This study investigated 160 student teachers' and 158 experienced teachers' self-reported computer usage and their future intentions to use computer applications for school assignments. The Technology Acceptance Model (TAM) was used as the framework to determine computer usage and intentions. Statistically significant results showed that after completing a student teaching practicum, students have greater intentions to integrate computer applications into their lessons. Both student and experienced teachers perceived computer usage as useful for their classroom lessons. No differences between the teacher groups were found for computer usage activities (i.e., student assignments, teacher-led classroom lessons, and administrative work). However, experienced teachers used spreadsheets and subject-specific and educational software to a greater extent than did student teachers. Practical suggestions are made for teacher preparation programs and educational technology researchers. Recommendations propose the expansion of TAM to include behavioral control issues when dealing with teachers.
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References
1.
AjzenI. (1985). From intentions to actions: A theory of planned behavior. In KuhlJ. & BeckmannJ. (Eds.), Action control: From cognition to behavior (pp. 11–39). New York: Springer-Verlag.
2.
AustR.NewberryB.O'BrienJ., & ThomasJ. (2005). Learning generation: Fostering innovation with tomorrow's teachers and technology. Journal of Technology and Teacher Education, 13(2), 167–195.
3.
BahrD. L.ShahaS. H.FarnsworthB. J.LewisV. K., & BensonL. F. (2004). Preparing tomorrow's teachers to use technology: Attitudinal impacts of technology-supported field experience on pre-service teacher candidates. Journal of Instructional Psychology, 31(2), 88–97.
4.
BeckerH. J. (1994). How exemplary computer-using teachers differ from other teachers: Implications for realizing the potential of computers in schools. Journal of Research on Computing in Education, 26(3), 291–321.
5.
BeckerH. J. (1998). Running to catch a moving train: Schools and information technologies. Theory Into Practice, 37(1), 20–30.
6.
BensonL. F.FarnsworthB. J.BahrD. L.LewisV. K., & ShahaS. H. (2004). The impact of training in technology assisted instruction on skills and attitudes of pre-service teachers. Education, 124(4), 649–663.
7.
BrentR.BrawnerC. E., & Van DykP. (2002). Factors influencing student teachers' use of technology. Journal of Computing in Teacher Education, 19(2), 61–68.
8.
BrownD., & WarschauerM. (2006). From the university to the elementary classroom: Students' experiences in learning to integrate technology in instruction. Journal of Technology and Teacher Education, 14(3), 559–621.
9.
BrzyckiD., & DudtK. (2005). Overcoming barriers to technology use in teacher preparation programs. Journal of Technology and Teacher Education, 13(4), 619–641.
10.
BullockD. (2004). Moving from theory to practice: An examination of the factors that preservice teachers encounter as the attempt to gain experience teaching with technology during field placement experiences. Journal of Technology and Teacher Education, 12(2), 211–237.
11.
CubanL. (2001). Oversold and underused: Computers in the classrooms.Cambridge, MA: Harvard University Press.
12.
CulpK. M.HoneyM., & MandinachE. (2005). A retrospective on twenty years of education technology policy. Journal of Educational Computing Research, 32(3), 279–307.
13.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
14.
DavisF. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 475–487.
15.
DavisF. D.BagozziR. P., & WarshawP. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
16.
DavisF. D., & VenkateshV. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Man-Machine Studies, 45, 19–45.
17.
DexterS.DoeringA. H., & RiedelE. S. (2006). Content area specific technology integration: A model for education teachers. Journal of Technology and Teacher Education, 14(2), 325–345.
18.
DishawM. T., & StrongD. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36, 9–21.
19.
DoeringA.HughesJ., & HuffmanD. (2003). Preservice teachers: Are we thinking with technology?Journal of Research on Technology in Education, 35(3), 342–361.
20.
DollW. J.HendricksonA., & DengX. (1998). Using Davis's perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869.
21.
DrennanJ.KennedyJ., & PisarskiA. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338.
22.
DuffieldJ. A., & MooreJ. A. (2006). Lessons learned from PT3. TechTrends, 50(3), 54–56.
23.
EdwardsV. B.ChronisterG., & HendrieC. (2006). The Information Edge. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 8–9.
24.
ErtmerR. A.AddisonP.LaneM.RossE., & WoodsD. (1999). Examining teachers' beliefs about the role of technology in the elementary classroom. Journal of Research on Computing in Education, 32(1), 54–73.
25.
FishbeinM., & AjzenI. (1975). Belief, attitude, intention and behavior.Reading, MA: Addison-Wesley Publishing Company, Inc.
26.
GaoY. (2005). Applying the technology acceptance model (TAM) to educational hypermedia: Afield study. Journal of Educational Multimedia and Hypermedia, 14(3), 237–247.
27.
GibsonS., & NocenteN. (1998). Addressing instructional technology needs in faculties of education. The Alberta Journal of Educational Research, 64(3), 320–331.
28.
HadleyM., & SheingoldK. (1993). Commonalities and distinctive patterns in teachers' integration of computers. American Journal of Education, 101, 261–315.
29.
HallL. (2006). Modeling technology integration for preservice teachers: A PT3 case study. Contemporary Issues in Technology and Teacher Education, 6(4), 436–455.
30.
HallL. D.FisherC.MusantiS., & HalquistD. (2006). What can we learn from PT3?TechTrends, 50(3), 25–31.
31.
HendricksonA. R., & CollinsM. R. (1996). An assessment of structure and causation of IS usage. The DATA BASE for Advances in Information Systems, 27(2), 61–67.
32.
IgbariaM.GuimaraesT., & DavisG. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87–114.
33.
IgbariaM.SchiffmanS. J., & WieckowskiT. J. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour & Information Technology, 13(6), 349–361.
34.
JudgeS., & O'BannonB. (2007). Integrating technology into field-based experiences: A model that fosters change. Computers in Human Behavior, 23, 286–302.
35.
KarahannaE., & StraubD. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35, 237–250.
36.
KayR. H. (2006). Evaluating strategies used to incorporate technology into preservice education: A review of the literature. Journal of Research on Technology in Education, 38(4), 383–408.
37.
KelleherT., & O'MalleyM. (2006). Applying the technology acceptance model to assess outcomes in a globally linked strategic communication project. Journalism & Mass Communication Educator, 60(4), 402–414.
38.
LedererA. L.MaupinD. J.SenaM. P., & ZhuangY. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29, 269–282.
39.
MaW. W. K.AndersonR., & StreithK. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21, 387–395.
40.
MimsC.PollyD.ShepherdC., & InanF. (2006). Examining PT3 projects designed to improve preservice education. TechTrends, 50(3), 16–24.
41.
MoonJ. W., & KimY. G. (2001). Extending the TAM for a World Wide Web context. Information & Management, 38, 217–230.
Mowrer-PopielE.PollardC., & PollardR. (1994). An analysis of the perceptions of preservice teachers toward technology and its use in the classroom. Journal of Instructional Psychology, 21(2), 131–138.
44.
OngC. S., & LaiJ. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22, 816–829.
45.
Ottenbreit-LeftwichA., & CullenT. A. (2006). Preserving the legacy of PT3 tools, strategies & resources: Knowledge capture artifacts. TechTrends, 50(3), 46–52.
46.
NgaiE. W. T.PoonJ. K. L., & ChanY. H. C. (2007). Empirical examination of the adoption on WebCT using TAM. Computers & Education, 48, 250–267.
47.
PanC. C.SivoS.GunterG., & CornellR. (2005). Students' perceived ease of use of an Elearning management system: An exogenous or endogenous variable?Journal of Educational Computing Research, 33(3), 285–307.
48.
PituchK. A., & LeeY. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47, 222–244.
49.
SelimH. M. (2003). An empirical investigation of student acceptance of course Websites. Computers & Education, 40, 343–360.
50.
SmarkolaC. (2004). Comparison between student teachers' and classroom teachers' computer usage intentions and self-reported computer usage. Dissertation Abstracts International (UMI No. AAT3128576).
51.
SmerdonB.CronenS.LanahanL.AndersonJ.IannottiN., & AngelesJ. (2000). Teachers' tools for the 21st century: A report on teachers' use of technology (NCES Publication No. 2000–102). Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Statistics.
52.
SwansonC. B. (2006). Tracking U.S. trends. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 50–55.
53.
SzajnaB. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92.
54.
TaylorS., & ToddP. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
55.
TrotterA. (2006). National effort. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 48–49.
U.S. Congress. (1995). Teachers and technology: Making the connection (OTA-EHR-616).Office of Technology Assessment. Washington, DC: U.S. Government Printing Office.
U.S. Department of Education. (2004). Toward a new golden age in American education—How the Internet, the law and today's students are revolutionizing expectations: National education technology plan 2004. Office of Educational Technology. Retrieved October 16, 2006, from http://www.ed.gov/about/offices/list/os/technology/plan/2004/plan_pg14.html#steps.
60.
WangY. M., & HolthausP. (1998–99). Facing the world: Student teachers' computer use during practicum. Journal of Educational Technology Systems, 27(3), 207–223.
61.
WedmanJ., & DiggsL. (2001). Identifying barriers to technology-enhanced learning environments in teacher education. Computers in Human Behavior, 17(4), 421–430.
62.
WillisE. M., & Sujo de MontesL. (2002). Does requiring a technology course in preservice teacher education affect student teacher's technology use in the classroom?Journal of Computing in Teacher Education, 18(3), 76–80.
63.
YuenA. H. K., & MaW. W. K. (2002). Gender differences in teacher computer acceptance. Journal of Technology and Teacher Education, 10(3), 365–382.
