Examining Undergraduate Student Acceptance of Virtual Laboratories in physics classes: An Extended TAM Model
Keywords:
virtual laboratory, technology acceptance model (TAM), e-learning, higher education, smart PLSAbstract
Following the COVID-19 pandemic, virtual laboratories have gained popularity in higher education. It is essential to explore the factors influencing students' acceptance of these virtual labs, particularly in the field of physics where research remains limited. This study proposes a framework based on the Technology Acceptance Model (TAM) to examine the factors of behavioral intention among undergraduate engineering students regarding virtual lab usage. The TAM model was extended by integrating prior experience, perceived enjoyment, facilitating conditions, and information quality. A partial least square structural equation modeling was employed to analyze the survey responses from 80 undergraduate engineering students at South Mediterranean University. Results indicate that students’ behavioral intention is directly influenced by perceived usefulness and indirectly influenced by information quality and perceived enjoyment. The findings of this study provide valuable insights for educators and instructional designers to improve the design and the effectiveness of virtual labs in physics classes.
References
Abdjul, T., & Ntobuo, N. (2018). Developing device of learning based on virtual laboratory through phet simulation for physics lesson with sound material. International Journal of Sciences: Basic and Applied Research (IJSBAR), 39(2), 105-115.
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood cliffs
Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology, 32(11), 1168-1176. https://doi.org/10.1080/0144929X.2013.774047
Almahri, F. A. J., Bell, D., & Merhi, M. (2020, March). Understanding student acceptance and use of chatbots in the United Kingdom universities: a structural equation modelling approach. In 2020 6th International Conference on Information Management (ICIM) (pp. 284-288). IEEE. https://doi.org/10.1109/ICIM49319.2020.244712
Alnagrat, A. J. A., Ahmed, K. M., Alkhallas, M. I., Almakhzoom, O. A. I., Idrus, S. Z. S., & Ismail, R. C. (2023, May). Virtual Laboratory Learning Experience in Engineering: An Extended Technology Acceptance Model (TAM). In 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) (pp. 474-479). IEEE. https://doi.org/10.1109/MI-STA57575.2023.10169123
Alnagrat, A. J. A., Ismail, R. C., & Idrus, S. Z. S. (2021, May). Extended reality (XR) in virtual laboratories: A review of challenges and future training directions. In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012031). IOP Publishing. https://doi.org/10.1088/1742-6596/1874/1/012031
Altalbe, A. A. (2019). Performance impact of simulation-based virtual laboratory on engineering students: A case study of Australia virtual system. Ieee Access, 7, 177387-177396. https://doi.org/10.1109/ACCESS.2019.2957726
Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of personality and social psychology, 41(4), 607. https://psycnet.apa.org/doi/10.1037/0022-3514.41.4.607
Bentler, P. M., & Speckart, G. (1979). Models of attitude–behavior relations. Psychological review, 86(5), 452. https://psycnet.apa.org/doi/10.1037/0033-295X.86.5.452
Bortnik, B., Stozhko, N., Pervukhina, I., Tchernysheva, A., & Belysheva, G. (2017). Effect of virtual analytical chemistry laboratory on enhancing student research skills and practices. Research in Learning Technology, 25. https://doi.org/10.25304/rlt.v25.1968
Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218-237. https://doi.org/10.1016/j.compedu.2015.07.003
Budai, T., & Kuczmann, M. (2018). Towards a modern, integrated virtual laboratory system. Acta Polytechnica Hungarica, 15(3), 191-204.
Byukusenge, C., Nsanganwimana, F., & Tarmo, A. P. (2023). Exploring Students’ Perceptions of Virtual and Physical Laboratory Activities and Usage in Secondary Schools. International Journal of Learning, Teaching and Educational Research, 22(5), 437-456. https://doi.org/10.26803/ijlter.22.5.22
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Sage publications.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307-341.
Christabel, H., & Prawira, I. (2023). Practical Study of Digital Learning and Virtual Laboratory in Post-Pandemic Era. In E3S Web of Conferences (Vol. 388). EDP Sciences. https://doi.org/10.1051/e3sconf/202338804012
Chu, E. T. H., & Fang, C. W. (2015). CALEE: A computer-assisted learning system for embedded OS laboratory exercises. Computers & Education, 84, 36-48. https://doi.org/10.1016/j.compedu.2015.01.006.
Clough, M. P. (2002). Using the laboratory to enhance student learning. Learning science and the science of learning, 85-94.
Corter, J. E., Esche, S. K., Chassapis, C., Ma, J., & Nickerson, J. V. (2011). Process and learning outcomes from remotely operated, simulated, and hands-on student laboratories. Computers & Education, 57(3), 2054-2067. https://doi.org/10.1016/j.compedu.2011.04.009
Cronbach, L. J. (1970). Essentials of psychological testing, Harper and Row. New York.
Cruz-Benito, J., Sánchez-Prieto, J. C., Therón, R., & García-Peñalvo, F. J. (2019, June). Measuring students’ acceptance to AI-driven assessment in eLearning: Proposing a first TAM-based research model. In International conference on human-computer interaction (pp. 15-25). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-21814-0_2
Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Ph. D. dissertation, Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
De Vries, L. E., & May, M. (2019). Virtual laboratory simulation in the education of laboratory technicians–motivation and study intensity. Biochemistry and Molecular Biology Education, 47(3), 257-262. https://doi.org/10.1002/bmb.21221
De la Torre, L., Heradio, R., Jara, C. A., Sanchez, J., Dormido, S., Torres, F., & Candelas, F. A. (2013). Providing collaborative support to virtual and remote laboratories. IEEE transactions on learning technologies, 6(4), 312-323. https://doi.org/10.1109/TLT.2013.20
DiSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Mit Press.
Diwakar, S., Kumar, D., Radhamani, R., Nizar, N., Nair, B., Sasidharakurup, H., & Achuthan, K. (2015, September). Role of ICT-enabled virtual laboratories in biotechnology education: Case studies on blended and remote learning. In 2015 International Conference on Interactive Collaborative Learning (ICL) (pp. 915-921). IEEE. https://doi.org/10.1109/ICL.2015.7318149
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of educational technology systems, 49(1), 5-22. https://doi.org/10.1177/0047239520934018
Drašler, V., Bertoncelj, J., Korošec, M., Pajk Žontar, T., Poklar Ulrih, N., & Cigić, B. (2021). Difference in the attitude of students and employees of the University of Ljubljana towards work from home and online education: Lessons from COVID-19 pandemic. Sustainability, 13(9), 5118. https://doi.org/10.3390/su13095118
Duan, B. I. N. G., Ling, K., Mir, H. A. B. I. B., Hosseini, M., & Gay, R. K. L. (2005). An online laboratory framework for control engineering courses. International Journal of Engineering Education, 21(6), 1068.
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
Ekmekci, A., & Gulacar, O. (2015). A case study for comparing the effectiveness of a computer simulation and a hands-on activity on learning electric circuits. Eurasia Journal of Mathematics, Science and Technology Education, 11(4), 765-775. https://doi.org/10.12973/eurasia.2015.1438a
Elawady, Y., & Tolba, A. S. (2009). Educational objectives of different laboratory types: A comparative study. arXiv preprint arXiv:0912.0932. https://doi.org/10.48550/arXiv.0912.0932
Estriegana, R., Medina‐Merodio, J. A., & Barchino, R. (2019). Analysis of competence acquisition in a flipped classroom approach. Computer Applications in Engineering Education, 27(1), 49-64. https://doi.org/10.1002/cae.22056
Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1-14. https://doi.org/10.1016/j.compedu.2019.02.010
Fazio, R. H., & Zanna, M. P. (1978). On the predictive validity of attitudes: The roles of direct experience and confidence 1. Journal of Personality, 46(2), 228-243. https://doi.org/10.1111/j.1467-6494.1978.tb00177.x
Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., & LeMaster, R. (2005). When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physical review special topics-physics education research, 1(1), 010103. https://doi.org/10.1103/PhysRevSTPER.1.010103
Fiscarelli, S. H., Bizelli, M. H. S. S., & Fiscarelli, P. E. (2013). Interactive simulations to physics teaching: a case study in Brazilian high school. International Journal of Learning and Teaching, 18-23.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention: An introduction to theory and research. Reading, PA: Addison Wesley.
Flowers, L. O. (2011). Investigating the effectiveness of virtual laboratories in an undergraduate biology course. The Journal of Human Resource and Adult Learning, 7(2), 110.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Freiberg, H. J. (Ed.). (1999). School Climate: Measuring, Improving, and Sustaining Healthy Learning Environments. Psychology Press.
Gallego-Gómez, C., De-Pablos-Heredero, C., & Montes-Botella, J. L. (2021). Change of processes in the COVID-19 scenario: From face-to-face to remote teaching-learning systems. Sustainability, 13(19), 10513. https://doi.org/10.3390/su131910513
Gamage, K. A., Wijesuriya, D. I., Ekanayake, S. Y., Rennie, A. E., Lambert, C. G., & Gunawardhana, N. (2020). Online delivery of teaching and laboratory practices: Continuity of university programmes during COVID-19 pandemic. Education Sciences, 10(10), 291. https://doi.org/10.3390/educsci10100291
Gamo, J. (2018). Assessing a virtual laboratory in optics as a complement to on-site teaching. IEEE Transactions on Education, 62(2), 119-126. https://doi.org/10.1109/TE.2018.2871617
García-Vela, M., Zambrano, J. L., Falquez, D. A., Pincay-Musso, W., Duque, K. B., Zumba, N. V., & Jordá-Bordehore, L. (2020). Management of virtual laboratory experiments in the geosciences field in the time of COVID-19 pandemic. In Iceri2020 Proceedings (pp. 8702-8711). IATED. https://doi.org/10.21125/iceri.2020.1925
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864
Groenewald, E. S., Kumar, N., Avinash, S. I., & Yerasuri, S. (2024). Virtual Laboratories Enhanced by AI for hands-on Informatics Learning. Journal of Informatics Education and Research, 4(1) https://doi.org/10.52783/jier.v4i1.600
Gunawan, G., Nisrina, N., Suranti, N. M. Y., Herayanti, L., & Rahmatiah, R. (2018, November). Virtual laboratory to improve students’ conceptual understanding in physics learning. In Journal of Physics: Conference Series (Vol. 1108, No. 1, p. 012049). IOP Publishing. https://doi.org/10.1088/1742-6596/1108/1/012049
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Advanced diagnostics for multiple regression: A supplement to multivariate data analysis. Advanced Diagnostics for Multiple Regression: A Supplement to Multivariate Data Analysis.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues.
Huang, F., Teo, T., & Scherer, R. (2022). Investigating the antecedents of university students’ perceived ease of using the Internet for learning. Interactive learning environments, 30(6), 1060-1076. https://doi.org/10.1080/10494820.2019.1710540
Jnr, B. A., Kamaludin, A., Romli, A., Raffei, A. F. M., Phon, D. N. A. E., Abdullah, A., & Baba, S. (2020). Predictors of blended learning deployment in institutions of higher learning: theory of planned behavior perspective. The International Journal of Information and Learning Technology, 37(4), 179-196. https://doi.org/10.1108/IJILT-02-2020-0013
Jones, B. D., & Carter, D. (2019). Relationships between students’ course perceptions, engagement, and learning. Social Psychology of Education, 22, 819-839. https://doi.org/10.1007/s11218-019-09500-x
Joshi, A., Vinay, M., & Bhaskar, P. (2021). Impact of coronavirus pandemic on the Indian education sector: perspectives of teachers on online teaching and assessments. Interactive technology and smart education, 18(2), 205-226. https://doi.org/10.1108/ITSE-06-2020-0087
Khan, W., Sohail, S., Roomi, M. A., Nisar, Q. A., & Rafiq, M. (2023). Opening a new horizon in digitalization for e-learning in Malaysia: Empirical evidence of Covid-19. Education and Information Technologies, 1-30. https://doi.org/10.1007/s10639-023-12176-8
Kollöffel, B., & De Jong, T. (2013). Conceptual understanding of electrical circuits in secondary vocational engineering education: Combining traditional instruction with inquiry learning in a virtual lab. Journal of engineering education, 102(3), 375-393. https://doi.org/10.1002/jee.20022.
Lang, J. (2012). Comparative study of hands-on and remote physics labs for first year university level physics students. Transformative Dialogues: Teaching and Learning Journal, 6(1).
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision support systems, 29(3), 269-282. https://doi.org/10.1016/S0167-9236(00)00076-2
Liao, H. L., & Lu, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416. https://doi.org/10.1016/j.compedu.2007.11.006
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & education, 54(2), 600-610. https://doi.org/10.1016/j.compedu.2009.09.009
Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioural intention to reuse e‐learning systems in rural China. British Journal of Educational Technology, 43(6), 933-948. https://doi.org/10.1111/j.1467-8535.2011.01261.x
Magin, D., & Kanapathipillai, S. (2000). Engineering students' understanding of the role of experimentation. European journal of engineering education, 25(4), 351-358. https://doi.org/10.1080/03043790050200395
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5
Marc, P. (2001). Digital natives, digital immigrants. On the horizon, 9(5), 1-6.
Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International journal of educational research open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012
Mercer, L., Prusinkiewicz, P., & Hanan, J. (1990, May). The concept and design of a virtual laboratory. In Graphics Interface (Vol. 90, pp. 149-155).
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
Nair, B., Krishnan, R., Nizar, N., Radhamani, R., Rajan, K., Yoosef, A., & Diwakar, S. (2012). Role of ICT-enabled visualization-oriented virtual laboratories in universities for enhancing biotechnology education–VALUE initiative: Case study and impacts. FormaMente, 7(1-2), 1-18.
Ng, D. T. K. (2022). Online lab design for aviation engineering students in higher education: A pilot study. Interactive learning environments, 1-18.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory New York. NY: McGraw-Hill.
Nunnally, J. C. (1978). Psychometric Theory 2" ed New York McGraw-Hill. Robbins, SP (1993). Organizational Beha.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46. https://doi.org/10.1016/j.eswa.2016.04.015
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. McGraw-hill education (UK).
Park, E., & del Pobil, A. P. (2013). Modeling the user acceptance of long-term evolution services. annals of telecommunications-annales des télécommunications, 68, 307-315. https://doi.org/10.1007/s12243-012-0324-9
Petersen, F. (2020, March). Students’ attitude towards using a mobile learning management system: A large, undergraduate Information Systems class. In 2020 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICTAS47918.2020.233991
Potkonjak, V., Gardner, M., Callaghan, V., Mattila, P., Guetl, C., Petrović, V. M., & Jovanović, K. (2016). Virtual laboratories for education in science, technology, and engineering: A review. Computers & Education, 95, 309-327. https://doi.org/10.1016/j.compedu.2016.02.002
Radhamani, R., Kumar, D., Nizar, N., Achuthan, K., Nair, B., & Diwakar, S. (2021). What virtual laboratory usage tells us about laboratory skill education pre-and post-COVID-19: Focus on usage, behavior, intention and adoption. Education and information technologies, 26(6), 7477-7495. https://doi.org/10.1007/s10639-021-10583-3
Radhamani, R., Sasidharakurup, H., Kumar, D., Nizar, N., Nair, B., Achuthan, K., & Diwakar, S. (2014, December). Explicit interactions by users form a critical element in virtual labs aiding enhanced education--a case study from biotechnology virtual labs. In 2014 IEEE Sixth International Conference on Technology for Education (pp. 110-115). IEEE. https://doi.org/10.1109/T4E.2014.37
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732
Raineri, D. (2001). Virtual laboratories enhance traditional undergraduate biology laboratories. Biochemistry and Molecular Biology Education, 29(4), 160-162. https://doi.org/10.1016/S1470-8175(01)00060-1
Regan, D. T., & Fazio, R. (1977). On the consistency between attitudes and behavior: Look to the method of attitude formation. Journal of experimental social psychology, 13(1), 28-45. https://doi.org/10.1016/0022-1031(77)90011-7
Rejón-Guardia, F., Polo-Peña, A. I., & Maraver-Tarifa, G. (2020). The acceptance of a personal learning environment based on Google apps: The role of subjective norms and social image. Journal of Computing in Higher Education, 32, 203-233. https://doi.org/10.1007/s12528-019-09206-1
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH. J. Appl. Struct. Equ. Model. https://www.smartpls.com
Rivers, D. J. (2021). The role of personality traits and online academic self-efficacy in acceptance, actual use and achievement in Moodle. Education and Information Technologies, 26(4), 4353-4378. https://doi.org/10.1007/s10639-021-10478-3
Robles-Gómez, A., Tobarra, L., Pastor-Vargas, R., Hernández, R., & Haut, J. M. (2021). Analyzing the users’ acceptance of an IoT cloud platform using the UTAUT/TAM model. IEEE Access, 9, 150004-150020. https://doi.org/10.1109/ACCESS.2021.3125497
Rotimi, O. C., Ajogbeje, O. J., & Akeju, O. O. S. (2012). A new kind of visual-model instructional strategy in physics. International Journal of Physics and Chemistry Education, 4(SI), 28-32.
Rutten, N., Van Joolingen, W. R., & Van Der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & education, 58(1), 136-153. https://doi.org/10.1016/j.compedu.2011.07.017
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE access, 7, 128445-128462. https://doi.org/10.1109/ACCESS.2019.2939467
Sell, R., & Seiler, S. (2012). Improvements of multi-disciplinary engineering study by exploiting design-centric approach, supported by remote and virtual labs. International Journal of Engineering Education, 28(4), 759.
Sheorey, T. (2014). Empirical evidence of relationship between virtual lab development and students learning through field trials on vlab on mechatronics. International Journal of Information and Education Technology, 4(1), 97. https://doi.org/10.7763/IJIET.2014.V4.377
Singh, D. K. (2023). AN INSIGHT INTO STUDENT’S ACCEPTANCE OF VARIOUS DIGITAL PLATFORMS USING TAM MODEL ACROSS THE INDIAN STATES DURING THE PANDEMIC. Academy of Marketing Studies Journal, 27(5).
Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337-1370. https://doi.org/10.1080/09500693.2011.605182
Steinberg, R. N. (2000). Computers in teaching science: To simulate or not to simulate? American Journal of physics, 68(S1), S37-S41. https://doi.org/10.1119/1.19517
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11). https://doi.org/10.1016/j.heliyon.2020.e05410
Tatli, Z., & Ayas, A. (2011, September). Sanal kimya laboratuvarı geliştirilme süreci. In 5th International Computer & Instructional Technologies Symposium (Vol. 22, p. 24).
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328. https://doi.org/10.1080/10494820.2015.1122635
Tobarra, L., Robles-Gomez, A., Pastor, R., Hernandez, R., Duque, A., & Cano, J. (2020). Students’ acceptance and tracking of a new container-based virtual laboratory. Applied Sciences, 10(3), 1091. https://doi.org/10.3390/app10031091
Tompson, R., Barclay, D., & Higgins, C. (1995). The partial least squares approach to causal modeling: Personal computer adoption and uses as an illustration. Technology Studies: Special Issue on Research Methodology, 2(2), 284-324.
Triandis, H. C. (1977). Interpersonal behavior. Brooks/Cole Publishing Company.
Triona, L. M., & Klahr, D. (2003). Point and click or grab and heft: Comparing the influence of physical and virtual instructional materials on elementary school students' ability to design experiments. Cognition and Instruction, 21(2), 149-173. https://doi.org/10.1207/S1532690XCI2102_02
Trundle, K. C., & Bell, R. L. (2010). The use of a computer simulation to promote conceptual change: A quasi-experimental study. Computers & Education, 54(4), 1078-1088. https://doi.org/10.1016/j.compedu.2009.10.012
Tüysüz, C. (2010). The Effect of the Virtual Laboratory on Students' Achievement and Attitude in Chemistry. International Online Journal of Educational Sciences, 2(1).
Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, 1-18. https://doi.org/10.1007/978-1-4419-6108-2_1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
Virani, S. R., Saini, J. R., & Sharma, S. (2023). Adoption of massive open online courses (MOOCs) for blended learning: The Indian educators’ perspective. Interactive Learning Environments, 31(2), 1060-1076. https://doi.org/10.1080/10494820.2020.1817760
Wang, F. (2018). Computer distance virtual experiment teaching application based on virtual reality technology. International Journal of Emerging Technologies in Learning (Online), 13(4), 83. https://doi.org/10.3991/ijet.v13i04.8472
Webster, J., Heian, J. B., & Michelman, J. E. (1990). Computer training and computer anxiety in the educational process: an experimental analysis.
Yusuf, I., & Widyaningsih, S. W. (2020). Implementing e-learning-based virtual laboratory media to students' metacognitive skills. http://repository.unipa.ac.id:8080/xmlui/handle/123456789/829
Zacharia, Z. C., & Olympiou, G. (2011). Physical versus virtual manipulative experimentation in physics learning. Learning and instruction, 21(3), 317-331. https://doi.org/10.1016/j.learninstruc.2010.03.001
Zacharia, Z. C. (2003). The effects of an interactive computer-based simulation prior to performing a laboratory inquiry-based experiment on science teachers' conceptual understanding of physics. Columbia University. https://www.learntechlib.org/p/127163/
Zabunov, S. S. (2013). Effect of Poinsot Construction in Online Stereo 3D Rigid Body Simulation on the Performance of Students in Mathematics and Physics. International Journal of Physics and Chemistry Education, 5(2), 111-119.
Zhang, Z., Cao, T., Shu, J., & Liu, H. (2022). Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments, 30(8), 1388-1401. https://doi.org/10.1080/10494820.2020.1723113
Zhou, Z., Oveissi, F., & Langrish, T. (2024). Applications of augmented reality (AR) in chemical engineering education: Virtual laboratory work demonstration to digital twin development. Computers & Chemical Engineering, 188, 108784. https://doi.org/10.1016/j.compchemeng.2024.108784
Zulkifli, Z., Azhar, A., & Syaflita, D. (2022). Application Effect of PhET Virtual Laboratory and Real Laboratory on the Learning Outcomes of Class XI Students on Elasticity and Hooke's Law. Jurnal Penelitian Pendidikan IPA, 8(1), 401-407. https://doi.org/10.29303/jppipa.v8i1.1274
Zurweni, Z., Wibawa, B., & Erwin, T. N. (2017, August). Development of collaborative-creative learning model using virtual laboratory media for instrumental analytical chemistry lectures. In AIP Conference Proceedings (Vol. 1868, No. 1). AIP Publishing. https://doi.org/10.1063/1.4995109
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Rim Gouia-Zarrad, Rim Gharbi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.