Publications and Abstracts
Hawk, N.A., Bowman, M.A., Xie, K. (2021). Theory-based intervention framework to improve mathematics teachers’ motivation to engage in online professional development. In K. Hollebrands, R. Anderson & K. Oliver (Eds), Online learning in mathematics teacher education. Springer.
Professional development for teachers is an important professional endeavor and is increasingly conducted online. One concern, however, is the extent of teachers’ motivation to engage. Among motivational theories, the Expectancy-Value Theory is one empirically tested framework which appropriately encompasses these values. Teacher-perceived task values, or the reasons to engage in a task, along with teachers’ expectancy for success, may help explain why teachers engage in online professional development. In this chapter, we synthesize the available research literature guided by this integrated motivational framework of Expectancy-Value Theory, and we propose five design principles to guide future online professional development in ways that may improve teachers’ motivation to engage. We discuss implications for researchers and designers of mathematics professional development.
Hawk, N.A., Vongkulluksn, V.W., Xie, K., & Bowman, M.A. (2021). Cognitive tasks in the core content areas: Factors that influence students’ technology use in high-school classrooms. Journal of Computer Assisted Learning.
Prior studies have focused on general technology use and technology use in domain‐general applications and quantity of technology use. Recent evidence suggests that investigations should consider how technology is used in more contextually specific ways, including how technology is used for various cognitive tasks in specific classrooms. The purpose of this study was to examine the ways in which classroom content area and student goal orientation have a coordinated influence for how students used technology to support learning. The sample included high school students in a Midwestern state who were surveyed on their motivation and how they used technology to support learning. The study employed hierarchical linear modelling to examine how goal orientation and classroom content area predicted various levels of Bloom's Digital Taxonomy. Students who adopted mastery‐oriented goals were more likely to use technology for various cognitive tasks, especially those at higher levels of complexity. Lastly, the association between mastery goal orientation and some aspects of technology use was conditioned on content area, although effect sizes were small. This study showed that, overall, technology is used differentially across four core content areas. Students in mathematics classrooms used technology less, however much of technology use was evident at lower cognitive levels. Second, students' goal orientation, and in particular their mastery goals influence how technology is used across content areas, and this is marginally conditioned on content area. Technology use should match the instructional context to maximize technology use and students' goal orientation.
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, 103006.
This study investigated how field experiences impacted the technology integration beliefs and intentions of prospective preservice teachers. Using structural equation modeling (SEM), a three-way interaction between the type, frequency, and quality of field technology observations predicted changes in beliefs and intentions. Beliefs about the utility of technology directly predicted intentions to use technology and intentions to use Meaningful Learning approaches to technology integration. Additionally, beliefs about technology’s importance in education indirectly predicted both variables. Positive impacts of field experiences on beliefs and intentions only existed when prospective preservice teachers saw technology used frequently by skilled teachers using Meaningful Learning approaches.
Duan, J., Xie, K., Hawk, N. A., Yu, S., & Wang, M. (2019). Exploring a Personal Social Knowledge Network (PSKN) to aid the observation of connectivist interaction for high- and low-performing learners in connectivist massive open online courses: Aid the observation of connectivist interaction in cMOOCs. British Journal of Educational Technology, 50(1), 199–217.
This study adds a new perspective to the observations about connectivist interaction behavior in cMOOCs by extending the notion of network building from the perspective of individuals. We explore the possibility of building a learning network named Personal Social Knowledge Network (PSKN) to support in the monitoring of learning performance and interaction in cMOOCs. The sample in this study included 284 preservice teachers and their learning lasted approximately 12 weeks. Data were primarily gathered by PSKN graphs. The results revealed a correlation between connectivist interaction measured by the PSKN (including density structure) and learning performance. The results also revealed differences in connectivist interaction behavior and patterns, indicated by PSKN (densities and structures), for high‐ and low‐performing learners in cMOOCs. The high‐performing learners show deeper knowledge interaction and social communication in addition to simple knowledge sharing and social communication. Additionally, as time passed and the PSKN of high‐performing learners extended further, their interaction behavior became more complex and their role had gradually changed from “learning” to “teaching” as well as from knowledge acceptance to knowledge creation in cMOOCs.
Xie, K., & Hawk, N.A. (2017). Technology’s role and place in student learning: What we have learned from research and theories. In J.G., Cibulka, & B.S. Cooper (Eds.), Technology in school classrooms: How it can transform teaching and student learning today (pp 1-17). Lanham, MD, USA: Rowman & Littlefield.
The use of technology in general, and computers more specifically, has increased in both our society and our classrooms. Modern technologies have become more powerful, more accessible, more distributed, and more intelligent. For example, mobile device ownership in the United States has steadily increased over the past ten years, with 90 percent of adults owning a mobile device and 60 percent owning a smartphone (Anderson, 2015). In addition, the participatory concept of Web 2.0 has reshaped the landscape of the Internet. The media and content on the web have grown substantially. Newer types of technology, such as location aware services, sensor technologies, open platform technologies, cloud computing technologies, artificial intelligence, and argument reality, are changing human experiences. New experiences with the technology are created that involve users being integrated within their real context, that use services for everyday tasks, such as driving directions and targeted marketing, and opportunities exist for greater collaboration with peers and experts around the world. These new forms of experiencing the world increase the authenticity of informal, in-time learning, central to the nonclassroom-based society today and critical to lifelong learning.
Hawk, N.A., Bartle, G., & Romine, M. (2012). The living labs: Innovation in real life settings. Quarterly Review of Distance Education, 13(4), 225-231.
The living lab (LL) is an open innovation ecosystem serving to provide opportunities for local stakeholders to practice research and to experiment with meaningful improvements for cities and other organizations. Living labs aim at involving the user as a cocreator. In this article the relationship between the LLs and a variety of stakeholders is discussed. Aspects of the leadership structures of LLs are critically analyzed and discussed, identifying opportunities and challenges. Examples of a LL in action are given. Recommendations for future development of the LLs are discussed in the concluding part of the article.[PUBLICATION ABSTRACT]