\n(p. 10).<\/li>\n<\/ul>\n
Educators should select appropriate instructional activities and materials, including technology, based on factors such as curriculum standards, students\u2019 needs, preferences, prior knowledge, and skill levels, and effective pedagogical practices and contextual factors such as time and available resources (Harris & Hofer, 2009; Kennedy & Deshler, 2010; King-Sears & Emenova, 2007).\u00a0The technology selected should not stand alone or be used in isolation, but rather should be used along with a logical sequence of activities that provide students with various opportunities to learn and practice instructional content (King-Sears & Emenova, 2007; Northrop & Killeen, 2013).\u00a0 While conducting technology-enhanced instruction, teachers should monitor students\u2019 responses to determine whether the instructional activity is effective and to make in-the-moment, as well as post-instructional decisions for future lessons, to adjust instruction to promote student success (Kennedy & Deshler, 2010; King-Sears & Evmenova, 2007).<\/p>\n
Evidence-Based Practices<\/h3>\n
Teachers need to select and utilize effective pedagogical strategies and technologies that are appropriate for the content and context. When integrating technology, educators may legitimately consider and select from a variety of pedagogical approaches that range from teacher-directed to student-centered (Harris, 2005; Koehler et al., 2013; Magliaro, Lockee, & Burton, 2005).\u00a0 Special educators can use the knowledge base on effective instruction to select technology applications that align with evidence-based instructional practices (Kennedy & Deshler, 2010; Smith & Okolo, 2010).<\/p>\n
Using evidence-based practices is critical for the success of technology-enhanced approaches for teaching students with disabilities (Kennedy & Deschler, 2010; Smith & Okolo, 2010).\u00a0 While educational technology leaders often favor student-centered or constructivist approaches to technology integration, teacher-directed approaches, such as explicit instruction, are appropriate for technology-enhanced learning in special education settings (Magliaro et al., 2005; Smith & Okolo, 2010).<\/p>\n
An extensive body of research supports the effectiveness of the elements of explicit instruction for teaching a variety of students, including those with learning difficulties (Archer & Hughes, 2011; Kroesbergen & Van Luit, 2003; Vaughn, Gersten, & Chard, 2000). Explicit instruction promotes high levels of engagement and success and is, therefore, appropriate for students learning basic skills, particularly when they have a history of failure and inadequate background knowledge (Archer, 2013; Magliaro et al., 2005).<\/p>\n
Components of explicit instruction include reviewing prior knowledge, adjusting the level of task difficulty, breaking down new material into small parts, providing clear descriptions, models, and examples of a skill, supporting sufficient amounts of practice, giving timely feedback, monitoring progress, and gradually withdrawing support as students become proficient.<\/p>\n
Features of explicit instruction, such as clear structure, opportunities for review, practice, immediate feedback, and progress monitoring, are integral to some technology applications, such as drill-and practice-software or apps (Magliaro et al., 2005; Maich & Hall, 2016; Smith & Okolo, 2010). When these features are not present within the technology, teachers can provide them.\u00a0 They can do so most easily in small group or one-on-one contexts.<\/p>\n
For example, researchers illustrated how they used an iPad app in combination with explicit teaching strategies while tutoring two students (Northrop & Killeen, 2013). They taught the concept without the iPad, explained and modeled the app, provided guided practice, and finally, allowed the student to practice independently with the app.\u00a0 The researchers determined that differentiated instruction, guidance, and feedback were essential to students\u2018 learning.<\/p>\n
Interactions and Decision-Making<\/h3>\n
Implementing technology-integrated instruction involves \u201cthree way\u201d interactions among teachers, students, and technology (Bull, Bull, & Harris, 1990). While engaging in interactions, teachers make in-the-moment decisions to adapt instruction based upon students\u2019 performance in relation to their instructional needs and goals (Griffith, Bauml, & Barksdale, 2015). For example, while guiding students through the learning process, teachers make decisions about components of explicit instruction, such as deciding how and when to provide prompts or feedback when students make errors (King-Sears & Evmenova, 2007).<\/p>\n
In-the-moment decisions may vary by context.\u00a0 During reading instruction teachers made decisions that focused upon motivation, engagement, and comprehension across contexts, but made additional types of decisions during small group lessons that allowed them to customize instruction to individual student needs (Griffith et al, 2015).\u00a0These other kinds of decisions related to assessment, modeling, thinking aloud, prompting, noticing, praising, and teaching problem solving strategies.<\/p>\n
Teachers develop situated knowledge through experience that informs future instructional decisions (Angeli & Valanides, 2009).\u00a0 Providing prospective special education teachers with opportunities to integrate technology and, thereby, to experience interactions and related decisions may help them develop the knowledge needed to make effective choices when using technology with students in the future (Marino et al., 2009).<\/p>\n
Theoretical Constructs<\/h2>\nPedagogical Content Knowledge<\/h3>\n
Lee Shulman (1986, 1987) originally proposed a model for representing the types of knowledge that form the basis for teachers\u2019 choices and actions. He asserted that in addition to knowing generally effective instructional practices, teachers also needed to know how to transform content knowledge while adapting their teaching strategies in relation to variations in students’ abilities and backgrounds. He defined this pedagogical content knowledge (PCK) as “the ways of representing and formulating a subject that make it comprehensible to others” (1986, p. 9).<\/p>\n
While scholars have described PCK in a variety of ways, many of the researchers who extended Shulman\u2019s work included the following two elements of PCK: (a) knowledge of students\u2019 understanding of conceptions and content-related difficulties and (b) knowledge of instructional strategies and representations of subject matter likely to improve students’ understanding of content (Angeli & Valanides, 2009; Graham, Borup, & Smith, 2012; Park & Oliver, 2008).<\/p>\n
Some researchers have also included knowledge of curriculum or media as components of PCK (Park & Oliver, 2008), but Shulman did not do so.\u00a0 He did acknowledge, however, that teachers should be aware of curricular alternatives (e.g., instructional materials and programs) that could be used in instruction and that they should tailor the materials to specific students based on relevant attributes such as \u201cconceptions, misconceptions, expectations, motives, difficulties, or strategies\u201d (Shulman, 1987, p. 17).<\/p>\n
Research suggests that teaching experience involving interactions with students and context play key roles in developing PCK in teachers (Angeli & Valanides, 2009).\u00a0 The results of one study showed that \u201ccomplimentary and ongoing readjustment\u201d of the integrated PCK components while planning, conducting, and reflecting on instruction strengthened science teachers\u2019 PCK (Park & Oliver, 2008, p. 280).\u00a0 When encountering a challenging situation during teaching, teachers used components of PCK as \u201cknowledge in action\u201d to determine an appropriate response.\u00a0 They also used \u201cknowledge on action\u201d when reflecting on lessons and planning subsequent instruction accordingly.<\/p>\n
Another study indicated that interactions among teachers\u2019 developing knowledge of curricular goals and objectives, instructional strategies, assessment practices, and judgements of students\u2019 comprehension, motivation, and abilities promoted growth in science teachers\u2019 PCK (Henze, van Driel, & Verloop, 2008).<\/p>\n
Technology, Pedagogy, and Content Knowledge<\/h3>\n
The TPACK model extends conceptions of PCK by adding technology as a specific type of teacher knowledge (Koehler et al., 2013; Mishra & Koehler, 2006). Since Shulman originally proposed the notion of PCK, technology has become much more prevalent and complex. The extensive knowledge needed to use it effectively in teaching goes beyond the original concept of PCK. Emerging technologies, in particular, are the focus of this added domain, because they are relatively new and their function may be unclear. Furthermore, they tend to be complex, change frequently, and can be used in many different ways (Brantley-Dias & Ertmer, 2013; Cox & Graham, 2009; Koehler et al., 2013). Thus, new technologies require teachers to spend considerable time learning about them and thinking about how to use them. In contrast, traditional and ubiquitous instructional materials, such as books and videos, are more transparent, familiar, simple, and stable, and thus, do not require as much thought.<\/p>\n
TPACK can be described as teachers\u2019 knowledge of when, where, and how to use technology, while guiding students to increase their knowledge and skills in particular content areas using appropriate pedagogical approaches (Brantley-Dias & Ertmer, 2013; Niess, 2011). When making instructional decisions, teachers strategically combine knowledge from multiple subdomains (Niess, 2011). The TPACK model emphasizes the importance of complex interactions among three domains\u2014technological (TK), pedagogical (PK), and content knowledge (CK)\u2014needed by teachers to successfully integrate technology into instruction (Koehler et al., 2013). Figure 1 displays the original TPACK model, and Appendix A shows definitions of each of the TPACK components.<\/p>\n <\/p>\n Scholars have criticized the TPACK model because of difficulties with measuring the components due to inconsistent definitions of, and unclear boundaries between, the domains (Angeli & Valanides, 2009; Brantley-Dias & Ertmer, 2013; Cox & Graham, 2009). Some researchers have suggested that the model should be simplified, since its many parts are difficult to discern (Brantley-Dias & Ertmer, 2013).<\/p>\n Though many researchers have considered TPACK holistically without distinguishing among the various subdomains, others have treated each area as distinct and have sought to clarify boundaries among the parts (Hofer & Harris, 2012). For instance, some researchers distinguished between TPK and TPACK by considering whether a technology integration practice was applicable across content areas (TPK) or pertained to instructional methods specific to particular subject matter (Cox & Graham, 2009; Graham et al., 2012).<\/p>\n Unfortunately, since researchers have defined various domains differently, inconsistencies in findings are inevitable. In addition, some researchers have been unable to detect sufficient evidence of each domain. For example, in studies of in-service teachers, researchers have observed TPK much more frequently than TCK (Hofer & Harris, 2012).<\/p>\n Finally, some scholars have questioned how teachers\u2019 knowledge of students fits into the model (Harris, van Olphen, & Hofer, 2013). Experts usually consider knowledge of students to be part of PCK; however, Angeli and Valanides (2009) created an alternate version of the TPACK model, which adds teachers\u2019 knowledge of students (e.g., their characteristics and preconceptions) as a separate domain.<\/p>\n The interaction among the TPACK model\u2019s domains may vary depending on the content area and other contextual factors that affect teaching and learning (Brantley-Dias & Ertmer, 2013; Koehler et al., 2013; Niess, 2011). Context is represented by the dotted circular line that surrounds the TPACK diagram in Figure 1. TPACK may be influenced by contextual factors such as grade level, curricular standards, student characteristics and background, instructional and social interactions, teacher motivation and beliefs, classroom layout, school-related expectations, support for technology, and types of technology available (Mishra & Koehler, 2006; Rosenberg & Koehler, 2015).<\/p>\n For example, two science teachers used TPACK to make instructional decisions based on combined knowledge of materials, students\u2019 skills and understandings, and activities that could support students\u2019 learning of specific content (Otrel-Cass, Khoo, & Cowie, 2012). In one instance, both teachers used videos for assessment but made different decisions about how to do so based upon the unique characteristics of their students. In turn, they used the assessment data to make planning decisions for subsequent lessons. Such attention to factors that influence teacher decision making while integrating technology can strengthen our understanding of how teachers\u2019 TPACK varies across different settings and with different students.<\/p>\n While there is a great deal of research on the TPACK model in general education settings, research regarding TPACK in special education contexts is scarce. Only a few studies to date have investigated the application of the TPACK model in special education settings. Other studies of preservice teachers\u2019 decision making have used the TPACK framework as a basis for understanding their choices when completing design tasks during educational technology courses or during fieldwork experiences in general education classrooms.<\/p>\n TPACK in Special Education.<\/strong><\/em> In one study, the researchers investigated the process in which exemplary technology-using special educators effectively implemented technology in their classrooms (Courduff et al., 2016). When selecting technology, the teachers considered learning objectives, observed students\u2019 preferences, and assessed academic needs. They focused more on these individual student characteristics and goals than on content. They also thought about whether the technology was appropriate for their students\u2019 skill levels. The teachers saw technology as a way to provide their students with differentiated, active, engaging, and multisensory learning experiences. They judged the effectiveness of technology integration by observing student engagement, progress toward academic goals, and perceptions of ease of use and utility. The teachers provided instruction and support so that their students could master the content and the technology. They allowed students time to explore and play with technology so that they could develop confidence and ownership of their learning. By gradually experimenting with technology and continuing to use applications that proved effective, the teachers learned to seamlessly weave technology into their teaching, sometimes by planning ahead and other times by using it spontaneously to meet students\u2019 needs in the moment.<\/p>\n Prospective special education teachers, on the other hand, are just beginning to develop the skills and knowledge needed to implement technology effectively. In a series of studies, researchers investigated the growth of preservice special education teachers\u2019 TPACK during a graduate-level course about teaching mathematics and science to learners with disabilities. Participants designed, implemented, and reflected upon two technology-integrated lessons (one in math and one in science) that they taught to students with disabilities. Results of pre- and posttest surveys indicated that the participants\u2019 perceived knowledge increased significantly in all integrated domains of the TPACK model (Tournaki & Lyublinskaya, 2014). In addition, the researchers evaluated the preservice teachers\u2019 lesson plans using the TPACK Levels Rubric and observed significantly increased scores during the semester (Lyublinskaya & Tournaki, 2014).<\/p>\n Another study examined prospective special educators\u2019 implementation of technology during an afterschool tutorial program for middle school students with disabilities (Pace & Blue, 2010).\u00a0 The participants worked one-on-one with students in literacy and math to address the individual needs and interests of the students. They chose to use technology in various ways to represent content, make accommodations, provide additional practice, and assess knowledge.\u00a0 More than a third of the preservice teachers felt that technology actively engaged and motivated their students. Some preservice teachers thought that incorporating technology was difficult and that finding just the right resource was challenging.\u00a0 Others learned the importance of vetting resources to identify potential problems ahead of time.<\/p>\n Preservice Teachers\u2019 Decision Making<\/strong><\/em>.\u00a0While many studies have investigated the development of preservice teachers\u2019 TPACK, few have used the framework as a basis for understanding preservice teachers\u2019 decisions in relation to technology-integration experiences (Graham et al., 2012; Hofer & Harris, 2012). In one such study, the researchers analyzed preservice teachers\u2019 rationales for technology-integration decisions while taking an educational technology course.\u00a0 Elementary teacher candidates explained how they would integrate technology in three design tasks that addressed specific curriculum standards. Results indicated significant growth in the domains of TK, TPK, and TPACK during the semester (Graham et al., 2012).\u00a0 However, the researchers did not find TCK in their data.<\/p>\n In another study, preservice teachers engaged in two design tasks while taking an instructional technology course. These tasks required students to make planning decisions while designing technology-enhanced learning activities on a topic of their choice.\u00a0 Results indicated that preservice teachers\u2019 scores on the design tasks increased from the first task to the second one, reflecting an increase in TPACK during the semester (Angeli & Valenides, 2009).\u00a0 However, the preservice teachers in these two studies did not actually implement the design tasks.<\/p>\n Some studies have investigated preservice teachers\u2019 emerging TPACK while making decisions about using technology in various content areas during field experiences in general education settings.\u00a0 These studies provide examples of specific kinds of actions that demonstrate components of preservice teachers\u2019 TPACK in different contexts.\u00a0 For instance, in one study, preservice teachers enrolled in a 2-year master\u2019s program used educational technology to support inquiry-based science instruction during student teaching (Maeng, Mulvey, Smetana, & Bell, 2013).\u00a0 Participants drew upon TPACK when selecting which technology to use and determining how and when to use it to support inquiry-oriented activities.\u00a0 They used their knowledge of science content and pedagogy when selecting and using images, videos, probeware, and simulations at appropriate times during instruction to engage students and meet curricular objectives.<\/p>\n In another study, researchers conducted a cross-case analysis of four preservice teachers who implemented technology in grade 4-8 science, social studies, or math lessons, with support and feedback from mentors during a seven-week practicum (Jaipal & Figg, 2010).\u00a0 The preservice teachers used TCK when selecting technology for instructional activities that helped students learn particular content. Instructional choices that exemplified TPK included providing models and examples, developing differentiated support materials, pairing students to work at the computer together, sequencing activities, and making back-up plans in case of technical difficulties.<\/p>\n Finally, Maher (2013) illustrated how preservice teachers used elements of TPACK when implementing iPad apps to support learning in K-6 classrooms. Participants chose to use apps to represent and explore concepts, demonstrate procedures, provide practice, give immediate feedback, support multimodal learning, differentiate instruction, and assess understanding.<\/p>\n Research is needed that provides insight into how educators with varying levels of experience and knowledge use TPACK to make decisions when planning for and using technology in different contexts (Cox & Graham, 2009; Maeng et al., 2013; Niess, 2011).\u00a0 In particular, research is needed to provide insight regarding the way TPACK is utilized in special education contexts.<\/p>\n The current study focused on how preservice teachers\u2019 instructional decision-making reflected the use of TPACK components in an elementary-level special education setting. Specifically, we sought to answer the following questions:<\/p>\n In addition, since the effectiveness of technology integration can influence future teaching decisions, we gathered social validity data regarding the participants\u2019 perceptions of the iPad implementation process during the study.<\/p>\n Fourteen junior early childhood special education majors at a private university participated in the project during two consecutive spring semesters.\u00a0 Ten participated in the study in the first year, and four more participated in a follow-up study during the second year. They volunteered to participate in the project for the following reasons: to motivate students through their interest in and enjoyment of technology, a desire for professional growth through learning to integrate the iPad into lessons, and an interest in determining whether iPad integration would improve teaching and learning during tutoring sessions.<\/p>\n All of the preservice teachers had completed an educational technology course that addressed a planning process consistent with the TPACK model (Harris & Hofer, 2009). However, most participants had little experience using mobile devices (tablets or smart phones) for educational purposes. Thus, at the time, the iPad was an emerging technology.\u00a0 Four participants owned an iPad and used it often for multiple purposes, whereas others had previously played with an iPad occasionally. One participant had never used an iPad before.<\/p>\n Each preservice teacher was paired with one or two students who had mild disabilities.\u00a0 During the first year, there were four girls and seven boys, and during the second year, there were four girls and two boys.\u00a0 The students were between the ages of 6 and 11 and were working on first through fourth grade curriculum, as appropriate, based upon their academic strengths and weaknesses.\u00a0 All of the students had been diagnosed with a learning disability and\/or attention deficit hyperactivity disorder.<\/p>\n All of the preservice teachers were enrolled in a special education course, taught by the third author, which included a 30-hour fieldwork component. The course focused on processes for teaching and assessing basic academic skills with an emphasis on explicit instruction. In addition, the participants were enrolled in an educational psychology course, as well as social studies, math, and reading methods courses. They completed approximately 3 hours of fieldwork each week at the university\u2019s private laboratory school for children with mild disabilities. Each participant tutored one or two students. Such individualized instruction is typical in special education settings (Courduff et al., 2016).<\/p>\n Prior to beginning the project, the preservice teachers provided a list of skill areas in which their students needed further instruction or practice. These areas included math facts, math computation, reading fluency, reading comprehension, handwriting, spelling, and grammar. The first author identified free apps for each of these topics, loaded them onto the college-owned iPads, and organized them by subject area. The preservice teachers could also request additional free apps; however, only a few of the first-year participants chose to do so. Between the first and second years of the study, the first author added more apps to the iPads, most of which were initially free or low cost.\u00a0 In addition, she created a database of all the apps, as suggested by the first-year participants.<\/p>\n During an introductory session in the first year of the study, the preservice teachers discussed their students’ needs, explored iPad apps, and reviewed a rubric that they could use to evaluate the apps (Walker, 2011). The first author conducted a similar session during the second year, but modified the content based upon feedback from the first group of participants.\u00a0 She gave the preservice teachers a case study and asked them to find apps on the iPads that would be appropriate for the scenario.\u00a0 In addition, she provided resources for locating apps, including the database of all the apps on the iPads, links to two other app databases, an app evaluation rubric, and two sample mini-lesson plans.<\/p>\n After an introductory session, the preservice teachers planned and implemented at least four lessons that incorporated iPad apps. In the first year, the project began midsemester. In the second year, the project started earlier in the semester, so that the participants would have more time to plan and implement their iPad-integrated lessons.\u00a0 They could check out an iPad the day before their field experience to preview apps while planning their lessons. Some preservice teachers used the borrowed iPad, while others used their own iPads during the lessons they conducted in the fieldwork setting.<\/p>\n We collected multiple forms of data. The primary data source was focus groups held at the end of the semester. Each of focus group lasted about an hour and a half, and there were four or five participants in each of the three focus groups.\u00a0 The first author led the focus groups, while the other two authors took notes or asked occasional follow up questions.\u00a0 The interviews were audio recorded and transcribed by a professional transcriber.\u00a0 In addition, graduate teaching assistants conducted brief individual interviews with 11 laboratory school students during the first year. (See Appendix B for focus group and interview questions.) These interviews were recorded and transcribed by the graduate assistants.<\/p>\n We also used several secondary data sources for clarification and contextual information. These sources included lesson plans and weekly journal entries reflecting on the lessons, which the preservice teachers submitted to an online learning management system.<\/p>\n Finally, we observed a small subset of lessons and took field notes during these observations.\u00a0 Since multiple lessons occurred at the same time, and the exact time of the iPad-integrated lessons was not known in advance, we were able to see only parts of some lessons.\u00a0 Nevertheless, these observations provided essential background information that helped us better understand the data from other sources.<\/p>\n The analysis focused on instructional decisions and the underlying knowledge that the preservice teachers used when making them. While we reviewed all of the data sources, we only coded and analyzed the interview data. The first two authors independently identified and coded instructional decisions mentioned in the focus group interviews using qualitative analysis methods, using Microsoft Word to color code instances of instructional decisions. \u00a0The first two authors then\u00a0met, discussed, and modified the coding scheme multiple times until reaching agreement in terms of the decisions identified, codes used to categorize them, and emerging themes.<\/p>\n Later, the first two authors entered the statements related to instructional decisions into an Excel spreadsheet. This approach allowed sorting of statements by the types of decisions made.\u00a0 In a second round of coding, the forms of teacher knowledge (i.e., TPACK dimensions) associated with each type of instructional decision were identified.<\/p>\n Finally, to determine the social validity of the process of using the iPads, the first author and a graduate student used a similar process to analyze transcripts from the student interviews, independently coding the transcripts and identifying\u00a0five themes.\u00a0 The\u00a0two met and discussed the instances of each theme to resolve any differences in coding.\u00a0 The graduate student entered the statements and their codes into an Excel spreadsheet so that they could be easily sorted by theme.<\/p>\n Like most research, the current study has some limitations.\u00a0 In qualitative research, the purpose is not to generalize to different settings but to provide information that can be transferred to comparable situations and applied in analogous contexts.\u00a0 Readers should keep the context in mind when considering the applicability of the results to other situations.\u00a0 The current study took place at a private university laboratory school for students with mild disabilities.\u00a0 Since the 14 participants were volunteers, they may have been more motivated to use technology in teaching than were their peers. They taught single students, and sometimes pairs or small groups.\u00a0 While such individualized instruction is typical in special education settings, there are other situations that a special education teacher could encounter in a public school setting (e.g., coteaching or inclusion).<\/p>\n Furthermore, participants used only one kind of emerging technology.\u00a0 The advantage of using iPads was that the devices could be easily transported to the school setting, and thus, we did not need to rely on the school\u2019s technology. The preservice teachers used the iPads primarily as instructional technology (a means to facilitate learning for students with and without disabilities).\u00a0 Participants generally did not use the iPads as assistive technology (to improve the functional performance of students with disabilities).\u00a0 Thus, the study did not address the full range of technology applications used in special education contexts.<\/p>\n In addition, there are some limitations to using focus groups for data collection. While focus groups are an efficient way to collect data, they do not provide as much raw data as do individual interviews.\u00a0 For example, in the group setting, sometimes one participant would make a statement, and then the other participants would simply nod their heads in agreement.\u00a0 Although we noted these occurrences, they did not allow us to quantify the prevalence of different types of statements.\u00a0 Nevertheless, we identified representative quotes that illustrated the participants\u2019 shared experiences.<\/p>\n Another limitation is that the interviews occurred late in the semester, and the participants may not have been able to accurately recall decisions that occurred earlier in the term. However, data from other sources (i.e., lesson plans, journals, and observations) provided a fuller picture of the preservice teachers\u2019 thoughts and actions while planning for and implementing iPad apps in the tutoring sessions.<\/p>\n In this section, we describe the types of teaching decisions the preservice teachers made, identify the kinds of knowledge underlying those decisions, and convey participants\u2019 perceptions regarding the implementation of iPad apps during the lessons. We also present a model illustrating the knowledge domains that contributed to preservice teachers\u2019 decisions. For each type of decision, we furnish representative quotes from focus group interviews that exemplify the preservice teachers\u2019 decision making while planning for and using iPad apps in their lessons. (Note that we replaced all participants\u2019 names with pseudonyms. Allison, Amelia, Amy, Andrea, Brenda, Erica, Jessica, Martha, Ramona, and Sheryl were first-year participants. Chloe, Janet, Kelly, and Lillian were second-year participants.)<\/p>\n We identified two primary kinds of teaching decisions: planning and in-the-moment (although occasionally we could not be certain whether a decision was made spontaneously or while planning the lesson).\u00a0 Some of the preservice teachers\u2019 decisions occurred prior to instruction, such as those associated with planning lessons, sequencing activities, and selecting iPad apps. Decisions made during instruction often occurred in the context of three-way interactions between the preservice teachers, their students, and the technology. Usually these in-the-moment decisions were in response to student actions, as well as technological issues that occurred. Decisions made during instruction involved monitoring progress, providing review, feedback, and extra practice, adjusting difficulty, offering support, providing modeling and guidance, withdrawing assistance, managing behavior, and dealing with technical problems.<\/p>\n Our analysis suggests that the preservice teachers drew upon two main categories of combined knowledge when making decisions.\u00a0 Decisions that were based upon TPK required combined knowledge of technology (i.e., iPads and apps), plus knowledge of appropriate pedagogical methods (i.e., explicit instruction) or management tactics. These generic pedagogical strategies were independent of content, because they could be used across various content areas (Cox & Graham, 2009; Graham et al., 2012).\u00a0Like Jaipal-Jamani and Figg (2015), we considered TPACK as primarily a combination of PCK with TK (or TPK). Decisions based on TPACK utilized integrated knowledge of technology and appropriate pedagogy (i.e., explicit instruction) for teaching specific content knowledge. When making these decisions, participants also drew upon their knowledge of students\u2019 instructional needs and characteristics, an element of PCK, in relation to the content and skills students were expected to master.<\/p>\n Since teachers may base their decisions on different combinations of knowledge depending on the context, we developed Figure 2 to illustrate how the preservice teachers displayed elements of TPACK while using iPad apps in a special education setting. The primary types of combined knowledge (TPK and TPACK) are bolded to highlight the domains reflected in the data.<\/p>\n Since we did not observe TCK in isolation, we arranged the overlapping circles in such a way that it is omitted. The grey areas indicate underlying domains (PK, CK, TK, PCK) that we inferred from the data but were not reflected in the focus group discussions. For example, the preservice teachers had to know the content they were teaching (CK) and how to operate an iPad (TK). However, they did not make statements that reflected this underlying knowledge. The circles for TK, PK, and CK are not the same size, reflecting the approximate relative contribution of each domain. The largest circle (PK) reflects the emphasis on pedagogy (i.e., explicit instruction) embedded in the course in which the students were enrolled. The TK circle was the smallest, since the preservice teachers were just beginning to learn about using iPads and educational apps.<\/p>\n Decisions related to selecting apps and planning lessons that incorporated them were primarily based upon TPACK.\u00a0 Such decisions required the preservice teachers to combine their PK about explicit instruction, awareness of students\u2019 content knowledge and instructional needs, and emerging TK of implementing iPads and apps.<\/p>\n Planning Lessons<\/em><\/strong>. \u00a0The preservice teachers chose to integrate apps in several different ways. They frequently used apps as a way for students to practice specific skills or knowledge. Sometimes they used apps as a way to represent information (e.g., show pictures, watch videos, view maps, write words, spell with magnetic letters, draw pictures, or create a graphic organizer).\u00a0 In addition, they occasionally used apps as a way to assess students\u2019 progress.\u00a0 The participants realized that using an app was just one part of a larger lesson.<\/em> Erica summarized how they combined and applied TPACK domains when planning lessons:<\/p>\n The iPad apps alone couldn’t constitute a whole lesson.\u00a0 I feel like we all added and supplemented to it to provide some structured form of instruction so the students are learning the content and then applying it to the iPad app.<\/p>\n Ramona echoed Erica\u2019s statement by saying, \u201cA lot of [apps] are really good for warm-up practices\u2026 or an introduction or just a part of the lesson, but I couldn\u2019t\u2026plan a lesson and only use the iPad.\u201d\u00a0 In these comments, participants acknowledged that the iPad apps could support certain pedagogical components, and while specific content was not mentioned, we know from the students\u2019 lesson plans that each was designed to meet particular content-related goals.<\/p>\n Some participants initially thought that planning lessons with the iPads would be easier than preparing for traditional instruction, but they found it to be more difficult, complex, and time consuming. For instance, Andrea reflected on the challenges of identifying an app that matched her student\u2019s needs and determining how to use it in teaching:<\/p>\n At first glance it seems like, oh, you’re using an iPad, that’s like an easy way to plan a lesson, but it was actually really challenging….You [had] to incorporate a lot of different resources and make sure that the app was appropriate and bring your own instruction into it….So, I was surprised by that.<\/p>\n Selecting Apps<\/em><\/strong>.<\/strong> The preservice teachers were sometimes overwhelmed by the process of selecting apps that matched learner needs and effective pedagogical methods. Participants collectively mentioned at least 30 different apps in their lesson plans, journals, and focus group interviews. Table 1 lists several commonly used apps, describes how the preservice teachers used them, and notes the students\u2019 responses to the app.\u00a0 The preservice teachers identified several reasons that app selection proved challenging: (a) there were many apps to choose from and app reviews were not always helpful; (b) many apps did not match the needs or levels of the students; and (c) apps did not always adequately address content matter.<\/p>\n Table 1![\"\"](\"https:\/\/citejournal.s3.amazonaws.com\/wp-content\/uploads\/v17i1GeneralFig1.jpg\")
<\/a>Related Research<\/h3>\n
Research Questions<\/h2>\n
\n
Method<\/h2>\n
Participants<\/h3>\n
Context<\/h3>\n
Data Collection<\/h3>\n
Data Analysis<\/h3>\n
Limitations<\/h3>\n
Findings<\/h2>\n
<\/a>Technology, Pedagogy, and Content Knowledge<\/h3>\n
\n<\/strong>Purpose of Use and Students\u2019 Response to Commonly Used Apps<\/p>\n