & Sailin, S. N. (2019). Investigating Student’s Acceptance of an EDMODO Content Management System. International Journal of Instruction, 12(4), 1–16. https://doi.org/10.29333/iji.2019.1241a.

Zhang, Y., & Wang, F. (2022). Developments and trends in flow research over 40 years: A bibliometric analysis. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/scuwf.

Zhao, J., & Wang, J. (2020). Health Advertising on Short-Video Social Media: A Study on User Attitudes Based on the Extended Technology Acceptance Model. International Journal of Environmental Research and Public Health, 17. https://doi.org/10.3390/ijerph17051501.

Ziraba, A., Akwene, G. C., Nkea, A. N. A. M., & Lwanga, S. C. (2020). The Adoption and use of Moodle Learning Management System in Higher Institutions of Learning: a Systematic Literature Review. American Journal of Online and Distance Learning, 2(1), 1–21. https://doi.org/10.47672/ajodl.489.

Published
2024-06-23
How to Cite
Nasution, N. E. A., & Rizka, C. (2024). INVESTIGATING UNIVERSITY STUDENT’S ACCEPTANCE OF VIRTUAL AND REMOTE LABS IN THEIR LEARNING. Lentera Pendidikan : Jurnal Ilmu Tarbiyah Dan Keguruan, 27(1), 47-62. https://doi.org/10.24252/lp.2024v27n1i4
Section
Article
Abstract viewed = 198 times