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Article

Development and Initial Validation Steps of a Standardized Video Test Assessing Professional Vision of Classroom Management and Instructional Support

by
Jasmin Lilian Bauersfeld
1,*,
Patricia Bourcevet
2,
Heike Hahn
2 and
Bernadette Gold
1
1
Department of Educational Sciences and Psychology, TU Dortmund University, 44227 Dortmund, Germany
2
Faculty of Education, University of Erfurt, 99089 Erfurt, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(6), 749; https://doi.org/10.3390/educsci15060749
Submission received: 20 September 2024 / Revised: 3 June 2025 / Accepted: 3 June 2025 / Published: 13 June 2025
(This article belongs to the Special Issue Enhancing the Power of Video in Teacher Education)
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Abstract

Teachers’ professional vision (PV) is important for implementing teaching quality in classrooms. PV entails noticing and reasoning on relevant events out of classrooms’ complexity. Many events entail situations of classroom management and instructional support, which are crucial for student learning. Standardized video-based instruments have been used to validly and reliably gmeasure PV of classroom management and PV of instructional support. However, most instruments focused on one teaching quality dimension (e.g., on classroom management or instructional support) and used several classroom videos for each focus. Therefore, the present study gives preliminary insights into the standardized assessment of PV of multiple foci of teaching quality (i.e., classroom management and instructional support) using a single classroom video from an elementary math lesson. Participants were 221 math master’s student teachers, 83 math bachelor’s student teachers, 40 math pre-service teachers in the induction program, 19 elementary math teachers, and 19 math students. The results of confirmatory factor analyses displayed a good fit for a two-dimensional structure with the following factors: PV of classroom management and PV of instructional support. Furthermore, our findings showed that master’s student teachers and pre-service teachers differed from bachelor’s student teachers and math students in PV of classroom management, but not when compared to experienced teachers. In conclusion, the findings mark an important first step in developing an instrument that captures classrooms’ complexity by simultaneously measuring PV of multiple foci of teaching quality using the identical classroom video.

1. Introduction

Professional vision (PV) is a core competency for teaching (Grossman et al., 2009). It encompasses noticing and knowledge-based reasoning (van Es & Sherin, 2002) on relevant events, from which teachers can make informed decisions about appropriate actions in the classroom (Blömeke et al., 2015; Jacobs et al., 2010). Many relevant events can be related to situations of teaching quality in which teachers appropriately deal with disruptions and implement effective classroom management to secure students’ attention and facilitate student learning by providing instructional support (Kunter et al., 2013; Pianta & Hamre, 2009; Praetorius et al., 2018).
In recent years, efforts have been made to measure PV with diverse instruments (Weyers et al., 2023). In this context, classroom videos have proven to be efficient in assessing PV (Gold & Holodynski, 2017; Seidel & Stürmer, 2014) because they can display the complexity of a classroom (Miller, 2007). While most instruments employ open-response formats to measure PV, there has been a call for the development of standardized instruments (Weyers et al., 2023), which provide a more economical approach to assessing PV. These tests can effectively measure the cognitive process of reasoning using descriptions and interpretations of classroom situations displayed in classroom videos that have to be evaluated by the teachers (Gold & Holodynski, 2017; Seidel & Stürmer, 2014). Because the rating items guide attention to specific events in the classroom and thereby simplify processes of perception and the selection of relevant events (Seidel & Stürmer, 2014), these standardized instruments mainly assess the cognitive process of reasoning (Weyers et al., 2023).
Moreover, existing standardized instruments typically regard different foci of teaching quality. Current models of teaching quality have proposed that teaching quality encompasses the dimensions of classroom management, instructional support, and emotional support (Pianta & Hamre, 2009; Praetorius et al., 2018). Depending on the focus, different categories of teacher knowledge become relevant (e.g., PV of classroom management draws on general pedagogical knowledge and PV of instructional support on pedagogical content knowledge and content knowledge). Because PV is a knowledge-based competency, instruments have measured PV with a focus on classroom management (Gold & Holodynski, 2017; König, 2015), instructional support (Jacobs et al., 2010; Meschede et al., 2015; Michalsky, 2014), or emotional support (Keppens et al., 2019) using separate classroom videos (Steffensky et al., 2015). However, the separate measurement of PV of different teaching quality dimensions only partially takes into account the complexity of classrooms displayed in the videos and only considers part of the skills required to deal with this complexity. The only instruments known to us that measured multiple foci of teaching quality with identical classroom videos used an unstandardized assessment (Dückers et al., 2022) or assessed the cognitive processes of PV (i.e., describing, explaining, and predicting) but not their specific focus (Seidel & Stürmer, 2014). Hence, a standardized assessment that measures PV of multiple foci of teaching quality is still missing.
Against this background, this study’s goal is to take a first step in developing a standardized instrument that uses an identical classroom video to assess PV of multiple foci of teaching quality (i.e., PV of classroom management and PV of instructional support). Specifically, the reliability, construct validity, and criterion validity of the new instrument will be analyzed. We acknowledge that teaching requires a diverse array of knowledge types. Nevertheless, events related to the teaching quality dimensions of classroom management and instructional support and their interplay cover many typical demands found in teaching.

1.1. Professional Vision

Classrooms are characterized by multidimensionality, simultaneity, immediacy, unpredictability, and complexity (Doyle, 1989). Teachers should be capable of paying attention to and selecting relevant events occurring in the classroom (noticing) and interpreting these events to figure out what is happening in the classroom (reasoning) (van Es & Sherin, 2002). These cognitive processes are components of PV, which is a core competency for teachers (Grossman et al., 2009) and has been shown to affect student learning and teaching quality (Blömeke et al., 2022; Kersting et al., 2012; Roth et al., 2011). Research on teacher expertise has shown that expert teachers’ knowledge is organized in highly integrated, interconnected, and easily accessible cognitive schemata and scripts (Borko & Livingston, 1989; Leinhardt & Greeno, 1986) that guide their perception and help encode visual information. As a result, teachers can quickly perceive, predict, categorize, and evaluate relevant events and relate them to abstract categories (Borko et al., 1990). Hence, their knowledge is flexibly organized and forms the basis for cognitive processes of noticing and reasoning on relevant events within the classroom (Baumert & Kunter, 2013; Borko & Livingston, 1989).
Those relevant events often concern dimensions of teaching quality, which encompass effective classroom management, instructional support, and emotional support (Kunter et al., 2013; Pianta & Hamre, 2009; Praetorius et al., 2018).1 Especially, the dimensions of classroom management and instructional support play an essential role in initiating and supporting student learning (Baumert et al., 2010; Fauth et al., 2014; Seidel & Shavelson, 2007) and fostering an environment that encourages student engagement and academic achievement (Evertson & Weinstein, 2006). Thus, depending on the focus of a specific relevant situation of teaching quality, different aspects of teachers’ knowledge are addressed (Kunter et al., 2013; Shulman, 1986). Teachers’ knowledge encompasses general pedagogical knowledge, pedagogical content knowledge, and content knowledge (Baumert & Kunter, 2013; Shulman, 1986). In relevant events that refer to effective classroom management, teachers’ general pedagogical knowledge is predominantly important (König et al., 2014; König & Kramer, 2016). Events related to instructional support trigger teachers’ content knowledge and pedagogical content knowledge (Kersting et al., 2012; Meschede et al., 2017). However, while student teachers typically focus on one dimension at a time because they lack sufficient knowledge and teaching experience (Carter et al., 1987; Putnam & Borko, 2000), research has shown that expert teachers can notice and reason on multiple classroom events simultaneously (Carter et al., 1988; Sabers et al., 1991; Star & Strickland, 2008) based on their highly interconnected knowledge. Hence, teachers need to use this knowledge simultaneously to make sense of a classroom situation (Dunekacke, 2016).
As the implementation of teaching quality is a crucial part of teaching, teachers need to be able to observe and analyze situations related to several aspects of teaching quality. Frameworks on teaching quality have identified the dimensions of classroom management, instructional support, and emotional support (Kunter et al., 2013; Pianta & Hamre, 2009; Praetorius et al., 2018). Based on these frameworks, research has shown a close connection between effective classroom management and instructional support (Kwok, 2021; Stahnke & Friesen, 2023) and that their combination influences student learning (Praetorius et al., 2018). In the next sections, important concepts of effective classroom management and instructional support will be described.

1.2. Classroom Management

The teaching quality dimension of effective classroom management is pivotal in creating and sustaining a positive and motivating atmosphere that enhances teaching and learning experiences (Doyle, 2006). Hence, teachers need to orchestrate classroom activities to secure learning time and organize academic work (Doyle, 2006), which supports effectively dealing with classroom disruptions. Effective classroom management has been shown to support student learning (Seidel & Shavelson, 2007), foster student motivation (Schiefele, 2017), decrease problematic student behavior (Oliver et al., 2011), and benefit teachers’ health (Dicke et al., 2014).
Studies have shown that strategies for effective classroom management are more preventive than reactive (Kounin, 1970; Shook, 2012). The teacher has to establish and enforce rules and routines in the classroom to provide structure and transparent expectations for the students (Evertson & Emmer, 2012; Korpershoek et al., 2016), which supports an undisturbed academic environment. Furthermore, the group focus can ensure learning time because the teacher mobilizes students’ attention, demands accountability, and secures students’ engagement (Kounin, 1970). Moreover, the teacher has to monitor the classroom through “withitness” and “overlapping” (Kounin, 1970, p. 74) to convey the impression to the students that they are aware of everything happening in the classroom (van Tartwijk et al., 2009; Wubbels et al., 2006). This also includes an appropriate position in the classroom, keeping track of students’ activities, interacting with students, and reinforcing appropriate student behavior (Marder et al., 2023; Simonsen et al., 2008). Furthermore, smoothness and momentum through effective time management and smooth transitions support the lesson flow and ensure learning time (Kounin, 1970; Thiel et al., 2023). Lastly, the teacher has to deal with disruptions effectively and involve inattentive students (Thiel et al., 2023). By appropriately addressing disruptions and imposing consequences, disruptions can be dealt with quickly and effectively. Hence, in the present study, PV of classroom management is conceptualized as the ability to notice and reason on events requiring teachers to establish and enforce rules and routines, uphold the group focus, maintain momentum and smoothness, monitor the classroom, and effectively deal with disruptions.

1.3. Instructional Support

The teaching quality dimension of instructional support depends on the lesson’s content. It is defined as cognitively activating instruction that lets students explore new ideas, explain and reflect on materials, and use learned concepts in different contexts (Brophy, 2004; Reiser, 2004). Mathematics lessons often include a problem-solving focus, interactive and hands-on activities, incremental learning, visual aids and representations, and an emphasis on critical thinking (Leuders & Holzäpfel, 2011). Therefore, a constructivist view of learning (Brophy, 2000; Staub & Stern, 2002) is preferred.
Teachers should explore students’ thoughts and prior knowledge (Brophy, 2000). Problem-based learning and challenging cognitively activating tasks (Maier et al., 2010) should be implemented to engage students in higher-order thinking, support metacognition, and enhance cognitive conflicts (Baumert et al., 2010; Lipowsky et al., 2009). Moreover, students’ cognitive engagement in the classroom is crucial to support their learning so that they can express ideas and develop a conceptual understanding (Windschitl et al., 2012). Strategies to foster student engagement include students’ contributions to the classroom discourse, demanding explanations for their solutions, and supporting students’ cognitive independence through openly expressing thoughts and ideas (Lotz, 2015). Hence, in the present study, PV of instructional support is defined as the ability to notice and reason on events in which teachers draw on students’ prior knowledge, explore students’ thought processes, include students’ contributions, implement problem-based learning and cognitively activating tasks, demand explanations, and support students’ cognitive independence.
Classrooms are highly multidimensional, unpredictable and complex as situations occur simultaneously and immediately (Doyle, 1989). Of course, this complexity of classrooms entails many more aspects (e.g., social settings, class composition, etc.) or demands on teachers (e.g., counseling and emotional support). Nevertheless, researchers have frequently proposed that the combination of classroom management and instructional support encompasses many demands associated with teaching (Dückers et al., 2022). For example, teachers establish rules and routines for how to behave in a discussion, through which they can explore students’ thought processes and work with students’ contributions. Therefore, teachers need PV of classroom management and instructional support for high teaching quality. This PV of teaching quality has been shown to positively influence teaching quality (Blömeke et al., 2022; Kersting et al., 2012; Roth et al., 2011) and student achievement (Kersting et al., 2012).

1.4. Standardized Assessment of Professional Vision

In recent years, efforts have been made to measure PV with diverse instruments (Gold et al., 2024; Weyers et al., 2023). Because of the situated nature of PV, classroom videos have been seen as an adequate stimulus for assessing PV (Gold & Holodynski, 2017; Weyers et al., 2023) as they can display the complexity, multidimensionality, simultaneity, and immediacy of a classroom (Miller, 2007).
Regarding the measurement method, most studies assess PV through qualitatively analyzing verbal or written analyses of classroom videos (König et al., 2022; Sherin et al., 2011), which provides deep insights into (future) teachers’ thoughts. However, these approaches are time-consuming and require complex coding procedures. As a result, there has been a call for the development of standardized instruments, which represents a more economical and valid means of measuring PV. Standardized instruments typically use rating items to assess PV (Gold & Holodynski, 2017; Seidel & Stürmer, 2014). Noticing can hardly be assessed with these instruments (Seidel & Stürmer, 2014) because the rating items already direct attention to specific events in the classroom, thereby excluding processes of independently perceiving and selecting from complex classroom situations. Consequently, standardized assessments lack the spontaneous and autonomous elements that are crucial for noticing, which in turn reduces cognitive demand and simplifies complex classroom dynamics and situations. Therefore, these instruments predominantly assess the cognitive process of reasoning. Research has introduced multiple but inconsistent conceptualizations of reasoning, including aspects of describing, explaining, interpreting, or reviewing relevant classroom situations (Blomberg et al., 2011; König et al., 2014). Specifically, some studies have suggested that the cognitive process of reasoning in PV of classroom management or PV of instructional support encompasses describing and interpreting relevant classroom events and displays a one-dimensional construct (Gold & Holodynski, 2017; Meschede et al., 2015).
Regarding the focus of PV, the instruments that already exist have primarily focused on aspects of classroom management or instructional support or have used different videos for each aspect of PV (Steffensky et al., 2015). Existing instruments have been mostly thoroughly validated, can reliably assess PV, and can identify differences between expert and novice teachers. Furthermore, they have been shown to elicit specific teachers’ PV in relation to specific foci (e.g., classroom management). This makes it easier to measure the progress of this focus-specific PV over time (Barenthien et al., 2023) and can be used to design trainings that explicitly target this focus (Gold et al., 2021). However, most classroom situations entail both aspects of classroom management and instructional support. Focusing on only one teaching quality dimension when measuring PV may not meet the requirement to notice and reason on the most relevant events in the complex teaching process (Dunekacke, 2016). Measuring both aspects of teaching quality using the same video clip as a test stimulus for multiple foci might capture teachers’ PV more comprehensively. Additionally, implementing the identical classroom video may be more feasible and practical in teacher education or evaluation contexts.
Nevertheless, the majority of existing instruments do not assess PV of multiple foci simultaneously within identical video clips of a classroom situation. An exception is the instrument by Dückers et al. (2022) and the instrument by Seidel and Stürmer (2014). Dückers et al. (2022) developed an unstandardized assessment in the context of elementary mathematics classrooms to investigate whether PV of classroom management and PV of learning support are focus-specific or focus-integrated constructs. The findings reveal separate foci of classroom management and instructional support for the cognitive process of reasoning. Seidel and Stürmer (2014) report on a standardized assessment that measured describing, explaining, and predicting but without considering the respective teaching quality dimensions (in this study, the dimensions of goal clarity, teacher support, and learning climate). A standardized assessment that measures focus-specific PV using the same video clip is still missing.
To ensure construct validity, studies have investigated the internal factorial structure. They have shown that PV is a focus-specific competency comprising the distinct dimensions of PV of classroom management and instructional support (Dückers et al., 2022; Steffensky et al., 2015). Studies investigating the internal structure of reasoning in standardized tests measuring PV usually only address one specific focus (e.g., Gold & Holodynski, 2017; Meschede et al., 2015; Seidel & Stürmer, 2014). Some of these studies have shown that reasoning is a one-dimensional construct, including aspects of describing and interpreting (Gold & Holodynski, 2017; Meschede et al., 2015). Other studies found that describing and interpreting are two distinct cognitive processes that are highly correlated (Seidel & Stürmer, 2014).
Furthermore, the validity of video tests is typically evaluated by showing that PV correlates strongly with a closely related construct (convergent validity) or that PV is distinct from other constructs (discriminant validity). Many studies have investigated convergent validity by analyzing the correlation between PV and knowledge because the two concepts are closely related but empirically separable (Gold & Holodynski, 2017; König, 2015; Meschede et al., 2015; Seidel & Stürmer, 2014). Discriminant validity can be evaluated using conceptually distinct concepts from PV, such as affective–motivational concepts. Only a few studies (König, 2015) have evaluated discriminant validity in connection with affective–motivational constructs such as self-efficacy. For example, König (2015) only found low, non-significant correlations between PV and self-efficacy and argued that the correlation’s direction served as evidence for the construct and discriminant validity of PV.
Moreover, some standardized instruments measuring PV have been checked concerning their criterion validity by showing differences between expertise levels in teacher education (e.g., student teachers and teachers; teacher education students and main subject students) (Weyers et al., 2023). These studies have indicated that bachelor’s student teachers at university significantly differed from experienced teachers with large effect sizes in PV of classroom management (Gold & Holodynski, 2017) and PV of instructional support (Meschede et al., 2015). However, both were less distinguishable between master’s student teachers and experienced teachers (Gold & Holodynski, 2017; Meschede et al., 2015). In this context, the development of PV throughout teacher education may not be completely linear when classroom experience (e.g., in the induction program or everyday teaching) comes into play (Barenthien et al., 2023; Bastian et al., 2022). Hence, standardized instruments of PV typically capture differences between novice student teachers (i.e., bachelor’s student teachers) and experienced teachers but often struggle to identify differences between intermediate student teachers (i.e., master’s student teachers or pre-service teachers in the induction program) and experienced teachers. Furthermore, whether the instruments capture differences between (future) teachers and students without a teaching-specific degree (e.g., a math degree) has rarely been investigated.

1.5. Aim of the Study

Against this background, the study aims to take the first steps in developing a video-based instrument measuring PV of classroom management and instructional support within the same elementary mathematics classroom situation by psychometrically evaluating the instrument and identifying areas for optimization and further development. Specifically, we investigated the construct validity of the instrument in terms of its factorial structure and explored whether it reliably measures PV with the foci of the teaching quality dimensions of classroom management and instructional support (RQ1: dimensionality and reliability). Based on previous studies (Dückers et al., 2022; Steffensky et al., 2015), we assumed a two-dimensional structure of the instrument with the following factors: PV of classroom management and PV of instructional support. Second, we investigated the discriminant validity of the instrument in connection with self-efficacy. Based on previous studies, we assumed low to moderate correlations (RQ2: discriminant validity compared to self-efficacy). Third, as an indicator of criterion validity, we explored whether the instrument captures differences between bachelor’s and master’s student teachers, pre-service teachers, elementary teachers, and math students (RQ3: differences between expertise groups). We assumed differences between (future) teachers and math students as well as differences between groups with different teaching expertise (i.e., between bachelor’s student teachers, master’s student teachers, and experienced teachers).

2. Materials and Methods

2.1. Sample and Procedure

Participants included master’s and bachelor’s student teachers at university, pre-service teachers in their induction program, experienced teachers teaching at school, and math students at university. Demographic data for the samples can be found in Table 1. Regarding previous learning opportunities, 16.7% of the master’s student teachers had not attended any seminars, while 83.3.% had only individual sessions related to classroom management. Additionally, 40.7% of the master’s student teachers had not attended any seminars and 59.3% had only individual sessions related to instructional support. Among the elementary pre-service teachers, 82.5% had at least one course, and 12.5% had no courses or only individual sessions on classroom management. In addition, 20% of the pre-service teachers reported having taken at least one course, while 75% had no courses or only individual sessions on instructional support. The bachelor’s student teachers, the math students, and the elementary teachers reported no additional data on previous learning opportunities. All participants gave informed written consent and were treated in line with guidelines laid down by the American Psychological Association and the Declaration of Helsinki. The teachers and future teachers voluntarily took part in the study and were recruited from seminars at universities or in the induction program.
The video test was conducted via the online survey tool Unipark. First, the participants were introduced to short definitions of effective classroom management and instructional support. Next, they had the opportunity to watch the video twice. After watching the video, they were asked to answer the items describing and interpreting classroom management and instructional support within the video clip.

2.2. Materials

Eleven 90-minute video clips of double math lessons from the first to fourth grades were carefully selected from the Provision platform (https://www.uni-muenster.de/ProVision/; 1 August 2021). The video clips were filmed with a front view of the class so that the students, the teacher, and their interactions could be observed (van Es et al., 2015). Seven approximately ten-minute-long video clips were pre-selected because they displayed positive and improvable events of classroom management (e.g., the teacher enforcing or not enforcing rules in the classroom) and instructional support (e.g., the teacher activating or not activating students’ prior knowledge). Events related to classroom management entailed rule clarity, dealing with disruptions, group focus, withitness and monitoring, smooth transitions, and effective time management (Lotz et al., 2013). The teacher activating students’ prior knowledge, exploring students’ thought processes, dealing with students’ contributions, implementing problem-based learning and cognitively activating tasks, demanding explanations, and supporting cognitive independence covered events related to instructional support (Lotz et al., 2013). The video clips needed to be about ten minutes long to cover the aspects of classroom management and instructional support thoroughly.
The authors discursively discussed the pre-selected video clips with four teacher educators. The teacher educators had at least five years of teaching experience and were teacher educators for at least one year. The final video clip was selected because, in their opinion, it covered many aspects of classroom management and instructional support (except smooth transitions and effective time management) and displayed positive and improvable events.
The final ten-minute video shows a math lesson in a first-grade elementary school classroom. The students are gathered around the blackboard, and the teacher introduces them to discovery packets. Discovery packets are cognitively activating tasks that enable students to discover, describe, and explain mathematical relations (Lipowsky & Hess, 2019). The teacher explains how discovery packets work and tries to maintain students’ attention. Table 2 provides an example situation related to the teaching quality dimensions of classroom management and instructional support.

2.3. Instruments

2.3.1. Professional Vision of Classroom Management and Instructional Support

We developed a pool of 50 items, with 26 items on classroom management and 24 items on instructional support, based on the abovementioned aspects of classroom management and instructional support portrayed in the video. The classroom management items included 13 items with descriptions and 13 items with interpretations connected to classroom management displayed in the classroom video. The instructional support items included 11 items with descriptions and 13 items with interpretations connected to instructional support shown in the video. The items were rated on a 4-point scale from agree (1) to disagree (4).
To determine the correct answers to the items, a master rating was drawn up by three experts in classroom management and instructional support. One expert was a teacher educator with more than ten years of teaching experience, and two experts were researchers on PV of teaching quality with at least ten years of experience. The experts independently answered the items on a 4-point scale. They showed 67.9% agreement for PV of classroom management and 75.0% agreement for PV of instructional support on the correct answers. The intraclass correlation coefficient (ICC) was moderate for PV of classroom management (ICC = 0.679) and PV of instructional support (ICC = 0.605). Because of the moderate ICCs and to determine unambiguous correct answers for the items, we discursively discussed the items and the answers with five further research experts on classroom management and instructional support. Based on the first master rating, we discussed the answers to each item, agreed on one correct answer, and then drew up the final rating for the video test. Moreover, we found that seven items were ambiguously displayed in the video (e.g., classroom management: “The students know exactly what is allowed and what is not.”; instructional support: “The teacher asks the students how they arrived at certain answers”). Two items for PV of classroom management and five items for PV of instructional support were excluded from further analyses. In the end, we used 43 items (24 items on PV of classroom management and 19 items on PV of instructional support).

2.3.2. Self-Efficacy

To investigate discriminant validity, we measured the self-efficacy of the student teacher sample using the validated German version of the scale of teacher’s sense of self-efficacy (Pfitzner-Eden et al., 2014). We used the sub-scales of self-efficacy in classroom management and self-efficacy in instructional support. For each sub-scale, student teachers rated themselves on a 9-point Likert scale, from whether they were absolutely certain they can do it (9) to whether they were not at all certain they can do it (1). The internal consistency of the student teacher sample was good for self-efficacy in classroom management, Cronbach’s α = 0.89, and self-efficacy in instructional support, Cronbach’s α = 0.77.

2.4. Analysis Plan

In preparation for analyses, participants’ answers to the video test were coded based on the master rating. Participants received two points for exact correspondence with the master rating, one point for the tendency of correspondence with the master rating (e.g., master rating answered rather agree (2), and participants answered agree (1)), and zero points for no correspondence with the master rating.
In RQ1, we aimed to investigate the instrument’s factorial structure. Based on the assumption that PV is focus-specific as well as on previous findings on the internal structure of PV of classroom management and instructional support (Dückers et al., 2022; Gold & Holodynski, 2017; Meschede et al., 2015; Steffensky et al., 2015), we specified three models in confirmatory factor analyses (CFA) with robust maximum likelihood estimation for the master’s student teachers. Model A: a one-dimensional model of PV with one factor of PV of teaching quality. Model B: a two-dimensional model of PV with the two dimensions, PV of classroom management and PV of instructional support (factor 1: classroom management; factor 2: instructional support). Model C: a four-dimensional model with the four dimensions, describing and interpreting of PV of classroom management and describing and interpreting of PV of instructional support. We evaluated the fit statistics of the three models based on Byrne (1994). The reliability of the final scales was evaluated with factor reliabilities of McDonald’s Ω and Cronbach’s α.
To investigate the discriminant validity of the instrument (RQ2), we calculated Pearson correlations between PV of classroom management and instructional support (based on the factor structure found in RQ1) and self-efficacy in classroom management and instructional support.
In RQ3, we tested whether the final scales could detect expertise differences. Because of the small sample sizes of math students, pre-service teachers, and elementary teachers, we could not test for measurement invariance across groups. Hence, we explored whether there were differences between the expertise groups with descriptive data and calculated multiple comparisons using the Tukey HSD test. Because of the small sample size of elementary teachers and math students, we conducted multiple comparisons with manifest means.
Data and syntax can be accessed via https://osf.io/2mj3g/?view_only=5c6d33d6130544e5b19692d8b08f4b7c (accessed on 1 September 2024).

3. Results

Descriptive statistics of means (item difficulty) and standard deviations are displayed in Table 3. Items regarding classroom management were of medium difficulty, whereas items of instructional support were more difficult for the master’s student teachers. The items did not display any floor (M < 0.2) or ceiling effects (M > 1.8). Master’s student teachers’ self-efficacy in classroom management was M = 6.55 (SD = 1.42), and self-efficacy in instructional support was M = 6.48 (SD = 1.09).

3.1. Dimensionality and Reliability (RQ1)

CFAs of all three models with all 43 items indicated an unsatisfactory fit for the Tucker–Lewis index (TLI) and comparative fit index (CFI; see Table 3). Based on the models, we successively deleted items with poor fit statistics, such as negative or low main loadings (<0.30). Two items with a lower factor loading of 0.30 still had to be included, as they covered aspects of classroom management not accounted for by the other items. In this step, we removed 13 items on classroom management and six items on instructional support.
CFAs (see Table 3 and Table 4) with the remaining 24 items (11 on classroom management, 13 on instructional support) showed that Model A (the one-factor solution) indicated a poor fit. Model B (the two-factor solution) indicated an acceptable fit with strong correlations of r = 0.51 (p < 0.001) between the factors, PV of classroom management and PV of instructional support. However, the low TLI indicated a slightly inadequate fit, suggesting a more complex structure to the model. Model C, the four-factor model, indicated a good fit to the model. However, correlations between describing and interpreting classroom management exhibited a Heywood case (r > 1.0), suggesting that the model was overestimated. Furthermore, the correlations indicated a strong correlation between describing and interpreting instructional support (r = 0.88). Hence, these findings led to the conclusion that PV is best displayed by the two dimensions of PV of classroom management and PV of instructional support. Reliability coefficients for the sub-scales, PV of classroom management (α = 0.76; ω = 0.81) and PV of instructional support (α = 0.87; ω = 0.89), were satisfactory.

3.2. Discriminant Validity (RQ2)

Regarding discriminant validity, Pearson correlations between PV of classroom management and self-efficacy in classroom management (r = −0.13, p = 0.050) were significant but weakly negatively correlated. Correlations between PV of instructional support and self-efficacy in instructional support were also negative but non-significant (r = −0.14, p = 0.055).

3.3. Differences Between Groups of Different Expertise (RQ3)

Table 5 displays descriptive statistics of the PV sub-scales. Means indicate the average of correct answers on a scale from 0 to 2.
Overall, the means show that, as expected, bachelor’s student teachers and math students attained the lowest means in PV of classroom management and PV of instructional support. However, regarding PV of classroom management, master’s student teachers and pre-service teachers performed similarly and better than bachelor’s student teachers and elementary teachers. In line with our assumptions, descriptive data on PV of instructional support showed that elementary teachers performed better than pre-service teachers, who outperformed bachelor’s and master’s student teachers. Math students performed similarly to bachelor’s student teachers but worse than master’s student teachers.
Multiple comparisons (see Figure 1) with manifest means of PV of classroom management showed that bachelor’s student teachers were outperformed by master’s student teachers, diff = −0.30, 95%CI [−0.43, −0.17], p < 0.001, f = −0.75, and pre-service teachers, diff = −0.30, 95%CI [−0.50, −0.10], p < 0.001, f = −0.76. Similarly, math students were outperformed by master’s student teachers diff = −0.41, 95%CI [−0.66, −0.16], p < 0.001, f = −1.03, and pre-service teachers, diff = −0.42, 95%CI [−0.70, −0.13], p < 0.001, f = −1.03. However, master’s student teachers, pre-service teachers, and elementary teachers did not significantly differ in PV of classroom management. All multiple comparisons regarding PV of instructional support yielded non-significant results.

4. Discussion

4.1. Interpretation of the Results

The study’s main goal was to take the first steps toward developing a video-based instrument that captures (future) teachers’ focus-specific PV of multiple teaching quality dimensions (i.e., classroom management and instructional support). Teachers need to implement effective classroom management and instructional support to create a learning atmosphere that fosters students’ socio-emotional and cognitive processes (Evertson & Weinstein, 2006). Hence, teaching is multidimensional and requires dealing with simultaneously occurring processes (Doyle, 1989), frequently related to classroom management and instructional support. The ability to notice and interpret such relevant events related to classroom management and instructional support enables teachers to make adequate decisions in the classroom (Blömeke et al., 2022; Kersting et al., 2012; Roth et al., 2011). As a result, PV is a core competency of teaching (Grossman et al., 2009). Most existing instruments for assessing PV of classroom management or PV of instructional support measured either one or the other with separate classroom videos or implemented qualitative coding procedures (Weyers et al., 2023). Therefore, the development of a standardized instrument that simultaneously captures these competencies in the domain of mathematics was necessary.
Regarding RQ1 (dimensionality and reliability), the findings showed that PV could be described best by two factors: PV of classroom management and PV of instructional support. Although the four-factor model with the two factors of describing and interpreting for classroom management and instructional support, respectively, displayed a slightly better fit to the data than the two-factor model, correlations between the describing factors and the interpreting factors were high (r > 0.88), and the correlation between describing and interpreting classroom management even showed that the model was overestimated. Hence, we concluded that PV is best displayed by a two-factor structure in our instrument consisting of PV of classroom management and PV of instructional support. These findings are in line with previous studies (PV of classroom management (Gold & Holodynski, 2017); PV of instructional support (Meschede et al., 2015); multiple foci (Dückers et al., 2022)), in which the PV of classroom management or instructional support displayed a one-dimensional construct, and the cognitive processes of PV were difficult to separate. In summary, our results support the assumption by Sherin (2007) that, although the cognitive processes of PV are conceptually distinct, they interact during the process of perception and are, therefore, practically inseparable. Moreover, although PV of classroom management and PV of instructional support are highly correlated, they are still two distinguishable constructs. In line with previous findings, this suggests that PV is a focus-specific ability (Dückers et al., 2022; Steffensky et al., 2015) and that the classroom video displays the complex nature of a classroom (Miller, 2007), in which aspects of classroom management and instructional support happen simultaneously and overlap.
Regarding RQ2 (discriminant validity), the results on the correlation between self-efficacy and PV showed that, apparently, student teachers with high self-efficacy performed worse in PV. However, these correlations were weak and even non-significant for instructional support. This finding suggests that PV and self-efficacy are two distinct constructs and provides initial indications of the discriminant validity of the instrument. Although supporting PV’s discriminant validity, compared to existing results (Gold & Holodynski, 2017; König, 2015; Meschede et al., 2015; Seidel & Stürmer, 2014), negative correlations were surprising. One possible explanation could be that PV of classroom management and instructional support are measured with a classroom video displaying a specific classroom situation. Contrastively, the teacher self-efficacy scale (Pfitzner-Eden et al., 2014) measures self-efficacy in a more general sense without reference to particular classroom situations. This methodological discrepancy may lead to negative correlations between the two instruments, indicating that student teachers struggle to use PV of classroom management and instructional support in a specific classroom situation but generally feel confident in implementing classroom management and instructional support in a classroom, or vice versa. However, another explanation could be that student teachers may struggle to evaluate their abilities, leading to overestimations or underestimations (see also: Dunning–Kruger effect; Kruger & Dunning, 1999). As a result, student teachers with low PV may not be aware of the challenge of implementing classroom management and instructional support and may, therefore, overestimate their abilities. Contrastively, student teachers with high PV see the complexities of a classroom, and thus may feel less confident in implementing classroom management and instructional support. Although these findings give indications for the discriminant validity of the instrument, a more adequate means of investigating convergent validity would be to implement knowledge tests to examine correlations between knowledge and PV. Furthermore, convergent validity could also be analyzed by including tests specifically related to knowledge about classroom management and instructional support.
Regarding RQ3 (differences between expertise groups), analyses of differences between different levels of expertise indicate that elementary teachers did not outperform master’s and bachelor’s student teachers, pre-service teachers, or math students in PV of classroom management or PV of instructional support. Nevertheless, the small sample size in the groups of experienced teachers and math students severely limits the validity of the results, so further studies are still required to test the instrument’s criterion validity. A careful interpretation of this finding still contrasts previous findings from standardized instruments measuring PV (Gold & Holodynski, 2017; Meschede et al., 2015), which showed significant differences between bachelor’s or master’s student teachers and experienced teachers. However, these differences sometimes could not be replicated (Meschede et al., 2015) or displayed only small effect sizes (Gold & Holodynski, 2017). Furthermore, these initial findings also contrast previous findings on teacher expertise (Jacobs et al., 2010), which suggested that experienced teachers significantly outperformed student teachers in PV. However, the development of PV may not be entirely linear throughout teacher education (Barenthien et al., 2023; Bastian et al., 2022; Bauersfeld et al., 2025). Therefore, standardized PV instruments tend to effectively capture differences between novice student teachers (e.g., bachelor’s student teachers) and experienced teachers. However, these instruments often face challenges in distinguishing between intermediate student teachers (such as university student teachers or pre-service teachers in induction programs) and experienced teachers (for example, the instrument by Gold and Holodynski (2017)). Supposedly, measuring PV at several time points could give further insight into whether it can capture expertise differences between student teachers, pre-service, and elementary teachers.
Moreover, a careful interpretation of differences between the (future) teachers and math students gave first indications that master’s student teachers and pre-service teachers outperformed math students in PV of classroom management; however, they were not outperformed by bachelor’s student teachers. This finding supports the claim that general pedagogical knowledge is necessary for PV of classroom management (Blömeke et al., 2015). However, apparently, math students did not differ from bachelor’s or master’s student teachers, pre-service, or elementary teachers in PV of instructional support, suggesting that their content knowledge helped with noticing and reasoning on instructional support in the classroom video.

4.2. Strengths and Weaknesses of the Study

Some strengths and weaknesses of this study should be addressed. The study provided initial steps to developing a standardized instrument that assessed PV of multiple foci of teaching quality. This instrument extends existing instruments, which either used unstandardized assessments that included elaborate coding procedures and required much time and resources, or instruments that measured the cognitive processes of PV but did not consider the focus specificity of PV. However, this study also presented some weaknesses, which explicitly relate to the initial investigations on discriminant and criterion validity. First, while investigating the discriminant validity of the instrument against self-efficacy may provide initial insights into whether PV displays a separate construct, the more appropriate means to show convergent validity would be to implement a knowledge test. However, to our knowledge, while many tests address aspects of instructional support, there is no test explicitly assessing knowledge about instructional support.
Second, the sample size for elementary teachers and math students was very small. Although we implemented non-parametric analyses to provide an initial insight into criterion validity, a larger sample size would have provided more robust results. Additionally, a more careful selection of experienced teachers could reveal a clearer distinction between the various stages of teacher expertise. For instance, choosing teachers who regularly participate in training programs or engage in other deliberate practices may better reflect teacher expertise.

4.3. Limitations and Implications

Certain limitations have to be addressed. First, although the two factors, PV of classroom management and PV of instructional support, provided an adequate fit to the data, the two dimensions, although very relevant to student learning (Fauth et al., 2014), are only two aspects of competencies that teachers need when handling the complexity of a classroom. For example, student–teacher relationships or emotional support may be further aspects of classroom complexity that can be focused on. Second, memory effects could play a role in the assessment of PV, as the video was ten minutes long. However, Dückers et al. (2025) did not find any correlations between PV and memory-processing abilities—although PV was measured with a shorter video clip. Furthermore, the conversations with expert teachers indicated that to be able to observe instructional support and student learning thoroughly, the classroom video needed to be at least 10 min long. Third, using only one classroom video to measure PV may limit the instrument’s validity and reliability. Several studies have shown that the variability of PV in an instrument could be due to the different situations displayed in classroom videos (Gold & Holodynski, 2017; Seidel & Stürmer, 2014). However, this study gave indications of the need to consider focus specificity in the conceptualization and measurement of PV. Fourth, we could not account for measurement invariance between the expertise groups because of the small sample sizes, particularly of the experienced teachers and the math students. This may be why we could not capture any expertise differences between student teachers, pre-service teachers, and elementary teachers. There are large organizational and motivational obstacles to overcome when assessing experienced teachers and math students. They may not only lack time and resources, but they may also not see value in participating in the study. Experienced teachers have been shown to be more motivated when analyzing their own teaching displayed in a classroom video (Seidel et al., 2011). Math students usually do not wish to become teachers and may, therefore, see no sense in analyzing elementary classroom videos in which only easy math principles are displayed. Fifth, although several instruments show that segments of classroom videos from unknown classrooms can validly and reliably assess PV (Gold & Holodynski, 2017; Seidel & Stürmer, 2014), there are also some drawbacks. For example, the classroom video may omit relevant background information, which could be important for an accurate assessment of the situation.
One implication from this study is that the PV of multiple foci cannot only be assessed with unstandardized assessments (see Dückers et al., 2022) but also with standardized video-based assessments. Hence, future studies should further consider including different foci within identical classroom videos to consider classroom complexity. Moreover, a second implication could relate to the need for connecting pedagogy with content in teacher education. Traditionally, teacher education has been divided into content and pedagogy (Ball, 2000), and future teachers are rarely encouraged to connect knowledge with pedagogy. Classroom videos could therefore increasingly provide (future) teachers with the opportunity to connect content with pedagogy.
Conclusively, besides its limitations, the development of the instrument is a first step in assessing the complexity of the classroom by measuring PV of multiple teaching quality dimensions simultaneously (i.e., classroom management and instructional support) using the identical classroom situation.

Author Contributions

Conceptualization, J.L.B., H.H. and B.G.; methodology, J.L.B., P.B., H.H. and B.G.; software, J.L.B.; validation, J.L.B. and B.G.; formal analysis, J.L.B.; investigation, J.L.B. and P.B.; resources, H.H. and B.G.; data curation, J.L.B.; writing—original draft preparation, J.L.B.; writing—review and editing, J.L.B. and B.G.; visualization, J.L.B.; supervision, B.G. and H.H.; project administration, H.H. and B.G.; funding acquisition, H.H. and B.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Thuringian Ministry of Economic Affairs, Science and Digital Society “ProDigital”, grant number 5575/10-2.

Institutional Review Board Statement

The participants in this study took part voluntarily and gave their informed consent. As the study posed no potential risks or discomfort, it was deemed low-risk research and did not require formal approval from an institutional or governing board. The researchers adhered to the ethical guidelines established by the American Psychological Association and the World Medical Association’s Declaration of Helsinki for research involving human subjects.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original data presented in the study are openly available in Open Science Framework (OSF) at https://osf.io/2mj3g/?view_only=5c6d33d6130544e5b19692d8b08f4b7c (accessed on 1 September 2024).

Acknowledgments

We thank the State Seminar for Teacher Training Thuringia, Department of Mathematics, for their support. We also thank Isabell Tucholka, Madeleine Müller, and Christopher Zietz for their invaluable advice and expertise. Finally, we extend our gratitude to all participants for their valuable contributions.

Conflicts of Interest

The authors declare no conflicts of interest.

Note

1
Although the models use different terminology to describe the teaching quality dimensions, most of the described aspects relate to classroom management, instructional support, and emotional support. Therefore, this study used classroom management, instructional support, and emotional support as a framework for teaching quality.

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Education 15 00749 g001
Figure 1. Group differences between student teachers, pre-service teachers, elementary teachers, and math students; PVCM = professional vision of classroom management; PVIS = professional vision of instructional support; significant differences are indicated by dashed lines.
Figure 1. Group differences between student teachers, pre-service teachers, elementary teachers, and math students; PVCM = professional vision of classroom management; PVIS = professional vision of instructional support; significant differences are indicated by dashed lines.
Education 15 00749 g001
Table 1. Demographic data on the samples.
Table 1. Demographic data on the samples.
SampleNAgeGenderUniversity SemestersPrevious School ExperiencePrevious Tutoring Experience
M (SD)in %M (SD)in %in %
Elementary bachelor’s student teachers 18321.89 (2.91)79.5% female4.30 (1.89)84.3%92.3%
Elementary master’s student teachers22122.95 (2.00)88.7% female8.05 (0.51)10.0%29.4%
Elementary pre-service teachers4025.41 (3.11)97.5% female9.97 (0.97)76.3%38.5%
Elementary math teachers1940.63 (12.69)100.0% female8.56 (2.04)78.9%-
Math students1926.26 (4.31)63.2% female5.50 (2.83)0%-
Note. 1 The sample of the elementary bachelor’s student teachers was assessed two years after initial data collection at a different university.
Table 2. Example situation related to the teaching quality dimensions of classroom management and instructional support.
Table 2. Example situation related to the teaching quality dimensions of classroom management and instructional support.
Description of the SituationTeaching Quality Dimension
The teacher says, “…and now for the toughest problem…” She asks the students to share their thoughts on why the result always increases by one. The students share and discuss possible solutions.Instructional support (exploring students’ thought processes and working with students’ contributions)
In the meantime, a student sitting in the back away from the class raises his hand, but the teacher tells him that if he wants to join in, he needs to come to the discussion circle. She silently signals another student to return to his seat.Classroom management (establishing and enforcing rules and routines, effectively dealing with disruptions)
Table 3. Summary of fit statistics of the one-factor, two-factor, or four-factor models.
Table 3. Summary of fit statistics of the one-factor, two-factor, or four-factor models.
Modelsχ2dfCFITLIRMSEASRMR
A: One-factor model (all items)1609.898600.6160.5970.0630.077
B: Two-factor model (all items)1442.979010.7350.7210.0520.071
C: Four-factor model (all items)1408.738960.7490.7350.0510.071
A: One-factor model (24 items)591.572520.7410.7160.0780.081
B: Two-factor model (24 items)375.002510.9050.8960.0470.056
C: Four-factor model (24 items)356.522460.9160.9050.0450.055
Table 4. Item statistics of item difficulty (M[SD]) and standardized factors loadings.
Table 4. Item statistics of item difficulty (M[SD]) and standardized factors loadings.
ItemTeaching Quality DimensionCognitive ProcessM (SD)Model A
λ		Model B
λ		Model C
λ
cmd2PVCMdescribing1.48 (0.59)0.2950.458 10.456 1a
cmd6PVCMdescribing1.37 (0.64)0.2540.333 10.336 1a
cmd9PVCMdescribing0.88 (0.90)0.2170.330 10.324 1a
cmd10PVCMdescribing1.14 (0.89)0.1710.262 10.259 1a
cmi1PVCMinterpreting1.13 (0.70)0.3840.543 10.543 1b
cmi2PVCMinterpreting1.62 (0.55)0.4510.626 10.625 1b
cmi3PVCMinterpreting1.15 (0.73)0.4260.688 10.687 1b
cmi4PVCMinterpreting1.36 (0.63)0.4150.686 10.686 1b
cmi7PVCMinterpreting1.19 (0.87)0.2690.438 10.438 1b
cmi8PVCMinterpreting0.47 (0.83)0.2370.285 10.283 1b
cmi12PVCMinterpreting0.93 (0.74)0.4670.573 10.574 1b
isd1PVISdescribing0.56 (0.88)0.4160.398 20.409 2a
isd5PVISdescribing0.90 (0.75)0.3340.325 20.324 2a
isd6PVISdescribing0.23 (0.52)0.5210.550 20.603 2a
isd8PVISdescribing0.43 (0.65)0.6630.681 20.702 2a
isd9PVISdescribing0.36 (0.57)0.6520.672 20.717 2a
isd11PVISdescribing0.39 (0.60)0.6140.649 20.677 2a
isi2PVISinterpreting0.49 (0.64)0.5320.541 20.530 2b
isi3PVISinterpreting0.60 (0.67)0.6550.684 20.705 2b
isi4PVISinterpreting0.91 (0.74)0.6720.698 20.724 2b
isi5PVISinterpreting0.52 (0.66)0.7230.743 20.773 2b
isi11PVISinterpreting0.54 (0.65)0.6180.607 20.611 2b
isi12PVISinterpreting0.36 (0.75)0.4780.494 20.484 2b
isi13PVISinterpreting0.75 (0.70)0.4670.459 20.474 2b
Note. PVCM = professional vision of classroom management; PVIS = professional vision of instructional support; 1 = factor ‘PVCM’; 2 = ‘PVIS’; a = ‘describing’; b = ‘interpreting’.
Table 5. Descriptive statistics of the PV-sub-scales PVCM and PVIS.
Table 5. Descriptive statistics of the PV-sub-scales PVCM and PVIS.
Sub-ScaleBachelor’s Student TeachersMaster’s Student TeachersPre-Service TeachersElementary TeachersMath Students
M (SD)M (SD)M (SD)M (SD)M (SD)
PVCM0.86 (0.34)1.16 (0.39)1.17 (0.35)1.08 (0.32)0.75 (0.47)
PVIS0.45 (0.32)0.53 (0.41)0.58 (0.43)0.64 (0.33)0.45 (0.44)
Note. PVCM = professional vision of classroom management; PVIS = professional vision of instructional support.
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Bauersfeld, J.L.; Bourcevet, P.; Hahn, H.; Gold, B. Development and Initial Validation Steps of a Standardized Video Test Assessing Professional Vision of Classroom Management and Instructional Support. Educ. Sci. 2025, 15, 749. https://doi.org/10.3390/educsci15060749

AMA Style

Bauersfeld JL, Bourcevet P, Hahn H, Gold B. Development and Initial Validation Steps of a Standardized Video Test Assessing Professional Vision of Classroom Management and Instructional Support. Education Sciences. 2025; 15(6):749. https://doi.org/10.3390/educsci15060749

Chicago/Turabian Style

Bauersfeld, Jasmin Lilian, Patricia Bourcevet, Heike Hahn, and Bernadette Gold. 2025. "Development and Initial Validation Steps of a Standardized Video Test Assessing Professional Vision of Classroom Management and Instructional Support" Education Sciences 15, no. 6: 749. https://doi.org/10.3390/educsci15060749

APA Style

Bauersfeld, J. L., Bourcevet, P., Hahn, H., & Gold, B. (2025). Development and Initial Validation Steps of a Standardized Video Test Assessing Professional Vision of Classroom Management and Instructional Support. Education Sciences, 15(6), 749. https://doi.org/10.3390/educsci15060749

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