Mapping the Contours of the Research on Learning to Teach with Technology: Clusters, Categories, and Missing Trajectories
Jeremy F. Price*, Josh Manlove*, Zachary Morgan**, Akaash Arora*, Ted Hall*
*Indiana University School of Education-Indianapolis at IUPUI, USA
**Bellevue College, USA
Education Thinking, ISSN 2778-777X – Volume 3, Issue 1 – 2023, pp. 19–40. Date of publication: 21 March 2023.
Cite: Price, J. F., Manlove, J., Morgan, Z., Arora, A., & Hall, T. (2023). Mapping the Contours of the Research on Learning to Teach with Technology: Clusters, Categories, and Missing Trajectories. Education Thinking, 3(1), 19–40. https://analytrics.org/article/mapping-the-contours-of-the-research-on-learning-to-teach-with-technology-clusters-categories-and-missing-trajectories/
Declaration of interests: The authors declare that they have no conflicts of interest.
Authors’ notes: Jeremy Price is Assistant Professor of Technology, Innovation, and Pedagogy in Urban Education at the IU School of Education-Indianapolis. Josh Manlove and Akaash Arora are students in the Urban Education PhD program at the IU School of Education-Indianapolis. Zachary Morgan is Executive Director of Effectiveness Research & Grants at Bellevue College. Ted Hall is Clinical Associate Professor in Urban Teacher Education at the IU School of Education-Indianapolis.
Copyright notice: The authors of this article retain all their rights as protected by copyright laws. However, sharing and adapting this article—although not for commercial purposes—and distributing it only under the same license is permitted under the terms of the Creative Commons Attribution-NonCommercial–ShareAlike BY-NC-SA 4.0 International license, provided that the article’s reference (including authors’ names and Education Thinking) is cited.
Journal’s areas of research addressed by the article: 42-Mixed Research Methods, 63-Teacher Education & Development, 65-Techologies for Learning.
Abstract
This review of the literature examines research reports on learning to teach with technology between 2013 and 2019 to illuminate the characteristics of the field at multiple levels of granularity and to call attention to what is missing. We ask the question: What does the overarching paradigm of the field of research on learning to teach with technology look like? Using a mixed paradigmatic and data science-based analysis that involved qualitatively coding the methodologies, purposes, and approaches in the manuscripts and applying a hierarchical clustering of principal components algorithm, five clusters emerged on a two-dimensional axis that centered on exploring the teacher pipeline versus social and individual experiences on one axis and behaviors and practices versus attitudes and beliefs on the other. The field was found to be tightly centralized, and clusters overlapped and intersected with methods and outcomes bundled together in a milieu buffeted by neoliberal logics and a sense of techno-utopianism to largely support default theories around technology as a “fix” and as an end in itself to build the teacher workforce. This review finds several critical areas underrepresented, such as time- and context-bound ethnographic studies, approaches that center on anti-oppressive critical media literacy, understanding the ways technology can bridge the classroom with families and communities, and learning to teach with technology for equity and inclusion to support the sustainability and development of identities, communities, and a more democratic society.
Keywords
Teacher education, Educational technology, Technology integration, Paradigmatic analysis, Hierarchical clustering of principal components analysis, Educational equity and inclusion.
Preparing preservice teachers to teach with technology is important for the contemporary educational context (Darling-Hammond & Bransford, 2005; Davis, 2010; Lemke, 2002). As a relatively new subfield within the broader field of teacher education, little is known about the paradigm and dynamics that shape research on learning to teach with technology. Literature reviews provide clarity into what is known and how this knowledge is garnered (Boote & Beile, 2005; Onwuegbuzie et al., 2012) and point the field in generative directions (Boote & Beile, 2005; Shulman, 1999). The concept of paradigm (Wideen et al., 1998)—how the field conducts research and what it values—is a central framework for this review.
Through this literature review, we explore the question, What does the overarching paradigm of the subfield of research on learning to teach with technology look like?. We use the following sub-questions to further guide our inquiry:
- What clusters and categories exist within the paradigm to shape and inform broader approaches to learning to teach with technology? And
- What is missing from the paradigm and in what directions can the paradigm grow?
Recognizing the mutually-informing intersections of research, practice, and policy (Cochran-Smith, 2006; Darling-Hammond, 2016), we look at manuscripts published between 2013 and 2019. The Council for the Accreditation of Educator Preparation (CAEP) assumed oversight for accreditation in 2013 and instituted “Technology and Digital Media” as a cross-cutting theme in their standards for teacher preparation (CAEP, 2013). CAEP is the accreditation body for schools of education in the United States, setting the standards and direction for what a successful teacher preparation looks like. The introduction of this theme in the CAEP standards served as our starting point because we assumed that this introduction would stimulate research on technology use across the teacher preparation curriculum, rather than solely within the confines of specific educational technology courses.
Our review is undergirded by the assumption that learning to teach with technology and researching learning to teach with technology are purposeful and genre-informed activities (Cochran-Smith et al., 2010; Wideen et al., 1998) that are influenced by social structures and politics within the classroom, the academy, and society at large (Bourdieu, 1988; Cochran-Smith & Villegas, 2015; Herndl & Nahrwold, 2000), yet the frameworks that underlie these activities are not always made explicit resulting in sets of default theories (Ladson-Billings, 1995). As such, we engage in bricolage, using various methods to elucidate underlying assumptions (Kincheloe, 2001, 2005; Levi-Strauss, 1968; Wideen et al., 1998). We used a combination of quantitative data science (Finzer, 2013; Mattmann, 2013) and qualitative paradigmatic analysis (Polkinghorne, 1995; Wideen et al., 1998) to map the field.
Methods
Identifying Manuscripts
To identify the manuscripts, we queried the online database ERIC using the following search phrase:
(descriptor:”technology integration” OR descriptor:”computer uses in education”) AND (descriptor:”preservice teachers” OR descriptor:”preservice teacher education” OR descriptor:”teacher education” OR descriptor:”teacher education programs”) AND (-descriptor:”foreign countries”).
The descriptors were selected from the ERIC thesaurus of official descriptor terms. Only peer-reviewed articles from 2013-2019 labeled in ERIC as “Reports-Research” were included. As can be seen from the minus sign in front of the last descriptor, research from outside the United States was excluded. The inclusion criteria ended with December 2019 because of the SARS-CoV-2 (COVID-19) pandemic. Starting in 2020, there was a marked increase in the number of manuscripts published in this area; the great majority of these manuscripts, however, focused on the contexts, practices, and challenges of delivering teacher education courses online due to widespread stay-at-home orders rather than on teaching future teachers to teach with technology. We additionally excluded manuscripts where technology was used solely as a tool within a teacher education course rather than to teach or model technology use or where technology was directed at preservice early childhood (pre-kindergarten) educators, a branch in itself of the research literature on teacher education. This process resulted in a list of 87 research manuscripts.
Coding Manuscripts
We used five methods to code the manuscripts: ERIC descriptors, data sources, research genres, purposes, and teacher education traditions. The ERIC descriptors were pulled directly from ERIC. The data sources were assigned by one co-author and confirmed by the first author. The co-authors each coded half of the manuscripts for genres, purposes, and traditions; the first author coded all manuscripts. Disagreements were negotiated until there was full agreement by all authors.
ERIC Descriptors
The ERIC-assigned descriptors (ERIC, n.d.) were used as one dimension for analysis. Similar descriptors were consolidated into broad categories. Where possible, existing ERIC descriptors were used although some categories were created post hoc. “Knowledge” was reclassified as “TPACK” (technological pedagogical content knowledge; Koehler & Mishra, 2008; Mishra & Koehler, 2006) as these manuscripts were grounded in TPACK.
Data Sources
The data sources for each manuscript were identified and used to describe each manuscript. Data source types were determined a priori and then iteratively refined based on the review. The final list included surveys, student (preservice teacher) artifacts, interviews, observations, and curriculum reviews.
Research Genres
The four research genres of teacher education identified by Borko et al. (2008) were used to classify the manuscripts. A genre is a “…particular way of thinking about whether or how teacher education makes a difference” (M. M. Kennedy, 1991, p. 3) and provides coherent structures for considering the ways in which teacher education occurs, what is studied and what the expected outcomes are. The four genres are effects of teacher education (identifying generalizable factors between approaches and ultimate outcomes), interpretive (complex local conditions and practices within particular sociocultural contexts), practitioner (research carried out by teacher educators on their own approaches), and design (iterative research negotiating design and practice).
Purposes
A framework was developed to identify the purposes informing the manuscripts. Academic learning (Hansen, 2008) posits preservice teachers must be provided with academic knowledge. Human capital development (A. Kennedy, 2015) proposes that nations benefit from well-prepared teachers in the workforce. Socialization (Biesta, 2009; A. Kennedy, 2015) refers to preparing preservice teachers to become part of the teaching community. Social justice (Hansen, 2008) involves preparing preservice teachers to promote diversity and engage in political action. Subjectification (Biesta, 2009; A. Kennedy, 2015) promotes the critical and creative thinking of preservice teachers.
Teacher Education Traditions
Zeichner (1993) identified traditions of teacher education used to code the manuscripts. Academic posits that preservice teachers should be prepared with core knowledge. The developmentalist tradition assumes that preservice teachers should be fostered on a natural trajectory of growth. The social efficiency tradition has two aspects: technical emphasizes the scientific study of teaching to acquire skills while deliberative focuses on fostering reflective practices (Feiman-Nemser, 1990).
Processing Manuscripts
The R statistical programming language (R Core Team, 2022) was used to conduct the quantitative analyses. A principal component analysis (PCA; Abdi & Williams, 2010; Bollen et al., 2009; De Leeuw, 2011) and a related hierarchical clustering on principal components was conducted using FactoMineR (Le et al., 2008) and factoextra (Kassambara & Mundt, 2020).
To calculate and identify the clusters, the variables were recombined, and their number was reduced to provide a clearer picture of the field. The first stage involved minimizing the “noise” by reducing the ERIC-designated Descriptors to a smaller number of categories. These categories were then visualized with PCA to detect overlapping categories. These went through a recombination process until the final categories in Table 1 were identified.
These categories then served as principal components for a hierarchical clustering algorithm using FactoMineR (Le et al., 2008) and factoextra (Kassambara & Mundt, 2020) to graphically determine the “shape” of the manuscripts in relation to each other and to the codes. The HCPC function (Hierarchical Clustering of Principal Components) of the FactoMineR package identifies the optimal number of clusters as it processes the data. Through the analysis, it identified five clusters as the optimal number. Once the clusters were identified, the manuscripts in each cluster were reexamined thematically to identify categories within the clusters. Each cluster was found to have two distinct categories within them. These clusters and their categories will be described and explored in the next section.
Results
This section will provide an overview of the findings, identifying the number and composition of the clusters. Each cluster will be identified and described and located along Dimensions 1 and 2. Additionally, each cluster is divided into two categories and described on the basis of representative manuscripts to provide both an overview of the field and the depth of each cluster.
The clusters can be visualized as a biplot, mapping the clusters onto two-dimensional space (Figure 1). In a biplot, the axes (the horizontal axis is Dimension 1 and the vertical axis is Dimension 2) have meaning. Determining what the axes represent requires examining the contributions of each of the principal components through a correlation plot (Figure 2). The larger and lighter the circle on the correlation plot, the greater the contribution of that component to the dimension. Examining both the biplot (Figure 1) and the correlation plot (Figure 2), Dimension 1 was found to represent Teacher Pipeline Frameworks on the left (negative) side and Social and Individual Experiences on the right (positive) side. Dimension 2 was found to represent the Attitudes and Beliefs of Preservice Teachers on the bottom (negative) side and the Behaviors and Practices of Teacher Candidates on the top (positive) side.
The biplot in Figure 1 highlights a tightly centralized set of literature within the field with little variance and overlapping clusters. Figure 3 provides another view of the manuscripts by dimension. This figure demonstrates that there are more studies exploring the teacher pipeline than experiences, and there are more studies focusing on attitudes and beliefs than behaviors and practices. This distribution visualization provides a broad understanding of the approaches in the research; the clusters and categories, which extend across the dimensional spans, provide a more detailed and nuanced understanding of the field. The following is a description of each of these clusters and categories.
Cluster 1: Learning to Teach with Games and Simulations
The manuscripts that compose Cluster 1 cover lines of research that examine the ways that video games, three-dimensional simulated environments, and the design of games as an educational activity can be used in teacher education. This cluster is situated strongly on Dimension 1 toward the Teacher Pipeline end and stretched across the spectrum from Attitudes and Beliefs to Behaviors and Practices. There were two categories that emerged in Cluster 1: preservice teachers’ attitudes toward games and game design and using online virtual worlds with teacher candidates.
Preservice Teachers’ Attitudes toward Games and Game Design
Akcaoglu and Kale (2016) documented a workshop for preservice teachers on incorporating game design into their future teaching practice. They provided a mix of overall findings and individual student case studies. The authors found that while the preservice teachers were able to master game design practices, the ways in which they incorporated
game design into the practice of lesson planning were largely dependent on the candidates’ previous experiences with gaming in general and recognizing how games could be used for learning. The teacher candidates were also greatly influenced by the nature of their placement classrooms. Ray et al. (2014) provide an analysis of findings from a survey of teacher candidates and their attitudes about the educational value of video games. The authors find that these teacher candidates hold conflicting beliefs: they see video games as potentially beneficial for specific learning tasks but also hold video games in low esteem.
Using Online Virtual Worlds with Teacher Candidates
Bahng and Lee (2017) examined how three-dimensional virtual reality simulations in Second Life for preservice teachers influenced the ways they considered inquiry learning. Using a constant comparative approach that relied on activity theory and communities of practice, the authors found that there was no significant impact, and that pre- and post-activity
scaffolding may be necessary to appropriately frame the experience for the teacher candidates. Nussli et al. (2014) also examined teacher candidates’ attitudes toward inquiry experiences in the virtual reality environment Second Life. The authors found that the teacher candidates’ attitudes changed to a statistically significant degree. Nussli et al. (2014) provide a “Virtual World 6-Step Model” for teacher preparation programs. Dooley et al. (2014) also used Second Life to allow teacher candidates to explore a virtual world while inhabiting the avatar of characters from children’s books, drawing on the concept of reader response theory. The authors found that the teacher candidates reported feeling like they had “stepped in” to the characters, and therefore disagreed with the assertion that a strong sense of knowledge was necessary before engaging in technology-mediated activities.
Cluster 2: Building a TPACK-Informed Teacher Workforce
Lines of research that examine how Technological Pedagogical Content Knowledge (TPACK; Mishra & Koehler, 2006) can be leveraged to prepare preservice teachers for their future careers represented Cluster 2. The cluster was situated predominantly on the Teacher Pipeline and Attitudes and Beliefs ends of Dimensions 1 and 2 of the biplot. Two categories emerged in this cluster: impacts of courses on preservice teachers’ attitudes and assessing measurement tools of preservice teachers’ attitudes.
Impacts of Courses on Preservice Teachers’ Attitudes
Hall (2018) measured teacher candidates’ change in self-perceived Technological Pedagogical Content Knowledge (TPACK) before and after participation in an educational technology course. There were small but statistically significant differences; Hall (2018) asserted that a larger sample size is required to demonstrate a larger effect size. Willis (2015) similarly found gains in self-efficacy by teacher candidates after they engaged in a preparation course that focused on technology and included specific scaffolding strategies to support this growth, but did not focus on TPACK-focused growth specifically. Mouza (2017) provided a longer view of these TPACK-related changes, as teacher candidates completed surveys over a period of two years while in a teacher education program. The authors found that the lack of continued exposure to educational technology coursework inhibited continued growth on these measures.
Assessing Measurement Tools of Preservice Teachers’ Attitudes
Using a survey (the Teacher Technology Proficiency Assessment, TTPA), observations, and the analysis of artifacts, Kovalik et al. (2013) investigated teacher candidates’ changing attitudes toward technology through the NETS-T national technology standards. The authors asserted that the use of the TTPA was a useful tool for understanding the growth of students’ technology knowledge and skills. Banister and Vannatta Reinhart (2013) documented the use of a particular survey tool as a way to measure teacher candidates’ relationships with the NETS-T during their final year of preparation and found it to be useful.
Cluster 3: Understanding What Teacher Candidates Do with Technology
The manuscripts that compose Cluster 3 examined how teacher candidates use technology in different learning contexts, in learning pedagogical methods, and in enacting methods in their field placements. This cluster was situated largely toward Behaviors and Practices and leaned toward Teacher Pipeline. Two categories emerged in this cluster: impacts of co-taught classes and measuring levels of integration.
Impacts of Co-Taught Classes
Green et al. (2013) conducted research on co-taught courses between instructional technology and teacher education faculty in methods courses. Using a mixed methods approach, the authors found that the courses were successful in helping candidates use technology in their teaching practices. Thomas et al. (2019) also examined a collaborative approach between a math methods course and an instructional technology course around Teacher Educator Technology Competencies. The authors described how this collaborative approach helped the teacher candidates achieve competencies and suggest further collaborations. Similarly, Lyublinskaya and Tournaki (2014) analyzed lesson plans of teacher candidates enrolled in a science and math methods course for special educators for evidence of TPACK. The authors found that the course had an impact, whereas most of the TPACK-centered research focuses on preservice general—rather than special—education teachers.
Measuring Levels of Integration
Lee and Kim (2014) reported on teacher candidates’ self-reported knowledge of technology integration through TPACK, focusing on the practice of teaching with technology through the analysis of artifacts in addition to surveys. The authors built upon prior research that found that knowledge of TPACK did not necessarily impact practice, contradicting the ingrained assumption that TPACK by itself will transform educational practices. Yet rather than looking broadly at likely external conditions—such as the preservice educators’ own experiences as students in K12 schools or broader social constructions of teaching and learning—the researchers instead looked to further “tinker” (Tyack & Cuban, 1997) with the course framework and curriculum to transform educational practices. Examining teacher candidates’ submitted interdisciplinary lesson plans, Polly and Rock (2016) analyzed the artifacts to determine patterns of technology integration. The authors found that technology integration was predominantly focused on the basic skills and mechanics of technology use rather than critical, higher-order, and social uses of technology.
Cluster 4: Shaping Beliefs and Attitudes around Technology Use through Socialization
Cluster 4 represented research that explored how the socialization of teacher candidates—inducting them into the social field of teaching—shaped their beliefs and attitudes about technology. These manuscripts were mostly gathered around the Attitudes and Beliefs and the Social and Individual Experiences ends of Dimensions 1 and 2. Two categories emerged in this cluster too: social experiences in coursework and socialization experiences in field placements.
Social Experiences in Coursework
Giles (2019) studied the impact of teacher candidates being placed into paired groupings on attitudes toward technology use in a technology integration course. Using the Attitude Toward Technology Scale, a standardized measurement, teacher candidates were paired during a required educational technology course based on their position on the scale. The author found that students’ attitudes shifted after this pair-based experience, with preservice educators exhibiting “improved attitudes” (Giles, 2019, p. 373) towards technology, indicating that technology was viewed as more beneficial in terms of educating and engaging students and organizing the work of teachers over time. Koehler et al. (2017) investigated what teacher candidates said about Web 2.0 technologies and services. They found that while students identified the tools as having potential to facilitate learning, the teacher candidates were not necessarily able to discuss the tools deeply. Akapame et al. (2019) looked at the attitudes of teacher candidates toward technology integration through self-reported and observed enacted TPACK levels during a three-semester integrated technology and mathematics course. The authors found that, due to the complexity of the attitudes, a model of developing TPACK for preservice teacher candidates would be advantageous for more clearly identifying attitudes toward technology integration.
Socialization Experiences in Field Placements
Hsu (2013) engaged in a mixed methods study to understand how teacher candidates’ attitudes toward technology integration changed through the student teaching experience. The author found that the mentorship of the cooperating teacher may have a negative influence on teacher candidates’ attitudes toward technology integration. Lux et al. (2017) similarly looked at how student teaching influenced teacher candidates’ attitudes and found that there can be both positive and negative influences that depend on the kinds of experiences that are provided for the candidate.
Cluster 5: Understanding Impressions Around Technology Use in the Field
The manuscripts that make up Cluster 5 were about understanding teacher candidates’ impressions of using technology in authentic field placements. They are situated strongly on Dimension 1 toward the Social and Individual Experiences end and stretched across the spectrum from Attitudes and Beliefs to Behaviors and Practices. The two categories in this cluster are about opportunities and challenges for technology integration and frameworks in the field.
Opportunities and Challenges for Technology Integration
Stover et al. (2014) conducted research around a multimedia blogging project in which teacher candidates engaged with elementary students around a shared reading. Using qualitative methods, the authors found that teacher candidates could successfully differentiate technology-based instruction and gained practices around real-world technology-mediated teaching. Al-Hazza (2017) investigated the use of mobile tablet computers by candidates to teach literacy skills. The author found that while the teacher candidates felt confident and enjoyed using the tablets, they encountered difficulties in leveraging the tablets to teach literacy skills. Vasinda et al. (2017) engaged in autoethnographic methods to better understand how teacher candidates used tablets throughout their elementary teacher education preparation program. The authors recognized that simple “access” does not necessarily lead to successful integration.
Frameworks in the Field
Miller et al. (2019) examined faculty beliefs around the use of tablets and found that having an explicit framework, such as the Substitution-Augmentation-Modification-Redefinition (SAMR) model, was an essential part of teaching technology integration to teacher candidates. Caniglia and Meadows (2018) investigated the websites and technologies that teacher candidates enrolled in a secondary mathematics methods course relied on and then analyzed artifacts based on TPACK and SAMR. The authors found that these frameworks provided teacher candidates with scaffolding for assessing technologies.
Discussion
Examining the paradigm through the research literature yields a view of the field through various levels of granularity. Levels of fine granularity by identifying clusters and categories were explored in the Findings section of this review. This section will zoom out to a broader scale to identify the general trends of the field and discern what is missing from the research on learning to teach with technology.
Overall, there is a high level of connection and agreement in terms of what the field “looks like” with little variance. From this overview, we can establish a narrativized account of how the field in general operates. Research in the paradigm focuses on moving preservice teachers into the workforce of professional educators who learn to use technology through the use of course- and program-based interventions such as games, simulations, and field experiences to improve their effectiveness in terms of reflective teaching and planning practices with technology. The focus is on factors that influence effectiveness, particularly by influencing the attitudes that preservice teachers hold concerning the use of technology in the classroom and impacting the practices they learn to enact. The research is primarily supported and warranted by surveys, although triangulation occurs through the collection of preservice teachers’ artifacts and interviews.
This narrative serves as an indication of the ways in which default theories (Delpit, 1988) that underlie the field are bundled with methodologies that support particular expected outcomes and ways of approaching research, practice, and policy.
The clusters and categories that emerge from the review support this general narrative. The two largest clusters, “Building a TPACK-Informed Teacher Workforce” and “Understanding What Teacher Candidates Do with Technology”, both connect deeply with this narrative. The studies focus on workforce-ready practices and knowledge of and about technology. The focus on changing attitudes and beliefs and the direct line drawn from simulations and coursework to changes in future teaching supports an implicit “if only” mindset in the scholarship: “if only” preservice teachers believed or felt a certain way about technology, “if only” teacher educators provided the “right” kinds of coursework or experiences to preservice teachers, “if only” preservice teachers scored sufficiently high on TPACK measures. The focus on specific factors as ways to impact results is indicative of these attitudes. Technology use in teaching under these conditions becomes both the deus ex machina (Postman, 2011)—the rescuer that literally drops from the heavens to save education—and the end of learning to teach with technology itself.
It is also possible to examine the research paradigms in the subfield of learning to teach with technology against the broader teacher education (learning to teach) field. Comparing the relative frequency of ERIC descriptors without any filtering yields distinct differences illustrated through Figure 4. The subfield of learning to teach with technology produces more research oriented toward (T)PCK, instructional design, and self-efficacy; surveys are over-represented in this research. Meanwhile, the subfield is very much underrepresented when engaging in research around issues of justice, inclusion, and contexts. The assumptions of neoliberal logics (Shutkin, 2005) and technological utopianism (Castañeda & Williamson, 2021; Dobozy, 2007) influence the use and study of educational technology in subtle and explicit ways, causing the deployment of technology to be the “fix” in and of itself, reinforcing a technology-driven single-pathway approach to teaching, learning, and creating (Delanty & Harris, 2021) at the expense of other purposes and genres.
This narrow focus also restricts research into the ecological effects of introduced technology as well, and the ways in which technological innovations that are meant to shape teaching practices influence and are influenced by assumptions and practices of teacher preparation. Video games and Web 2.0—a collection of technologies and services that place an emphasis on the social production of materials (An et al., 2010)—are two such categories that are meant to “disrupt” teaching that are undertheorized and under-researched in the field. As we look forward to new generations of AI (artificial intelligence) and their projected impacts on teaching and teacher preparation (Gillani, 2023; Talagala, 2023), this kind of deep exploration of the relationships between the technologies themselves and the broader practices and profession of teaching (rather than solely focusing on narrow factors) is necessary. The work of Postman (1998), himself a former teacher, provides a guide for this work, allowing researchers to recognize and highlight the tradeoffs, inequalities, assumptions, socioecological changes, and mythic qualities that tend to accompany technological change and impact.
As primarily a field of research that supports the licensure of teachers, it is not surprising that there is a focus on moving preservice teachers into the teaching profession—the teacher pipeline—but this approach is bundled with a set of methods and assumptions and is distinct from the broader teacher education field. The concepts of effectiveness, impact, and self-efficacy play central roles in the research on learning to teach with technology, which in turn further inculcates this bundled set as core attributes of teacher education policy and research. Without awareness of and resistance to these bundled attributes, educational technology will reinforce, sustain, and expand structural racism (Benjamin, 2019; Noble, 2018; Tettegah, 2016), existing economic inequalities (Postman, 1998), and ingrained ableism (Hehir, 2007; Wolbring, 2012).
Conclusion
There are two pervasive underlying default theories in the research: 1) the purpose of learning to teach with technology is to develop the educational workforce and 2) there are explicit connections between coursework experiences in teacher preparation programs and effectiveness as a practicing teacher. The use and refinement of survey instruments to collect data, particularly around self-efficacy and TPACK levels, is prevalent in the studies in this review. Neoliberal logics and techno-utopianism combine to fuel an “if only” approach in which technology itself is tacitly framed as both a deus ex machina that is assumed to “fix” education and as the end of educational technology in and of itself. This is evident in comparing the subfield of Learning to Teach with Technology with the broader field of Learning to Teach, highlighting areas that have yet to be explored and opportunities for growth and expansion in the field.
While there is a great deal of scholarship that reports on “snapshots”—of practice, attitudes, and beliefs—that rely on surveys and artifact reviews, there is little research that explores learning to teach with technology in context and over time. This line of scholarship can rely on both longitudinal survey methods and ethnographic methodologies that provide multilayered, meaning-sensitive, and time-based analyses (Emerson et al., 1995; Erickson, 1982; Firth, 1981; Van Maanen, 2011). This latter approach to understanding contextualized meanings is particularly important to undercut the assumption that an intervention can be transplanted from one situation to another with little to no adaptation to the new environment (Bartlett & Vavrus, 2016; Fenwick, 2011).
The subfield of learning to teach with technology outpaced the broader teacher education field in terms of examining “media” but the focus, genre, and purpose of research on media in learning to teach with technology is unclear. Studies of critical media literacy—especially when addressing societal and material modes of oppression and disinformation (Marlatt, 2020; Share et al., 2019)—comprise an important yet under-researched trajectory. This is particularly the case when seriously considering teaching in context, such as urban (Domine, 2010) and rural (Reynolds et al., 2018) settings, and teaching with and for identities and communities (Evans, 2020; McArthur, 2019; Stanton et al., 2020). The response to the COVID-19 pandemic has laid bare the affordances of technology to facilitate connections between home and school (Castells, 2000; Nardi & O’Day, 1999; Santamaría Graff et al., 2022). Preparing teachers with technology to become community partners and advocates (Kretchmar & Zeichner, 2016; Santamaría Graff, 2021) is a promising trajectory given the realities of today’s society and educational systems.
Lastly, the COVID-19 pandemic and the Black Lives Matter movement have thrown the racial, social, geographic, and ableist inequalities already found in the US education system into sharp relief (Chetty et al., 2020; DeMatthews, 2020; Dorn et al., 2020; ExcelinEd, 2020; Horowitz, 2020; Kurtz, 2020). It is generally well-documented that social conditions, issues, biases, and inequities cannot be simply checked at the classroom door (Aronson & Laughter, 2016; Blanchett et al., 2009; Nespor, 1997); it is further demonstrated that when students’ lived and cultural experiences are valued and incorporated into educational experiences, students thrive and learning improves (Fritzgerald, 2020; Ladson-Billings, 1995). Despite growing recognition of these real concerns and promising approaches in educational technology and the need for such a trajectory in learning to teach with technology programs (Fingal & Mack, 2022), research on learning to teach with technology within the contexts of a multicultural society with changing demographics (Causey et al., 2000; Grossman & McDonald, 2008; Lowenstein, 2009) and for the purposes of social justice (Cochran-Smith, 2010; Grant, 2012; Ladson-Billings, 1995; Paris, 2012; Zeichner, 2014) are outliers in the field, but require much closer attention if we are to assume that education can lead to a more just, equitable, and democratic society.
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