Introduction

Metacognition is made up of a number of components. The following study focuses on one component in particular: meta-strategic knowledge (MSK). MSK is defined as the “general and explicit knowledge of the manipulated cognitive procedures” (Zohar and Ben David 2008, p. 59). It seems that MSK has regulative significance for our thinking because it informs us how to apply correct cognitive processes to specific, contextually rich situations that are often “messy” in terms of their underlying logical structures. Metacognitive knowledge may do so by directing our attention to the general structures that are embedded in specific situations and contexts. An important benefit from using explicit metacognitive knowledge in teaching thinking is that it may enhance transfer, which requires the ability to identify the shared, deep logical structures in situations that differ in their surface structures (Brown et al. 1983; Soodla et al. 2016; Veenman 2011; Yanqun 2019).

In particular, this study focused on the cognitive processes that are involved in higher-order thinking strategies. For example, higher-order thinking strategies are utilized when constructing good arguments; solving problems; classifying, establishing, and analyzing causal relationships; formulating research questions, testing hypotheses, drawing valid conclusions, and deciding which variables to control (Kuhn 2000a, 2002; Kuhn and Modrek 2018). The vast amount of traditional scientific inquiry about thinking strategies utilizes these cognitive methods (National Academies of Sciences, Engineering, and Medicine 2016; Zoller 2000). The metacognitive knowledge that is relevant to the current study is the awareness of the particular thinking strategy used in a specific situation. Although this type of knowledge can be both implicit and explicit, it is always taught explicitly in school settings; in other words, metacognitive knowledge is openly discussed in the classroom (Kuhn et al. 2004; Zohar and Ben David 2008). Metacognitive knowledge consists of different cognitive methods: (1) utilizing generalizations to decipher the rules about a thinking strategy and naming the thinking strategy; (2) clarifying why and when the strategy would be used, and the manner in which it is used; (3) listing the task characteristics needed to use the strategy; and (4) naming the disadvantages of failing to use the correct strategy (Kuhn 2000a, 2002).

Of interest in this present work is the MSK skill known as control of variables strategy (CVS). This strategy is considered to be a core skill in scientific reasoning (Inhelder and Piaget 1958). An understanding of CVS helps to build interpretable research, as it involves the researcher’s manipulation of only the variable of interest while, at the same time, keeping all other variables constant (Chen and Klahr 1999). In addition, CVS can be used to deduce logical inferences from research studies, as one compares the outcomes of the different conditions that differ only in one manipulated variable (Ross 1988; Zohar and Ben David 2008). Previous studies of young adolescents showed that cognitive regulation, rather than behavioral regulation, was predictive of students’ skill in learning how CVS was related to a variable outcome (Modrek et al. 2019).

This study argues that a useful way to enhance students’ reasoning abilities is by maintaining general cognitive structures during specific teaching contexts. This can be achieved using a number of pedagogical strategies that teach MSK. For example, such strategies can include reflecting on others’ performance on an assignment or engaging in multiple meta-level written tasks (e.g., Kuhn et al. 2004; Pearsall 1999). In the United States, most states have adopted versions of the Next Generation Science Standards (NGSS Lead States 2013). The shift to the new standards entails radical changes to teaching practice. One very big change is that the NGSS ask teachers to organize curriculum around natural phenomena that require explanation, rather than around content students should learn. This shift is intended to subordinate science concepts to the phenomena they help to explain. A second change is that students should engage in science practices to explain and model phenomena, such that concepts are learned through practices of investigation, modeling, explanation, and so on, rather than maintaining a false dichotomy between science concepts and processes. This reorganization of instruction requires a third shift in teaching practice in order to be successful: Teachers must reorganize the discursive practices of their classrooms (Modrek and Kuhn 2017).

Popular educational notions suggest that in order for learning to be meaningful and useful, the learner must actively construct the knowledge. This belief mainly refers to the learning of concepts and strategies, but it is also relevant to learning meta-strategies. In the current study, the teaching of MSK is explicit and does not include instruction by “transmission learning” or rote learning (Tan et al. 2015; Zohar 2004; Zohar and Peled 2008). Rather, the instruction involved in teaching MSK utilizes explicit and verbal communication that focuses on encouraging the learner to engage in active thinking and to form a deep understanding of the material (Michalsky 2013). Modrek and Kuhn (2017) showed that inductive reasoning skills, including CVS, can be lost if students are not given opportunities to construct their own learning and instead are expected to memorize. Modrek and Sandoval (2020) found that giving students more autonomy in fact increases their causal reasoning skill, where more autonomy increases the amount of evidence they are likely to attribute to an outcome variable.

The teaching and learning of MSK in schools include two main features. The first feature refers to the linguistic element of constructing statements to be discussed both in a social and an individual context. The second feature is that, because of the abstractness of MSK, many students will not be able to understand this sort of knowledge if they do not have personal experience with it (Dean Jr. and Kuhn 2007; Zohar and Ben David 2008; Zohar and Peled 2008). Given these features, it can be concluded that when teaching concepts such as addressing rules, generalizations, and good thinking principles, the best strategy is to eliminate abstractness by connecting the material to students’ personal experiences (Kuhn 1999, 2000b; Kuhn et al. 2004).

One problem that prevents teachers from incorporating MSK teaching is that they lack the knowledge on how to implement it practically in a classroom setting (e.g., Perry et al. 2015; Zohar and Lustov 2018). Teachers, especially those who have not had prior experience as reflective practitioners, struggle in choosing the best methods to help students develop MSK skills (Michalsky 2014; Perry et al. 2008). To ensure students’ MSK-learning progress, teachers must make sure to maintain constant awareness of their students’ development and must assess and reflect on their work, as well as simultaneously using supported tactics that encourage metacognitive activity (Perry et al. 2002; Veenman et al. 2006).

To increase the understanding of how to enhance teachers’ ability to teach MSK, the present study suggested a model for integrating reflective frameworks on MSK into pre-service teachers’ training through the utilization of a video-based laboratory learning environment. This study explored the value of pre-service physics teachers’ systematic reflection on students’ classroom behavior during the practicum phase of their preparatory programs, as a complementary approach to the more traditional systematic reflection on teachers’ classroom behavior. Both reflective approaches – learning from student behavior (LFSB) and learning from teacher behavior (LFTB) – are conceptualized as professional vision (PV) processes. The current study examined two systematic PV video-analysis approaches – one that instructed pre-service teachers’ reflection on LFTB alone and the other where video-analysis reflected on LFTB + LFSB jointly – as differentially contributing to the dependent variables: the participating pre-service teachers’ actual teaching of MSK, measured both implicitly and explicitly, and their students’ application of MSK.

Theoretical background

The goals of science education and the methods used to achieve these goals must be changed to meet the challenges of the world today, in particular the rapid and dynamic changes in the fields of science and technology. The emphasis in the classroom is shifting from a focus on learning large amounts of information and acquiring basic skills, to prioritizing the development of higher-order and reasoning skills as well as gaining a deeper understanding of concepts and strategies that will assist in learning and processing new information (National Academies of Sciences, Engineering, and Medicine 2016; National Research Council 2012; Organisation for Economic Co-operation and Development – OECD 2017). Metacognition – an individual’s awareness and control over their own thinking and learning strategies – is one of the main components of effective science learning, which consists of both higher-order thinking and self-regulated learning (Brown 1987; Flavell 1979; Schraw and Moshman 1995). Since the innovative writings of Flavell (1979) and Brown (1987), the development of students’ metacognition has become a primary educational goal (Garner and Alexander 1989). Flavell et al. (2002) separated the term metacognition into three main components. The first component, metacognitive knowledge, was further separated into another three sub-components: strategies, tasks, and knowledge about persons. The first two of these sub-components are connected to MSK, as they focus on the nature of the tasks and strategies aimed at learning specific cognitive goals. Another perspective, introduced by Schraw (1998), concerns the separation between regulation of cognition and knowledge of cognition. Knowledge of cognition specifically consists of declarative, procedural, and conditional knowledge. Both regulation and knowledge of cognition are related to MSK because they refer to the effective use of strategies, including knowing when and why to use particular strategies.

The current study, in contrast, uses the definition constructed by Kuhn, who dedicated a considerable amount of time to the study of MSK (Kuhn 1999, 2000b, 2001a, 2001b; Kuhn and Dean 2005). Kuhn’s procedural meta-level knowing focuses on what one can accomplish while using known strategies, as well as when, why, and in what manner the strategies should be used. According to Kuhn, meta-strategic understanding includes two main elements: (1) being aware of and understanding both the nature and necessities of the task; (2) being aware of and understanding the way in which one can use the strategies in one’s repertoire in order to effectively accomplish the task. The combination of these two components – understanding the task and understanding the strategies – is what Kuhn sees as the challenge of effective meta-strategic thinking (Kuhn and Pearsall 1998).

Researchers agree that teachers have a great deal of influence over students’ MSK development (Schraw et al. 2006; Schunk and Zimmerman 1997). Therefore, a number of pedagogical models for teaching MSK have been developed (Cleary and Zimmerman 2004; Perry et al. 2002; Schunk and Zimmerman 1997; Zimmerman 1998). These models can be organized into three complementary approaches (Dignath et al. 2008): (1) providing key contextual elements or learning conditions that facilitate MSK (e.g., giving students choices, opportunities for control, and peer collaboration; Perry et al. 2002); (2) modeling to facilitate learners’ movement from emulator (an individual who imitates a more experienced other) to self-regulator (an individual who independently adapts learning strategies to meet new contextual demands; e.g., Schunk and Zimmerman 1997); and (3) engaging in direct MSK strategy instruction (e.g., Dignath and Büttner 2008). The third pedagogical approach entails giving students explicit directions on how to use strategies, when to use them, what goals to set, how to pursue those goals, and how to monitor strategies and movement toward goal achievement. Additionally, it includes providing students with explicit information about the meaning and importance of those strategies, as well as information to help them explicitly understand the goal of the learning task, which may offer students a metacognitive understanding that can lead to applying the learned strategies in the future. Direct MSK instruction contrasts with teachers’ mere modeling of a strategy’s use and verbalization of thought processes, which can implicitly induce students to engage in certain behaviors, but it does not inform students about the activity’s significance.

Despite the considerable literature on pedagogical strategies and learning environments for promoting MSK, more empirical research is sorely needed to analyze authentic pedagogical situations in which pre-service teachers attempt to promote their students’ MSK. Such research efforts would address several questions raised in the literature: How do pre-service teachers actually inculcate MSK in various teaching and learning contexts (Randi and Corno 2000)? How can educators help pre-service teachers to design tasks and to engage in practices that promote their students’ MSK (Perry et al. 2006)?

Butler et al. (2017) highlighted the importance of training pre-service teachers in how to define the explicit teaching of MSK, monitor student successes, and interpret outcomes with implications for practice. To meet the growing call to create a cadre of flexible, yet effective, MSK-promoting teachers, the present study focused on facilitating pre-service teachers’ development of professional vision (PV) for explicit MSK teaching, while comparing two scaffolding methods to determine which reflection approach best promotes MSK teaching.

Pre-service teachers’ professional vision

The PV concept (Goodwin 1994) refers to teachers’ reflective ability to notice features of classroom events that are relevant for student learning, and to analyze and interpret those events using prior content knowledge and prior pedagogical content knowledge, namely, domain-related pedagogical principles and concepts (Sherin 2007; van Es and Sherin 2002). PV is conceptualized as a complex “dynamic interplay of top-down and bottom-up processes” (Sherin 2007, p. 384) that should be guided by teachers’ timely application of higher-order thinking skills before and during multifaceted classroom situations, as well as after making decisions and taking actions (Kunter 2013; Kunter and Baumert 2006; Seidel 2012). PV reflective expertise allows teachers to perceive and respond flexibly to students’ understanding and reasoning at any given moment (Berliner 2000), thereby helping teachers provide effective learning opportunities. Blömeke et al. (2015) conceptualized situation-specific skills like PV as a mediator between professional knowledge and actual classroom practice. The competence model developed by Blömeke et al. indicates that situation-specific skills of PV interpretation and decision-making are predictors of actual teaching ability.

Research has identified two component processes of teachers’ PV reflection. Noticing refers to teachers’ ability to direct their attention to relevant classroom situations (van Es and Sherin 2002). Knowledge-based reasoning describes teachers’ cognitive processing of the noticed events, which is grounded in their knowledge about effective teaching and learning (Borko 2004; Sherin 2007; van Es and Sherin 2002), and their ability to transfer that knowledge to authentic instructional situations (Palmeri et al. 2004; Seidel et al. 2008).

A qualitative empirical analysis of teachers’ reflections while observing videotaped classroom situations yielded three qualitatively distinct but highly interrelated levels of PV reasoning – description, explanation, and prediction (Blomberg et al. 2011; Seidel and Stürmer 2014; Stürmer et al. 2013a, 2013b). Description is defined as identifying and differentiating particular classroom events based on teaching knowledge. Explanation refers to classifying a noticed classroom event based on teaching knowledge while bridging between theories and classroom practice (e.g., “Work in discussion groups led to student-centered learning, in which students cooperated with one another, negotiated differences of opinion, and shared various ideas and ways of thinking during a cooperative inquiry learning process”). Prediction is a pedagogical skill in which teachers utilize noticed events to forecast future classroom events and experiences that will impact student learning processes (e.g., “When students work in social interaction, they shape more cognitive, metacognitive, and motivation skills than when they work individually”). The current study followed the PV reflective instruction based on van Es and Sherin (2002), focusing on these noticing and knowledge-based reasoning processes.

Unsurprisingly, in contrast to experienced teachers, pre-service teachers have been shown to identify fewer critical classroom events, to describe classroom situations in more limited and naïve terms (Carter et al. 1987; Seidel and Stürmer 2014), to explain and predict fewer classroom events and practices (Seidel 2012; Seidel and Prenzel 2007), and to apply theoretical knowledge less effectively to authentic classroom situations (Gruber 2001; Putnam and Borko 2000; Shulman 1987). Educators agree that equipping pre-service teachers with PV skills would allow them to continuously reflect on their teaching processes as they mature professionally, as well to generate knowledge that would guide their gradual acquisition of expertise (Hiebert et al. 2007; Santagata and Angelici 2010). Nevertheless, little is known about the connections between pre-service PV reflection skills and actual MSK teaching, which is the question at hand in the present study.

Video-based PV training for MSK-mapping

Video observation and reflective analysis of classroom situations has been shown to promote teachers’ application of PV abilities, such as noticing and reasoning about students’ learning and thinking behaviors (Franke et al. 2001; Santagata et al. 2007; van Es and Sherin 2010; Yeh and Santagata 2013), as well as predicting alternative instructional strategies (Kersting et al. 2009; van Es and Sherin 2002). Although the educational potential of video-analysis has largely been recognized, the simple provision of opportunities to analyze video cases is not sufficient to enhance pre-service teachers’ PV skills; rather, the analysis needs to be accompanied by specific instruction and guidance (Santagata and Angelici 2010; Seidel and Prenzel 2007). Moreover, this instruction must be targeted toward specific learning goals because different university modules for learning from videos may lead to different pre-service teacher outcomes (Seidel et al. 2013).

Yet, what counts as effective PV guidance, or PV scaffolding, for reflective video-analysis remains an open question. Gaudin and Chaliès (2015, p. 41), in their review of 255 articles, posed a question to profitably guide future research: “How can we best articulate the diverse objectives of video viewing and the diverse types of videos in teacher education and professional development programs?” (p. 41). Based on science education reforms that call for an inquiry into student-centered teaching and learning, van Es and Sherin (2008) appealed to teacher educators to help pre-service teachers go beyond analyzing and interpreting teachers’ classroom behaviors by using scaffolding techniques with teacher trainees that emphasize students as the main actors in classroom interactions. Researchers have argued that learning from students’ behavior (LFSB) is imperative for the successful development of students’ MSK (Ganda and Boruchovitch 2018; Kuhn and Dean 2005; Randi and Corno 2000; Sabourin et al. 2013; Santagata and Angelici 2010). Emphasizing a focus on the student perspective, Santagata and Guarino (2011) even recommended that pre-service teachers should first analyze student thinking and learning before analyzing teacher behavior. These appeals call for a more specific inquiry into how pre-service teachers can optimally capitalize on reflective analysis, with a complementary focus on all major actors in classrooms today (Perry et al. 2006). Analyzing not only teacher behavior but also student behavior in authentic pedagogical situations should help develop pre-service teachers’ PV skills for mapping MSK teaching by engaging in effective practices and creating appropriate environments that promote students’ MSK (Kramarski and Michalsky 2009; Randi 2004; Randi and Corno 2000).

The present study

Education reforms call for teacher education programs to shift the emphasis of MSK pedagogical knowledge from teacher regulation of students’ learning to students’ regulation of their own learning (Kuhn, Modrek and Sandoval 2020). As such, this study examined a new conceptual PV approach for teachers, which blends reflective LFSB with LFTB while analyzing MSK-teaching modes as portrayed in videotaped lessons (National Research Council 2012; van Es and Sherin 2008).

Learning from teacher behavior (LFTB)

University-based teacher education has been criticized for not bridging the gap between theory and practice (Borko 2004; Seidel and Stürmer 2014; van Es and Sherin 2008). Many pre-service teachers struggle with applying their basic knowledge of pedagogy to dynamic real-time teaching situations. To bridge the gap between pedagogical knowledge and actual classroom practices, preparatory programs utilize video observation to critically reflect on pre-service teachers’ behavior (Borko 2004; van Es and Sherin 2008). Video observation elicits conscious post-action reviews (noticing, describing, explaining, predicting) and stimulates a process of sense-making (Janik et al. 2015; Richter et al. 2019). The effectiveness of video observation in promoting teacher reflection and change has led to a consensus recommendation among leaders in educational programs and teaching professionals to incorporate LFTB into organizational practices.

Learning from student behavior (LFSB)

The recommended shift to a student-centered view of teaching and learning, including an emphasis on knowledge construction (National Research Council 2012), emphasizes the necessity for teachers to carefully observe student behavior. Teachers are expected to, at least in part, make appropriate pedagogical decisions while adapting MSK-instructional practices and learning environments in a manner that meets their students’ diverse needs (van Es and Sherin 2008). For example, if a teacher notices two students whispering about how to solve a problem under discussion, she can approach the situation in multiple ways. She can decide to ask the pair to share their dilemma aloud with the class in order to elicit class interest, she can provide more data on the problem to prevent misconceptions about the task, or she can divide the class into small groups for peer discussion to encourage the learner-centered practice of building problem-solving skills. In the more specific case of inquiry-based science projects, teachers are encouraged to analyze and interpret students’ behavior in order to promote students’ MSK and science understanding while giving students the opportunity to independently investigate authentic questions (Hammer 2000; National Research Council 2012; van Zee and Minstrell 1997).

This learner-centered view of teaching and learning requires teachers to develop new ways of noticing and interpreting classroom interactions (e.g., Blomberg et al. 2011; Seidel and Stürmer 2014; Stürmer et al. 2013a, 2013b). Prior research has shown that some experienced teachers may already engage in these practices (Berliner 2000; Heyd-Metzuyanim and Shabtay 2019). However, current teacher education programs do not explicitly focus on helping pre-service teachers learn to analyze and interpret the behavior of students. In particular, preparatory programs do not address the topic of how student behavior can trigger teachers’ MSK-teaching behavior and thus affect students’ thinking. Instead, programs usually focus on helping teachers analyze their own MSK-teaching behavior and provide frequent instruction concerning new pedagogical techniques or activities (Berliner 2000; Day 1999; Huling et al. 2001; Niess 2001; Putnam and Borko 2000; Zohar and Lustov 2018). Teacher-focused activities are certainly important, but they do not necessarily ensure that pre-service teachers gain PV reflective expertise in noticing, describing, explaining, and predicting students’ verbal and nonverbal behaviors.

Understanding students’ MSK-learning behavior has been the subject of increasing attention (Sabourin et al. 2013). Unfortunately, monitoring this behavior in real-time has proven to be challenging. However, understanding and scaffolding students’ MSK-learning behavior is especially important in open-ended learning environments where goals may be less clear, and students do not necessarily receive clear indications of their progress. In particular, in the context of science teaching and learning (e.g., laboratory research learning and project-based learning), open learning environments offer students opportunities to develop and practice their MSK-learning processes. Such environments provide students with active learning tasks and a set of tools for exploring, hypothesizing, and building solutions to authentic and complex problems. In order to be successful in this type of learning environment, students must actively identify and select their own goals and evaluate their progress accordingly. However, research has shown that students do not consistently demonstrate sufficient MSK behaviors during interactions with these environments, which may reduce the potential contributions to learning (Alfieri et al. 2011; Kirschner et al. 2006). Consequently, further investigation of the role of MSK in open-ended learning environments is necessary to understand how the teaching and learning of MSK can be incorporated most effectively in these environments.

A complementary “dual” approach

The present study draws on the increasing value attributed to student-centered teaching for promoting effective teaching practices, as well as students’ MSK acquisition and domain-specific academic achievements (Alles et al. 2018; Dignath and Büttner 2008). Moreover, there is a widespread call to systematically analyze teacher behavior, with the goal of developing teachers’ PV and building their pedagogical knowledge (Seidel and Stürmer 2014). Therefore, the present quasi-experimental study aims were to examine the possible added benefit of incorporating the LFSB-reflective approach with the LFTB-reflective approach during pre-service science teachers’ practicum phase. This study is innovative as it expands pre-service teachers’ perspective during video-analysis to include both teacher-centered as well as student-centered behavior. Further, the study advances the field by examining how pre-service teachers’ actual teaching in schools may benefit from PV reflective development via an explicit model that scaffolds mapping of MSK teaching based on these different foci (see Table 1).

Table 1 Instructions for Reflective Video-Analysis Approaches Given to Two Professional Vision (PV) Research Groups
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Research questions and hypotheses

The goals of this study were threefold. The first goal was to design the two reflective PV approaches for mapping direct and indirect MSK-teaching modes based on videotaped science-teaching vignettes viewed during pre-service university-based workshops. Trainings were thus based either (1) solely on traditional LFTB or (2) on the dual complementary approaches (LFSB + LFTB). The second goal was to compare the effectiveness of LFTB vs. LFTB + LFSB for pre-service teachers’ actual teaching of MSK to their students (Q1). Lastly, the third goal was to examine the contribution of pre-service teachers’ LFTB vs. LFTB + LFSB reflective PV approaches for students’ own MSK (Q2).

Research hypotheses were based on: (a) researchers’ assertions that pre-service teachers will likely need more explicit, systematic reflective support during their training (Linnenbrink and Pintrich 2002; Veenman et al. 2006); and (b) several prior claims that systematic reflection prompts might foster pre-service teachers’ use of MSK in ill-defined domains such as pedagogy education (Michalsky and Kramarski 2015; Davis 2003; Ifenthaler and Seel 2011).

  1. Research Q1:

    Will the effectiveness of pre-service teachers’ actual MSK-teaching practices differ between the group using the LFTB approach and the group using the LFTB + LFSB approach?

  2. Hypothesis 1:

    The group of participants exposed to the LFTB + LFSB prompts condition was expected to teach MSK more effectively than the group exposed only to the LFTB prompts condition. This hypothesis corresponded with initial studies that pointed to the benefit of specific prompts emphasizing student behavior for developing PV for teaching (Kramarski and Kohen 2017; Seidel and Stürmer 2014).

  3. Research Q2:

    Will students’ MSK differ between the group using the LFTB approach and the group using the LFTB + LFSB approach?

  4. Hypothesis 2:

    Students in the group exposed to LFTB + LFSB prompts were expected to demonstrate better MSK outcomes than the group exposed only to LFTB prompts. This hypothesis corresponded with prior research studies that found a relationship between explicit teaching of MSK and students’ MSK achievements (Zohar and Ben David 2008; Kuhn and Pearsall 1998; Zohar and Peled 2008).

Method

Participants

Participants included 82 s-year pre-service physics teachers who were enrolled in a practicum teacher education course at one of two major Israeli research universities (65% females, 35% males; 88% Jewish, 12% Arab). The average age of participants and acceptance criteria were similar across the two programs (age: M = 26.3 years, SD = 6.1; GPA: M = 85 out of 100, SD = 5.6). Each university was assigned to one of the two intervention procedures. At the start of the course, pre-service teachers’ responses to ten open-ended questions from the standard Israeli high-school physics curriculum (e.g., on electric potential, velocity, force, temperature, volume, friction, etc.; see Appendix) found no significant differences in physics content knowledge across participants from the two university programs, t(80) = 0.8, p > 0.05. For their practicum field training, the pre-service teachers were assigned to teach 10th grade physics in fourteen high schools (4–5 pre-service teachers from each university were assigned to each school).

Intervention procedure

Informed written consent to participate in the study was obtained from all pre-service teachers and the parents of the 10th graders who were enrolled in the study as well as from the chief scientist in the Israeli Ministry of Education. Participants were informed that the course methods and materials and the pretest/mid-test/posttest assessment measures were part of an Israeli government-funded research study to determine the effectiveness of pre-service teachers’ MSK-teaching training. As seen in Table 2, both reflective groups shared the same training structure based on van Es and Sherin’s (2002) four-step PV reflection method (noticing, describing, explaining, predicting), but each had different instructor training content. All participating pre-service teachers attended the same two academic courses: the Science Teaching and Learning Methods video-analysis course (in which they viewed the same eight authentic video vignettes) and the Practical Teaching fieldwork practicum training course. The two groups studied at different universities. All teachers and students were administered the same assessments in both groups. The only component distinguishing between the two intervention groups (see bold highlights in Table 2) was the PV reflective approach used for theoretical instruction and for video-analysis in the Science Teaching and Learning Methods course: in the LFTB-only group, the emphasis was solely on how MSK-teaching behavior affects student thinking/behavior, and in the LFTB + LFSB group, the emphasis was on how student behavior influences teachers’ MSK-teaching behavior, which in turn impacts students’ MSK and science achievements.

Table 2 Summary of the Practical Teaching Course and the Two Professional Vision (PV) Reflective Groups
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Measures

Two dependent variables were measured at three different time points: at the beginning (October), middle (February), and end (May) of the year-long practicum course.

Pre-service teachers’ actual MSK teaching (N = 82)

To assess the possible differential effects of the two PV reflective methods on actual MSK-promoting teaching skills among pre-service physics teachers, video lesson analysis utilized a mixed-method approach. Pre-service teachers’ teaching of MSK was examined at three time points during the practicum. The time points chosen for administering the measures were based on research indicating that pre-service teachers’ reflection skills develop predominantly through practicing the reflection process in various teaching situations (Schön 1983), and on research showing that the more opportunities there are to practice reflection, the better the development of pre-service teachers’ reflection skills (Zeichner and Liston 1987).

MSK-teaching data collection

Videotapes were collected from all pre-service teachers’ actual teaching experiences with high school students. Each pre-service teacher was videotaped giving three different lessons in his/her practicum classroom (each lesson lasted approximately 45 min). These lessons took place on the first, middle, and last days of the pre-service teaching period. The lessons dealt with the solar system (Time 1), global warming (Time 2), and electromagnetism (Time 3), and were in line with the standard Israeli Ministry of Education high school physics curriculum. Videotaping began as soon as the teacher started the lesson and continued until the lesson ended. The high school students and pre-service teachers were informed that the videotaping was part of a research study that aimed to determine the effectiveness of pre-service training. Moreover, the study was approved by the chief scientist in the Israeli Ministry of Education, and parental consent was obtained.

MSK-teaching data coding

Each videotape was coded by two trained observers using the ATES observation instrument (Assessing How Teachers Enhance Self-Regulated Learning; Dignath-van Ewijk et al. 2013). This observation instrument consisted of a low-inferent coding system, which was used to assess the level of MSK teaching. The videos were coded in one-minute increments, because briefer units of time turned out to be impractical, and longer units of time increased the risk of losing information. This produced approximately 45 segments per lesson; however, not all observed lessons were exactly the same length as they ranged from 35 to 45 min. For coding purposes, the observed amount of time per each strategy was standardized to 45 min, and a standardized average frequency related to the total length of each lesson was computed.

The low-inferent coding system, based on Kuhn’s (1999) MSK model, was used to assess small-unit features of pre-service teachers’ actual instruction of MSK. Specifically, as seen in Table 3, the observers coded, minute-by-minute, whether the teacher instructed with cognitive strategies for meta-strategy and meta-task teaching. Teachers’ verbal statements, as well as nonverbal behavior, were taken into account. If the teacher instructed with different components within the same minute, each component was coded for that minute. For example, meta-strategy teaching included naming the thinking strategy and clarifying why and when a particular strategy would be used and how to use it. Meta-task teaching included naming the tasks’ goal characteristics needed to use the strategy and naming the disadvantages of failing to use the correct strategy/task.

Table 3 Examples of Low-Inferent Coding for Pre-Service Teachers’ MSK Instruction
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Moreover, for each coded statement/behavior, observers specified whether the pre-service teacher promoted that component implicitly (prompting students to use it without directly referring to it) or explicitly (telling students directly to use it). For example, if, while students tackled a physics task, a teacher stated, “the goal of the task is knowing/understanding that variables need to be controlled in science experiments… applying CVS, whenever we need to establish the existence of causal relationships,” it would be coded as explicit instruction of a meta-task component according to Kuhn’s (1999) MSK model. In contrast, the following teacher’s statement would be coded as an implicit instruction of the same meta-task component: “The goal is to find out how fast the ball would drop down in the different cases.” Table 3 provides some examples of teachers’ statements/behaviors that were coded as instruction of explicit and implicit MSK in the classroom. A total score for each explicit and implicit meta-strategy and meta-task component was calculated by summing the number of instances that the observers coded that component (Min. = 0 / Max. = 22).

Coders’ training and interrater reliability

A total of 246 videos were observed and coded (82 participants X 3 videotaped lessons). Before starting the coding procedure, eight observers underwent 50 h of observation training, during which they were introduced to the ATES observation instrument and practiced coding of videotaped lessons that were collected from an unrelated sample of pre-service teachers. After training, all eight observers then independently coded the same 20 videos that were randomly selected from the current dataset of 246 videos, to test for interrater reliability. For the low-inferent coding system measuring participants’ actual teaching of MSK (which yielded nominal data), Cohen’s kappa was computed. Disputable ratings and/or disagreements in the coding of MSK processes were resolved through discussion. In the rare cases in which coders did not reach consensus, an external coder (a university professor with expertise in teacher education) was summoned until agreement could be reached. After the coding of the first 10 videos, Cohen’s kappa was .78 and generalizability coefficients ranged between .76 and 1.00. After the coding of all 20 videos, Cohen’s kappa was .74 and generalizability coefficients ranged between .77 and .94.

High school students’ MSK learning (N = 136)

The rationale behind the design of the MSK interview was to provide students with multiple opportunities to showcase their MSK and their understanding of it. The interview consisted of four parts. Part 1 was an adaptation of the interview protocol designed by Kuhn and Pearsall (1998). Students were presented with a fictitious story about a classmate who had been absent from the lessons in which the students engaged in a solar system task (Time 1). The interviewer then asked students to explain to their classmate what they were supposed to do in the solar system task (i.e., a question designed to assess the understanding of the task component of MSK) and how they had decided which features to investigate (i.e., a question designed to assess the understanding of the strategy component of MSK). Parts 2 and 3 consisted of fictitious stories about children who planned an experiment in order to find out which features made a difference in the solar system (Time 1). The interviewees were asked to explain the goal of the experiments (i.e., task component) and to evaluate their conclusions (i.e., strategy component). The difference between Part 2 and Part 3 was that the children in the stories of Part 2 failed to control variables, whereas the children in Part 3 did control variables. Part 4 of the interview consisted of explicit questions about MSK: “Why is it important to control variables?’” “How do you control variables?” (i.e., strategy components of MSK), or “In what cases is it important to control variables?” (i.e., task components of MSK).

MSK-learning data analysis

Interview transcripts were analyzed using the coding scheme developed and validated by Kuhn and Pearsall (1998), adapted to the specific details of the four parts of the MSK interview. Namely, data from each of the interview’s four parts were scored twice, following Kuhn and Pearsall’s (1998) guidelines: once for the task component (0–5) and once for the strategy component (0–6). Table 4 presents sample scoring for the first part of the meta-strategic interview.

Table 4 Scoring of Students’ Task and Strategy Components (based on Kuhn and Pearsall 1998) in MSK Learning, with Excerpts from Part 1 of Student Interviews
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To establish interrater reliability, a sample of 30 responses for each part of the interview was coded independently by two different coders. The percentage of agreement between the two coders was at least 90% for each of the four parts of the interview. Next, coding for the two meta-strategic components of the interview (i.e., the task component and the strategy component) ensued for all students’ interview data. Consequently, each student in each session received an average total score for MSK learning (the mean of the task and strategy components), which was then used to compute a mean MSK-learning score for each of the two PV instruction groups (LFTB and LFTB + LFSB).

Results

To address the research questions and hypotheses, multivariate analysis of variance (MANOVA) was performed. The data were found to satisfy the three preconditions necessary for conducting MANOVA (Weinfurt 1995): (a) multivariate normality, (b) homogeneity of the covariance matrices, and (c) independence of observations. Learning condition (group) was the between-subjects factor, and time was the within-subjects factor. Follow-up ANOVAs with repeated measures were conducted.

Pre-service teachers’ MSK teaching

To compare the MSK-teaching processes of the two PV instruction groups (LFTB vs. LFTB + LFSB) at the two teaching quality levels (explicit vs. implicit), analysis utilized the ATES observation instrument’s coding of teachers’ actual MSK teaching at the three intervals (Times 1, 2, and 3). Thus, the analysis utilized a 2 (treatment) by 2 (teaching level) by 3 (time) design. Figure 1 presents the mean actual MSK-teaching scores (mean of the strategy and task components), across the three time intervals, calculated separately for four scores: explicit MSK teaching in the LFTB + LFSB group, explicit MSK teaching in the LFTB group, implicit MSK teaching in the LFTB + LFSB group, and implicit MSK teaching in the LFTB group.

Fig. 1
figure 1

Mean scores of explicit and implicit MSK-teaching quality by LFTB + LFSB and LFTB-only treatment groups across the practicum course

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To examine initial differences according to treatment group and teaching explicitness level, a 2 X 2 ANOVA was performed on the four different scores at pretest (Time 1): LFTB + LFSB explicit, LFTB + LFSB implicit, LFTB explicit, and LFTB implicit. There was no main effect for treatment, suggesting that there were no significant differences between the LFTB + LFSB and LFTB-only groups at pretest. There was a main effect for MSK-teaching level showing that, at pretest, pre-service teachers’ mean implicit MSK-teaching score was significantly higher than their mean explicit MSK-teaching score, F(1, 78) = 7.81, p < 0.01, partial η2 = 0.19.

To determine the effects of the PV instruction intervention over time, a repeated measures ANOVA was performed, with time as the within-subject factor and with treatment group and teaching level as the between-subject factors. The analysis revealed significant main effects for time, treatment group, and teaching level as well as some significant interactions. The main effect for time, F(2, 74) = 32.17, p < 0.001, partial η2 = 0.61, indicated that pre-service teachers significantly improved their MSK-teaching performance over the three intervals of the pre-service course. The main effect for treatment group, F(1, 77) = 21.36, p < 0.001, partial η2 = 0.41, indicated that the MSK-teaching performance of the pre-service teachers who were trained to analyze video cases using the LFTB+LFSB approach was significantly better than that of their peers who were trained using only the LFTB approach. The main effect for MSK-teaching level, F(1, 77) = 6.11, p < 0.05, partial η2 = 0.19, indicated that pre-service teachers’ implicit MSK-teaching performance significantly surpassed their explicit MSK-teaching performance.

The repeated measures ANOVA also yielded a significant 2-way interaction between treatment group and time, F(4, 72) = 3.18, p < 0.01, partial η2 = 0.27, indicating differences between the LFTB + LFSB group and the LFTB group in terms of the changes in their MSK teaching that took place across the practicum course. The other 2-way interactions (treatment by teaching level and time by teaching level) were not significant. However, the analysis revealed a significant 3-way interaction for treatment by teaching level by time, F(2, 74) = 5.21, p < 0.01, partial η2 = 0.31. To determine the source of this 3-way interaction, a simple main effect analysis was performed, with separate 2 × 3 ANOVAs (two teaching levels by three time intervals) for each of the two treatment groups. The ANOVA for the LFTB group was not significant. However, the ANOVA for the LFTB + LFSB group showed a significant interaction between teaching level and time, F(2, 39) = 4.33, p < 0.01, partial η2 = 0.15, indicating that in the LFTB + LFSB group, there were significant differences between explicit and implicit MSK teaching in terms of the changes in pre-service teachers’ performance across time. These findings support the pattern observed in Fig. 1 for the LFTB + LFSB group, demonstrating very large improvement in explicit MSK teaching across the three time points (a steep increase from initially very low scores to very high scores), compared to more modest improvement in this treatment group’s implicit MSK teaching across the three time intervals (increasing from already high initial scores to very high scores).

To identify the specific time points at which these significant changes in explicit and implicit MSK-teaching performance took place in the LFTB + LFSB group, repeated measures contrast analysis was performed using this group’s mean explicit and implicit MSK-teaching scores at the three times. The contrast analysis showed significant improvement in this treatment group between Times 1 and 2, F(1, 41) = 29.25, p < 0.001, partial η2 = 0.56, as well as between Times 2 and 3, F(1, 41) = 16.23, p < 0.001, partial η2 = 0.41. For implicit MSK-teaching, this group’s means were 7.31 (SD = 1.8) at Time 1, 8.63 (SD = 1.9) at Time 2, and 8.89 (SD = 1.7) at Time 3, and the Cohen’s d effect sizes (p < .001) were 0.48 for Times 1 vs. 2 and 0.16 for Times 2 vs. 3. For explicit MSK-teaching, this group’s means were 3.61 (SD = 1.6) at Time 1, 7.32 (SD = 1.8) at Time 2, and 8.63 (SD = 1.7) at Time 3, and the Cohen’s d effect sizes (p < .001) were 2.17 for Times 1 vs. 2 and 0.74 for Times 2 vs. 3. [Effect sizes between Times 1 and 2 were calculated as the ratio between (Time 2 minus Time 1) and (the mean SD of Times 1 and 2), and effect sizes between Times 2 and 3 were calculated similarly.]

Students’ MSK learning

As seen on Table 5, students’ meta-strategic learning was assessed using the MSK interviews held at Times 1 (pretest), 2, and, 3, based on interview protocol analysis instruments following Kuhn and Pearsall (1998). A t-test for independent groups, used to examine differences between the group means for students’ MSK prior to the intervention, showed that the two student groups did not significantly differ at the pretest interval, t(80) = 0.79, p > 0.05. To determine the effects of the MSK intervention on students’ MSK learning, a repeated measures ANOVA was performed with time as the within-subjects factor and with treatment (LFTB + LFSB vs. LFTB) as the between-subjects factor. As seen on Table 5, the mean scores of the LFTB + LFSB group were considerably higher than those of the LFTB group at both Time 2 and Time 3. The ANOVA revealed a main effect of time, F(2, 78) = 35.16, p < 0.001, partial η2= 0.59, indicating that students improved their MSK performance over the course of the instructional sessions, as well as a main effect of treatment, F(1, 77) = 14.31, p < 0.001, partial η2 = 0.27, indicating that students in the LFTB + LFSB group outperformed students in the LFTB group. The interaction between time and treatment was also significant, F(2, 78) = 12.51, p < 0.01, partial η2 = 0.40.

Table 5 Mean Scores (and Standard Deviations) of Students’ MSK by Treatment Group and Time
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Discussion

The discussion section focuses on the contribution of the study to the literature, suggests implications for practice and research, and concludes with an acknowledgement of several limitations of this study.

Contribution to the literature

Findings indicated that teachers receiving dual PV reflection prompts (LFTB + LFSB) outperformed the LFTB-only group on both of the current study’s measures. More specifically, when pre-service teachers developed PV for MSK in a way that reflected on both teacher (LFTB) and student (LFSB) perspectives, this enhanced not only teachers’ actual skills for teaching MSK but also their students’ actual MSK achievements. This finding is important in light of research indicating that pre-service teachers have difficulties implementing MSK in various teaching and learning contexts (Zohar and Lustov 2018). Teacher educators find themselves asking (Michalsky 2017; Yanqun 2019): How can we help pre-service teachers to design tasks and engage in practices that promote their students’ MSK? In this regard, Hong and Van Riper (2019) argued: “Although numerous research studies have documented the benefits of the use of videos for teacher development, teachers’ analysis of their own videos or others is not considered a routinized practice in teacher education programs” (p. 94). The current study supports previous studies (e.g., Alles et al. 2018; Osipova et al. 2011) indicating that guided video-analysis in teacher education helps pre-service teachers to analyze teaching strategies and thus to change their teaching practices in ways that meet their students’ needs.

Why did the LFTB + LFSB group outperform the LFTB group in teaching MSK? The current study findings suggested that although both groups were actively exposed to the same video-analysis activities, the additional element of engagement with PV reflective instruction that focused directly on students’ behaviors may have helped the pre-service teachers in the LFTB + LFSB group to adapt their teaching more effectively to their students’ needs immediately, as challenges arose during learning. Hassrick et al. (2017) found that adaptability – “the ability to skillfully assess students behaviors’ and instruct” (p. 127) – was a central capacity demonstrated by expert elementary teachers. The standards that define teacher quality also reveal this attention to teacher adaptability. For example, the Interstate Teacher Assessment and Support Consortium (Council of Chief State School Officers 2011) – a nonpartisan, nationwide, nonprofit organization of public officials who lead departments of elementary and secondary education in the United States – emphasizes adaptability as an essential factor for quality teaching, as outlined in the following standard: “The teacher continuously monitors student learning, engages learners in assessing their progress, and adjusts instruction in response to student learning needs” (Council of Chief State School Officers 2011, p. 17).

Prior research has pinpointed pre-service teachers’ difficulties in implementing adaptive teaching in their lessons, possibly due in part to pre-service teachers’ difficulties in noticing students’ behaviors that manifest students’ difficulties in understanding and learning (Gröschner et al. 2018). As described in the current literature (e.g., Blomberg et al. 2014; Sandoval et al. 2018a, 2018b; Stürmer et al. 2013a, 2013b), teachers need to notice and interpret student behavior as part of their everyday classroom work. Considering that few teacher education programs to date have included explicit instruction in how to engage in analysis of student behaviors to usefully promote adaptive teaching (Alles et al. 2018), the current study findings suggest a means to help pre-service teachers develop their facility for adaptive teaching practice, by incorporating noticing of meaningful student behaviors at an early stage in their teacher education programs. Although research has shown that some experienced teachers may engage in such practices already, with positive effects on students’ achievements (Berliner 2000), at the pre-service stage, programs usually explicitly focus only on helping teachers analyze teachers’ own MSK-teaching behaviors and how they may impact student thinking or behavior, while frequently providing instruction concerning new pedagogical techniques or activities (Berliner 2000; Day 1999; Gröschner et al. 2018; Huling et al. 2001; Modrek et al. 2019; Niess 2001; Putnam and Borko 2000). Current teacher preparatory programs often do not explicitly focus on helping pre-service teachers learn to analyze and interpret student behavior and understand how it may influence teachers’ MSK-teaching behaviors, which in turn may affect students’ thinking and achievements.

With regard to the two quality levels of teaching to promote students’ MSK – implicit and explicit – when the current sample of pre-service teachers (in both groups) began the intervention, they taught MSK in a much more implicit than explicit manner. These results are in line with previous studies on science education (Dignath-van Ewijk 2016) demonstrating that teachers rarely teach MSK and, when they do, mostly do so intuitively and implicitly. Earlier studies showed that teachers lack pedagogical knowledge and practice in how to teach explicit MSK (Dignath-van Ewijk 2016; Zohar 2004). One of the unique contributions of the current study was the finding that, in comparison to the LFTB-only group, incorporating LFSB into teacher training contributed to a significant increase in the explicitness of MSK instruction, which, in turn, affected the development of students’ MSK achievements. Purposefully reflecting on and analyzing videotaped students’ behaviors as a trigger for their videotaped teachers’ teaching behaviors may have led our pre-service participants to organize and construct their pedagogical knowledge and practices regarding how to answer students’ needs in specific situations via more explicit MSK teaching. These findings also coincide with other research that found a relationship between explicit teaching of MSK and students’ MSK achievements (Ben-David and Zohar 2008; Kuhn and Pearsall 1998; Zohar and Peled 2008).

Implications for practice and research

At the practical level, the current study contributes added value to the existing tools for developing MSK teaching. The clear instructions provided for analyzing teachers’ and students’ behaviors observed in video lessons may help in developing pre-service teachers’ PV levels. Furthermore, developing PV reflective abilities can contribute to teachers’ MSK-teaching abilities and to their students’ MSK achievements. The PV reflective model in the current study can offer a platform for improving teacher education and for constructing intervention programs that promote teachers’ capacities to maintain MSK. Utilization of this PV model in teacher education programs may also have the potential to deepen teachers’ understanding of the dynamic interplay between the two MSK-teaching delivery modes and their subcomponents, thereby guiding teachers in simultaneously adapting each of the two knowledge bases and helping teachers infuse MSK into the required material by using student-centered learning pedagogies. The current outcomes suggest that program planners should explicitly embed formal structures or tools within the curriculum to help pre-service teachers enrich their PV and evaluate information derived from both teachers’ and students’ behaviors, practices which are often otherwise ignored.

Second, the findings described above suggest that teacher preparatory programs should consider “switching cognitive gears” between LFTB and LFSB. For example, LFTB may be a more conducive approach when the goal is to learn well-validated instructional strategies or skills. However, the LFSB approach may be more appropriate when aiming to develop a professional identity, thus fostering awareness through adaptive teaching processes. The integration of the two approaches can provide a more concrete link between inductive and deductive methods of teaching in science education. It may be the case that, in science education, deductive reasoning would be an appropriate goal for both pre-service science teachers and students, especially when integrated with inductive reasoning.

To conclude, the traditional instructional approach to teacher education, based on the technical-rational model of knowledge generation, has been criticized as inappropriate for developing pre-service teachers’ understanding of how theory unfolds in the practical world (e.g., Korthagen 2001). To bridge this theory-practice gap, LFTB has been applied quite extensively in teacher preparatory programs around the world, especially in North America (Dean 1999; Edens 2000; Edwards and Hammer 2006; Goodnough 2003). The current study’s findings reframe the learning-from-teacher focus to an instructional framework of teacher education programs that additionally include learning-from-student focus. Although LFSB has been perceived as the enemy of experimentation and innovation (Levitt and March 1996), the deliberate choice to integrate learning from both student and teacher behavior may nurture the practical wisdom necessary to work in dynamic school contexts.

Finally, an important practical implication of the current findings is the relatively rapid rate of improvement, especially in explicit MSK teaching, that already occurred at Time 2 of the practicum course, after pre-service teachers in the complementary “dual” group had only been exposed to four opportunities for reflective video-analysis. Considering the dearth of resources often characterizing educational systems, this promising learning curve may suggest the possible cost-effectiveness of the current complementary LFTB + LFSB training program, even if it can only be implemented partially, to help pre-service teachers better externalize their implicit MSK teaching skills. Future research should pursue this speculation.

Study limitations

The limitations of this work need to be acknowledged. First, this study could not test the contribution of learning solely from students’ behaviors (as compared to learning solely from teachers’ behaviors) to the improvement in the two dependent constructs (MSK teaching and students’ MSK achievements). As teacher education programs in Israel have been primarily centered on LFTB, testing the contribution of learning solely from students’ behaviors was not permitted by the higher education institutions.

Second, the present study did not include a control group of pre-service teachers who did not use any PV reflective method. Instead, this study assessed the dependent constructs (pre-service teachers’ teaching of MSK and their students’ MSK) at the beginning of the practical teaching period and found no significant differences between the two PV groups. Third, in the group that integrated PV prompts/instruction of both teachers’ and students’ behaviors into the lesson, the PV reflective process was conducted by probing teachers’ behaviors first and students’ behaviors second. The current study did not include a group that analyzed students’ behaviors first, followed by teachers’ behaviors. Thus, the current findings cannot be used to describe the patterns of interaction and mutual influences between LFTB and LFSB reflective processes. Fourth, one further limitation of the study is that pre-service teachers’ professional knowledge and vision of MSK were not measured; therefore, conclusions cannot be drawn about the direct connection between teachers’ PV of MSK and their actual MSK-teaching. Fifth, in this quasi-experimental design, the relatively small sample of participants, in addition to the logistical requirements, limited the randomization process, enabling only random assignment of each location to one of the two conditions instead of random assignment of participants to conditions.