Abstract
This study examined and compared attitudes of both students and instructors, motivated by an interest in improving the development and delivery of online oral communication learning (OOCL). Few studies have compared student and instructor attitudes toward learning technologies, and no known studies have conducted item response theory (IRT) analyses on these factors. Two independent and anonymous surveys resulted in 255 participants (124 university students, and 131 instructors). Exploratory factor analyses produced final item sets and a two-factor model for student attitudes (Technology Self-efficacy [TSE], and Positive Attitudes [PA]), and a three-factor model for instructors (TSE, Behavioral Intentions, and PA). The OOCL attitude factors showed strong validity through both IRT and classical test theory analyses. Comparisons between students and instructors showed students generally had higher TSE and more positive attitudes towards OOCL. The attitudes most relevant to OOCL were intrinsic interest, behavioral intentions, and perceived usefulness of the technology. This study revealed that technological self-efficacy may be useful for differentiating students and instructors, but not for assessing OOCL attitudes. Further development in this field could focus on the improvement of instructors’ attitudes and skills, as well as exploring the role of intrinsic interest.
Similar content being viewed by others
References
Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–561.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683.
Ardoin, S. P., Christ, T. J., Morena, L. S., Cormier, D. C., & Klingbeil, D. A. (2013). A systematic review and summarization of the recommendations and research surrounding curriculum-based measurement of oral reading fluency (CBM-R) decision rules. Journal of School Psychology, 51(1), 1–18.
Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology, 41(3), 586–598.
Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175–191. doi:10.1016/j.chb.2004.04.004.
Bennett, S., & Maton, K. (2010). Beyond the ‘digital natives’ debate: Towards a more nuanced understanding of students’ technology experiences. Journal of Computer Assisted learning, 26(5), 321–331. doi:10.1111/j.1365-2729.2010.00360.x.
Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. doi:10.1111/j.1467-8535.2007.00793.x.
Carter, L. M., Salyers, V., Myers, S., Hipfner, C., Hoffart, C., MacLean, C., et al. (2014). Qualitative insights from a Canadian multi-institutional research study: In search of meaningful e-learning. Canadian Journal for the Scholarship of Teaching and Learning, 5(1), 21.
Chien, S. P., Wu, H. K., & Hsu, Y. S. (2014). An investigation of teachers’ beliefs and their use of technology-based assessments. Computers in Human Behavior, 31(1), 198–210. doi:10.1016/j.chb.2013.10.037.
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 173–178.
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (create). Psychology and Aging, 21(2), 333–352. doi:10.1037/0882-7974.21.2.333.
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.
De Grez, L., Valcke, M., & Roozen, I. (2009). The impact of goal orientation, self-reflection and personal characteristics on the acquisition of oral presentation skills. European Journal of Psychology of Education, 24(3), 293–306.
De Grez, L., Valcke, M., & Roozen, I. (2012). How effective are self- and peer assessment of oral presentation skills compared with teachers’ assessments? Active Learning in Higher Education, 13(2), 129–142. doi:10.1177/1469787412441284.
De Grez, L., Valcke, M., & Roozen, I. (2014). The differential impact of observational learning and practice-based learning on the development of oral presentation skills in higher education. Higher Education Research and Development, 33(2), 256–271. doi:10.1080/07294360.2013.832155.
DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed., Vol. 26). Los Angeles: Sage.
Dwyer, K. K., & Davidson, M. M. (2013). General education oral communication assessment and student preferences for learning: E-textbook versus paper textbook. Communication Teacher, 27(2), 111–125.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah: Lawrence Erlbaum Assoc.
Gray, F. E. (2010). Specific oral communication skills desired in new accountancy graduates. Business Communication Quarterly, 73(1), 40–67. doi:10.1177/1080569909356350.
Gu, X., Zhu, Y., & Guo, X. (2013). Meeting the “digital natives”: Understanding the acceptance of technology in classrooms. Educational Technology and Society, 16(1), 392–402.
Harris, K. M., Syu, J. J., Lello, O. D., Chew, Y. L. E., Willcox, C. H., & Ho, R. C. M. (2015). The ABC’s of suicide risk assessment: Applying a tripartite approach to individual evaluations. PLoS ONE, 10(6), e0127442. doi:10.1371/journal.pone.0127442.
Heiman, H. L., Uchida, T., Adams, C., Butter, J., Cohen, E., Persell, S. D., & Martin, G. J. (2012). E-learning and deliberate practice for oral case presentation skills: A randomized trial. Medical Teacher, 34(12), e820–e826.
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. doi:10.1177/0013164405282485.
Hernández-Ramos, J. P., Martínez-Abad, F., García Peñalvo, F. J., García, M. E. H., & Rodríguez-Conde, M. J. (2014). Teachers’ attitude regarding the use of ICT. A factor reliability and validity study. Computers in Human Behavior, 31(1), 509–516. doi:10.1016/j.chb.2013.04.039.
Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820–831. doi:10.1016/j.chb.2012.01.011.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
Kreijns, K., Vermeulen, M., Van Acker, F., & van Buuren, H. (2014). Predicting teachers’ use of digital learning materials: Combining self-determination theory and the integrative model of behaviour prediction. European Journal of Teacher Education, 37(4), 465–478. doi:10.1080/02619768.2014.882308.
Lai, K. W., Khaddage, F., & Knezek, G. (2013). Blending student technology experiences in formal and informal learning. Journal of Computer Assisted learning, 29(5), 414–425. doi:10.1111/jcal.12030.
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61(1), 193–208.
Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended learning context. International Review of Research in Open and Distance Learning, 5(2), 1–16.
Ma, C. M., Chao, C. M., & Cheng, B. W. (2013). Integrating technology acceptance model and task-technology fit into Blended E-learning System. Journal of Applied Sciences, 13(5), 736–742.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.
McBain, B., Drew, A. J., James, C., Phelan, L., Harris, K. M., & Archer, J. (2016). Student experience of oral communication assessment tasks online from a multi-disciplinary trial. Education + Training, 58(2), 134–149. doi:10.1108/ET-10-2014-0124.
McGill, T. J., & Hobbs, V. J. (2008). How students and instructors using a virtual learning environment perceive the fit between technology and task. Journal of Computer Assisted learning, 24(3), 191–202. doi:10.1111/j.1365-2729.2007.00253.x.
Nistor, N. (2014). When technology acceptance models won’t work: Non-significant intention-behavior effects. Computers in Human Behavior, 34, 299–300.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
O’Bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education, 74, 15–25. doi:10.1016/j.compedu.2014.01.006.
Osborne, R., Kriese, P., Tobey, H., & Johnson, E. (2009). And never the two shall meet? Student vs. faculty perceptions of online courses. Journal of Educational Computing Research, 40(2), 171–182. doi:10.2190/EC.40.2.b.
Ostini, R., & Nering, M. L. (2006). Polytomous item response theory models. Thousand Oaks, CA: Sage.
Palaigeorgiou, G. E., Siozos, P. D., Konstantakis, N. I., & Tsoukalas, I. A. (2005). A computer attitude scale for computer science freshmen and its educational implications. Journal of Computer Assisted learning, 21(5), 330–342. doi:10.1111/j.1365-2729.2005.00137.x.
Prensky, M. (2008). Students as designers and creators of educational computer games: Who else? British Journal of Educational Technology, 39(6), 1004–1019. doi:10.1111/j.1467-8535.2008.00823_2.x.
Reisslein, J., Seeling, P., & Reisslein, M. (2005). Video in distance education: ITFS vs. web-streaming: Evaluation of student attitudes. Internet and Higher Education, 8(1), 25–44. doi:10.1016/j.iheduc.2004.12.002.
Reynolds, P. A., & Mason, R. (2002). On-line video media for continuing professional development in dentistry. Computers & Education, 39(1), 65–98. doi:10.1016/S0360-1315(02)00026-X.
Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16(5), 473–491. doi:10.1016/S0747-5632(00)00025-X.
Rieber, L. P. (1992). Computer-based microworlds: A bridge between constructivism and direct instruction. Educational Technology Research and Development, 40(1), 93–106. doi:10.1007/BF02296709.
Ritzhaupt, A. D., & Martin, F. (2014). Development and validation of the educational technologist multimedia competency survey. Educational Technology Research and Development, 62(1), 13–33. doi:10.1007/s11423-013-9325-2.
Rizopoulos, D. (2006). ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17(5), 1–25.
Samejima, F. (1969). Estimation of the latent ability using a response pattern of graded scores. Psychometrika, 34(Monograph Supplement), 100–114.
Sherer, P., & Shea, T. (2011). Using online video to support student learning and engagement. College Teaching, 59(2), 56–59. doi:10.1080/87567555.2010.511313.
Spector, J. M. (2008). Cognition and learning in the digital age: Promising research and practice. Computers in Human Behavior, 24(2), 249–262. doi:10.1016/j.chb.2007.01.016.
Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahway: Lawrence Erlbaum.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. doi:10.1016/j.compedu.2006.11.007.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education.
Tao, Y. H., Cheng, C. J., & Sun, S. Y. (2012). Alignment of teacher and student perceptions on the continued use of business simulation games. Educational Technology and Society, 15(3), 177–189.
Thomas, M. L. (2011). The value of item response theory in clinical assessment: A review. Assessment, 18(3), 291–307. doi:10.1177/1073191110374797.
Traphagan, T., Kucsera, J. V., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37. doi:10.1007/s11423-009-9128-7.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Voogt, J., Erstad, O., Dede, C., & Mishra, P. (2013). Challenges to learning and schooling in the digital networked world of the 21st century. Journal of Computer Assisted learning, 29(5), 403–413. doi:10.1111/jcal.12029.
Wang, L., Chen, Y., & Shi, J. (2007). Attitudes toward computers: A new attitudinal dimension. Cyberpsychology and Behavior, 10(5), 700–704. doi:10.1089/cpb.2007.9967.
Waycott, J., Bennett, S., Kennedy, G., Dalgarno, B., & Gray, K. (2010). Digital divides? Student and staff perceptions of information and communication technologies. Computers & Education, 54(4), 1202–1211. doi:10.1016/j.compedu.2009.11.006.
Webb, M. (2014). Pedagogy with information and communications technologies in transition. Education and Information Technologies, 19(2), 275–294. doi:10.1007/s10639-012-9216-x.
Acknowledgments
This study was funded in part by a University of Newcastle teaching and learning grant. We thank colleagues for providing instrumental support to the project: Luke Boulton, Terry Burns, Bronwyn Hemsley, Nimay Kalyani, and Megan Rollo. YouSeeU provided access to their platform for the purposes of the trial.
Funding
The project funding body and technology service provider were not involved in the development, implementation, analyses, or write-up of the study. All co-authors had complete access to data supporting the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical Approval
This study received approval from the University of Newcastle Ethics Review Board under quality assurance number QA72.
Conflicts of interest
There are no conflicts of interest.
Appendix
Appendix
Student questionnaire
-
1.
Overall, my experience of the online oral communications task was positive for my learning.
-
2.
The task was interesting.
-
3.
In general, I think the online oral communication task made it easier for me to learn.
-
4.
I enjoyed this task.
-
5.
Overall, this task helped me to learn the disciplinary content of this course.
-
6.
Overall, this task helped me to improve my oral communication skills.
-
7.
I would like the opportunity to complete online oral communication tasks in other courses.
-
8.
I felt that I understood the benefits of undertaking an oral communication task.
-
9.
Overall I felt that I was provided with adequate guidance on how to successfully give an online oral presentation.
-
10.
I found the technical aspects of completing this task to be fairly straightforward.
-
11.
Overall I felt that I was provided with adequate guidance on how to use the online oral presentation technology.
-
12.
I am comfortable using Blackboard.
-
13.
I am comfortable using computers.
Instructor questionnaire
-
1.
Online tasks can be developed that significantly improve students’ oral communication skills.
-
2.
Online oral communication tasks can be interesting.
-
3.
In general, I think online oral communication tasks can make it easier for students to learn.
-
4.
I believe online oral communication tasks are of comparable quality as similar face-to-face tasks.
-
5.
Online oral communication tasks can be developed that significantly improve students’ ability to learn the disciplinary content of my course(s).
-
6.
I feel I can provide adequate guidance on how to successfully give an online oral presentation.
-
7.
I am aware of benefits of oral communication tasks for my course(s).
-
8.
I am comfortable with the technical aspects of creating online oral communication tasks.
-
9.
I am comfortable using online oral presentation technology.
-
10.
I am comfortable using Blackboard.
-
11.
I am comfortable using computers.
-
12.
I would like the opportunity to develop online oral communication tasks for my courses.
-
13.
I would enjoy learning how to design and implement online oral communication tasks.
Rights and permissions
About this article
Cite this article
Harris, K.M., Phelan, L., McBain, B. et al. Attitudes toward learning oral communication skills online: the importance of intrinsic interest and student-instructor differences. Education Tech Research Dev 64, 591–609 (2016). https://doi.org/10.1007/s11423-016-9435-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11423-016-9435-8