Relation of teacher self-efficacy and classroom practices: A preliminary investigation

Abstract

Teachers’ perceptions of self-efficacy are one of the few individual characteristics that predict teacher practice. There is limited research linking teachers’ perceptions of self-efficacy and actual classroom practices. The study examined teacher’ perceptions of self-efficacy and actual instructional and behavior management practices using the Classroom strategies assessment system (CSAS), a multidimensional validated observation system. Fifty-eight Greek teachers completed the Teacher sense of efficacy scale (TSES) and were observed using the CSAS by independent observers. Significant differences between teachers’ self-reported self-efficacy and observers’ ratings were found in the domain of instructional strategies. Implications for research, professional development and school psychological practice are discussed.

Keywords

classroom management classroom strategies assessment system in-service teachers self-efficacy teacher assessment

Teachers’ self-efficacy––the belief that they can produce changes in student learning––is one of the most studied aspects of the classroom context (Miller, Ramirez, Murdock, 2017). A plethora of studies since the 1970s have investigated the relationship between teachers’ self-efficacy and a host of teaching constructs such as: Student achievement outcomes (Ross, 1992), teacher motivation (Midgley, Feldlaufer, & Eccles, 1989), teacher behavior in the classroom (Ghaith & Yaghi, 1997; Guskey, 1988; Milner, 2002), ideology about the control of pupils (Woolfolf & Hoy, 1990), enthusiasm for teaching (Allinder, 1994; Ashton, 1984), work-related stress (Smylie, 1988), burnout (Browers & Tomic, 2000), quality of teaching (Raudenbush, Bhumirat, & Kamali, 1992), and commitment to teaching (Coladarci, 1992). Recently, research has also found teachers’ self-efficacy related to their passion for teaching, with greater amounts of harmonious passion predicting increased efficacy (Fernet, Lavigne, Vallerand, & Austin, 2014), specifically in the early years of a teachers’ career (Moe, 2016). Teachers’ confidence in their ability to perform the actions that lead to student learning (i.e., self-efficacy) is one of the few individual teacher characteristics that reliably predicts teacher practice and student outcomes (Pendergast, Garvis, & Keogh, 2011; Woolfolk & Hoy, 1990; Zee, de Jong, & Koomen, 2016). In sum, teachers’ self-efficacy is an important motivational construct that shapes teachers’ thoughts, behaviors, and emotions (e.g., Bandura, 1997; Pendergast et al, 2011; Woolfolk, Rosoff & Hoy, 1990).

Teachers’ efficacy beliefs are task- and context-specific (Chao, Chow, Forlin, & Ho, 2017; Dicke, Parker, Marsh, Kunter, Schmech, & Leutner 2014), and they may vary according to different types of tasks, students and circumstances in a class (Raudenbush et al., 1992; Tschannen-Moran, & Hoy, 2001). One domain of teacher task-specific self-efficacy beliefs studied in the research literature is classroom practices. Studies suggest teachers’ with high self-efficacy create positive classroom learning environments with high-quality lesson planning, meaningful instruction, and effective classroom management (Woolfolk, Rosoff, & Hoy, 1990). Teachers who report greater self-efficacy in the school environment report closer relationships with students (e.g., Hamre, Pianta, Downer, & Mashburn, 2008; Mashburn, Hamre, Downer, & Pianta, 2006), and interact in ways that enhance students’ behavioral functioning (Hamre et al., 2008; Poulou, 2017).

Classroom management

Effective classroom management is a prerequisite of effective teaching and students’ learning (Jones & Jones, 2012; Marzano, Marzano, & Pickering, 2003) as both cannot occur in classrooms that are ineffectively managed. Kounin’s (1970) classic study was the first systematic large-scale study that identified characteristics of effective classroom managers (Marzano, Marzano, & Pickering 2003) and numerous studies, analyses, and books published afterwards have reached similar conclusions about classroom management’s important relationship to effective teaching (e.g., Brophy & Evertson, 1976; Anderson and Everston, 1980; Gable, Hester, Rock, & Hughes, 2009; Jones, & Jones, 2012; Korpershoek, Harms,de Boer, van Kuijk, & Doolaaard., 2016; Van de Grift, Van Der Wal, & Torenbeek, 2011). Classroom management encompasses teachers’ efforts to create a supportive environment for students’ learning, interactions, and social-emotional behavior (Good & Brophy, 2000) through the use of various teacher and student focused strategies. Such strategies include, but are not limited to: Establishing rules, behavioral expectations, procedures, and routines; providing positive reinforcement; using reprimands; arranging physical space; and promoting self-regulation (e.g., Hui, Kennedy & Cheung, 2006; Jones & Jones, 2012; Martin & Sass, 2010; Reddy, Fabiano, Barbarasch, & Dudek, 2012; Reddy, Newman, & Verdesco, 2016). Generally, research has identified that use of proactive and preventative strategies are more effective than reactive strategies; however, reactive strategies are also a necessary part of effective classroom management (Korpershoek et al., 2016). Although much has been written about effective classroom management, two landmark meta-analyses have identified classroom management as a main teacher concern (Korpershoek et al., 2016; Martin, Yin, & Mayall, 2008), and research suggests teachers receive minimal training in this area (Begeny & Martens, 2006).

Teachers’ self-efficacy and classroom management

Teachers’ self-efficacy in classroom management is a dimension of teachers’ self-efficacy (Brouwers, & Tomic, 2000; Dicke et al., 2014), and refers to teachers’ beliefs about their capabilities to organize and execute actions that lead to a positive learning environment. Woolfolk and Hoy (1990) found relations between teacher efficacy (i.e., confidence in positively managing the classroom) and student achievement. Generally, teachers with higher self-efficacy are more likely to manage the classroom effectively (Tschannen-Moran, Woolfolk, & Hoy, 2007), show higher instructional quality (Holzberger, Philipp, & Kunter, 2013), use more differentiated instruction and constructivism (Suprayogi, Valcke, & Godwin, 2017), develop challenging lessons (Deemer, 2004), use classroom management and instructional methods to encourage student autonomy, and keep students on task (Chao et al., 2017; Miller et al., 2017). Dicke et al. (2014) specifically investigated the role of teachers’ self-efficacy in classroom management and argued that lower levels of self-efficacy in classroom management predicts emotional exhaustion via classroom disturbances. However, this study was based solely on teachers’ self-report and did not include measurement of actual teacher skills and knowledge. Similarly, others have supported the finding that teachers’ perceptions of self-efficacy are associated with successful classroom management (Mujis & Reynolds, 2002).

Current limitations in self efficacy research

Although Bandura argued that efficacy beliefs are partly formed by actual skills and knowledge, which independently contribute to performance (Bandura, 1977), there are limited studies linking teachers’ self-efficacy beliefs in classroom practices and how teachers actually utilize these practices in their classroom. In fact, most of the studies on teachers’ self-efficacy and classroom practices are based on teachers’ self-reports of both constructs (Klassen, Tze, Betts, & Gordon, 2011). While self-report research is appropriate, there is a need to expand research to examine teachers’ self-reported efficacy with other methods, such as classroom observations of teachers’ classroom practices (Holzberger et al., 2013). This is especially true for how teachers’ self-efficacy of classroom management strategies relate to actual classroom management practices in schools (Gibbs & Powell, 2012).

Compared to teachers’ self-reports of classroom management practices, the use of externally conducted observations can serve as an alternative, more objective assessment of the classroom (Pas, Cash, O’ Brennan, Denham, & Bradshaw, 2015). Classroom observations are almost universally used to assess classroom teachers’ effectiveness worldwide (Cohen & Goldhaber, 2016; Grossman, Cohen, Ronfeldt, & Brown, 2014; Martin & Sass, 2010). Typically, classroom observations describe teachers’ instructional practices, provide formative feedback to teachers, and facilitate teachers’ adoption of or changes in instructional practices (Halpin & Kieffer, 2015). Classroom observations by well-trained observers have the advantage in that they evaluate teachers using similar norms, and thus are more objective assessment tools (Muijs, 2006) compared to self-report methodology.

Study purpose

The current investigation aims to address limitations in the literature by examining teachers’ self-reported efficacy of classroom management in relation to direct observation of classroom practices. This study builds on the teacher self-efficacy literature by examining self-efficacy of classroom management practices in relation to independently observed evidence-based instructional and behavior management practices using an empirically validated classroom observation tool.

School psychologists are uniquely positioned to monitor and support teachers on their instructional and behavior management practices. Likewise, understanding teachers’ beliefs in producing positive change in the school success of their students are critically important for enhancing the ecology of the classroom, teacher-student interactions and school climate (Poulou, 2017). Decades of research have underscored that a problem solving, data driven consultation and coaching process can help teachers identify specific strengths and areas for improvement related to student learning and social development (e.g., Bergan & Kratochwill, 1990; Reddy, Barboza-Whitehead, Files, & Rubel, 2000, Reddy, Dudek, & Lekwa, 2017). Through collaboration consultation or coaching school psychologists can assess teachers’ practice needs and beliefs, establish specific goals and design plans for improving specific practices and beliefs that lead to positive student learning, engagement and social behavior for all students, especially those with disabilities.

Furthermore, the current study adds to the knowledgebase of teachers and teaching practices situated in the country of Greece. Research on classroom management strategies in the Greek context are rather limited. The only published empirically peer reviewed study which used observations on classroom behavior management with a Greek sample of kindergarten teachers was conducted by Beazidou, Botsoglou, and Andreou (2013). This study utilized observation checklists based on common classroom practices conceptualized into two broad categories, positive non-punitive and negative punitive practices (Beazidou, Botsoglou, & Andreou, 2013).

Therefore, the current study addressed the following research questions:

What is the nature and extent of teachers’ self-reported efficacy of instructional, classroom management, and student engagement?

What is the relationship between teachers’ self-reported efficacy of instructional, classroom management, and student engagement and actual classroom instructional and behavior management practices measured through direct observation?

Based on previous research, it was hypothesized that teachers would perceive high levels of self-efficacy in classroom management (Poulou, 2017). However, it was also hypothesized that teacher reported self-efficacy would be moderately related to actual classroom management practices (Bandura, 1972; Miller et al., 2017).

Method

Participants

The sample included 58 in-service teachers, who voluntarily participated in the study. The teacher participants came from 17 public, urban schools in central Greece (11 preschools, 5 primary and 1 secondary school). Of these, 56.9% were women and 43.1% were men. Teachers’ grade level assignments included 18 (31.03%) in preschool (student ages 4- to 5-years-old), 33 (56.89 %) in primary school (student ages 6- to 12-years-old), and 7 (12.06%) in secondary school (student ages 16- to 17-years-old). The majority of teachers were between 31- and 40-years-old (44.82 %) and 41- to 50-years-old (39.93 %), and possessed teaching experience between 11 and 20 years (56.89%). Of the 58 teachers, 37 received two classroom observations (74 single observations), and 21 received one classroom observation (due to changes in school routines that prevented a second observation), for a total of 95 single observations.

A total of three trained independent observers completed the classroom observations. Independent observers were graduate students in psychology and education programs. Two of the observers were preservice teachers (preschool and elementary) and one was an in-service elementary teacher. Independent observers were all women, with ages ranging from 22- to 49-years-old.

Instrumentation

Classroom strategy assessment system (CSAS)

The Classroom strategies assessment system (CSAS), is an evidence-based multi-rater teacher classroom observation assessment designed to assess teachers’ use of instructional and behavioral management strategies (Reddy, Fabiano, Dudek, & Hsu, 2013; Reddy, Dudek, Fabiano, & Peters, 2015). The CSAS measures how often teachers utilize effective instruction and classroom behavioral management practices found related to student learning (e.g., Gable et al., 2009; Moore Partin, Robertson, Maggin, Oliver, & Wehby, 2010; Reddy, Fabiano, Dudek, & Hsu 2013c; Reddy & Dudek, 2014).

Detailed information of the CSAS’s evidence of reliability and validity have been noted in previous publications (Reddy, Fabiano, Dudek, & Hsu, (2013a, 2013b; Reddy, Dudek, Fabiano, & Peters, 2015). In brief, the CSAS evidences good reliability, content, construct and predictive validity (e.g., Reddy, Fabiano, Dudek, & Hsu, 2013a; Reddy, Fabiano, & Jimerson, 2013d). High levels of internal consistency have been documented across the CSAS multiple components (Cronbach’s alphas > 0.90) with fair to good test–retest reliability across a 2–3 week span (r > 0.70). Acceptable estimates of inter-rater reliability have also been found for the CSAS’ multiple components (rs between 0.72 and 0.94). The assessment evidences freedom of item bias for teacher age, educational degree, and years of teaching experience. Additionally, the CSAS has demonstrated convergent and discriminant validity with classroom observational assessments, such as the Classroom Assessment Scoring System (Reddy, Fabiano, & Dudek, 2013) and the Danielson Framework for Teaching (Dudek, Reddy, Kettler, & Kurz, 2017; Dudek, Reddy, Lekwa, & Kettler, 2016). Similarly, the CSAS has demonstrated convergent and discriminant validity with the Cooperative Learning Observation Code For Kids (CLOCK; Volpe & DiPerna, 2010), a direct student observation behavioral ratings system (Lekwa, Reddy & Shernoff, 2018) as well as student ratings of the instructional environment on the Responsive Environmental Assessment for Classroom Teaching (REACT; Nelson, Reddy, Dudek, & Lekwa, 2017). The CSAS has demonstrated predictive validity with students’ academic outcomes on statewide testing (Dudek, Reddy & Lekwa, 2018; Reddy et al., 2013d) and Northwestern Educational Assessments (NWEA), Measures of Academic Progress (Lekwa, Reddy, Dudek, & Hua, in press).

The CSAS Observer Form includes three parts: strategy counts, strategy rating scales, and classroom checklist (see Tables 1 and 2, for definitions and example items). The Strategy counts consists of eight teaching strategies, where observers note (tally) each time a strategy was used by the teacher for either an individual student or a group of students. Scores are summed within observation and between individual students and group of students. The Strategy rating scales consists of the Instructional strategies (IS) and Behavioral management strategies rating scales (BMS), which are completed after each classroom observation. The Instructional strategies scale includes 28 items, comprising five scales: Adaptive instruction (4 items), Student-directed instruction (5 items), Direct instruction (8 items), Promotes student thinking (5 items), and Academic performance feedback (6 items). The Behavioral management strategies scales includes 26 items, and four scales: Directives (6 items), Proactive methods (8 items), Praise (5 items), and Corrective feedback (7 items). The Strategy rating scales contains two metrics per item that utilize a seven-point Likert scale (1 = Never, 4 = Sometimes, 5 = Almost Always) and provide information about how teachers used instructional and classroom behavior management strategies. The observed frequency ratings asks how often did teachers use the specific observed instructional and behavioral management strategies and the recommended frequency rating asks the degree to which these strategies should have been used in the specific classroom context. A discrepancy score is calculated between by subtracting the observed frequency from the recommended frequency rating and taking the absolute value of the difference (i.e., recommended––observed). Small discrepancy scores indicate appropriate use of classroom strategies, whereas large discrepancy scores suggest areas of improvement or need for change. The CSAS-Observer Form scores provides scores and graphs (visual representation of teachers’ performance), thus providing teachers feedback with the areas of strengths and consideration for professional development, as measures by the CSAS. The Classroom checklist is completed prior to leaving the classroom. It assesses the presence of ten specific classroom structure procedures (e.g., posting of rules, and charts for monitoring behavioral or academic progress). The Classroom checklist was not used for this study.

Table 1.

Descriptions of the CSAS strategy counts.

Instructional strategies (IS)
Concept summaries (CS)	Teacher summarizes or highlights key concepts or facts taught during the lesson. Summarization statements are typically brief and clear. This teaching strategy helps students organize and recall material taught
Academic response opportunities (ARO)	Teacher creates opportunities for students to share their understanding of the lesson content with the teacher or class. These opportunities can be verbal or nonverbal response (e.g., explain answers, repeat key points, brainstorm ideas, and show answers on the board)
Academic praise (AP)	Teacher gives a verbal or nonverbal statement or gesture to provide feedback for appropriate academic performance
Academic corrective feedback (ACF)	Teacher gives verbal or nonverbal statement or gesture to provide feedback for incorrect academic performance
Behavior management strategies (BMS)
Clear directives (CD)	Teacher gives a verbal instruction that specifically directs a behavior to occur immediately. These directives are clear and they provide specific instructions to students to perform a behavior. They are declarative statements (not questions), describe the desired behavior, and include no more than two steps
Vague directives (VD)	Teacher gives a verbal instruction that is unclear when directing a behavior to occur immediately. These directives are vague, may be issued as questions, and often include unnecessary verbalizations of more than two steps
Behavior praise (BP)	Teacher gives a verbal or nonverbal statement or gesture to provide feedback for appropriate behavior
Behavior correct feedback (BCF)	Teacher gives verbal or nonverbal statement or gesture to provide feedback for inappropriate behavior

Table 2.

CSAS Instructional and behavior management strategies rating scale definitions.

Instructional strategies (IS)	The Total Instructional Strategies scale reflects the overall use of instructional methods and academic monitoring/feedback
Adaptive instruction	Strategies teachers use to respond to their students’ learning needs while teaching. These practices reflect teacher flexibility and responsiveness to students’ needs, as well as methods of differentiated instruction
Student-directed instruction	Strategies teachers use to actively engage students in the learning process. These practices encompass constructivist and hands-on instructional techniques, linking lesson content to prior learning, personal experiences, and cooperative learning
Direct instruction	Strategies teachers use to deliver academic content or convey information to students. These practices include direct instruction techniques, modeling, identifying, and summarizing
Promotes students’ thinking	Strategies teachers use to activate students’ thinking about the lesson material. These practices assess teachers’ efforts to get their students to think about their thinking process (i.e., open-ended, what, how, and why
Academic performance feedback	Strategies teachers use to provide specific feedback to their students on their understanding of the material. These practices assess teachers’ efforts to explain what is correct or incorrect with student academic performance
Behavioral management strategies (BMS)	The total behavioral management strategies scale reflects the overall use of proactive methods and behavior feedback
Proactive management	Verbal and nonverbal strategies teachers use to prevent student disengagement, and problem behaviors from occurring in the classroom. These practices assess how teachers create a positive classroom environment
Behavior directives	Strategies teachers use for issuing directions or instructions to students and behavioral expectations in the classroom
Behavior praise	Verbal and nonverbal strategies teachers use to positively reinforce specific appropriate behaviors in the classroom. These practices assess how teachers respond to positive behavior in the classroom
Behavior corrective feedback	Verbal and nonverbal strategies teachers use to correct student inappropriate behavior. These practices assess how teachers respond to negative behavior in the classroom

Observer training

The three independent observers (graduate students at University of Patras) were trained on the CSAS by a master trainer. The CSAS Observer training is designed for observers with various levels of teaching experience. Training consisted of processes gradually increasing exposure to content knowledge and observation skills in reference to CSAS (Reddy & Dudek, 2014). The Greek observers received five didactic training sessions (3 hours each) from a CSAS trainer/master coded through webinars. Observers received training on the research evidence guiding the development of CSAS and the recommended frequency of CSAS strategies. Following, the observers practiced coding classroom videos using the CSAS. Specific feedback and instruction was additionally provided to observers due to the differential classroom context and teacher practices between the US and Greek classrooms. Effort was made to ensure that Greek observers operated with the same knowledge base for judging the frequency and the recommended frequency of strategies.

CSAS administration and scoring

At minimum a single observation can be used to complete the CSAS Observer Form (Reddy et al., 2013d), however, it is advised by the authors to use multiple observations and follow aggregation procedures. As such, for the teacher participants receiving two observations, the Strategy counts eight teacher strategies were averaged across observations (both individual and group scores). For the Strategy rating scales, item level discrepancy scores, which are generated from the observed frequency and recommended frequency ratings, were calculated for the IS and BMS scales for each classroom observation separately. The IS and BMS discrepancy scale scores were then calculated for each observation separately by summing the discrepancy scores of the associated items. We then added the respective scale scores from Observation 1 to the corresponding scales in Observation 2, and then divided by 2 to obtain the average absolute value discrepancy score across both observations.

CSAS and TSES translation process

The CSAS and TSES’s instructions, scales, and items were translated into Greek using a systematic three-phase process. The CSAS and TSES were initially translated to Greek by the lead author and then two bi-lingual English-Greek graduate students from the University of Patras reverse translated the CSAS and TSES Greek versions back into the English version to check for accuracy. A word-count agreement method was used to statistically compute the translation agreement between the two versions [English and Greek] such that: [total number of words in agreement between the two languages / total number of possible words on the CSAS English version = translation agreement]. Next, the Greek versions of the CSAS and TSES were presented to a sample of Greek elementary teacher volunteers (N = 5) to assess word accuracy as well as face/content validity. The same word-count agreement method was used to statistically compute translation agreement. The CSAS and TSES Greek versions yielded adequate agreement (70% or above).

Teachers’ sense of efficacy scale (TSES)

The TSES was used to measure teachers’ efficacy beliefs (Tschannen-Moran & Hoy, 2001). The TSES evidences reliability and construct validity of three-factor model consisting of: reflecting instructional practices (e.g.,To what extent can you use a variety of assessment strategies?), classroom management (e.g., How much can you do to control disruptive behaviour in the classroom?), and student engagement (e.g., How much can you do to get students to believe they can do well in schoolwork?) . For this study, the TSES Long Form (24 items) was translated into Greek by the lead author’s research team. In the current study, TSES nine-point scale was modified to a five-point scale (ratings of 1 ‘Not At All’ to 5 ‘A Great Deal’) based on focus group feedback to enhance the clarity and ease of completing the measure with the participating teachers. This change was based on a previous study that utilized a five-point scale of the TSES and yielded acceptable reliability levels for the TSES scales (alphas ranging from 0.78–0.82; Poulou, 2007).

Procedure

All participating teachers volunteered to be observed by trained observers. Teachers and their school directors were informed about the study by the lead author. Informed consent was obtained from all participating teachers and participants were assured of the confidentiality of the information collected by observers and the self-ratings provided.

CSAS observations ranged between 10–50 minutes with an average number of minutes per observation of 24.57 (SD = 8.49). Of the teachers observed, the most common subjects taught were Greek language (N = 24), mathematics (N = 18), and environmental science (N = 11). Prior to the observation, teachers were asked to complete the Teachers’ sense of efficacy scale (Tschannen-Moran & Hoy, 2001).

Results

Teachers’ self-efficacy scores on the three TSES subscales evidenced similar means and standard deviations, with the class management subscale yielding the highest score (M = 3.85, SD = 0.53; self-efficacy on instruction: M = 3.75, SD = 0.49; and self-efficacy on student engagement: M = 3.83, SD = 0.57). Cronbach’s alphas for instruction, class management, and student engagement were 0.81, 0.86 and 0.87 respectively.

Descriptive statistics and alpha coefficients for the CSAS are presented on Table 3. For the Strategy counts, the most frequent strategy used was Academic response opportunities (M = 28.62, SD = 20.37), whereas the least frequent strategy used was Behavioural corrective feedback (M = 0.33, SD = 0.93). Table 3 also presents the Strategy rating scale discrepancy scores that represent the recommended frequency minus the observed frequency of specific strategy usage. As noted, larger discrepancy scores reflect greater need for change in specific practices based on the lessons observed. In general, mean discrepancy scores for IS (M = 6.07; SD = 2.93) and BMS (M = 8.32; SD = 3.49) were similar and respectively. The largest discrepancy scores were found for the Direct instruction (M = 7.98, SD = 4.71), and Academic performance feedback (M = 7.65, SD = 3.45) dimensions under IS, and Praise (M = 9.59, SD = 4.54), and Behaviour corrective feedback (M = 8.90, SD = 5.44) dimensions under BMS.

Table 3.

CSAS Descriptive statistics for strategy counts and strategy rating scales.

	Scale	Mean	SD	Range	Cronbach’s Alpha
Strategy counts	Strategy counts	66.98	29.00	24–185	–
	Concept summaries (CS)	4.65	3.46	0–14	–
	Academic praise (AP)	7.83	5.86	0.5–26	–
	Academic response opportunities (ARO)	28.62	20.37	1–106	–
	Academic corrective feedback (ACF)	5.94	3.17	0–14	–
	Clear directives (CD)	7.65	6.41	0–31	–
	Behavioral praise (BP)	1.82	1.72	0–7	–
	Vague directives (VD)	10.16	10.04	0–43	–
	Behavioral corrective feedback (BCF)	0.33	0.93	0–5.5	–
Discrepancy scores	Strategy Rating Scales [IS + BMS]	7.20	2.85	0.44–14.2	0.914
	Instructional strategies (IS) total	6.07	0.93	0.25–15.42	0.876
	Adaptive instruction (ADI)	3.61	3.26	0–12	–
	Student directed instruction (SDI)	3.96	3.19	0–12	–
	Direct instruction (DI)	7.98	4.71	0–20	–
	Promotes students thinking (PST)	6.28	3.87	0–20	–
	Academic performance feedback (APF)	7.65	3.45	1–17
	Behavioral management strategies (BMS) total	8.32	3.49	0–15.5	0.865
	Proactive methods (PM)	8.41	4.16	0–22	–
	Directives (D)	6.40	3.79	0–14	–
	Praise (P)	9.59	4.54	0–17	–
	Behavioral corrective feedback (BCF)	8.90	5.44	0–21	–

Table 4 shows the correlations of TSES Scale scores and CSAS Strategy counts. Correlations with magnitudes in the 0.00 s are considered nonexistent, 0.10 s and 0.20 s are considered small, 0.30 s and 0.40 s considered medium, 0.50 s and 0.60 s considered large, 0.70 s and 0.80 s considered very large, and 0.90 s considered nearly perfect (Cohen, 1992). Results revealed TSES Instructional strategies subscale were positively related to Vague directives (r = 0.394). The TSES Classroom management subscale was positively related to CSAS Academic praise (r = 0.322), and Academic corrective feedback (r = 0.291). The TSES Student engagement subscale was positively related to CSAS Strategy count total (r = 0.286), Academic response opportunities (r = 0.281), and Vague directives (r = 0.293). Strategy counts results suggest that participants with higher scores in teaching efficacy were more likely to use instructional strategies such as Academic praise, Academic corrective feedback and Academic response opportunities, but were also more likely to use behavior management strategies such as Vague directives.

Table 4.

Correlations of CSAS strategy counts and TSES means (observations 1 and 2).

	TSES Total	Instructional strategies	Classroom management	Student engagement
Strategy count total	0.273	0.217	0.22	0.286^*
Concept summaries (CS)	0.02	−0.052	0.113	−0.011
Academic praise (AP)	0.249	0.18	0.322^*	0.163
Academic response opportunities (ARO)	0.215	0.111	0.172	0.281^*
Academic corrective feedback (ACF)	0.268	0.233	0.291^*	0.193
Clear directives (CD)	−0.241	−0.224	−0.162	−0.256
Behavioral praise (BP)	−0.14	−0.115	−0.126	−0.132
Vague directives (VD)	0.292^*	0.394^**	0.10	0.293^*
Behavioral corrective feedback (BCF)	−0.235	−0.195	−0.225	−0.206

Note: *p < 0.05; **p < 0.01.

Table 5 presents the correlations of the TSES Scale score and CSAS Strategy rating scale discrepancy scores. Results indicate the TSES Total score was positively related to CSAS BMS Praise and corrective feedback subscale discrepancy scores, reflecting high self-reported efficacy and greater need for improvement on teacher independently observed use of behavioural Praise (r = 0.291), and Corrective feedback (r = 0.319). The TSES Instructional strategies subscale was positively related to discrepancy scores on the CSAS total rating scale (r = 0.291), as well as the IS Adaptive instruction (r = 0.395), Direct instruction (r = 0.308), and Promotes students’ thinking (r = 0.289) subscales. TSES Instructional strategies subscale was also positively related to CSAS BMS Praise (r = 0.339), and Corrective feedback (r = 0.345) subscale discrepancy scores. TSES Classroom management and student engagement subscales were not related to either the CSAS IS and BMS Total scales, suggesting that teachers’ self-efficacy ratings in the domain of instructional strategies are not related to discrepancy ratings in CSAS (i.e., greater need for practice changes).

Table 5.

Correlations of the CSAS strategy rating scales discrepancy scores and TSES means (observations 1 & 2).

	TSES Total	Instructional strategies	Classroom management	Student engagement
CSAS total	0.238	0.291^*	0.223	0.133
IS total	0.191	0.274	0.175	0.074
Adaptive INSTRUCTION	0.237	0.395^**	0.158	0.100
Student-directed instruction	−0.069	−0.064	0.000	−0.115
Direct instruction	0.205	0.308^*	0.136	0.118
Promotes students’ thinking	0.239	0.289^*	0.232	0.128
Academic performance feedback	0.093	0.132	0.119	0.007
BMS Total	0.238	0.250	0.227	0.164
Proactive methods	0.025	−0.006	0.066	0.005
Directives	0.007	−0.012	0.060	−0.028
Praise	0.291^*	0.339^**	0.257	0.190
Corrective feedback	0.319^*	0.345^*	0.248	0.263

Note: *p < .05; **p < 0.01.

Discussion

Understanding the relations between teachers’ beliefs about their ability to produce positive changes in the classroom and the actual classroom practices used to orchestrate the instructional environment can help to support teacher professional growth and students’ learning and social behavior (Poulou, 2017; Reddy et al., in press). The current study aimed to inform our understanding about teachers’ classroom management practices, combining teachers’ self-efficacy reports on instructional, classroom management, and student engagement with a direct observational measure designed to capture quantitative information on the use of evidence-based instructional and classroom management strategies. Overall, teachers in the study perceived high levels of self-efficacy in the domains of instruction, student engagement, and classroom management. This finding was expected, given the self-reported years of experience of the teacher participants in the current study. Research has demonstrated teachers’ self-efficacy beliefs present a curvilinear relationship with years of teaching experience such that self-efficacy increases in early and mid-career, levels out in mid-career, and then declines in later career stages (Dicke et al., 2014). Additionally, Holzerger et al, (2013) argued that teachers’ self-efficacy––even for those with many years of teaching experience––not only changes over the course of a school year, but also increases in response to experiences of success in the classroom. Consistent with the studies above, most teachers in the current study were in their early and mid-career stages, and combining this information with their mastery experiences may explain the teachers’ high self-efficacy scores on the TSES.

In this study CSAS results demonstrated that teachers use academic response opportunities, academic praise, and academic corrective feedback frequently. The use of these instructional strategies implies that teachers create verbal or nonverbal opportunities for students to comprehend the teaching content. This is an interesting finding given Pas and colleagues’ (2015) argument that effective teachers maximize time for academic instruction, demonstrate the importance of clear expectations, and provide adequate opportunities to respond. Moreover, teachers in our study gave high ratings of efficacy in classroom management and student engagement. In conjunction with the instructional strategies frequently used, the current study corroborates similar research in elementary classrooms, which has shown that teachers with high efficacy levels utilize instructional practices associated with mastery-oriented approaches, while teachers with lower levels of efficacy display a performance oriented approach in their teaching (e.g., Gibson & Dembo, 1984; Midgley, Anderman, & Hicks, 1995). In addition, research suggests high levels of teaching efficacy predict the use of instructional practices focused on creativity, understanding, and meaningfulness (Deemer, 2004).

In regards to teachers’ behavior management strategy usage, teachers in our study did not use behavior praise and behavior corrective feedback frequently. In fact, behavior praise and behavior corrective feedback were the BMS which received the highest discrepancy scores by the observers. Taking into consideration that teachers in our study gave high self-efficacy ratings in the classroom management domain, this finding may signify differences between teachers’ classroom management efficacy perceptions and their actual strategy usage, and highlights the gap between teachers’ hypothetical knowledge and the application of this knowledge in the classroom (Almog & Shechtman, 2007). At the same time, the non-anticipated positive correlation between vague directives in BMS and self-efficacy perceptions of instructional strategies is congruent with research indicating that teachers tend to give vague directives before receiving school-based consultation or coaching from school psychologists (Fabiano, Reddy & Dudek, 2018; Reddy et al., 2013c). Teachers in our study had not previously received any consultation for classroom management strategies.

While differences between teachers’ self-efficacy and observed CSAS discrepancy scores were not found for the classroom management and student engagement domains, significant differences were found between the domain of instructional strategies and CSAS scores. However, this finding does not minimize the fact that teachers’ self-efficacy perceptions align with their actual classroom management practices. In self-report studies, teachers have claimed they feel efficient in using classroom management strategies, but they do not seem to use these strategies in real classroom situations For example, in a study based on information provided by exemplary technology teachers, Ertmer, Gopalakrishnan, and Ross (2000) similarly concluded that technology use, as perceived and practiced by teachers, does not align with the descriptions of best practice provided in the literature, even in the exemplary classrooms.

In contrast to teachers’ beliefs, what happens in classrooms depends on a variety of variables such as grade level, teachers’ goals, students’ needs, student-teacher relationships, the school culture, and the learning materials, as well as the constraints under which teachers have to implement their strategies, such as curricular expectations or availability of resources (Fang, 1996). Teachers’ self-efficacy beliefs in particular, have been found to vary depending on the particular students (Zee et al., 2016). Besides the direct link between teacher self-efficacy and their behavior, there is also an indirect relationship through the way they perceive their students’ abilities which influence teachers’ perceptions during instruction (Miller et al., 2017). The relationship between teaching experience and innovation adoption could be another factor to explain the divergence between teachers’ beliefs and practices, as well. It has been argued (Suprayogi, Valcke, & Godwin, 2017) that early career teachers (5 or less years of experience) seem more eager to adopt innovations, whereas late-career teachers (over 20 years of experience) are more likely to resist change and criticize the new instructional practices. Mid-career teachers (6–20 years of experience) have mixed reactions to educational innovations. These latter teachers feel competent and confident but are cautious about innovations that require the development of new competencies (Suprayogi et al., 2017). Finally, the difference between teachers’ self-reported efficacy and their actions regarding classroom management practices may be due to teachers’ social desirability in their responses (Deemer, 2004). Teachers’ responses to survey questions might reflect their preferences, which may not actually be implemented in their daily flow of instruction.

A study by Almog and Shechtman (2007) also found significant differences between teachers’ theoretical knowledge and their actual behavior. The authors attributed this difference to the fact that teachers respond spontaneously to classroom incidents, and do not always react to these incidents with the appropriate theoretical knowledge previously acquired. When such gaps between teachers’ knowledge and their actual behavior arise, we must ask why teachers do not apply their knowledge in real classroom situations. One potential answer is to examine whether the expectations outlined in the research literature and best practice documents are unrealistic, and to what extent can teacher educators expect from their teacher trainees to utilize best practices, McLaughlin (1991) suggests that best practice may be an unrealistic goal because instruction reflects a teacher’s response to various elements in school and classroom setting (e.g. students, competing demands, instructional goals, norms and expectations)

It is the case that teachers have difficulties in linking their knowledge to practice. In fact, there is a disconnect between specific teachers’ practices, which are related to positive student outcomes as revealed by effective instruction literature and teachers’ support to implement these effective practices (Reddy et al., 2013d). Our findings underscore the need to reconsider teacher education programs and evolving professional development approaches. Most teacher evaluation measures focus exclusively on the identification of effective teaching practice, but they provide little information about how to improve effectiveness and offer little insight on teachers’ professional development needs or activities (Van der Lans, van de Grift, & van Veen, 2016). What teachers really need from an evaluation process is specific information about effective instructional and behavioral management practices, support to implement these practices in the classroom, and implementation feedback that adds to their professional development. Suggestions for teacher training and professional development include preparing teachers to reflect on teaching practice, while considering examples of theoretical principles and their application to real-life classroom situations and constraints (Almog, & Shechtman, 2007; Ertmer et al., 2000). Only by helping teachers in their actual classroom environment can we expect for best practice implementation (Ertmer et al., 2000).

Limitations

The current study has certain limitations that preclude firm conclusions being drawn from the findings. First, 58 Greek teachers voluntarily participated in the study, which raises questions about the generalizability of these results. A larger and more diverse sample would build on this line of research. Second, a limited amount of classroom observations were conducted. Research on the stability of teacher-student interactions within one school day indicates that similar interactions would have been observed if multiple observations had been conducted (Curby et al., 2001). Future studies are warranted that include larger teacher samples, teachers who teach different content areas (science, mathematics, reading, foreign language), and multiple observations within and across school days. Finally, the lack of association between personal teaching efficacy and classroom management practices may be, in part, a result of the way these practices were measured in this study. The CSAS was used for the first time in the Greek context, and therefore, more research is needed to validate its implementation in Greek classrooms and other countries.

Implications for school psychologists

Throughout the history of the profession, school psychologists have been collaborating with teachers to improve students’ academic, behavioral, and social outcomes (Benjamin, 2007). Although school psychologists may typically spend the majority of their time in traditional assessment, intervention, and consultative roles, in light of current educational policy trends school psychologists may find themselves in new and emerging roles that require them to work directly with teachers (Fagan & Wise, 2007). For example, providing professional development workshops to teachers on Tier 1 or universal instructional practices, or working directly with a teacher through consultation or instructional coaching to implement evidence-based behavior management practices in response to student behavioral needs. Tier 1 or universal instructional and behavior management practices (e.g., academic or behavior corrective feedback, behavior praise) are commonly used by teachers across grade, content area and settings. Moreover, Tier 1 classroom practices can be reliably observed (quantified) in Kindergarten through 12th grades in general education and special education classrooms in schools (Dudek, Reddy, & Lekwa, 2018; Reddy et al., in press). With this in mind, the current study can inform the practice of school psychologists by enhancing their knowledge of how teacher self-efficacy beliefs and their classroom practices are related, which may in turn may shape the procedures and methods used by school psychologists.

Typical professional development approaches for teachers aim to alter knowledge and belief structures with the idea that changes in belief or knowledge precede and will lead to changes in classroom practice (Guskey, 2002; Mitchell, Hirn, & Lewis, 2017). However, changing belief structures alone is not sufficient to produce changes in classroom practices since teachers appear to change their belief structures in response to improved student outcomes, ultimately suggesting teacher practice changes proceed belief changes (Guskey, 2002; Tschannen-Moran, & Hoy, 2007). In light of the current study, understanding potential differences between teachers’ actual classroom practices and their self-efficacy beliefs about these practices can help school psychologists identify related practices and belief structures that can be targeted for professional development activities and interventions utilizing a more direct service delivery approach such as instructional coaching.

This study serves as the first known investigation to examine the association between teachers’ self-efficacy reports and direct observational assessment of classroom management in Greece. Although teachers’ self-efficacy beliefs on classroom management are important, it is equally important to address teachers’ actual usage of classroom management strategies. Studies that examine the relationship between teacher self-reports of students’ classroom management and classroom observations provide a comprehensive picture of classroom that is useful for generating targeted interventions and supports for classroom teachers (Nelson et al., 2017). Beside the conceptual congruence between the two measures used in our study, our measures offered complimentary and unique perspectives of observers and teachers when evaluating classroom management issues. Observations provided information of the class environment at a given point in time, whereas teachers’ self-efficacy perceptions of these classroom management issues represented views that are shaped after spending a large amount of time in the class environment.

In addition, both methods yield unique information that can assist school psychologists in supporting teachers’ professional development via instructional consultation/coaching (Fabiano, Reddy, Dudek, 2018; Reddy, Dudek, & Lekwa, 2017). Teachers need data-based feedback on their implementation of classroom management strategies. By combining different data collection methods and examining their convergence, school psychologist and teacher mentors could facilitate teachers’ clearer decision making in practice and enhance professional development efforts (Nelson et al., 2016). In 1977, Bandura argued that although persons may know that certain achievements result in desired outcomes, this information becomes virtually useless when they lack the belief that they have the ability to produce such actions. Similarly, we argue that although teachers have self-efficacy beliefs to implement classroom management strategies, this information needs to be complimented by additional support structures, since it is not yet clear how this information translates into practice. The current study suggests the CSAS observation measure is a user-friendly assessment tool of teachers’ instructional and behavioral management strategies that can be used by teacher educators and school psychologists to shape classroom practice. Addressing the gap between teachers’ efficacy beliefs and classroom decisions could help school professionals to develop interventions to minimize this gap, which may in turn promote positive school outcomes, such as students’ behavior adjustment and academic achievement.

Conclusion

Current research literature presents substantial findings on teachers’ self-reports of self-efficacy and their classroom practices. However, there are limited studies linking teachers’ perceptions of self-efficacy and direct observations of classroom practices. The current study adds to the literature by combining both teachers’ self-efficacy and direct observational measures of classroom management. The administration of Teachers’ Sense of Efficacy Scale (TSES) already validated in the Greek context and the Classroom Strategy Assessment System (CSAS) implemented first time with Greek in-service teachers revealed significant differences between teachers’ self-efficacy and observers’ ratings in the domain of instructional strategies. The current study supports the need for teachers’ data-based feedback on their classroom management, in order to advance classroom environment improvements and teacher professional development.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Maria S. Poulou is an Assistant professor of Educational Psychology at University of Patras, in Greece. She has published on classroom management, self-efficacy, students’ emotional and behavioral difficulties, teacher-student relationships, school and family relationships and social and emotional learning, in both Greek and international publications. She is a Fellow of the American Psychological Association (APA), the American Educational Research Association (AERA), the British Psychological Society (BPS), the British Educational Research Association (BERA), and European Network for Social and Emotional Competence (ENSEC). She received the 2018 International School Psychology Research Initiative Grant, by the Society for the Study of School Psychology and International School Psychology Association.

Linda A. Reddy is a Professor of School Psychology at Rutgers University. She has published over 100 publications and six books on classroom assessment, coaching, ADHD, and measurement development. She has been a Principal Investigator (PI) or Co-PI on numerous grants focused on school system improvement, coaching and assessment (over 52 million). She is the lead author of the Classroom Strategies Assessment System. She is a Fellow of the American Psychological Association (APA), Fellow of the American Educational Research Association (AERA) and inducted member of the Society for the Study of School Psychology (SSSP). Dr. Reddy has held numerous national leadership roles. She was the President of APA Division 16 (School Psychology), member of the APA Board of Professional Affairs, and member of the APA Violence Towards Teachers Task Force.

Christopher M. Dudek is an experienced Research Project Manager in large multi-site large-scale research projects focused on the development of teacher practice assessments, teacher evaluation, and teacher professional development. He completed his graduate training at Rutgers University where he received a M.Ed. in Counseling Psychology. He is the co-author of the Classroom Strategies Assessment System (CSAS) and a contributing author on numerous articles related to teacher practices, evaluation, and instructional coaching with the CSAS. Mr. Dudek’s expertise includes teacher practice assessments, teacher observation, data management, and technology integration.

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