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Navigating Transition: Supporting Technology-enabled Learning During Student Teaching & First-Year Teaching

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Dr. Jessica Herring  

This qualitative case study aimed to describe participants’ intentions to use technology-enabled learning (TEL) compared to their actual use of TEL as they navigated the transition from preservice to novice teaching. Interviews, observations and digital artifacts were collected to illuminate supports and challenges to their use of TEL.

Audience: Coaches, Principals/head teachers, Teacher education/higher ed faculty
Attendee devices: Devices not needed
Participant accounts, software and other materials: Since this is intended to be a presentation of a research study, no accounts, software, or other materials are required of attendees.
Topic: Teacher education
Subject area: Higher education, Preservice teacher education
ISTE Standards: For Coaches:
Change Agent
  • Facilitate equitable use of digital learning tools and content that meet the needs of each learner.
For Educators:
Learner
  • Set professional learning goals to explore and apply pedagogical approaches made possible by technology and reflect on their effectiveness.
Leader
  • Shape, advance and accelerate a shared vision for empowered learning with technology by engaging with education stakeholders.
Additional detail: Graduate student

Proposal summary

Framework

Ajzen’s (1985) Theory of Planned Behavior (TPB) guided the inquiry. According to Ajzen (1985; 1991), a person’s intention to pursue a particular behavior, in this case, the use of TEL, is informed by three constructs: attitude toward the behavior (i.e., what a person thinks about the behavior), subjective norms (i.e., what a person perceives others think about the behavior), and perceived behavioral control (i.e., whether a person feels they have enough control over their abilities and environment to engage in the behavior). While the TPB was first applied to research in the health sciences (Armitage & Connor, 2001; Blue, 1995), it has since seen broad application in education research related to teachers’ technology adoption (e.g., Cullen & Greene, 2011; Gretter & Yadav, 2018; Li et al., 2016; Sadaf et al., 2012; Sadaf & Johnson, 2017). In the present study, the TPB was used to guide the development of interview protocols and as a theoretical lens from which to derive a priori codes as a means of focusing on significant data elements.  

Additionally, this study is grounded in research informing the role of technology in facilitating constructivist pedagogical practices. Scholars have established the effectiveness of TEL over the last two decades (Barak, 2017b; Bower et al., 2013; Ertmer & Ottenbreit-Leftwich, 2012; Hirsh-Pasek et al., 2015; Jonassen, 2005), and professional and accrediting organizations have centered the need to prepare educators and students to be engaged, constructive users of technology (CAEP, 2022; ISTE, 2017). However, the use of TEL aligned with constructivist practices has evolved slowly (Barak, 2017a; Lee, 2018; Nelson & Hawk, 2020; Tondeur et al., 2016). Thus, this study fills an existing gap in the literature by focusing on the intention to use and use of TEL during the critical and complex transition period from preservice to in-service novice teaching.

Methods

This study, in alignment with the qualitative approach, which limits the scope of participation, delimited the sample to preservice teachers who are middle level education majors (i.e., certified to teach grades 4-8). The case or “bounded system” was defined as a shared cohort that have participated in and graduated from the same program of study in the college of education at a mid-size public teaching university in the southeastern United States. Convenience and purposeful sampling methods were used. After gaining IRB approval, participants were recruited from a cohort of middle level education majors at the college of education where I am a faculty member. Participants had to meet specific criteria for inclusion to the study: (1) completion of all coursework in their shared program of study by December 2020; and (2) willingness to participate in interviews and be observed while teaching. Four individuals volunteered their participation and provided their informed consent. Study participants completed their student teaching semester in the same suburban school district during the Spring 2021 semester and began their first year of teaching during the Fall 2021 semester in various public-school districts in the same state in which they attended college.  
In order to provide a rich description, data were collected from a variety of sources, which represent what Yin (2018) called the “full variety of evidence” (p. 12). Interviews were conducted via Zoom video conferencing with the participants at the beginning and end of student teaching and at the beginning and mid-point of the first year of teaching. Direct observations were conducted via Zoom video conferencing during both student teaching and first-year teaching semesters, and lesson plans and TEL artifacts were also examined for evidence of TEL use. These layers of evidence provide important insight regarding potential barriers to TEL use and how teacher educators and teacher education programs can cultivate intention to use TEL among preservice teachers and better support educators in their intention to use and actual use of TEL during novice teaching.
Per Yin’s (2018) guidance, the study employs two analytic strategies. In the first cycle of analysis, which is ongoing, theoretical propositions are guiding the coding process. Since this case study employed Ajzen’s (1985) Theory of Planned Behavior as a theoretical lens, the constructs of the theory (e.g., attitude, subjective norm, perceived behavioral control, intention, and actual use) were established as a priori codes (Saldana, 2014), which will allow me to focus on the significant data elements. The themes derived from the theory will be applied to each data source; interview transcripts, observation field notes, and written artifacts will be examined. Words and phrases that align with the established a priori codes will be highlighted and categorized accordingly. However, open and inductive coding will also be conducted during a second cycle of analysis in order to explore how, if at all, the established theory might be extended, given the experiences and perception of the study participants, especially where phrases and words did not fit within the a priori codes (Yin, 2018). Throughout each cycle of analysis, I will collaborate with my doctoral program advisor. We have had and will continue to have several meetings to discuss and refine the themes I am identifying across the data sources. While all a priori codes were agreed upon before proceeding with data collection, we will individually identify themes that expand the TPB. These emerging themes will be discussed, and agreed upon themes will be collaboratively developed. This will allow me to confirm patterns in the data and establish ways in which the TPB might be extended based on the findings in this study.  

Results

Data collection and analysis are ongoing. Data were collected during the participants’ student teaching experiences in the Spring 2021 semester and are currently being collected during the first semester of their first year of teaching (Fall 2021 semester). Should this paper be accepted, results will be available and presented. These results will include a rich, robust description of the case and participants’ teaching contexts as well as discussion of salient themes identified within the body of evidence.

Importance

This case study allowed for an in-depth exploration and rich description of the intention to use and actual use of TEL by middle level education majors during student-teaching and first-year teaching experiences. While the Theory of Planned Behavior has often been used as a theoretical framework to guide research into preservice teachers’ intentions to use various types of technology (Sadaf et al., 2012; Salleh & Laxman, 2015; Valtonen et al., 2015) and to adopt specific curricula or teaching strategies (Gretter & Yadav, 2018; Sadaf & Johnson, 2017; Voet & De Wever, 2020), a common criticism of the TPB is that intention does not equal action. Therefore, these study results addressed this criticism regarding the gap in the literature as participants’ use of TEL was examined through their transition from preservice to in-service teaching. This study created the opportunity to determine not only what factors appear to influence intention but also whether, if at all, intention to use TEL leads to actual use. Theoretically, the study results provide further validation for the application of TPB in describing preservice and first-year in-service teachers’ intention to use and actual use of TEL. The themes generated from the study will also illuminate ways in which the TPB may be expanded when studying TEL.
Results from this study also yield practical implications for colleges of education by providing insight into what learning experiences within the participants’ shared program of study they deemed most impactful on their intention to use and actual use of TEL. This, then, can inform program development within colleges of education that are seeking to advance the use of TEL among their preservice teachers. As this study follows participants into their first-year teaching experiences, the findings also have practical implications for educators who work to support novice teachers during the induction period (i.e., 1-3 years of teaching). Supporting the pedagogical and technological skill development of novice teachers is one way to ensure their success in improving K-12 students’ engagement, motivation, and learning outcomes. The study findings can also inform the development and implementation of interventions designed to increase intention to use and actual use of TEL with both preservice and novice teacher populations.    

References

Ajzen I. (1985) From intentions to actions: A theory of planned behavior. In: J. Kuhl & J. Beckmann (Eds.), Action control (pp. 11-39). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-69746-3_2
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40, 471-499. https://doi.org/10.1348/014466601164939
Barak, M. (2017). Cloud pedagogy: Utilizing web-based technologies for the promotion of social constructivist learning in science teacher preparation courses. Journal of Science Education and Technology, 26(5), 459-469. doi:10.1007/s10956-017-9691-3
Barak, M. (2017). Science teacher education in the twenty-first century: A pedagogical framework for technology-integrated social constructivism. Research in Science Education, 47(2), 283-303. https://doi.org/10.1007/s11165-015-9501-y 
Blue, C. L. (1995). The predictive capacity of the theory of reasoned action and the theory of planned behavior in exercise behavior: An integrated literature review. Research in Nursing & Health, 18, 105-121. https://doi.org/10.1002/nur.4770180205
Bower, M., Highfield, K., Furney, P., & Mowbray, L. (2013). Supporting preservice teachers' technology-enabled learning design thinking through whole of programme transformation. Educational Media International, 50(1), 39-50. https://doi.org/10.1080/09523987.2013.777183
Council for the Accreditation of Educator Preparation [CAEP]. (2022). 2022 CAEP Standards. Retrieved from http://www.caepnet.org/standards/2022/introduction
Cullen, T. A., & Greene, B. A. (2011). Preservice teachers’ beliefs, attitudes, and motivation about technology integration. Journal of Educational Computing Research, 45(1), 29-47.  https://doi.org/10.2190/ec.45.1.b 
Ertmer, P. A., & Ottenbreit-Leftwich, A. (2012). Removing obstacles to the pedagogical changes required by Jonassen's vision of authentic technology-enabled learning. Computers & Education, 1-8. https://doi.org/10.1016/j.compedu.2012.10.008
Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of preservice teachers explained by attitudes and beliefs, competency, access, and experience. Computers & Education, 130, 81-93. https://doi.org/10.1016/j.compedu.2018.11.010 
Gretter, S., & Yadav, A. (2018). What do preservice teachers think about teaching media literacy? An exploratory study using the theory of planned behavior. Journal of Media Literacy Education, 10(1), 104-123. https://doi.org/10.23860/jmle-2018-10-1-6 
Han, I., Shin, W. S., & Ko, Y. (2017). The effect of student teaching experience and teacher beliefs on pre-service teachers’ self-efficacy and intention to use technology in teaching. Teachers and Teaching, 23(7), 829-842.
Hirsh-Pasek, K., Zosh, J. M., Golinkoff, R. M., Gray, J. H., Robb, M. B., & Kaufman, J. (2015). Putting education in “educational” apps: Lessons from the science of learning. Psychological Science in the Public Interest, 16(1), 3-34. https://doi.org/10.1177/1529100615569721 
International Society for Technology in Education [ISTE]. (2017). ISTE standards for educators. Retrieved from https://www.iste.org/standards/for-educators
Jonassen, D. H. (2005). Technology as cognitive tools: Learners as designers. ITForum Paper, 1, 67-80.
Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers' intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Educational Technology & Society, 21(3), 48-59.
Lee, Y. (2018). Internet-based epistemic beliefs, engagement in online activities, and intention for constructivist ICT integration among preservice teachers. Australasian Journal of Educational Technology, 34(5), 120-134. https://doi.org/10.14742/ajet.3747
Li, K., Li, Y., & Franklin, T. (2016). Preservice teachers' intention to adopt technology in their future classrooms. Journal of Educational Computing Research, 54(7), 946-966.   https://doi.org/10.1177/0735633116641694 
Nelson, M. J., & Hawk, N. A. (2020). The impact of field experiences on prospective preservice teachers’ technology integration beliefs and intentions. Teaching and Teacher Education, 89, 1-12. https://doi.org/10.1016/j.tate.2019.103006
Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring factors that predict preservice teachers' intentions to use web 2.0 technologies using decomposed theory of planned  behavior. Journal of Research on Technology in Education, 45(2), 171-196. https://doi.org/10.1080/15391523.2012.10782602 
Sadaf, A., & Johnson, B. L. (2017). Teachers’ beliefs about integrating digital literacy into classroom practice: An investigation based on the theory of planned behavior. Journal of  Digital Learning in Teacher Education, 33(4), 129- 137. https://doi.org/10.1080/21532974.2017.1347534 
Saldana, J. (2014). Coding and analysis strategies. In P. Leavy (Ed.), The Oxford guidebook of qualitative strategies. (pp. 581-605). Oxford Handbooks. https://doi.org/10.1093/oxfordhb/9780199811755.013.001
Salleh, S., & Laxman, K. (2015). Examining the effect of external factors and context-dependent beliefs of teachers in the use of ICT in teaching: Using an elaborated theory of planned  behavior. Journal of Educational Technology Systems, 43(3), 289-319. https://doi.org/10.1177/0047239515570578
Sanchez-Prieto, J. C., Hernandez-Garcia, A., Garcia-Penalvo, F. J., Chaparro-Pelaez, J., & Olmos-Miguelanez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019 
Savin-Baden, M. & Major, C. H. (2013). Qualitative research: The essential guide to theory and practice. Routledge.
Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2016). Understanding the relationship between teachers' pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555-575. https://doi.org/10.1007/s11423-016-9481-2 
Valtonen, T., Kukkonen, J., Kontkanen, S., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on preservice teachers' intentions to use ICT for  teaching and learning. Computers & Education, 81, 49-58. https://doi.org/10.1016/j.compedu.2014.09.008 
Voet, M., & De Wever, B. (2020). How do teachers prioritize instructional goals? Using the theory of planned behavior to explain goal coverage. Teaching and Teacher Education,  89, 103005. https://doi.org/10.1016/j.tate.2019.103005 
Yin, R. K. (2018). Case study research: Design and methods. (6th ed.). SAGE Publications.

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Presenters

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Dr. Jessica Herring, University of Central Arkansas
Graduate student

ISTE Certified Educator

Jessica Herring-Watson is an Instructor in the College of Education at the University of Central Arkansas. She works with K-20 teachers and students in a variety of settings as a teacher and trainer. With over a decade of teaching experience, Jessica focuses her work on cultivating meaningful relationships between technology-enabled learning and engaging pedagogy. She earned a Masters in Teacher Leadership and is a doctoral candidate pursuing an Ed.D. in Instructional Design & Technology. Jessica has presented and served as a featured speaker at a variety of regional, national, and international conferences, including ISTE, AERA, and SXSWedu.

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