Using TPACK

TPACK DECISION MAKING

1

Running Head: TPACK DECISION MAKING

This is a prepublication draft of a article to be published in: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.13652729.2011.00472.x

Using TPACK as a Framework to Understand Teacher Candidates’ Technology Integration Decisions

Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


2 Abstract

This research uses the TPACK framework as a lens for understanding how teacher candidates make decisions about the use of information and communication technology (ICT) in their teaching. Pre- and post-treatment assessments required elementary teacher candidates at UNIVX to articulate how and why they would integrate technology in three content teaching design tasks. Researchers identified themes from student rationales that mapped to the TPACK constructs. Rationales simultaneously supported sub-categories of knowledge that could be helpful to other researchers trying to understand and measure TPACK. The research showed significant student growth in the use of rationales grounded in content-specific knowledge and general pedagogical knowledge, while rationales related to general technological knowledge remained constant.

Key Words: pedagogical content knowledge, technological pedagogical content knowledge, preservice teacher education, technology integration, information and communication technology



3

Using TPACK as a Framework to Understand Teacher Candidates’ Technology Integration Decisions Introduction In an effort to explain the types of knowledge teachers need in order to integrate technology into their teaching, Mishra and Koehler (2006, 2008) outlined the technological pedagogical and content knowledge (TPACK) framework. This framework explicitly acknowledges that effective pedagogical uses of information and communication technology (ICT) are deeply influenced by the content domains in which they are situated. According to the framework, teachers who demonstrate TPACK can effectively integrate their knowledge of ICT with their pedagogical and content knowledge to promote student learning. Teacher educators are beginning to use the TPACK framework as a lens for exploring ways in which teachers integrate technology into lesson planning (Harris, 2009; Hofer & Harris, 2010). Some have recommended teaching technology to teacher candidates in the context of a design problem. By this method the candidates are simultaneously exposed to the complexities and interrelated nature of pedagogical knowledge (PK), content knowledge (CK), and technological knowledge (TK) (Koehler & Mishra, 2005; Koehler, Mishra, & Yahya, 2007). This study looks at the instructional decisions teacher candidates make before and after completing an educational technology course. Subjects were given simplified design challenges and asked to articulate how they would use technology to address specific curriculum standards. Researchers analyzed teacher candidates’ rationales for incorporating ICT into their responses. 2. Theoretical Framework Technological pedagogical content knowledge (TPACK) is a framework that proposes a set of knowledge domains involved in integrating technology into teaching (Koehler & Mishra, Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


4

2008; Mishra & Koehler, 2006). The TPACK framework builds on the idea of pedagogical content knowledge (PCK) that was introduced by Lee Shulman (1986, 1987). PCK has been used by many researchers as a way of investigating teacher knowledge of pedagogy (PK), content (CK), and the intersection of these constructs (PCK) (Segall, 2004). The TPACK framework introduces technological knowledge (TK) to Shulman’s PCK framework and by doing so describes the complex interaction between a teacher’s knowledge of content (CK), pedagogy (PK), and technology (TK). This interaction results in three additional knowledge domains: technological content knowledge (TCK), technological pedagogical knowledge (TPK), and technological pedagogical and content knowledge (TPACK) (see Figure 1). This section of the paper will outline our understanding of PCK and describe how that understanding has helped us to conceptualize and measure the TPACK constructs. Insert Figure 1 approximately here 2.1 Pedagogical Content Knowledge (PCK) as a Foundation Pedagogical content knowledge (PCK) is used as a foundation for the TPACK framework. However, there are many different conceptions of the knowledge domains that make up PCK (van Driel, Verloop, & de Vos, 1998; Lee & Luft, 2008; Graham, 2011). In a review of nine prominent models of PCK, Lee and Luft (2008) identified eight categories of teacher knowledge included across PCK models: (1) subject matter, (2) representations and instructional strategies, (3) student learning and conceptions, (4) general pedagogy, (5) curriculum and media, (6) context, (7) purpose, and (8) assessment. Of the eight elements considered in the analysis, teacher knowledge of “student learning and conceptions” was included as a part of PCK by all nine models. Teacher knowledge of “representations and instructional strategies” was included as part of PCK by all the models except Cochran et al. (1993). While Cochran et al. do not Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


5

explicitly address representations, they include “teaching strategies for teaching specific content” as a part of PCKg (p. 266). The majority of the models included teacher knowledge of “curriculum and media” and “purpose” as elements of PCK, respectively mentioned by six and five of the nine models. The most common elements to be excluded from PCK models and be recognized as distinct categories of knowledge were teacher knowledge of “subject matter” and “general pedagogy.” This research focuses only on the categories of teacher knowledge for which there is almost unanimous agreement by the nine PCK models described by Lee and Luft (2008). These categories include knowledge of “representations and instructional strategies” and knowledge of “student learning and conceptions.” Figure 2 represents ways the researchers are trying to differentiate the elements of (1) representations, (2) instructional strategies, and (3) knowledge of learners across the PCK construct boundaries, which is the basis for the theoretical framework used in the study. The researchers chose to split representations from instructional strategies to help define the boundary conditions in the model because they felt that content representations tend to be more closely related to subject matter knowledge and instructional strategies tend to be more closely related to pedagogical knowledge. In this model the essential boundary condition between PK and PCK is whether the element is a content-specific form of knowledge or a more general form of knowledge. The essential boundary condition between CK and PCK has to do with a teacher’s knowledge of subject matter representations versus knowledge of how to transform the subject matter representations to make the content more comprehensible to students. Insert Figure 2 approximately here



6

Shulman made the distinction between general pedagogical knowledge and contentspecific pedagogical knowledge represented in PCK by defining general pedagogical knowledge as “those broad principles and strategies of classroom management and organization that appear to transcend subject matter” (Shulman, 1987, p. 8). For the purposes of this paper, the instructional strategy dimension on the PK side will involve strategies that transcend subject matter or are independent of subject matter. Examples of PK include teaching strategies that apply generally across subject matter domains: e.g., classroom management strategies, collaborative or active learning strategies, presentation strategies, etc. Alternatively, the instructional strategy dimension on the PCK side includes content-specific strategies like those often taught in methods courses: e.g., inquiry learning in science, primary source investigations in history, writers’ workshop in literacy, etc. The current research will follow the pattern of Carlsen (1999), Grossman (1990), Magnusson et al. (1999), and Tamir (1988) and include knowledge of the learner as a subset of pedagogical knowledge. This dimension of knowledge will be distinguished across the PK/PCK boundary using a general versus a content specific condition. Van Driel et al. (1998) articulate the distinction between general and content-specific knowledge of student learning by differentiating between a knowledge of “learner characteristics in a general sense” and an “understanding of specific learning difficulties and student conceptions” (1998, p. 677). For this study, PK will include a general knowledge of learner characteristics (e.g., understanding what motivates a group of children, what is age appropriate, or what their general learning preferences are). PCK will entail a content-specific understanding of the learners (e.g., students’ common misconceptions of a particular topic, their prior experiences related to the topic, etc.).



7

Finally, representations are an important aspect of content knowledge (see Figure 2). Content representations are used by practitioners in the content domain as well as by educators trying to teach the domain as subject matter. Often teachers will modify or transform representations used in the content domain to make them more comprehensible to learners. Teachers also invent representations that aid in teaching. This act of providing the learner with “comprehensible representations of subject matter” is in the PCK domain, while the practice of using representations by practitioners in the content domain is CK (van Driel et al., 1998, p. 675). 2.2 From PCK to TPACK TPACK adds technological knowledge to the PCK framework as a third knowledge domain (see Figure 1). Shulman (1986) explained that the concept of curricular knowledge was included in the larger concept of content knowledge. Curricular knowledge was defined as teachers’ knowledge of the available educational tools and materials including software, programs, visual materials, and films. Angeli and Valanides (2009) contended that Shulman intended for technology to be included in his PCK framework but “did not explicitly discuss technology and its relationship to content, pedagogy, and learners, and thus PCK in its original form does not specifically explain how teachers use the affordances of technology to transform content and pedagogy for learners” (p. 156). The introduction of the technological knowledge (TK) construct to create TPACK from PCK is useful because it provides an explicit mechanism for discussing the tools teachers use in the service of teaching and learning. Koehler and Mishra (2008) did not distinguish between the types of technology encompassed within TK (including older technologies like the pencil as well as newer digital technologies). However, most researchers currently using TPACK as a theoretical framework Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


8

are investigating teachers’ integration of digital technologies. Some researchers have made this explicit by identifying a particular flavor of TPACK. For example, Angeli and Valanides (2009) used the term ICT-TPCK to represent a focus on the use of information and communication technologies (ICT); Lee and Tsai (2010) used the term TPCK-W to represent a focus on web technologies; and Doering et al. (2007, 2009) have used the term G-TPACK to represent a focus on geospatial (geography) technologies. This research will focus on the use of information and communication technologies (ICT). In our model, information and communication technologies will fall within the TK boundaries and may contribute to TK-related constructs such as TPK, TPACK, and TCK. Non-ICT-related technologies will not be considered part of the TPACK constructs. The TPACK framework adds a significant level of complexity to the already complex PCK framework by more than doubling the number of framework constructs (from three in PCK to seven in TPACK). However, the additional complexities of the framework are outweighed by the descriptive power that the new framework affords. 3. TPACK and Decision Making The TPACK framework provides an analytical lens with which to look at the instructional decisions that teachers make. Very little research has focused on the how and why behind teacher candidates’ technology integration decisions. However, existing studies do provide some insight into the teachers’ rationales for their technology decisions. Harris and Hofer (2009), studying how a group of social studies teachers planned lessons, found that teachers first focused on the content and then organized lessons using activities that supported students’ learning of the content. In contrast, many technology integration programs begin with the affordances and constants of technology. Harris, Mishra, and Koehler (2009) Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


9

recommended that technology integration be consistent with the planning process of teachers. Thus teachers would first determine the curriculum to be taught, then select activities to support student learning, and last identify technology to support the chosen activities. This approach shifts the focus to content-based pedagogy and student needs, away from affordances and constraints of technologies. Through interviews following this intervention, Harris and Hofer (2009) showed that teachers became more conscious and strategic in their planning; not only did their range of activities increase, but the activities became more student centered. Manfra and Hammond’s (2008) qualitative case study of two social studies teachers also found that pedagogy, not technology, drove teachers’ decisions on lesson plans. Although both teachers used digital storytelling to teach similar content, the pedagogical aims of the two were very different. One teacher used technology to present information for his students to absorb and reproduce. The second teacher used technology as a way to help students construct content knowledge and develop critical thinking skills. Manfra and Hammond explained that the pedagogical aims of each teacher dominated the lesson planning decisions, while the content and the technology were used to support the pedagogy. Although technology may not be a teacher’s focus when planning lessons, research has shown that teachers recognize that technology can enhance their pedagogy and improve student content understanding. Niess (2008) performed a pooled data analysis of multiple inservice teacher education programs that emphasized mathematics teachers’ development of TPACK. Researchers reported that teachers considered using technology to be beneficial in a number of ways, which included enabling graphical representations, allowing concepts to be applied to a real problem-solving environment, making assessment easier, promoting enjoyment, and improving efficiency. Cavin (2008) added that teacher candidates who had integrated Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


10

technology into a mathematics lesson viewed technology as a way to improve the speed and organization of the computation portion of the lesson and allow students to develop a stronger conceptual understanding. Kersaint (2007) also found that candidates viewed technology as a way to help students visualize concepts, engage in active learning, maintain positive attitudes, and build confidence. Although these reflections occurred after the lessons were taught, these advantages represent the kind of reasoning teachers are likely to use in deciding which technology to implement. Most of the teacher candidates’ rationales expressed in the studies above (Cavin, 2008; Kersaint, 2007; Ness, 2008) (i.e. helping students build self-efficacy, develop positive attitudes, engage in active learning, improve the efficiency of learning, enjoy the learning process more, etc.) seem to be more closely related to general pedagogical practices than to content-specific pedagogical practices. Little research has been done to clarify this distinction. This article highlights the differences between general and content-specific rationales, specifically guided by the following questions: 1. What are teacher candidates’ general and content-specific rationales for using ICT as part of a design task addressing curriculum standards? 2. How do teacher candidates’ rationales change following a course designed to help them develop knowledge, skills, and dispositions related to the use of ICT in the K-6 teaching? 4. Method Shulman (1987) contended that teaching begins with a process of cognitive reasoning that culminates in the act of imparting knowledge to others. Baxter and Lederman (1999) further stated, “PCK [and TPACK by extension] is both an external and internal construct, as it is constituted by what a teacher knows, what a teacher does, and the reasons for the teacher’s Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


11

actions” (p. 158). These authors continued with the caution that teacher cognition is a “complex and slippery construct to study,” similar to Kagan’s (1990) statement that evaluating teacher cognition is a complex task fraught with limitations to the ecological validity of the findings. A wide range of methods have been used in attempting to understand teachers’ decision making. These methods include surveys, concept mapping, lesson plan analysis, case scenario responses,, interviews, video performance reflection , etc. (Baxter & Lederman, 1999). Each method provides a different insight into the cognitive decision-making process but ultimately comes short of a full understanding of the process. Additionally, the methods vary widely in the time and effort required for implementation and thus in the number of participants who can be included in the inquiry. In this study, researchers chose to use common design tasks based on curriculum standards as a means of eliciting student decision-making processes and rationales. Shulman (1986) recognized the potential of using such performance exercises “as a means for developing strategic understanding, for extending capacities toward professional judgment and decision making” (p. 13). Haertel (1990) placed performance exercises such as the design tasks used in the current research “somewhere between observing teachers in their classrooms and tallying the circles darkened on their answer sheets” (p. 278). Haertel added that such methods provide more information than is possible in multiple choice tests and are more feasible and reliable than classroom observations. This balanced approach allowed researchers to collect and analyze data from a large number of teacher candidates. In addition, using identical writing prompts allowed for comparisons across individuals and groups (Greenhow, Dexter, & Hughes, 2008). Researchers of the current study chose to measure teacher candidates’ TPACK by analyzing their rationales for selecting technology in three simplified design tasks. The Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


12

candidates were provided with specific grade-level content objectives, then asked to describe how and why they would teach the objectives using technology. The design tasks contained limited contextual constraints in order to allow teaching candidates the broadest opportunity for exploring their knowledge of technology integration. Student rationales were then qualitatively analyzed for evidence of TPACK constructs related to technology. The following sections describe the context, participants, and details of the data collection and analysis for the study. 4.1 Context Each semester teacher candidates majoring in elementary or early childhood education at X University enroll in a required introductory educational technology class. The course was designed to help them develop knowledge, skills, and dispositions related to the use of technology in teaching all K-6 core content areas. Teacher candidates take the course during the second semester of a three-semester sequence. They simultaneously enroll in a literacy methods course and a math content course specifically designed for elementary education majors. When they take the course, they have not yet taken science or social studies methods courses, which occur during their third semester, the final semester before student teaching. Since 2002 professors and administrators have tried to move the course away from productivity-oriented course projects (like creating newsletters and spreadsheet grade books) to instruction and projects that use technology to enhance content pedagogies and facilitate teaching core content standards (Graham, Culatta, Pratt, & West, 2004; Wentworth, Graham, & Monroe, 2009). Teacher candidates taking the course are introduced to TPACK through a brief online tutorial that outlines the major TPACK components. Then the bulk of the course is spent on projects designed to introduce new information and communication technologies that can Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


13

support technology integration in language arts, math, science, and social studies. (See Table 1 for a description of core projects in the content areas.) Insert Table 1 approximately here The first three projects listed in Table 1 were developed to provide a scaffolded design experience for the candidates by limiting the decision making required. For example, the virtual tour project specified which technology would be used to create the virtual tour, but gave candidates some flexibility in choosing the affordances of the tool that they would use and the social studies content standard they would address. The digital storytelling and science inquiry projects increased the complexity of the design tasks by allowing wider selections of technology to choose from. Reflections around candidates’ rationales for their design decisions were shared publicly. Reflection prompts included questions like “Who was using the technology?” and “What value did the technology add to the instruction?” The final integration project involved candidates applying their skills in a practicum context in which the design constraints were authentic. Candidates had to assess the resources available to them, select an appropriate lesson for technology integration from the lessons that the mentor teacher needed taught, and determine which technologies to use that would add value to the lesson. We felt that the strength of this approach can be summarized in the following provisions: • Multiple opportunities to practice decision making with different curriculum objectives • Increasing complexity of the design decisions that needed to be made • Deep experience with their own design and implementations, but with the opportunity to see dozens of peer examples and rationales that were variations on the same task Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


14

4.2 Participants Participants in this research were enrolled in four sections of the educational technology course during the 2009 winter semester. Of the 137 students enrolled in the four sections, 133 agreed to participate in the research: 22 (16.5%) early childhood education majors and 111 elementary education majors. Only 3 of the candidates were male. An introductory survey in the course revealed that participants began the course with a wide range of levels of confidence in their ICT skills, ranging from not confident at all to completely confident. 4.3 Data Collection As part of a pre- and post-course assessment, all enrolled students were given three design tasks and asked to describe how they would teach a particular core curriculum standard using technology (see Figure 3). The instructional design tasks were administered as an online survey during the first week of the semester and as part of the final exam. Insert Figure 3 approximately here Each teaching candidate received a design task involving the lower elementary grades (K-2) or the upper elementary grades (3-6). Then they were randomly assigned (1) one language arts design task, (2) one math design task, and (3) one science or social studies design task—all of which had been randomly selected from a pool of Utah core curriculum standards identified by instructors as having high potential for technology integration. For each design task the candidates were given two prompts, shown in Table 2. They were asked to describe and provide a rationale for their instructional strategy using ICT. Insert Table 2 approximately here



15

4.4 Data Analysis To explore the evidence of TPACK constructs in student planning and decision making, researchers analyzed the data obtained from candidates’ open-ended responses to the request that they supply rationales for the technology they selected for the design tasks. Open coding was initially used to identify the range of rationales and to develop a codebook of evidences for the TPACK constructs. Researchers knew that there was significant overlap in the rationales that participants were using to justify their technology use because all of the responses were read as an aspect of grading the final assessment in the course. During the open coding phase, two researchers coded randomly selected candidate responses. All researchers met regularly to compare codes, identify themes, and organize identified rationales into the coding categories outlined in the theoretical framework. Through several iterations of this process researchers appeared to have reached thematic saturation and a codebook was formed. Afterwards the two researchers separately coded approximately 10% (n=83) of the total design task responses to confirm that saturation had been achieved and to obtain practice using the newly formed codebook. Only minor changes were made to the codebook (see coding categories in Appendix A). The next phase of coding consisted of using the themes identified during the first phase of the research to code a stratified random sample of the design task responses. Researchers randomly selected 200 design task responses (approximately 25% of the total pool of 798) stratified evenly across the four content areas (language arts, math, science, social studies) and evenly across the pre- and post-course assessments. Researchers justified the selection of 200 design task responses due to the overlap among rationales identified during the open coding phase of the research and due to the time-intensive nature of the coding. Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


16

Two researchers independently coded all 200 design task responses. For each design task response, researchers determined whether there was evidence or no evidence for each of the six coding categories shown in Appendix A. The 1200 discrete coding decisions for each rater were compared. Inter-rater reliability was 74% for agreement on existence of a code and 92% for agreement of both existence and non-existence of a code. Initial work looking at differences in coding revealed that resolving four minor differences between coders changed the inter-rater reliability to 81% and 94% respectively. Raters met and discussed each of the coding differences, coming to 100% agreement for the data presented in the findings. Descriptive statistics and examples were used to identify patterns in the decision making of the candidates. Additionally, paired sample t-tests were conducted to determine if pre- and post-course differences for the participants were statistically significant. 4.5 Limitations of Approach The researchers recognize several limitations to the study approach. One limitation is that student decision making in response to instructional design tasks is removed from classroom realities. Haertel (1990) contended that performance exercises like the one used in this study cannot measure all of the knowledge required for effective technology enhanced instruction, and they do not tend to elicit typical behavior. Thus Baxter and Lederman (1999) stressed the importance of using multiple sources of data. Examining only student rationales for selecting technologies in response to design tasks that address curriculum standards limits the scope of the current study results. Additionally, the methods used are time consuming and thus difficult for researchers to reproduce (Kagan, 1990).



17

5. Results This section is divided into two major parts: (a) evidence and examples of each of the coding categories from the student rationales, and (b) evidence for patterns of change in students’ rationales between pre- and post-course examinations. 5.1 Identifying Rationales This section of the paper outlines the nature of the rationales that researchers discovered which support TPACK constructs that relate to technology integration. Overall, 48% of codings were related to TPK, with 42% related to TPACK and only 10% related to TK. Technological knowledge (TK) represents the knowledge required to understand and use various technologies independent of the knowledge required to apply a technology in a specific pedagogical context. Teachers who used TK rationales focused on the value of the technical skill itself, independent of its value in facilitating classroom learning. Thus TK focuses on technology as a content domain itself to be learned, rather than as a tool to be used in the service of learning other content. Researchers expected to find a low number of TK rationales because the prompts asked candidates to use technology to address curriculum standards. Table 3 shows that evidence for this coding category involved teacher candidates using technology because they felt the technical skills would have intrinsic value for their students now or in the future. Some examples of this theme can be found in Table 3. Insert Table 3 approximately here 5.1.1 Technological pedagogical knowledge (TPK). Although, the design tasks asked teacher candidates to integrate ICT in a way that would benefit student learning in a specific content objective, TPK-oriented rationales were the ones most commonly used by teacher candidates as they tried to explain their planned technology use. TPK rationales are rooted in the use of Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


18

general teaching strategies (TPK.sg) and/or a general understanding of learner characteristics (TPK.lg) and do not require content-specific knowledge. Many different kinds of evidence, identified in Table 4, mapped to the category of general instructional strategies (TPK.sg). All of them (e.g., classroom management, collaboration, active learning, etc.) have to do with general strategies typically used across multiple content domains. TPK rationales rooted in knowledge of general learner characteristics (TPK.lg) were also identified. Table 4 shows specific examples of how teacher candidates used their knowledge of general learner characteristics (e.g., developmental abilities, learning styles/preferences, etc.) as rationales for their planned technology use. Insert Table 4 approximately here While a wide range of TPK rationales were found, not all of the themes were equally prevalent in the design task responses. Some of the most commonly used TPK.sg themes were collaboration, active learning, and present/display information. Rationales that were used only sporadically included practice/feedback and student research. The TPK.lg category included many rationales rooted in the motivation of children, and relatively few focused on knowledge of developmental abilities or learning styles/preferences. 5.1.2 Technological pedagogical content knowledge (TPACK). Of the rationales Teacher candidates provided, 42% were related to TPACK categories and themes. TPACK rationales differed from TPK rationales because they included some form of content-specific knowledge, represented in the TPACK framework by the overlap with content knowledge (CK). Themes were organized into three TPACK categories: knowledge of content-specific instructional strategies, knowledge of learner content understanding, and knowledge to transform content representations for teaching. [Note: In this research, knowledge of representations in the content Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


19

domain (Rd in Figure 2) is knowledge as used by practitioners within the domain (no overlap with pedagogical knowledge). Once a teacher transforms a domain representation to make it comprehensible to learners, it is coded as a representation transformed for teaching (Rt in Figure 2). This explains why the code Rd from the PCK foundational framework in Figure 2 is not coded in the study.] Understanding learner content knowledge includes teachers’ knowledge about students’ prior knowledge of the content, knowledge of common misconceptions or difficulties with specific content, and/or understanding of how particular practices influence content learning. Knowledge to transform content representations for teaching has to do with how technology changes a content representation to make it more comprehensible to learners. Table 5 contains specific examples of the categories and themes coded in student rationales. Insert Table 5 approximately here Rationales based on content-specific instructional strategies (TPACK.sc) were found in only 7.8% of the teacher candidates’ responses, making this the least coded of the categories. In the category knowledge to transform content representations for teaching (TPACK.rt), the most common rationale by far was that the technology transformed the content to make it more visual. In the learner content understanding category (TPACK.lc), the most common rationale was that the use of the technology had a positive impact on student content learning outcomes. Though the PCK literature includes knowledge of learner misconceptions (Cochran et al., 1993; Lee et al., 2007; Magnusson et al., 1999; van Driel et al., 1998), none of the participants included the use of technology as a tool for addressing student misconceptions. This outcome is likely due to teaching candidates’ limited exposure to learner misconceptions at this point in their program and teaching experiences. Publication Info: Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546. doi:10.1111/j.1365-2729.2011.00472.x


20

5.2 Changes in Rationales This section will address how student responses improved in both the quantity and quality (richness) of rationales used from the pre- to post-course assessments. 5.2.1 Changes in quantity. Table 6 shows the number of categories coded in each of the content areas for both the pre- and post-course assessments. These data indicate a dramatic increase for all of the TPK and TPACK categories, with a slight reduction in the number of teacher candidates using TK as a rationale for their technology use. A paired-samples t-test found the change in all of the categories to be significant (p