Teaching to Tech: A Quasi-Experimental Assessment of a Technology-Enhanced Social Work Course

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Battista-Frazee, K. (2017). The high-tech social worker—Myth or reality? Social Work Today, 17(1), 10–14. https://www.socialworktoday.com/archive/011917p10.shtml
   Google Scholar
• Berzin, S. C., & Coulton, C. J. (2018). Harness technology for social good. In R. Fong, J. Lubben, and R.P. Barth (Eds.), Grand challenges for social work and society (pp. 161–180). Oxford University Press.
   Google Scholar
• Berzin, S. C., Singer, J., & Chan, C. (2015). Practice innovation through technology in the digital age: A grand challenge for social work (Working Paper 12). American Academy of Social Work & Social Welfare. https://grandchallengesforsocialwork.org/wp-content/uploads/2015/12/WP12-with-cover.pdf
   Google Scholar
• Cauble, A. E., & Thurston, L. P. (2000). Effects of interactive multimedia training on knowledge, attitudes, and self-efficacy of social work students. Research on Social Work Practice, 10(4), 428–437. https://doi.org/10.1177/104973150001000404
   Web of Science ®Google Scholar
• Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148–158. https://doi.org/10.1016/j.compedu.2012.06.008
   Web of Science ®Google Scholar
• Chandrasekhar, C. P., & Ghosh, J. (2001). Information and communication technologies and health in low-income countries: The potential and the constraints. Bulletin of the World Health Organization, 79(9), 850–855. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566653/
   Web of Science ®Google Scholar
• Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003
   Web of Science ®Google Scholar
• Christensen, R., & Knezek, G. (2017). Validating the technology proficiency self-assessment questionnaire for 21st century learning (TPSA C-21). Journal of Digital Learning in Teacher Education, 33(1), 20–31. https://doi.org/10.1080/21532974.2016.1242391
   Google Scholar
• Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158. https://doi.org/10.2307/249749
   Web of Science ®Google Scholar
• Council on Social Work Education. (2018). Results of the nationwide survey of 2017 social work graduates. The George Washington University Health Workforce Institute. https://cswe.org/Centers-Initiatives/Initiatives/National-Workforce-Initiative/Survey-of-2017-SW-Grads-Report-FINAL.aspx
   Google Scholar
• Finnegan, D. J. (1996). Unraveling social workers’ ambivalence toward computer technology: An analysis of the attitudes of social work students towards computers and social work practice. Computers in Human Services, 13(2), 33–49. https://doi.org/10.1300/J407v13n02_03
   Google Scholar
• Geller, S. L., Abramson, A. J., & de Leon, E. (2010). The nonprofit technology gap—Myth or reality (Communique no. 20). Johns Hopkins University: Center for Civil Society Studies.
   Google Scholar
• Gershenfeld, N., Gershenfeld, A., & Cutcher-Gershenfeld, J. (2017). Designing reality: How to survive and thrive in the third digital revolution. Hachette UK.
   Google Scholar
• Goldkind, L., Wolf, L., & Jones, J. (2016). Late adapters? How social workers acquire knowledge and skills about technology tools. Journal of Technology in Human Services, 34(4), 338–358. https://doi.org/10.1080/15228835.2016.1250027
   Web of Science ®Google Scholar
• Gubrium, A. (2009). Digital storytelling: An emergent method for health promotion research and practice. Health Promotion Practice, 10(2), 186–191.
   PubMedGoogle Scholar
• Hafford-Letchfield, T., Dayananda, A., & Collins, D. A. (2018). Digital storytelling for interprofessional collaborative practice to develop quality and service improvements. Social Work Education, 37(6), 804–812.
   Web of Science ®Google Scholar
• King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
   Web of Science ®Google Scholar
• Mishna, F., Bogo, M., & Sawyer, J. L. (2015). Cyber counseling: Illuminating benefits and challenges. Clinical Social Work Journal, 43(2), 169–178. https://doi.org/10.1007/s10615-013-0470-1
   Web of Science ®Google Scholar
• National Association of Social Workers. (2017). Technology in social work practice. https://www.socialworkers.org/includes/newIncludes/homepage/PRA-BRO-33617.TechStandards_FINAL_POSTING.pdf
   Google Scholar
• Odgers, C. L., Caspi, A., Bates, C. J., Sampson, R. J., & Moffitt, T. E. (2012). Systematic social observation of children’s neighborhoods using Google Street View: A reliable and cost‐effective method. Journal of Child Psychology and Psychiatry, 53(10), 1009–1017. https://doi.org/10.1111/j.1469-7610.2012.02565.x
   PubMed Web of Science ®Google Scholar
• Perron, B. E., Taylor, H. O., Glass, J. E., & Margerum-Leys, J. (2010). Information and communication technologies in social work. Advances in Social Work, 11(1), 67–81. https://doi.org/10.18060/241
   PubMedGoogle Scholar
• Pew Research Center (2019). Internet and technology fact sheets. https://www.pewresearch.org/internet/fact-sheet/
   Google Scholar
• Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003
   Web of Science ®Google Scholar
• Quinn, A., & Fitch, D. (2014). A conceptual framework for contextualizing information technology competencies. Journal of Technology in Human Services, 32(1–2), 133–148. https://doi.org/10.1080/15228835.2013.860367
   Google Scholar
• R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Reamer, F. G. (2015). Digital technology in social work. Encyclopedia of Social Work. https://doi.org/10.1093/acrefore/9780199975839.013.1160
   Google Scholar
• Schoech, D. (2008). Technology. In T. Mizrahi & L. E. Davis (Eds.), Encyclopedia of social work (pp. 204–213). National Association of Social Workers and Oxford University Press.
   Google Scholar
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
   Google Scholar
• Sousa, C. A., Lemuel Yutzy, L., Campbell, M., Cook, C., & Slates, S. (2020). Understanding the curricular needs and practice contexts of macro social work: A community-based process. Journal of Social Work Education 56(3) , 533–547. doi:10.1080/10437797.2019.1656686
   Web of Science ®Google Scholar
• Svinicki, M. D. (2004). Learning and motivation in the post-secondary classroom. Anker.
   Google Scholar
• Teixeira, S., & Hash, K. M. (2017). Teaching note—Tweeting macro practice: Social media in the social work classroom. Journal of Social Work Education, 53(4), 751–758.
   Web of Science ®Google Scholar
• Ting, L., Mellinger, M. S., & Morris, K. (2014). Perceived skills, self-efficacy, and attitudes toward technology: A pilot study of BSW students. Journal of Baccalaureate Social Work, 19(1), 65–81. https://doi.org/10.18084/basw.19.1.e08736w784506p10
   Google Scholar
• United Nations. (2006). Information and communication technology vital to development. UN Assembly Chief. http://www.un.org/apps/news/story.asp?NewsID=20780&Cr=information&Cr1=technology
   Google Scholar
• Whitaker, T., Torrico Meruvia, R., & Jones, A. (2010). Child welfare social workers’ attitudes toward mobile technology tools: Is there a generation gap. NASW.
   Google Scholar
• Wilkinson, A., Roberts, J., & While, A. E. (2010). Construction of an instrument to measure student information and communication technology skills, experience and attitudes to e-learning. Computers in Human Behavior, 26(6), 1369–1376. https://doi.org/10.1016/j.chb.2010.04.010
   Web of Science ®Google Scholar
• Wolf, L., & Goldkind, L. (2016). Digital native meet friendly visitor: A Flexner-inspired call to digital action. Journal of Social Work Education, 52(Suppl. 1), S99–S109. https://doi.org/10.1080/10437797.2016.1174643
   Google Scholar
• Young, J. A. (2015). Assessing new media literacies in social work education: The development and validation of a comprehensive assessment instrument. Journal of Technology in Human Services, 33(1), 72–86. https://doi.org/10.1080/15228835.2014.998577
   Web of Science ®Google Scholar