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Technology Acceptance Model and Use of Twitter by Preservice Teacher Candidates

Listen and learn

Listen and learn : Research paper
Roundtable presentation


Thursday, December 3, 11:15 am–12:00 pm PST (Pacific Standard Time)
Presentation 1 of 3
Other presentations:
Preservice Teachers' Changing Perceptions on Mobile Phone Use in the Classroom
Energizing Language Teaching and Learning with VR Games and AI Characters

Dr. Michael Mills  
Jessica Herring  
Mia Morrison  

Twitter has been shown to improve collaboration and increase participatory learning in educational settings. The findings indicate a strong link between measures of the Technology Acceptance Model (perceived usefulness, relevance, and ease of use), not only of Twitter itself but also, in some circumstances, of the LMS being used.

Audience: Teachers, Teacher education/higher ed faculty, Professional developers
Attendee devices: Devices not needed
Topic: Online tools, apps & resources
Grade level: Community college/university
Subject area: Preservice teacher education
ISTE Standards: For Educators:
Collaborator
  • Use collaborative tools to expand students' authentic, real-world learning experiences by engaging virtually with experts, teams and students, locally and globally.
Facilitator
  • Foster a culture where students take ownership of their learning goals and outcomes in both independent and group settings.
Citizen
  • Create experiences for learners to make positive, socially responsible contributions and exhibit empathetic behavior online that build relationships and community.
Additional detail: ISTE author presentation, Session recorded for video-on-demand

Proposal summary

Framework

To determine the significance of the use of Twitter as a component of classroom assignments, the Technology Acceptance Model (TAM-3) was used. The TAM-3, having undergone two revisions from Davis’s seminal work (1989), has been validated in numerous studies (Venkatesh & Davis, 2000; Venkatesh & Bala, 2008; Donaldson, 2011; Fan, 2014; Hidayanto & Setyady, 2014; Ramayah et al., 2002; Shroff, Deneen, & Ng, 2011; Yuan, et al, 2017), and has been used to classify various components of technology use that relate to increased usage and acceptance of technology systems.

The TAM-3 has identified two key factors specifically related to increased technology usage and acceptance: perceived usefulness and perceived ease of use (Ramayah, Ma’ruf, Jantan, & Mohamad, 2002). Davis (1989) said if a user feels a particular technology application (software or hardware) will aid him or her at his or her job, the user is more likely to implement the application. Further, if the user perceives the technology as easy to use, he or she is more likely to utilize it (Davis, 1989). Essentially, if students view a technology application (e.g., Twitter) as easy to use and useful, they are more likely to use the technology in broader applications. In this case, Twitter was selected because of its popularity as a social media application and its research-supported benefits to learning applications, particularly in the area of collaboration and participatory learning (Blessing, Blessing, & Fleck, 2012; Mills, 2014; Carpenter & Krutka, 2014; Carpenter 2015; Krutka & Carpenter, 2016; Chawinga, 2017).

To add further dimensionality to the study, perceived relevance from the TAM-3 as a measure of technology acceptance was also included because of its ability to quantify students’ perception of how relevant Twitter was to their classroom assignments.

Methods

Methodology & Instrumentation

Students from two suburban/rural regional universities, one in the South and one in the Northeast, engaged in reflection and creation activities related to assignments in an introductory educational technology course. This was a sample of convenience as the study relied on enrollment in the courses and the assignments given in the classes. These students were asked to share a reflection or summary of their understanding both in traditional text form and also through visual media. The students were then required to share their reflection/summaries through Twitter and through the traditional LMS.

Prior to the administration of the first assignment, participants completed a survey that measured their general comfort with technology and social media in particular (including preference for specific social media apps), attitude toward learning and media creation, along with demographic information intended for later disaggregation.

Participants in the experimental group (n=46; Twitter+LMS) completed four assignments on a range of topics, and at intermittent opportunities within the semester, participants created a reflection artifact for the four assignments. The reflection artifacts included the students’ summary of what was learned, a takeaway of sorts. Participants shared these takeaways in two modes and through two mediums, resulting in four assignments.
• Assignment #1: Participants brainstormed their summary/reflection and then shared a concise textual summation of that reflection (takeaway) with their classmates in a closed LMS (learning management system).

• Assignment #2: Participants created a multimedia artifact (e.g., short video, digital poster, infographic) Â that articulated their reflection (takeaway) with their classmates in a closed LMS (learning management system).

• Assignment #3: Participants brainstormed their summary/reflection and then shared a concise textual summation of that reflection through a social media (Twitter), tagged with hashtags to ensure high visibility of the reflection.

• Assignment #4: Participants created a multimedia artifact (e.g., short video, digital poster, infographic) that articulated their reflection through a social media (Twitter), tagged with hashtags to ensure high visibility of the reflection.

Participants in the control group (n=43; LMS only) completed their assignments by interacting only within a traditional LMS. Subsequent surveys were sent after each assignment to measure participants’ level of ownership of the content they shared.

Instrumentation
The demographic survey administered at the beginning of the term was adapted from a survey used in an earlier study on social media use, Twitter in particular. The TAM-3 portion of the survey was adapted per the use of TAM-3 in previous studies.

Results

A multivariate analysis of covariance (MANCOVA) examining differences between those using Twitter along with the learning management system (LMS) of the class was conducted, and the result was a statistically significant difference between the control and experimental groups on the combined dependent variables after controlling for the pre-survey results, F(6,66)=6.536, p<.001, Wilks Lambda = .627, partial eta squared=.373).

Compared to the students who used a learning management system only to share their classwork with fellow classmates and the instructor, students who were instructed to use Twitter for at least two assignments showed an increased likelihood to find overall relevance, usefulness, and ease in using Twitter and overall usefulness and ease in using the LMS as well. Bonferroni corrections were used to reduce the possibility of Type I error for all univariate analyses. Concurrently, there was little significance in how the students in the experimental group found relevance in the learning management system (LMS).

Subsequent univariate tests of covariance (ANCOVA) revealed identical findings and indicated varying levels of effect size on select components of the TAM3 relative to perceptions of Twitter use and are reported in Table 2 below.

Maxwell and Delaney (2004, p. 548) state “a major goal of developing effect size measures is to provide a standard metric that meta-analysts and others can interpret across studies that vary in their dependent variables as well as types of designs.”
Partial eta squared is the preferred measure for effect size when conducting tests of ANCOVA (Field, 2009; Cohen, 1988). While eta squared measures the proportion of total variance, partial eta squared measures proportion of total variance including what is not explained by other variables in the analysis (Field, 2009), with measures of .01, .06, and .14 representing small, medium, and large effects respectively (Cohen, 1988). In light of findings and suggestions from Levine and Hullett (2002), eta squared should be also reported whenever partial eta squared is used. As such, those values can be found in Table 2.

The experimental (Twitter+LMS) group overall showed large effects in each TAM-3 item that relates to the perceived usefulness (p<.001, partial η2 = .204), ease of use (p<.001, partial η2 = .306), and relevance (p<.001, partial η2 = .251) of using Twitter as a medium for completing course assignments along with the LMS.

Additionally, the experimental group showed a significant, medium effect on perceiving usage of the LMS as useful (p<.019, partial η2 = .070) and easy to use (p<.004, partial η2 = .105).

Importance

This research confirms earlier studies (Mills, 2014; Krutka & Carpenter, 2016; Meredith, 2018) that suggest educators perceive Twitter as being easy to use, useful, and relevant and expands the earlier findings by linking the use of Twitter as a required part of class assignments. Further, the findings indicate a strong link between perceived usefulness, relevance, and ease of use not only of Twitter itself but also, in some circumstances, of the LMS being used. These findings further suggest the use of Twitter actually increased students’ feelings of the LMS being used, specifically perceived ease of use and usefulness. In short, assigning Twitter as a required part of classroom assignments may increase students’ perceived ease of use, usefulness, and relevance of Twitter and also may increase students’ perceived ease of use and usefulness of an LMS being used. Notably, the strongest effects were found for Twitter and only modest effects were found for the LMS being used.

The extension of these findings is to analyze the how the social media component specifically contributed to an environment that fostered collective learning and social rapport (Jang, 2014; Meredith, 2018). Based on earlier empirical studies of using Twitter in educational settings, the indication of a high level of perceived ease of use, usefulness, and relevance of using Twitter for educational purposes may result in the cultivation of a collaborative learning culture that features transformational learning opportunities and better instructional practices all around (Mills, 2014; Carpenter & Krutka, 2014; Carpenter 2015; Krutka & Carpenter, 2016; Chawinga, 2017; Visser, Evering, & Barrett, 2014).

References

Blessing, S.B., Blessing, J.S., & Fleck, B.K. (2012). Using Twitter to reinforce classroom concepts. Teaching of Psychology, 39(4), 268-271.
Carpenter, J. (2015). Preservice teachers’ microblogging: Professional development via Twitter. Contemporary Issues in Technology and Teacher Education, 15(2), 1–21.
Carpenter, J.P., & Krutka, D.G. (2014). How and why educators use Twitter: A survey of the field. Journal of Research on Technology in Education, 46(4), 414–434. Retrieved from ttps://doi.org/10.1080/15391523.2014.925701
Chawinga, W.D. (2017). Taking social media to a university classroom: teaching and learning using Twitter and blogs. International Journal of Educational Technology in Higher Education, 14(1), 3.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Retrieved from http://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Donaldson, R.L. (2011). Student acceptance of mobile learning (Unpublished Doctoral Dissertation). The Florida State University College of Communication and Information, Tallahassee, FL. Retrieved from http://diginole.lib.fsu.edu/islandora/object/fsu%253A168891
Fan, C. (2014). Applied the Technology Acceptance Model to survey the mobile-learning adoption behavior in science museum. International Journal of Innovation and Scientific Research, 12(1), 22–29. Retrieved from http://www.ijisr.issrjournals.org/abstract.php?article=IJISR-14-272-01
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage.
Hidayanto, A. N., & Setyady, S.T. (2014). Impact of collaborative tools utilization on group performance in university students. Turkish Online Journal of Educational Technology, 13(2), 88–98. Retrieved from https://doi.org/10.1371/journal.pone.0183994
Jang, Y. (2014). Convenience matters: A qualitative study on the impact of use of social media and collaboration technologies on learning experience and performance in higher education. Education for Information, 31(1), 73–98. doi:10.3233/EFI-150948
Krutka, D.G. & Carpenter, J.P. (2016). Participatory learning through social media: How and why social studies educators use Twitter. Contemporary Issues in Technology and Teacher Education, 16(1), 38-59. Retrieved from https://www.learntechlib.org/primary/p/150963/.
Levine, T., & Hullett, C. (2002). Eta squared, partial eta squared, and misreporting of effect size in communication research. Human Communication Research, 28(4). 612-625. Retrieved from https://msu.edu/~levinet/eta%20squared%20hcr.pdf
Maxwell S. E., & Delaney H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd Ed.). Mahwah, NJ: Erlbaum.
Meredith, L. (2018). Technology acceptance model and the influence of Twitter on K-12 educators (Unpublished doctoral dissertation).
Mills, M. (2014). Effect of faculty member’s use of Twitter as informal professional development during a preservice teacher internship. Contemporary Issues in Technology and Teacher Education, 14(4), 451–467.
Ramayah, T., Ma’ruf, J., Jantan, M., & Mohamad, O. (2002). The role of harmonization of economics and business discipline in global competitiveness. The proceedings of The International Seminar, Indonesia-Malaysia. Banda Aceh, Indonesia.
Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology, 27(4), 600–618.
Venkatesh, V. & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2): 273–315. doi:10.1111/j.1540-5915.2008.00192.x
Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2): 186–204. doi:10.1287/mnsc.46.2.186.11926
Visser, R. D., Evering, L. C. & Barrett, D. E. (2014). Twitter for teachers: The implications of Twitter as a self-directed professional development tool for K-12 teachers. Journal of Research on Technology in Education, 46(4). doi:10.1080/15391523.2014.925694.
Yuan, Y.H., Tsai S.B., Dai, C.Y., Chen, H.M., Chen, W.F., Wu, C.H., Li, G., & Wang, J. (2017). An empirical research on relationships between subjective judgment, technology acceptance tendency and knowledge transfer. PLoS ONE, 12(9).

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Presenters

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Dr. Michael Mills, University of Central Arkansas
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Jessica Herring, University of Central Arkansas
ISTE Certified Educator
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Mia Morrison, University of Maine

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