FAKTOR-FAKTOR YANG MEMPENGARUHI INTENTION TO USE VIRTUAL LEARNING (Studi Kasus Mahasiswa Ekonomi Islam FIAI UII)

Baharuddin Muslim Efendy; Ryan Yuniawan; 2Rheyza Virgiawan

doi:10.20885/tullab.vol2.iss2.art3

FAKTOR-FAKTOR YANG MEMPENGARUHI INTENTION TO USE VIRTUAL LEARNING

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March 8, 2021

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April 6, 2021

Published

April 6, 2021

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Abstract

Perkembangan revolusi industri 4.0 menuntut perguruan tinggi saat ini untuk menerapkan pembelajaran berbasis Virtual Learning (VL). Namun dipentingkan meneliti niat pengadopsiaan VL (Intention Use) (IUVL) mahasiswa dipengaruhi oleh Resistance to Change (RCVL), Perceived Usefulness (PUVL), Perceived Ease of Use (PEUVL), Self Efficacy (SEVL). Attitude Toward Using (ATVL) pembelajaran ekonomi dimungkinkan menjadi salah satu mediasi dalam membangun niat pengadopsian (IUVL). Penelitian untuk menguji peran mediasi Attitude Toward Using (ATVL) pembelajaran ekonomi pada dimensi individual terhadap niat pengadopsian VL pada pembelajaran ekonomi. Penelitian ini adalah penelitian kuantitatif. Populasi penelitian mahasiswa pendidikan ekonomi yang pernah mengikuti program pembelajaran ekonomi dengan VL, sampel penelitian sejumlah 169 orang yang ditetapkan dengan simple random sampling. Teknik pengambilan data dengan angket. Teknik analisis data yang digunakan adalah Structural Equation Model (SEM) dengan metode analisis AMOS. Hasil penelitian ini menunjukkan bahwa Resistance to Change (RCVL), Perceived Usefulness (PUVL)), Perceived Ease of Use (PEUVL), Self Efficacy (SEVL) mampu berpengaruh signifikan terhadap Attitude Toward Using (ATVL). Selain itu penelitian ini juga menemukan bahwa Attitude Toward Using (ATVL) mampu memdiasi hubungan antara Perceived Usefulness (PUVL)), Perceived Ease of Use (PEUVL), Self Efficacy (SEVL) terhadap niat pengadopsian VL, akan tetapi Attitude Toward Using (ATVL) tidak mampu memediasi hubungan antara Resistance to Change (RCVL) dan niat pengadopsian VL.

Keywords

Intention Use (IUVL) Resistance to Change (RCVL) Perceived Usefulness (PUVL) Perceived Ease of Use (PEUVL) Self Efficacy (SEVL)

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References

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Dr. Simon Moss. (2009). Theory of planned behavior Theory of planned behavior, 1–8. http://doi.org/10.1037/t15668-000
Ferdousi, B., Carolina, S., & Levy, Y. (2010). Development and Validation of a Model to Investigate the Impact of Individual Factors on Instructors ’ Intention to Use E-learning Systems, 6(2006).
Ferreira, J. J., Raposo, M. L., Rodrigues, R. G., Dinis, A., & Paço, A. Do. (2012). A model of entrepreneurial intention: An application of the psychological and behavioral approaches. Journal of Small Business and Enterprise Development, 19(3), 424–440. http://doi.org/10.1108/14626001211250144
Fokides, E. (2017). Greek Pre- service Teachers ’ In tentions to Use Computers as In-service Teachers. CONTEMPORARY EDUCATIONAL TECHNOLOGY, 8(1), 56–75.
Fokides, E., & Author, C. (2017). P RE -S ERVICE T EACHERS ’ I NTENTION TO U SE MUVE S AS P RACTITIONERS – A S TRUCTURAL E QUATION M ODELING A PPROACH, 16, 47–68.
Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102, 79–89. http://doi.org/10.1016/j.compedu.2016.07.003
Higgins, E. T., Locke, J., & James, W. (n.d.). Elf fficacy, 814–817.
Hilao, M. P. (2017). GENDER DIFFERENCES IN MOBILE PHONE USAGE FOR LANGUAGE LEARNING , ATTITUDE , AND PERFORMANCE, (April), 68–80.
Huang, N. N., & Chiu, L. (2016). Relationship Among Students ’ Problem-Solving Attitude , Perceived Value , Behavioral Attitude , and Intention to Participate in a Science and Technology Contest, 1419–1435. http://doi.org/10.1007/s10763-015-9665-y
Hussein, A., & Zolait, S. (2014). The nature and components of perceived behavioural control as an element of theory of planned behaviour, 33(1), 65–84.
Ifinedo, P. (2009). The Technology Acceptance Model ( TAM ) and the Continuance Intention of Using WebCT : A Case of College Students in Estonia. Information Communication Technologies for Enhanced Education and Learning: Advanced Applications and Developments, 29–44. http://doi.org/10.4018/978-1-60566-150-6.ch003
Kavanagh, S., Luxton-Reilly, A., Wüensche, B., & Plimmer, B. (2017). A Systematic Review of Virtual Reality in Education. Themes in Science and Technology Education, 10(2), 85–119.
Kenny, R. F., Neste-kenny, J. M. C. Van, Park, C. L., Burton, P. A., & Meiers, J. (2009). Mobile Learning in Nursing Practice Education : Applying Koole â€TM s FRAME Model, 23(3), 75–96.
Lee, Y., Hsieh, Y., & Chen, Y. (2013). An investigation of employees ’ use of e-learning systems : applying the technology acceptance model, 32(2), 173–189.
Lynch, R. (2004). International Review of Research in Open and Distance Learning ISSN : 1492-3831 The Relationship Between Self-Regulation and Online Learning in a Blended Learning Context Self-Regulatory Attributes Predictive of Distance Learner Success, 1–14.
Makoe, M. (2012). Teaching digital natives : Identifying competencies for mobile learning facilitators in distance education, 26(1), 91–104.
Orientations, M., Beliefs, E., Students, T., & Learning, E. F. L. (2017). Eurasian Journal of Educational Research, 67, 251–267.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. http://doi.org/10.1111/j.1467-8535.2011.01229.x
Parson, V., & Bignell, S. (2017). An Investigation into Cooperative Learning in a Virtual World using Problem-Based Learning. Online Learning, 21(2). http://doi.org/10.24059/olj.v21i2.796
Press, I. R. M. (2008). Examining . Consumer Broadband . Adoption ,. Usage ,. and . Impact.
Quinn, B. C. N. (n.d.). LEARNING ABOUT There seems to be little doubt that the introduction of smartphones and handheld devices have ushered in a new age of mobility that will have profound implications on how people interact with the world and with each other . In his book , De.
Razzaq, A., Samiha, Y. T., & Anshari, M. (n.d.). Smartphone Habits and Behaviors in Supporting Students Self-Efficacy, 94–110.
Şahİn, S., & Mcilroy, D. (2014). TECHNOLOGY ACCEPTANCE MEASURE FOR TEACHERS : T-TAM, 10(4), 885–917.
Tang, J. E., Tang, T., & Chiang, C. (2014). Blog learning : effects of users ’ usefulness and efficiency towards continuance intention, 33(1), 36–50.
Technology, A., Tam, M., & Study, A. C. (2010). Investigating Students ’ Behavioral Intention to use, 17(1).
Vilkonis, R., Bakanovienė, T., & Turskienė, S. (2013). Readiness of Adults to Learn Using E-learning , M-learning and T-learning Technologies, 12(2), 181–190.
Waely, S. A., & Aburezeq, I. M. (2013). Using Blogs to Facilitate Interactive and Effective Learning: Perceptions of Pre-service Arabic Teachers. Journal of Language Teaching and Research, 4(5), 975–985. http://doi.org/10.4304/jltr.4.5.975-985
Wang, D., Xu, L., & Chuan, H. (2015). Understanding the continuance use of social network sites : a computer self-efficacy perspective, 34(2), 204–216.
Wang, F. (2018). Computer Distance Virtual Experiment Teaching Application Based on Virtual Reality Technology, 13(4), 83–95.
Wang, M., Shen, R., Novak, D., & Pan, X. (2009). blended classroom, 40(4), 673–696. http://doi.org/10.1111/j.1467-8535.2008.00846.x
Zhang, M., Zhang, Z., Chang, Y., Aziz, E.-S., Esche, S., & Chassapis, C. (2018). Paper—Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the … Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the Microsoft Kinect, 13, 138–159. http://doi.org/10.3991/ijet.v13i01.7773

References

Alnabhan, M., & Aljaraideh, Y. (2014). [JOURNAL BI] Collaborative M-Learning Adoption Model: A Case Study for Jordan. International Journal of Emerging Technologies in Learning (iJET), 9(8), 4. http://doi.org/10.3991/ijet.v9i8.3639

Cliffe, A. D. (2017). A review of the benefits and drawbacks to virtual field guides in today’s Geoscience higher education environment. International Journal of Educational Technology in Higher Education, 14(1). http://doi.org/10.1186/s41239-017-0066-x

Dr. Simon Moss. (2009). Theory of planned behavior Theory of planned behavior, 1–8. http://doi.org/10.1037/t15668-000

Ferdousi, B., Carolina, S., & Levy, Y. (2010). Development and Validation of a Model to Investigate the Impact of Individual Factors on Instructors ’ Intention to Use E-learning Systems, 6(2006).

Ferreira, J. J., Raposo, M. L., Rodrigues, R. G., Dinis, A., & Paço, A. Do. (2012). A model of entrepreneurial intention: An application of the psychological and behavioral approaches. Journal of Small Business and Enterprise Development, 19(3), 424–440. http://doi.org/10.1108/14626001211250144

Fokides, E. (2017). Greek Pre- service Teachers ’ In tentions to Use Computers as In-service Teachers. CONTEMPORARY EDUCATIONAL TECHNOLOGY, 8(1), 56–75.

Fokides, E., & Author, C. (2017). P RE -S ERVICE T EACHERS ’ I NTENTION TO U SE MUVE S AS P RACTITIONERS – A S TRUCTURAL E QUATION M ODELING A PPROACH, 16, 47–68.

Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102, 79–89. http://doi.org/10.1016/j.compedu.2016.07.003

Higgins, E. T., Locke, J., & James, W. (n.d.). Elf fficacy, 814–817.

Hilao, M. P. (2017). GENDER DIFFERENCES IN MOBILE PHONE USAGE FOR LANGUAGE LEARNING , ATTITUDE , AND PERFORMANCE, (April), 68–80.

Huang, N. N., & Chiu, L. (2016). Relationship Among Students ’ Problem-Solving Attitude , Perceived Value , Behavioral Attitude , and Intention to Participate in a Science and Technology Contest, 1419–1435. http://doi.org/10.1007/s10763-015-9665-y

Hussein, A., & Zolait, S. (2014). The nature and components of perceived behavioural control as an element of theory of planned behaviour, 33(1), 65–84.

Ifinedo, P. (2009). The Technology Acceptance Model ( TAM ) and the Continuance Intention of Using WebCT : A Case of College Students in Estonia. Information Communication Technologies for Enhanced Education and Learning: Advanced Applications and Developments, 29–44. http://doi.org/10.4018/978-1-60566-150-6.ch003

Kavanagh, S., Luxton-Reilly, A., Wüensche, B., & Plimmer, B. (2017). A Systematic Review of Virtual Reality in Education. Themes in Science and Technology Education, 10(2), 85–119.

Kenny, R. F., Neste-kenny, J. M. C. Van, Park, C. L., Burton, P. A., & Meiers, J. (2009). Mobile Learning in Nursing Practice Education : Applying Koole â€TM s FRAME Model, 23(3), 75–96.

Lee, Y., Hsieh, Y., & Chen, Y. (2013). An investigation of employees ’ use of e-learning systems : applying the technology acceptance model, 32(2), 173–189.

Lynch, R. (2004). International Review of Research in Open and Distance Learning ISSN : 1492-3831 The Relationship Between Self-Regulation and Online Learning in a Blended Learning Context Self-Regulatory Attributes Predictive of Distance Learner Success, 1–14.

Makoe, M. (2012). Teaching digital natives : Identifying competencies for mobile learning facilitators in distance education, 26(1), 91–104.

Orientations, M., Beliefs, E., Students, T., & Learning, E. F. L. (2017). Eurasian Journal of Educational Research, 67, 251–267.

Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. http://doi.org/10.1111/j.1467-8535.2011.01229.x

Parson, V., & Bignell, S. (2017). An Investigation into Cooperative Learning in a Virtual World using Problem-Based Learning. Online Learning, 21(2). http://doi.org/10.24059/olj.v21i2.796

Press, I. R. M. (2008). Examining . Consumer Broadband . Adoption ,. Usage ,. and . Impact.

Quinn, B. C. N. (n.d.). LEARNING ABOUT There seems to be little doubt that the introduction of smartphones and handheld devices have ushered in a new age of mobility that will have profound implications on how people interact with the world and with each other . In his book , De.

Razzaq, A., Samiha, Y. T., & Anshari, M. (n.d.). Smartphone Habits and Behaviors in Supporting Students Self-Efficacy, 94–110.

Şahİn, S., & Mcilroy, D. (2014). TECHNOLOGY ACCEPTANCE MEASURE FOR TEACHERS : T-TAM, 10(4), 885–917.

Tang, J. E., Tang, T., & Chiang, C. (2014). Blog learning : effects of users ’ usefulness and efficiency towards continuance intention, 33(1), 36–50.

Technology, A., Tam, M., & Study, A. C. (2010). Investigating Students ’ Behavioral Intention to use, 17(1).

Vilkonis, R., Bakanovienė, T., & Turskienė, S. (2013). Readiness of Adults to Learn Using E-learning , M-learning and T-learning Technologies, 12(2), 181–190.

Waely, S. A., & Aburezeq, I. M. (2013). Using Blogs to Facilitate Interactive and Effective Learning: Perceptions of Pre-service Arabic Teachers. Journal of Language Teaching and Research, 4(5), 975–985. http://doi.org/10.4304/jltr.4.5.975-985

Wang, D., Xu, L., & Chuan, H. (2015). Understanding the continuance use of social network sites : a computer self-efficacy perspective, 34(2), 204–216.

Wang, F. (2018). Computer Distance Virtual Experiment Teaching Application Based on Virtual Reality Technology, 13(4), 83–95.

Wang, M., Shen, R., Novak, D., & Pan, X. (2009). blended classroom, 40(4), 673–696. http://doi.org/10.1111/j.1467-8535.2008.00846.x

Zhang, M., Zhang, Z., Chang, Y., Aziz, E.-S., Esche, S., & Chassapis, C. (2018). Paper—Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the … Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the Microsoft Kinect, 13, 138–159. http://doi.org/10.3991/ijet.v13i01.7773

FAKTOR-FAKTOR YANG MEMPENGARUHI INTENTION TO USE VIRTUAL LEARNING (Studi Kasus Mahasiswa Ekonomi Islam FIAI UII)

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