Chapter 4. Teachers’ pedagogical knowledge: What it is and how it functions

Sonia Guerriero
OECD

This chapter gives an overview of the literature on teachers’ knowledge and how it relates to teacher quality and student outcomes. First, we illustrate the complexity of the teaching and learning process through some models. Second, we review how teachers’ knowledge has been conceptualised and what specific content it is composed of in the different models, focusing in particular on pedagogical knowledge. Third, we summarise the empirical evidence on the relationship between teachers’ knowledge and student learning outcomes. We then describe how pedagogical knowledge is used in decision-making, how it is learned and how it is developed into expertise. We conclude with some implications on teacher education.

  

Introduction and policy context

The imperative in recent years about improving student outcomes is also about improving the quality of the teaching workforce. In recent years, however, recruiting and retaining quality teachers have become challenges among some OECD countries. In addition to the ageing of the teaching workforce, some countries experience high rates of attrition among new teachers and a shortage of quality teachers in high-demand subject areas and disadvantaged schools. There is also concern about attracting high-achieving and motivated candidates into teacher education programmes and the lowering of qualification requirements in the certification and licensing of new teachers (OECD, 2005).

Issues such as these have an impact on the quality of the resulting teaching workforce that is tasked with improving student outcomes. For example, the ageing of the teacher workforce entails the loss of experienced teachers. High attrition rates among new teachers are costly to the system and may prompt education authorities to fill teacher shortages by lowering qualification requirements for the certification of new teachers or by assigning teachers to teach subjects or grades for which they are not trained (for a review, see Santiago, 2002). In such cases, the quality of the teaching workforce is negatively affected.

The quality of the teaching workforce has implications for student outcomes, as empirical research has shown that teacher quality has an impact on student achievement. Research studies using econometric approaches have shown that teacher quality is an important factor in determining gains in student achievement, even after accounting for prior student learning and family background characteristics (e.g. Darling-Hammond, 2000; Hanushek, Kain and Rivkin, 1998; Muñoz, Prather and Stronge, 2011; Wright, Horn and Sanders, 1997). In these types of studies, predictors of teacher quality have included factors such as class size, certification, type of qualification, degrees earned or years of experience. However, findings have been inconsistent (e.g. Darling-Hammond, Berry and Thoreson, 2001; Muñoz and Chang, 2007; Wayne and Youngs, 2003) or minimal when teacher characteristics are examined independently of overall teacher effects (e.g. Goldhaber and Anthony, 2007; Hanushek, Kain and Rivkin, 1998; Rivkin, Hanushek and Kain, 2005).

It is not surprising that such studies have shown inconsistent results. Factors such as teacher certification, qualifications or years of experience are proxies assumed to measure quality. In these studies, it is hypothesised that such proxies will differentiate quality teaching (Hill, Rowan and Ball, 2005). But in actuality, teachers’ competency in the classroom, such as the quality of the instructional skills employed, are not directly examined. In the same way, any policy changes directed at certification or qualification requirements that do not target the actual competences underlying teaching itself risk of being unsuccessful.

In a different set of studies, a more direct indicator of teacher quality has been explored by investigating the substance of teachers’ knowledge. In this approach, the study of teacher quality goes beyond an investigation of distal indicators, such as qualifications, degrees earned or years of study, to a more conceptual investigation of quality by studying the knowledge base of teachers presumed to underlie competent teaching (Hill, Rowan and Ball, 2005). Studies of teacher knowledge hypothesise that differences in the conceptual quality of teachers’ knowledge can better differentiate quality teaching because (competent) performance is based on an underlying pedagogical knowledge base. This approach to understanding teacher quality is more complex, but it is more likely to lead to policy changes that can have an impact on student learning, for example, by exerting a direct influence on the content of teacher education programmes. In fact, as will be discussed later, research shows that teacher knowledge is a better predictor of student outcomes than distal factors.

In this chapter, we review the research on how teachers’ knowledge has been conceptualised in the literature in order to gain a better understanding of what it is and how it relates to teacher quality and student outcomes. This review resulted as part of the development of the Innovative Teaching for Effective Learning project introduced in Chapter 1. But before beginning the review, we start with a brief overview of the teaching-learning process. It would not be possible to investigate the knowledge base of teachers (a psychological concept) without first understanding how the teaching-learning process works (a cognitive concept). As a result, this paper adopts a cognitive-psychological view of teaching and learning where “learning” is a cognitive process and defined as a change (i.e. growth) in student knowledge. Under this approach, the learner is the focus of the teaching-learning process and teaching is viewed from the perspective of the learner. In other words, (effective) teaching is interpreted from the perspective of its effect on student learning growth. Implications for teacher education are discussed at the end.

The teaching-learning process

The teaching-learning process is complex, and to this day, not yet fully understood. A simplistic view in diagram form is given below, adapted from Mayer (2011):

Figure 4.1. The teaching-learning process
picture

Source: Adapted from Mayer (2011), Applying the Science of Learning.

Under a cognitive view, instruction is defined as the manipulation of the learner’s environment to cause a change in the learner’s experience. A change in the learner’s experience is interpreted as new knowledge and is, thus, a learning process. These are represented in the light blue boxes. The grey box represents the hypothesised cognitive changes in the learner’s mind as the learner interprets and represents the instruction. Evidence of the change in the learner’s knowledge is inferred via a change in the learner’s performance in an assessment test. Assessment is a crucial component of the teaching-learning process as this is how the effectiveness of the instructional manipulation is determined. In this simplistic diagram, Mayer has nicely captured the complexity of the cognitive processes underlying teaching and learning.

An example of a more sophisticated model of teaching and learning is one proposed by Seidel and Shavelson (2007) who built on a previous model of teaching and learning developed by Bolhuis (2003). Seidel and Shavelson conceptualise learning as the intentional construction of knowledge in a specific subject domain, in a regulated, goal-directed and social learning environment, with continuous monitoring and assessment of learning. Effective teachers employ teaching practices that best exploit the learning process. For example, effective teachers are those who differentiate teaching in different content areas (maths vs. reading), allow sufficient learning time for students to actively construct their knowledge, structure learning by setting and orienting students towards goals, establish a social learning climate in the classroom, and provide feedback and support for continuous monitoring and evaluation of student learning. Although teaching effects are differentiated through measures of outcomes in student learning, motivational-affective and cognitive components, the model is based on the perspective of teaching and the student’s contribution to the teaching-learning process is not made explicit.

In a different set of models, the student’s contributions to the teaching-learning process are made explicit and taken into account as inputs to the process of effective teaching. Carroll (1963) proposed a model that is comprised of five elements that contribute to effectiveness of instruction: students’ general ability, prior knowledge, motivation to learn, opportunity to learn (i.e. amount of time allowed for learning) and quality of instruction. Three of these five elements (general ability, prior knowledge and motivation to learn) are student inputs to the teaching-learning process, while the other two (opportunity to learn and quality of instruction) are under the control of the teacher. Carroll’s model introduces the concepts of “learning differences” and “adaptive instruction” in the teaching-learning process. Teachers need to balance the time needed for instruction with the time available for instruction, given that students within a classroom will vary in ability, knowledge and motivation. For example, students with lower abilities or motivation will require more time for instruction. At the same time, the higher the instructional quality (e.g. ensuring students have the requisite prior knowledge for successfully learning a new lesson), the less time will be needed for a lesson. Thus, student and teacher are interconnected and interdependent in the teaching-learning process.

Slavin (1984) built upon Carroll’s (1963) model by concentrating on the alterable components of effective instruction that are under the teacher’s control: quality of instruction, appropriate levels of instruction, incentive (i.e. motivating students) and time allocated for learning. According to Slavin’s model, the alterable components under the teacher’s control combine with student inputs (student aptitude and student motivation) to result in student achievement. Although student inputs are considered fixed in the short-term, they are not immutable and can be affected by classroom practices in the long-term. Slavin also proposed two mediating variables: instructional efficiency (a product of quality of instruction, appropriate levels of instruction and incentive) and engaged time on task (a product of incentive and time allocated for learning). Like in Carroll’s model, Slavin views students and teachers as interconnected and interdependent in the teaching and learning process. For example, student aptitude and student motivation contribute to instructional efficiency and engaged time, which ultimately affect student achievement.

This brief overview is meant to illustrate the complexity of the teaching-learning process and the complex relationship between students and teachers. This contrasts with econometric models that attempt to predict effective teaching by controlling for student factors. While it makes sense to control for differences in student characteristics that are beyond the control of teachers or schools in value-added models of accountability, the above review indicates that student factors are part of, and interdependent with, the teaching-learning process. A model of effective teaching would need to account for these.

More importantly, these models presuppose that a teacher’s knowledge goes beyond mere knowledge of content (e.g. of maths, science or reading) and classroom management, but should also include knowledge of learners and learning. In the next section, we review various conceptualisations of teacher knowledge and how it functions in the teaching-learning process. We begin with definitions.

Teacher knowledge: Theoretical/scientific knowledge and practice-based knowledge

Verloop, Van Driel and Meijer (2001) define the pedagogical “knowledge base” of teachers to refer to all the pedagogical-related knowledge that is relevant to teachers’ activities in a teaching-learning situation. This would include both theoretical or scientific knowledge (e.g. theories of learning and of teaching-learning processes, as described above) and practical or practice-based knowledge (e.g. situated knowledge), but it is not meant to refer to guidelines or prescriptions for practice. Essentially, this knowledge base would comprise all the required cognitive knowledge for creating effective teaching and learning environments. Identifying what specifically is the content of this knowledge is the purpose of this paper. As proposed by Verloop, Van Driel and Meijer, teachers’ knowledge base can be made explicit and studied.

But what exactly is meant by “knowledge”? In cognitive psychology, “knowledge” does not refer to a single concept – a distinction is made between “declarative” knowledge and “procedural” knowledge. This distinction is important because it forms the basis for understanding how knowledge is acquired and developed into mastery, such as in expert teaching. Research from cognitive psychology is of particular relevance here because of the focus on understanding how knowledge is related to behaviour (e.g. as in teaching performance). Simplistic conceptualisations of declarative and procedural knowledge define these as “knowing that” (e.g. knowledge of facts) and “knowing how” (e.g. knowledge of how to ride a bike), respectively. But the issue is actually more complex, and essentially has to do with how knowledge of skills is stored and organised in memory and how it is used and developed into mastery performance (Anderson, 1982).

Based on the work of Anderson (1982, 1987; also Corbett and Anderson, 1995), declarative knowledge can be either factual or experiential knowledge, whereas procedural knowledge is goal-oriented knowledge that mediates problem-solving behaviour. Knowledge starts out in declarative form, which is then converted to procedural form and embodied as performance of a skill. For example, knowledge of a skill such as teaching is initially encoded in memory in declarative form through experiences such as listening to lectures or reading a textbook (e.g. facts about instruction or classroom management). Through practice, declarative knowledge is interpreted into procedural knowledge to generate behaviours (e.g. knowing when and how to use a particular instructional strategy). Performance improves through subsequent practice as both declarative and procedural knowledge is strengthened.

These distinctions are relevant to the work on teacher knowledge as they explain how novice teachers develop into expert teachers. Specifically, a novice teacher starts with the acquisition of declarative knowledge and then begins to apply that knowledge to teaching students. Research on teacher expertise shows that novice and expert teachers differ in their ability to apply their knowledge (i.e. procedural knowledge), which suggests differences in the quality of pedagogical knowledge based on experience.

When applying their pedagogical knowledge, teachers are making a “professional judgement”. Shalem (2014) argues that teachers’ professional judgement derives from theoretical knowledge and from practice-based knowledge, which she defines as working knowledge of contextually-specific experiences. According to Shalem, teachers’ professional judgement depends on their theoretical knowledge, which is what binds the judgement to a specific situation. Importantly, Shalem argues, it is the theoretical or scientific knowledge that makes teaching a true profession. In other words, placing teachers’ judgement primarily on situated knowledge or knowledge that is acquired primarily experientially is what contributes to de-professionalising teaching. This discussion is picked up again later in the chapter in the discussion on teacher decision-making and expertise.

General pedagogical knowledge

The study of the structure and content of teacher knowledge began in earnest with Shulman (1986, 1987) who proposed that teacher knowledge was comprised of the following categories:

  • general pedagogical knowledge (principles and strategies of classroom management and organisation that are cross-curricular)

  • content knowledge (knowledge of subject matter and its organising structures)

  • pedagogical content knowledge (knowledge of content and pedagogy)

  • curriculum knowledge (subject- and grade-specific knowledge of materials and programmes)

  • knowledge of learners and their characteristics

  • knowledge of educational contexts (knowledge of classrooms, governance and financing of school districts, the culture of the school community)

  • knowledge of educational ends, purposes, values, and their philosophical and historical grounds.

In this seminal work, Shulman formally proposed the concept of “pedagogical content knowledge”, in which he referred to knowledge that integrates the content knowledge of a specific subject and the pedagogical knowledge for teaching that particular subject. Specifically, pedagogical content knowledge “represents the blending of content and pedagogy into an understanding of how particular topics, problems, or issues are organized, represented, and adapted to the diverse interests and abilities of learners, and presented for instruction” (Shulman, 1987: 8). This category would also include knowledge of strategies for reorganising the understandings of learners because students of different ages and backgrounds bring with them prior knowledge, some of which may include misconceptions (Shulman, 1986). According to Shulman, this is the category of knowledge that is fundamental to teachers’ knowledge of teaching. This conceptualisation, in fact, fits well with models that take account of student individual differences in the teaching-learning process as described in the previous section.

The concept of “pedagogical content knowledge” became the focus of study for many educational researchers because it gave rise to the idea that teachers held a unique form of “technical” knowledge available only to the profession of teachers (Ball, Thames and Phelps, 2008; Depaepe, Verschaffel and Kelchtermans, 2013). For example, Ball, Thames and Phelps (2008) proposed that pedagogical content knowledge, as defined by Shulman (1986, 1987), is actually comprised of two categories: “knowledge of content and students” (knowledge of students’ (mis)conceptions) and “knowledge of content and teaching” (knowledge of instructional strategies). They further proposed that Shulman’s concept of “content knowledge” is comprised of “specialised content knowledge” (knowledge unique to the work of teachers) and “common content knowledge” (knowledge common to teachers and non-teachers). With the addition of two more categories (“horizontal content knowledge” and “knowledge of content and curriculum”), these six categories were hypothesised to contribute to teachers’ knowledge of teaching specific subject content. In Ball, Thames and Phelp’s research, the domain of study is mathematics teaching. Most investigations of pedagogical content knowledge have focussed on mathematics (e.g. Baumert et al., 2010; Hill, Schilling and Ball, 2004; Depaepe, Verschaffel and Kelchtermans, 2013) or science teaching (e.g. Abell, 2008; Kind, 2009; Schneider and Plasman, 2011).

For the purposes of this review, the focus is on “general pedagogical knowledge”, originally defined by Shulman (1986, 1987) as principles and strategies of classroom management and organisation that are cross-curricular. While research on pedagogical content knowledge proliferated the field after Shulman’s seminal work, empirical studies of general pedagogical knowledge are still few in number.

However, research is beginning to show that general pedagogical knowledge is just as essential as content knowledge and pedagogical content knowledge for developing quality teachers. First, it is clear that content knowledge alone is insufficient. For example, when teachers’ content knowledge is controlled (via a direct assessment), higher levels of knowledge do not predict better student scores (this study, Baumert et al. (2010), is further described below). This result should not be confused with those from effectiveness studies that have reported a relationship between indicators of teacher knowledge and student learning gains (e.g. Wayne and Youngs, 2003). Those studies used distal indicators (i.e. the measures used are coursework or degrees in a subject) and have not directly assessed teachers’ content knowledge. This lack of control could likely explain the mixed findings in these types of analyses.

As more and more researchers are beginning to conduct empirical studies of teacher professionalism, the concept of general pedagogical knowledge as part of professional competence is becoming more relevant (e.g. Blömeke et al., 2008; Kunter et al., 2013; Voss, Kunter and Baumert, 2011). Finally, on a theoretical level, the study of teachers’ general pedagogical knowledge might help the field move towards a common framework that can bridge the gap between research on teaching and research in teacher education, which, as argued by Grossman and McDonald (2008), has developed in isolation from research on teaching in general. Such a framework would be useful in informing the content of teacher education and making the existing body of knowledge available to novice teachers. Grossman and McDonald propose a framework that is common across subjects, grade levels, students and school contexts, independent of teaching methods, that identifies the key underlying components involved in successful teaching. For example, establishing student-teacher relationships or engaging students in the learning process are common factors across teaching domains.

Shulman’s (1986, 1987) original definition of general pedagogical knowledge was restricted to classroom management and organisation that are cross-curricular. Recent conceptualisations have become more refined to integrate components of the teaching-learning process. For example, Voss, Kunter and Baumert (2011) proposed a model of general pedagogical knowledge that combines aspects of pedagogy and psychology to account for the social environment of the classroom and heterogeneity of individual student learning. Their model of “general pedagogical/psychological knowledge” is comprised of five sub-dimensions:

  • knowledge of classroom management (maximising the quantity of instructional time by having awareness of what is going on in all parts of the classroom, handling two or more classroom events at the same time, teaching at a steady pace throughout the lesson to maintain momentum, maintaining clear direction in lessons and keeping the entire group of students alert)

  • knowledge of teaching methods (making productive use of instructional time by having a command of various teaching methods [e.g. direct instruction, discovery learning, etc.] and knowing when and how to apply each method in promoting students’ conceptual involvement with learning tasks)

  • knowledge of classroom assessment (knowledge of different forms and purposes of formative and summative classroom assessments and knowledge of how different frames of reference [e.g. social, individual, criterion-based] impact students’ motivation)

  • knowledge of learning processes (supporting and fostering individual learning progress by having knowledge of various cognitive and motivational learning processes, including learning strategies, impact of prior knowledge, memory and information processing, causal attributions and how they foster student engagement, effects and quality characteristics of praise, and opportunities for increasing student engagement)

  • knowledge of individual student characteristics (meeting individual student needs by having knowledge of the sources of student cognitive, motivational and emotional heterogeneity, such as test anxiety, ADHD, dyslexia, mental abilities and giftedness, and influence of ethnic background).

In contrast to knowledge of classroom management, teaching methods and classroom assessment, which are pedagogical components, knowledge of learning processes and individual student characteristics are “psychological” components. Psychological components are included in this model because learning occurs in a social context and learning success depends on the general cognitive and affective-motivational characteristics of individual students. In other words, psychological aspects such as general cognitive abilities, motivational and affective characteristics, and prior knowledge will differ among students and thus impact on individual learning success. As such, teachers need to know how to deal with what the authors refer to as “heterogeneity” in student learning, which is subsumed in the sub-dimensions of “learning processes” and “individual student characteristics”. In this manner, Voss, Kunter and Baumert’s (2011) model is similar to those of Slavin (1984) and Carroll (1963) where students and teachers are considered interconnected and interdependent in the teaching and learning process. According to Voss, Kunter and Baumert (2011), their model is domain-general and necessary for “creating and optimising teaching-learning situations” (p. 953). This model also addresses an important issue brought up by Grossman and McDonald (2008) about the centrality of relationships in teaching and the lack of research investigating this pedagogical relationship and how it influences student engagement. Voss, Kunter and Baumert’s model, “knowledge of learning processes”, which encompasses factors involved in the teacher-student relationship, can begin to address this issue.

Another cognitive model also based on student learning is proposed by König et al. (2011). König et al. used a task-based framework of teacher competence to define and operationalise “general pedagogical knowledge” by adopting Slavin’s (1994) model of effective teaching as a theoretical framework. According to König et al., general pedagogical knowledge is comprised of four dimensions:

  • structure (structuring of learning objectives, lesson planning and structuring the lesson process, and lesson evaluation)

  • motivation and classroom management (achievement motivation, strategies to motivate a single student or a whole group, strategies to prevent and counteract interferences, and effective use of allocated time and routines)

  • adaptivity (strategies of differentiation and use of a wide range of teaching methods)

  • assessment (assessment types and functions, evaluation criteria and teacher expectation effects).

Within this framework, effective teachers are proposed to have acquired general pedagogical knowledge if they showed competency in tasks requiring them to prepare, structure and evaluate lessons; to motivate and support students and make effective use of time to manage the classroom; to deal with heterogeneous learning groups in the classroom by making use of differentiated strategies and methods of instruction; and to assess students. This model is similar to Voss, Kunter and Baumert’s (2011) model, except that König et al.’s model does not make the existence of psychological factors underlying learning processes and student characteristics explicit as their model is based on the alterable elements of instruction that are under the control of the teacher (as per Slavin, 1994).

One of the reasons for the dearth of empirical studies investigating general pedagogical knowledge could be due to the difficulty in defining this concept. For instance, some such as König et al. (2011) and Blömeke et al. (2008) suggest that cultural differences in perspectives on the role of teachers and schooling contribute to difficulties in defining a construct like general pedagogical knowledge. By contrast, a construct such as “mathematics” is relatively universal. Despite this, researchers such as König et al. (2011) and Blömeke et al. (2008) have shown that it is possible to develop a standardised assessment instrument to investigate general pedagogical knowledge across different countries. These studies show that such an instrument is valid cross-culturally, namely, across Germany, United States, South Korea, Chinese Taipei and, thus, give support to the existence of a culturally-independent fundamental knowledge base for teaching.

Relationship to student learning outcomes

A positive relationship between teacher knowledge and student learning outcomes would indicate that efforts aimed at improving the quality of teacher knowledge should lead to improvements in learning outcomes. However, like investigations of the (theoretical) components of teacher knowledge, most studies of the impact of teacher knowledge on student learning outcomes are restricted to investigations of pedagogical content knowledge or simply content knowledge. We review this evidence here.

In a study investigating the quality of teachers’ content knowledge of mathematics, Hill, Rowan and Ball (2005) reported a significant relationship to student achievement gains. In this study, the knowledge category of interest was conceptualised as “content knowledge for teaching mathematics” and comprised two components: (1) common knowledge of content (the knowledge of the subject that most adults would have); and (2) specialised knowledge used in teaching mathematics to students (the knowledge needed for evaluating solutions, at the level of a mathematician). Results showed that for every standard deviation difference in teachers’ knowledge, students gained about one-half to two-thirds of a month of growth. Data was also collected on the number of mathematics and mathematics methods courses taken as reported by the teachers and whether teachers were certified. Results revealed that neither certification nor courses taken were significantly related to student achievement gains. Furthermore, neither variable showed a strong significant relationship with mathematical content knowledge for teaching as assessed in this study, indicating that these approaches to improving teacher quality do not target the necessary knowledge underlying quality instruction. One limitation of this study, however, is that “pedagogical content knowledge”, conceptualised as the specialist or technical knowledge of both content and pedagogy that is in the professional domain of teachers, was not assessed.

Baumert et al. (2010) investigated the specific impact of pedagogical content knowledge (of mathematics) on student achievement. Pedagogical content knowledge was operationalised into three assessment dimensions: (1) a “tasks” dimension, which assessed teachers’ ability to identify multiple solution paths; (2) a “student” dimension, which assessed teachers’ ability to recognise students’ misconceptions, comprehension difficulties and solution strategies; and (3) an “instruction” dimension that assessed teachers’ knowledge of different representations and explanations of standard mathematics problems. In their model, “instructional quality” was used as a mediator and operationalised into three components: (1) cognitively challenging and well-structured learning opportunities, for example, by drawing on students’ prior knowledge and ensuring alignment between the cognitive demands of the tasks and materials chosen by the teacher and curricular demands; (2) learning support through monitoring of the learning process, individual feedback (including motivating students) and adaptive instruction for addressing student difficulties; and (3) efficient classroom and time management, for example, preventing disruptions and using classroom time effectively. All student tests, examinations, tasks and homework assignments given by the teachers were collected and analysed for the level of cognitive activation demanded and level of alignment with the curriculum. For measuring individual learning support and classroom management, students and teachers completed rating scales. Finally, student achievement was assessed via a German national mathematics exam in combination with the Programme for International Student Assessment (PISA) 2003 results in mathematics and reading literacy.

Multilevel analyses showed that teachers’ pedagogical content knowledge was a significant predicator in explaining differences in student achievement between classes. Moreover, the relationship between pedagogical content knowledge and mathematics achievement was linear. Analyses of instructional quality showed that the cognitive level of tasks, the curricular level of tasks and effective classroom management were significant predictors of mathematics achievement. However, individual learning support and classroom management were not found to have a specific effect on mathematics achievement. The mediation model revealed that pedagogical content knowledge influenced the cognitive level, curricular level and learning support dimensions of instructional quality, suggesting the important contribution of pedagogical knowledge specifically.

There is still a debate in the teacher knowledge literature in connection with whether content knowledge, that is, knowledge of subject matter, falls within the concept of pedagogical content knowledge or is independent of it. According to Shulman (1986, 1987), pedagogical content knowledge and content knowledge are separate categories, but not all agree to this separation (see Depaepe, Verschaffel and Kelchterman, 2013 for a review). Under Baumert et al.’s (2010) model, these two constructs represent distinct knowledge categories, which were empirically tested in their study as the teachers also completed a mathematics test assessing their conceptual understanding of mathematics topics taught in Grades 5 through 10. Results revealed that teachers’ pedagogical content knowledge has more of an impact on student achievement than content knowledge. In regards to measures of instructional quality, teachers’ content knowledge predicted the curricular level of tasks (i.e. teachers with higher levels of content knowledge were better able to align the material with the curriculum). However, higher levels of content knowledge had no direct impact on cognitive activation or on the individual learning support that teachers are able to provide when learning difficulties occurred. Only pedagogical content knowledge seems to have an impact on the quality of instruction, suggesting that it is a separate category of knowledge, and an important component of teachers’ knowledge required for impacting student learning.

Empirical investigations of the relationship between teacher knowledge and student outcomes are also few in number, and these have focused on pedagogical content knowledge. One study has attempted to investigate teachers’ general pedagogical knowledge, and this study, by Voss, Kunter and Baumert (2011), also discussed above, has investigated this relationship empirically by examining student perceptions of instructional quality.

Based on their conceptualisation of teachers’ “general pedagogical/psychological knowledge”, Voss, Kunter and Baumert (2011) developed an assessment instrument to investigate the impact of this knowledge on instructional quality. As part of their validation work, a sample of students was asked to rate their teachers on five aspects of instructional quality: (1) cognitive activation (giving students cognitive autonomy, such as comparing and evaluating different ways of solving a problem); (2) pace of instruction (giving students sufficient time to think before responding to a question); (3) classroom management (managing disruptions during lessons); (4) student-teacher relations (the caring nature of the teacher); and (5) awareness of students’ comprehension problems during a lesson. Results showed that student perceptions of instructional quality were related to teachers’ general pedagogical/psychological knowledge: Students of teachers with higher general pedagogical/psychological knowledge reported higher cognitive activation, better instructional pacing, better student-teacher relationships, fewer disruptions and higher teacher awareness of students’ comprehension problems.

Regrettably, this is the only study that has conducted an empirical investigation of the relationship between teachers’ general pedagogical knowledge and student learning outcomes, where teachers’ knowledge is properly operationalised and controlled. Although there is a long history of discussion and debate around the connection between teacher knowledge and quality instruction, there is a lack of empirical research testing this hypothesis or even connecting knowledge to student learning. The studies reviewed here show that while much research is still needed to fully support this relationship, as well as to test a cross-cultural conceptualisation of general pedagogical knowledge, research thus far is beginning to show that teachers’ general pedagogical knowledge is relevant to understanding quality teaching as understood by its impact on student learning outcomes.

A related area of work to understanding teacher knowledge is the work on teacher decision-making. Research on teacher decision-making addresses the question of how teacher knowledge functions in the teaching-learning process. This research is reviewed next.

How is teacher knowledge used in decision-making?

For most, teaching is viewed as a profession similar to the professions of medicine or engineering and, just like doctors and engineers hold knowledge specific to their professions, teachers hold specialist knowledge of learning. As “learning specialists”, teachers are assumed to have the requisite pedagogical knowledge for decision-making and judgement as required by the profession, for example, for deciding on how to design a lesson for a diversity of learners or making on-the-spot judgements while the classroom dynamics are unfolding. A similar view is proposed by Alter and Coggshall (2009) who conceptualise teaching as a “clinical practice profession” because of its similarity to the professions (as opposed to crafts) of clinical psychology and medicine (also McLean Davies et al., 2013).

Based on a systematic review of the teacher and medical education literature, Alter and Coggshall (2009) identified five key characteristics of a clinical practice profession: (1) centrality of clients; (2) knowledge demands; (3) use of evidence and judgement in practice; (4) community and standards of practice and (5) education for clinical practice. Our view of the learner (student) as the central focus of the teaching-learning process is in accordance with the “centrality of clients” in a clinical practice profession such as medicine or clinical psychology. “Knowledge demands” refers to the various knowledge domains held by the professional, which for teaching would include general pedagogical knowledge, pedagogical content knowledge, content knowledge, and so on. “Community and standards of practice” and “education for clinical practice” refer to the use of evidence-based standards for accountability and evaluation, how knowledge is codified in the profession, how knowledge is transformed into practice and professional responsibility for continued learning. In this section, we are concerned with the “use of evidence and judgement in practice” as this addresses how teacher knowledge is used in decision-making.

If we take the view of teachers as learning specialists who hold profession-specific technical knowledge of teaching and learning, we also assume that teachers must use their professional knowledge for judgement and decision-making. This idea is not new. Shavelson (1973) argued that decision-making is actually a basic teaching skill. According to Shavelson, decisions are regularly made by teachers during teacher-student interactions while processing cognitively complex information about the student (e.g. the prior or current knowledge available to the student) in order to decide alternatives for increasing the student’s understanding.

Others have made similar arguments, and more recent research has refined our understanding of the complexity involved in teaching (e.g. Kagan, 1988; Leinhardt and Greeno, 1986; Shavelson and Stern, 1981; Westerman, 1991). For example, Leinhardt and Greeno (1986) characterise teaching as a complex cognitive skill that requires making rapid decisions in an ill-structured and constantly changing environment, where information that arises while in the performance of teaching must be used to inform performance as it is occurring. In this line of research, teachers’ decision-making is analogous to that of physicians who engage in clinical problem-solving (in the case of physicians, for diagnosing and treating physical dysfunctions). In a US National Institute of Education review reported in Kagan (1988), teachers are compared to physicians because teachers are professionals who diagnose and treat learning dysfunctions using clinical problem-solving strategies similar to physicians, such as collecting and interpreting information about the client (the student), applying new research findings to the current (classroom) situation and using data to make judgements.

All decisions taken by a teacher interact to create a learning environment (Clough, Berg and Olson, 2009). Clough, Berg and Olson (2009) developed a “decision-making framework” to illustrate how teacher decisions about content and activities, as well as decisions about teaching strategies and behaviours, interact with teaching goals and how students learn. Key teacher decisions rest on the centrality of assessment that is part of a continual feedback loop within the learning environment. For example, teachers are continually assessing the learner (e.g. collecting information about the student’s thinking, self-efficacy, prior knowledge, developmental level, etc.) to inform their decisions on selecting the strategies, tasks and activities, as well as how to behave and interact with the student, in order to move the student forward in their learning. Clearly, in this highly interactive, continually changing environment, teaching is complex and cognitively demanding.

In an earlier and more comprehensive model of teacher decision-making, Shavelson and Stern (1981) included factors beyond the classroom as variables influencing teachers’ decisions, as well as characteristics of the teacher themselves. Based on their review of the research, teachers’ decisions, judgements and behaviours are influenced by:

  • the students (e.g. general ability, gender, self-concept, social competence, behaviour problems, work habits)

  • the nature of the instructional task (e.g. subject matter, learning goal(s), availability of materials, student characteristics that impact decisions about the task such as how to group students)

  • the classroom environment (the social and physical context of the classroom influence teachers’ decisions as part of a continual process of negotiating learning goals with students; e.g. decisions around establishing a sense of community, reward structures, routines)

  • the school environment (e.g. school policies around evaluation, curriculum, content, parents and community)

  • teacher characteristics (e.g. teachers’ beliefs about teaching (traditional vs. progressive) and conceptions of subject matter influence decisions about instructional methods and strategies)

  • teachers’ cognitive processes used in selecting and integrating information for decision-making (e.g. heuristics, attributions of student abilities and inferences).

Information about the students, the nature of the instructional task, and the classroom and school environments serve as antecedent conditions that influence teachers’ judgements. Antecedent conditions combine with teachers’ characteristics and cognitive processes to impact the pedagogical decisions that guide teaching behaviours, such as planning instruction and interacting with students. Pedagogical decisions in turn impact antecedent conditions and the decision-making cycle continues as such (Shavelson and Stern, 1981).

How teacher knowledge is used in decision-making is not an easy question to investigate. Kersting et al. (2012) conducted a study to investigate how teachers use mathematical pedagogical knowledge by asking teachers to analyse videotaped episodes of classroom instruction. It was hypothesised that teachers’ analyses of videotaped events might explain how teachers access and apply knowledge in a classroom situation and thus reveal their pedagogical thinking. To measure the degree of teachers’ knowledge, teachers’ written analyses of the videotaped instructional episodes were coded for evidence of content knowledge, whether they made suggestions for improvement, depth of interpretation of the instructional event, and awareness of student thinking. Results showed that more knowledgeable teachers were those who were able to connect content knowledge (in this case, of mathematics) with student thinking, which in turn informed their choice of teaching strategies (by making suggestions for improvement). Further analyses revealed that teachers who provided more sophisticated analyses of the videotaped events were judged to exhibit higher quality teaching in their classrooms and that higher instructional quality was a significant predictor of student learning gains (also Kersting, 2008; Kersting et al., 2010).

Other researchers have studied how teacher knowledge is used in decision-making by investigating teachers’ ability to notice and reason about classroom events (Sherin and van Es, 2009; van Es, 2009; van Es and Sherin, 2008). “Noticing” refers to teachers’ ability to direct their attention to relevant classroom situations as they are occurring. “Reasoning” refers to teachers’ cognitive processing and interpreting of the instructional events to which their attention is directed based on their knowledge of teaching and learning. Research has identified three aspects to this reasoning process: (1) the ability to describe what has been noticed; (2) higher-order processes to connect the observed classroom event to prior knowledge and understanding of teaching and learning; and (3) knowledge-based reasoning processes to evaluate and predict what might happen as a result of connecting the observed situation to prior knowledge of teaching and learning (Seidel et al., 2011). Together, noticing and reasoning about classroom events interact in a dynamic manner and require (1) a high level of domain general pedagogical knowledge about effective teaching and how it relates to students’ learning processes and (2) the ability to apply such knowledge for the planning and implementation of instruction to the current situation (Stürmer, Könings and Seidel, 2013). Thus, teachers make (and implement) decisions as a result of identifying and interpreting important events in the midst of instruction based on their pedagogical knowledge.

In sum, empirical research seems to be suggesting that in order to make informed pedagogical decisions, teachers must be able to analyse and evaluate specific learning episodes, in combination with contextual and situational factors (e.g. students’ prior knowledge, ability levels, motivational factors, lesson objectives, curriculum goals, etc.) and to be able to connect all this information to their specialist knowledge of the teaching-learning process in order to guide subsequent teaching actions. Thus, making good pedagogical decisions hinges on the quality of the pedagogical knowledge held by the teacher.

It should be clear at this point that complex knowledge is involved in effective teaching, which begs the question about how pedagogical knowledge is learned and developed. A related area of work on teacher knowledge is the field of research on teacher expertise and how teachers acquire their knowledge. This is reviewed next.

How is pedagogical knowledge learned and developed into expertise?

Defining expert teaching and identifying expert teachers are complex issues given that conceptualisations of “expert teaching” are culturally-dependent and often lack objective criteria (Berliner, 2001). Based on a review of the literature reported in Berliner (2001, 2004), expert teachers are characterised as having the following features: better use of knowledge; extensive pedagogical content knowledge, including deep representations of subject matter knowledge; better problem-solving strategies; better adaptation and modification of goals for diverse learners; better skills for improvisation; better decision-making abilities; giving students more challenging objectives; better classroom climate; better perception of classroom events; better ability to read cues from students; greater sensitivity to context; better monitoring of learning and providing feedback to students; more frequent testing of hypotheses; greater respect for students; and display of more passion for teaching. Many of the features reported by Berliner have been discussed in preceding sections of this paper, for example, teachers’ use of pedagogical knowledge.

Sternberg and Horvath (1995) used findings from psychological research on expert performance to characterise the features of the prototypical expert teacher and identified three basic ways in which experts differ from novices: (1) experts bring more knowledge to bear in solving problems than do novices; (2) experts are able to solve problems more efficiently than are novices; and (3) experts are more able to arrive at insightful solutions to problems than are novices. The main driver behind expert teachers’ ability to solve problems more efficiently and to arrive at more insightful solutions than novices is the knowledge they hold, which Sternberg and Horvath propose to be the most important feature of expertise. Westerman (1991) investigated development of teacher decision-making and reported that integration of knowledge (e.g. combining new subject content knowledge with prior knowledge) was one of the notable differences between novices and experts.

Empirical studies on the nature of expertise have revealed that teaching expertise is developed over time and that it takes about five to seven years for new teachers to learn the knowledge and skills to a sufficient degree where they can have an impact on student outcomes (e.g. Berliner, 2004). It has been proposed that learning to teach is more complex and different from other forms of learning, because the learning growth of student teachers goes beyond simply assimilating new academic knowledge; it must also incorporate the new knowledge derived from experiential and practical experiences in the classroom (Calderhead, 1991). This is consistent with the view of teaching as a clinical practice profession as proposed by Alter and Coggshall (2009).

A new body of research is investigating “opportunity to learn” as a measure of the quality of teachers’ knowledge by looking at the types of pedagogical content that teachers are exposed to in teacher preparation programmes and the extent to which teachers have opportunities to learn the various content (Schmidt, Cogan and Houang, 2011; Schmidt et al., 2008; Schmidt et al., 2007). These studies are beginning to show that variations in opportunities to learn in teacher preparation are related to differences in student achievement as assessed by international studies such as PISA and Trends in International Mathematics and Science Study (TIMSS). More specifically, teachers from countries that are top performers in PISA and TIMSS tend to have more opportunities to learn mathematical content (content knowledge), mathematical pedagogy (pedagogical content knowledge), and general pedagogy.

In sum, empirical studies are beginning to show that teacher knowledge is related to quality teaching, and that pedagogical knowledge can be leaned and developed over time given the right opportunities. This has implications for teacher education, discussed next.

Implications for teacher education

The purpose of this chapter was to review the theoretical and empirical research on teacher knowledge, specifically teachers’ general pedagogical knowledge, and to give an overview of how it functions in the teaching-learning process. The research studies reviewed indicate that teachers’ pedagogical knowledge is related to higher student achievement and better instructional quality. This research also suggests that while content knowledge and pedagogical content knowledge are necessary for improved student achievement, they are not sufficient. General pedagogical knowledge is another key factor underlying teacher quality.

In reviewing how teacher knowledge functions in the teaching-learning process, it becomes clear that teaching is a complex and cognitively-demanding activity and that improving teaching so as to improve learning outcomes will require more than just superficial reforms to certification requirements, qualifications or programme durations. Expert teachers are capable of enacting informed professional judgements that integrate extensive theoretical and practice-based knowledge. Becoming an expert teacher takes time and requires years of deliberate practice. The research shows that expert teachers are effective at helping their students learn successfully because of quick decision-making that hinges on a well-developed foundational pedagogical knowledge base. Improving teaching so as to have a corresponding improvement in learning outcomes will thus require substantive reforms to the scope and depth of the knowledge that teachers are meant to acquire in initial teacher education and throughout their professional careers.

This has implications for how teachers can be more effectively trained. The research reviewed above indicates that the quality of teachers’ knowledge is related to having access to opportunities to learn a range of pedagogical content and scope. For example, research by Blömeke et al. (2008) indicates that teacher education courses in high-achieving Asian countries are more focused on the processes of student thinking and cognitive development in comparison to the USA and European countries. Access to such “opportunities to learn” will have differential effects on teacher quality.

Another, generally overlooked, factor related to variation in opportunities to learn in initial teacher education is teacher educators. According to a report published by the European Commission (2013), teacher educators are key players in improving education quality, but the roles and responsibilities of teacher educators are not well understood. For example, variation exists in the level of qualifications required of teacher educators (e.g. Bachelor, Master or PhD), area of expertise (e.g. pedagogical or subject-matter experts) or professional profile (university lectures, researchers or school teachers). This variation in the “profession” of teacher educator also affects opportunities to learn, not only in initial teacher education institutions, but also in the provision of professional development.

Teacher educators are key to teaching students how to link theory to practice (European Commission, 2013), which, as argued above, is how professional judgements are made. Teacher education is the mechanism through which teachers are trained and inducted into the profession (Berliner, 2004), and it is through these formal learning opportunities that profession-specific knowledge is learned (Kunter et al., 2013). However, as some have argued (e.g. Révai and Guerriero, this volume), it is not clear whether the profession’s knowledge base is up-to-date due to the complexity of the knowledge dynamics in the teaching profession.

To this end, teacher educators can play a crucial role in contributing to the development of the profession’s pedagogical knowledge base through knowledge production, knowledge use, knowledge management and knowledge translation. Engaging with research would enable the profession to become better consumers of research, and by consequence, enable the profession to be informed by research-based knowledge in a more systematic way (BERA, 2014). Teacher educators, holding specialist knowledge of both theory and practice, are well-positioned to undertake research to further understanding of teaching and learning. In fact, teacher educators can be seen as sitting at the nexus of practice, research and policy, as exemplified by the figure below. Teacher educators are also well-positioned to inform policy with evidence-based recommendations.

Figure 4.2. Building a pedagogical knowledge base and a teaching profession through teacher educators
picture

The debate around teachers’ knowledge and its role in the teaching profession continues. It has been argued that access to a specialist knowledge base will elevate teaching to a status that is afforded to other professions with a rich knowledge base, such as medicine or law (e.g. Calderhead, 1991). In this chapter, we argue that teacher educators can play a key role in building the profession’s knowledge base. But it is not so simple. As made clear by the European Commission (2013) report, despite acknowledging the important role of teacher educators to improving learning outcomes, national policies to promote the development of the profession are lacking. More importantly, professionalising teacher educators is likely to have a positive impact on the profession as a whole.

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