“Trends Come and Go”: Early Childhood Rural Special Education Teachers’ Use of Reported Practices During Mathematics Instruction

Abstract

Data suggest students are not meeting grade-level expectations in mathematics. If high-quality instruction is not provided, the inequitable outcomes observed in schools will be maintained. The goal of this study was to identify the frequency in which rural special education teachers (SETs) serving students in prekindergarten through third grade self-report using practices during mathematics instruction. A secondary focus was to investigate whether rural SETs differed in their reported use of practices compared with (a) suburban and urban SETs and (b) rural general education teachers. Data from 901 teachers serving students in prekindergarten through third grade were retained. Of this sample, 150 teachers identified as SETs in rural (n = 55), suburban (n = 50), and urban (n = 45) schools. Results suggest teachers reported using research-based and unsubstantiated practices frequently. Rural SETs reported using research-based and unsubstantiated practices with similar frequency to urban SETs. Suburban SETs reported using explicit instruction more frequently than rural SETs.

Keywords

rural special education teachers instructional practices mathematics

As researchers and practitioners continue to discuss the best mathematics instructional practices (Ansari & Lyons, 2016), the mathematics performance of students continues to decline. However, the observed regressions do not impact all students equally. For example, students from the most economically advantaged backgrounds perform at or above grade-level mathematics proficiency standards, whereas students from less economically advantaged backgrounds, students of color, and students with disabilities do not (see National Assessment of Educational Progress [NAEP], 2019). Less is known about the reasons for the discrepancies.

Rural educators face significant barriers. Generational poverty (Rude & Miller, 2018), the number of teachers working toward state licensure rather than entering the field with comprehensive training (J. E. King, 2018), increased teacher mobility (Sullivan et al., 2017), personnel shortages (Sutcher et al., 2016), and lack of resources (Hott et al., 2019) all disproportionately impact rural students’ educational progress.

Evidence-Based Practice (EBP) Movement

The notion of identifying EBPs was first considered in medicine during the 1990s (Sackett et al., 1996). EBPs were defined as practices with rigorous scientific evidence documenting their effectiveness at improving client (viz., student) outcomes. Over the past two decades, the field of special education has moved to establish quality indicators to delineate the level of rigor employed in scientific experiments and aid in the categorization of practices as EBPs (i.e., highest level), research-based (i.e., some research evidence), or unsubstantiated (i.e., no evidence) for students with disabilities (Odom et al., 2005). Throughout the remainder of the article, we will use the term “research-supported practices” to refer to practices with high levels of empirical support and unsubstantiated practices to refer to practices with limited or no empirical support. Students with disabilities have an increased likelihood of exhibiting poor in-school and post-school outcomes (e.g., Test et al., 2009); thus, the field of special education aims to promote and implement practices with evidence of effectiveness through rigorous research because the likelihood that students with disabilities will make meaningful progress toward achieving socially meaningful outcomes increases (Odom et al., 2005). The anticipated outcomes from systematically evaluating the quality and quantity of empirical support for specific practices include (a) the identification of research-supported practices, (b) the dissemination of practices with evidence of effectiveness, (c) the training of preservice and in-service teachers in the implementation of these practices, and (d) ultimately, the large-scale implementation of these practices (Cook et al., 2009; Cook & Odom, 2013). However, despite the progress in identifying research-supported practices, the implementation of these practices has lagged behind. This has been coined the “research-to-practice gap” (Carnine, 1997)—with others calling it a “chasm” (Donovan & Cross, 2002).

To compound issues, most students (i.e., 63.1%) being served through the Individuals with Disabilities Education Improvement Act (2004) receive a majority of their instruction (i.e., 80% or more) in a general education environment provided by general education teachers (U.S. Department of Education, Office of Special Education and Rehabilitative Services, Office of Special Education Programs, 2019). Research suggests that special education teachers (SETs) report using EBPs and research-based practices for students with a disability at a low rate (Burns & Ysseldyke, 2009), leaving general education teachers less apt to implement them (Gable et al., 2012; Maccini & Gagnon, 2006; Stormont et al., 2011). Observational research has substantiated the self-reported data. Morgan and colleagues (2015) found classrooms with higher percentages of students with mathematics difficulties were more likely to use unsubstantiated practices. There are several reasons why general education teachers may be less likely to implement EBPs and research-based practices. First, general educators complete less coursework during a teacher preparation program focused on research-supported practices for students with disabilities (Holdheide & Reschly, 2008). Second, in the domain of mathematics, general education preparation programs approach instruction through a different paradigm, which may leave them less likely to use research-supported practices for students with disabilities (e.g., Powell, 2015). However, instruction continues in schools each year, which raises the question, “What practices are teachers implementing and how can we increase the use of research-supported practices?”

Several factors have been identified as crucial when addressing the research-to-practice gap: (a) teacher content and pedagogical knowledge, (b) training and feedback on their implementation of research-supported practices, (c) access to resources for implementation, and (d) contextual factors (e.g., funding, collaborations; Fixsen et al., 2005). The quality of the training provided to preservice and in-service teachers is linked to higher levels of teacher knowledge and implementation of research-supported practices (Cook & Odom, 2013). For rural districts, an inequitable access to resources can inhibit large-scale implementation efforts (Archibald et al., 2011).

The field of special education is attempting to address these concerns by rethinking the dissemination process. One of the main outlets researchers use to translate research-based practices are practitioner journals (e.g., TEACHING Exceptional Children, Intervention in School and Clinic, and Beyond Behavior). Unfortunately, practitioner articles are typically blocked by a “paywall” and, to access the articles, teachers need to be members of an organization (e.g., Council for Exceptional Children, National Council for Teachers of Mathematics) or be a current student at an institution of higher education. Funding inequities for rural locales may inhibit teachers from having access to practitioner articles. The Institute of Education Sciences (IES) has identified that current mechanisms for disseminating research findings to practitioners is broken. IES Director Mark Schneider recently stated, “I have been concerned with the ‘last mile’ problem—how to get information into the hands of the people who need it the most. My goal is to make information useful, usable, and used” (Schneider, 2018). The What Works Clearinghouse produces practice guides and webinars as avenues to disseminate research findings and address the research-to-practice gap. Resources available through the What Works Clearinghouse are free (https://ies.ed.gov/ncee/wwc/), which increases the usability for underfunded districts. However, several barriers may impact teachers’ use of these resources. First, the understandability of the resources or the type of information provided may not support teachers’ implementation of the practice. Second, norms for the locale context may be more reinforcing to teacher behavior. For example, a teacher’s implementation of research-supported practices may be counter to current norms for practice in their current context and may decrease the likelihood of sustained implementation. Finally, internet access in some rural locales may be unreliable, which would make accessing the What Works Clearinghouse materials difficult. However, Hott and colleagues (2019) identified that both Pinterest and Teachers Pay Teachers were used frequently by rural teachers, and both of these sources require internet connectivity.

Teacher Use of Practices

Prior survey research investigating teachers’ self-reported strategy use has yielded multiple consequential findings (Burns & Ysseldyke, 2009; Gable et al., 2012; Hott et al., 2019). The results of these studies indicate that a majority of teachers reported using practices with research support; however, not all practices with strong research support were used at a high frequency. Moreover, teachers reported using unsubstantiated practices at similar or higher rates than research-based practices.

Leveraging findings from a previous meta-analysis (Forness, 2001), Burns and Ysseldyke (2009) surveyed a random selection of SETs and school psychologists to identify the frequency with which research-supported practices were used in comparison with unsubstantiated practices. The study produced mixed results, with the practice of direct instruction having a high frequency of use and sufficient research support. At the same time, modality instruction (viz., learning styles) was reported at a similar frequency despite a lack of evidence of effectiveness. Other practices, including applied behavior analysis and social skills training, which shared similar rates of reported use, differed considerably in their effect size (i.e., large and small, respectively). This study evidences and further illuminates the research-to-practice gap, suggesting that SETs not only use some research-supported practices at high rates but also engage in practices that only have perceptions of efficacy while lacking true empirical support.

The purpose of this study was to extend previous research on teachers’ self-reported use of practices during mathematics instruction. We focused on mathematics because data suggest students are failing to make adequate progress, inhibiting their ability to obtain positive in-school and post-school outcomes. Identifying the rate at which teachers self-report using research-based and EBPs, along with unsubstantiated practices, can inform in-service and preservice professional development. We focused specifically on early childhood educators (i.e., pre-K–3) to extend Hott et al.’s (2019) focus on secondary educators. In addition, we evaluated whether early childhood SETs differed in strategy use by locale (i.e., rural, urban, and suburban). Finally, we investigated whether rural SETs differed in strategy use compared with general education teachers.

Research Questions

Research Question 1 (RQ1): What is the frequency of early childhood teachers’ use of practices during mathematics instruction with and without research support?

Research Question 2 (RQ2): What is the frequency of rural special educators’ use of practices during mathematics instruction with and without research support?

Research Question 3 (RQ3): Do rural SETs differ from urban and suburban SETs in their use of practices during mathematics instruction?

Research Question 4 (RQ4): Do rural SETs differ in their use of practices during mathematics instruction from rural general education teachers?

Method

Context

More than half of Oklahoma’s schools are located in rural settings and the students there are among the most diverse in the United States (Showalter et al., 2019). The average academic achievement of students in Oklahoma is low in comparison with other states; however, the achievement gap between students from varying levels of socioeconomic backgrounds is narrower than those observed by rural localities in other states (Showalter et al., 2019). Results from the most recent NAEP indicate that Oklahoma’s fourth-grade mathematics scores fell 3 points and eighth-grade scores remained the same between 2015 and 2017 (NAEP, 2019). Only 13 states performed lower than Oklahoma. Oklahoma’s students are less likely to graduate high school, participate in ACT or SAT exams, or complete at least one Advanced Placement (AP) exam than the majority of their peers attending rural schools in other states. In addition, a recent Rural School and Community Trust report classified Oklahoma’s rural and small districts among the neediest in the nation (Showalter et al., 2019).

There are several factors that contribute to Oklahoma’s poor educational performance. First, Oklahoma spends less per pupil than any other state, with the exception of Idaho, and adjusted teacher salaries are approximately US$13,000 below the national average (Showalter et al., 2019). Second, due to critical teacher shortages, the Oklahoma State Department of Education (OSDE) continues to approve large numbers of emergency certified teachers. From the school year 2011–2012 to 2018–2019, there was roughly a 9,659% increase in the number of emergency certifications approved (i.e., 32 in 2011–2012 to 3,091 in 2019). OSDE State Superintendent of Public Instruction Joy Hofmeister stated,

There are some exceptional individuals who have stepped up to fill the void, the great need to fill the teacher (vacancies) where we have students ready to learn, but each of those individuals deserves to have the training and the support to be able to be effective. (Stecklein, 2019)

We provide this context to highlight that the sample of teachers recruited for the study may not have strong external validity to other states, given the unique contextual factors at play in Oklahoma. The interpretation of data and results should be couched within this and further replication is warranted.

Participants

The OSDE’s listserv was used to identify email addresses associated with teachers in the state. The target population were teachers who provided mathematics instruction to students in prekindergarten through third grade. To reduce the likelihood of contacting teachers outside the target population, email addresses were removed for teachers working in middle and high schools (i.e., level identified by name of school). After deleting extraneous emails, the initial survey contact was sent in January 2019 to 23,612 Oklahoma email addresses; 1,875 email addresses were returned due to failed delivery notifications. This could be explained by teachers leaving their positions, while their inactive email addresses were retained in the listserv until it is again updated once a year.

In this initial wave of participation, a total of 936 teachers completed the survey with another 1,330 starting the survey but not completing it. A reminder email was sent 1 week later using the same listserv as the initial round to 23,152 email addresses. This discrepancy is because we removed 460 email addresses upon request by teachers or district personnel. A total of 1,892 email addresses were returned due to failed delivery notifications. We are unsure why more emails were returned in the second round; the most likely rationale is teachers had left their role and the email was terminated. For this second wave, a total of 508 teachers completed the survey with another 780 starting the survey but not completing it. In the end, 1,444 teachers completed the survey with 901 teachers meeting the specific inclusion criteria for the study.

The majority of respondents reported teaching in a rural locality (n = 370, 41.1%), followed by suburban and urban areas. An operational definition was not provided to participants, so their selection of rural, suburban, or urban was based on their own perceptions. As expected, the majority of respondents were general education teachers; however, 150 (16.6%) SETs completed the survey. The majority of respondents held a degree in education and were traditionally certified, with a smaller proportion holding a degree in a different field and being nontraditionally certified. Teachers were distributed normally across years of experience. See Table 1 for demographic information for the full sample and unique data for rural special educators.

Table 1.

Demographic Information of Teacher Respondents.

	Frequency (percentage)
Variable	All respondents (N = 901)	Special educators (n = 150)
Degree in education	No, n = 32 (3.6%) Yes, n = 869 (96.4%)	No, n = 4 (6%) Yes, n = 141 (94%)
Path to certification	Nontraditional, n = 115 (12.8%) Traditional, n = 786 (87.2%)	Nontraditional, n = 38 (25.3%) Traditional, n = 112 (74.7%)
Current position	General education, n = 751 (83.4%) Special education, n = 150 (16.6%)	—
Years of experience	0–5 years, n = 276 (30.6%) 6–10 years, n = 199 (22.1%) 11–15 years, n = 141 (15.6%) 16–20 years, n = 110 (12.2%) 21–30 years, n = 175 (19.4%)	0–5 years, n = 56 (37.3%) 6–10 years, n = 27 (18%) 11–15 years, n = 20 (13.3%) 16–20 years, n = 18 (12%) 21–30 years, n = 29 (19.3%)
Geography	Urban, n = 256 (28.4%) Suburban, n = 275 (30.5%) Rural, n = 370 (41.1%)	Urban, n = 45 (30%) Suburban, n = 50 (33.3%) Rural, n = 55 (36.7%)

Instrument Development

To identify mathematical practices or activities included in our survey, prior systematic reviews, meta-analyses, research reports, and online resources were consulted (Browder et al., 2008; Frye et al., 2013; Gersten et al., 2009; King et al., 2016; National Mathematics Advisory Panel, 2008; Nelson & McMaster, 2018). We selected practices and activities that were generalizable across populations of students and indicated to be effective. We also aimed to include practices that may be routinely used but lack empirical support. To identify these practices, we consulted related literature (e.g., Ruhaak & Cook, 2018). The following practices were included: (a) modeling, (b) guided practice, (c) independent practice, (d) math vocabulary, (e) student explanations, (f) error correction, (g) small group, (h) worked examples, (i) peer learning, (j) explicit instruction, (k) curriculum-based measures (CBMs), (l) 5 and 10 frames, (m) concrete-representation-abstract framework, (n) learning styles, (o) math centers, (p) math crafts, (q) constructivism, and (r) left brain/right brain.

Explicit instruction has consistently been shown to be an integral component of effective interventions for students with mathematics difficulties (Gersten et al., 2009; Morgan et al., 2015). Due to the multiple components, or pillars (see Hughes et al., 2017), of explicit instruction, we opted to include explicit instruction as an item along with some of the core components: (a) modeling, (b) guided practice, (c) independent practice, (d) error correction, (e) worked examples, and (f) student explanations. Mathematical vocabulary proficiency is required for students to use mathematical language (Schleppegrell, 2007) and to develop an understanding of mathematical concepts (Forsyth & Powell, 2017). The use of CBMs to track progress and make data-informed decisions has been shown to improve the mathematical performance of struggling learners (van Geel et al., 2016). Small-group instruction, rather than strictly whole class, has been shown to improve the early numeracy skills of kindergarten students (Clarke et al., 2017). Peer learning opportunities have been shown to increase the mathematical performance of struggling learners (Kunsch et al., 2007), although the variation in effects is large depending on the structure of the peer learning opportunities. Finally, the concrete-representational-abstract (CRA) instructional framework combines the use of manipulatives, representations, and explicit instruction within a system to promote student learning and has substantial evidence of its effects with struggling mathematics learners (Bouck & Park, 2018). We also included an item related to 5 and 10 frames because these use manipulatives or representations, which align with the CRA framework and are embedded within many commonly purchased curricula (e.g., Eureka Math).

In addition to providing research-supported practices, we also included unsubstantiated practices: use of learning styles to plan instruction (Willingham et al., 2015) and gearing instruction toward left/right brain dominant students (Dekker et al., 2012). We included two activities frequently used but with variability in how these are implemented: math centers (Clements & Sarama, 2018) and math crafts (Kokko et al., 2015). Finally, we included one practice with limited support for struggling students or students identified with a disability: constructivist-oriented pedagogy (Xin et al., 2016).

Thereafter, we disseminated the survey to three experts in the field of mathematics instruction for students at risk of, or identified with, a disability. We provided a brief description of the purpose of the study and asked the expert panel to conduct two evaluations (a) on the clarity of the instrument (i.e., directions, rating scale, and language) and (b) the mathematical practices included in the instrument (i.e., should any be eliminated, should any be added). Reviewer feedback was then incorporated into the revision of the survey instrument. Feedback consisted of adding a practice (i.e., math centers), revising answer choices for a demographic item (i.e., highest degree in the field of education), and revising the language of the Likert-type scale options.

The final survey included seven teacher demographic items and six demographic items related to the students they served. Teachers were asked to rate the frequency in which they used these practices during mathematics instruction on the following Likert-type scale: 0 = do not know, 1 = know, but never, 2 = rarely, 3 = occasionally, and 4 = frequently. The following research-supported practices were included on the instrument (a) CRA framework, (b) CBMs, (c) 5 and 10 frames, (d) vocabulary instruction, (e) worked examples, (f) error correction, (g) modeling, (h) guided practice, (i) student explanations, (j) peer learning, (k) explicit instruction, (l) small-group instruction, and (m) independent practice. We also included the following unsubstantiated practices: (a) math crafts, (b) learning styles, (c) constructivist-oriented pedagogy, (d) math centers, and (e) left/right brain.

The Cronbach’s alpha for the instrument was .73 across the full sample of respondents (n = 851; 50 were excluded due to missing data). We also estimated a reliability coefficient for rural educators (Cronbach’s α = .74 [n = 342; 28 were excluded due to missing data]) and special educators (Cronbach’s α = .74 [n = 150; five were excluded due to missing data]). General guidelines suggest a Cronbach’s alpha above .70 is acceptable (Cortina, 1993).

Data Analyses

The first research question addressed the frequency in which teachers used research-supported practices during mathematics instruction and unsubstantiated practices. Therefore, we computed the mean and standard deviation for each practice along with the percentage of responses per frequency of use on the Likert-type scale. In addition, we reported mean, standard deviation, and frequency on a Likert-type scale for only rural SETs. The second research question addressed whether rural SETs differ in their use of each practice compared with urban SETs and suburban SETs. To address this research question, we ran a one-way between-subjects analysis of variance (ANOVA) to compare use of mathematical practices by rural SETs, urban SETs, and suburban SETs. We used a Bonferroni correction to control our study’s Type I error rate. The third research question addressed whether rural SETs differ in their use of each practice compared with rural general education teachers. To address this research question, we ran a one-way between-subjects ANOVA to compare use of practices during mathematics instruction by rural SETs and rural general education teachers.

Results

What Is the Frequency of Teachers’ Reported Use of Practices With and Without Research Support?

For the full sample, respondents reported using a variety of research-supported practices and unsubstantiated practices. Five of the six most frequently used practices were modeling (94% used frequently), guided practice (93% used frequently), independent practice (82.5% used frequently), student explanations (74% used frequently), and error correction (73% used frequently). These five components are embedded within explicit instruction (Hughes et al., 2017). However, teachers reported using explicit instruction itself less frequently (57% used frequently). Learning styles were the seventh most frequently reported practice (70% used frequently). The two least frequently used practices were the CRA framework (35% used frequently) and constructivist-related pedagogy (26% used frequently). In addition, these two practices were unknown by a large percentage of respondents: CRA framework (18% do not know) and constructivism (18% do not know).

What Is the Frequency of Rural Special Educator’s Reported Use of Practices With and Without Research Support?

Similar findings were found for rural SETs. Five of the six most frequently used practices by rural SETs were modeling (98% used frequently), guided practice (96% used frequently), small group (84% used frequently), error correction (78% used frequently), and independent practice (76% used frequently). Similar to the full sample, rural SETs reported using five core principles of explicit instruction at high rates (i.e., all above 75% used frequently), but only 53% reported using explicit instruction frequently. Rural SETs reported using learning styles at the same frequency as the full sample (i.e., 73% reported frequently). Similar to the full sample, the least used practices were the CRA framework (25.5% used frequently, 13% reported I do not know what this is) and constructivist-related pedagogy (24% used frequently, 22% reported I do not know what this is). Table 2 contains means and standard deviations for each practice along with the frequency and percentage of respondents per Likert-type scale category.

Table 2.

Descriptive Statistics for Teacher Self-Reported Usage of Practices During Mathematics Instruction.

Strategy	M (SD)	Do not know	Never	Rarely	Occasionally	Frequently	Missing
Modeling^a
Full sample	3.94 (0.28)	1 (<1%)	1 (<1%)	3 (<1%)	37 (4%)	847 (94%)	12 (1%)
Rural SPED	4.00 (0.00)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	54 (98%)	1 (2%)
Guided practice^a
Full sample	3.92 (0.34)	1 (<1%)	3 (<1%)	4 (<1%)	50 (5.5%)	834 (93%)	9 (1%)
Rural SPED	3.96 (0.20)	0 (0%)	0 (0%)	0 (0%)	2 (4%)	53 (96%)	0 (0%)
Independent practice^a
Full sample	3.79 (0.51)	2 (<1%)	2 (<1%)	24 (3%)	122 (13.5%)	743 (82.5%)	8 (<1%)
Rural SPED	3.65 (0.70)	0 (0%)	1 (2%)	4 (7%)	8 (14.5%)	42 (76%)	0 (0%)
Math vocabulary^a
Full sample	3.75 (0.525)	2 (<1%)	2 (<1%)	21 (2%)	169 (19%)	700 (78%)	7 (<1%)
Rural SPED	3.64 (0.56)	0 (0%)	0 (0%)	2 (4%)	16 (295)	37 (67%)	0 (0%)
Student explanations^a
Full sample	3.70 (0.575)	0 (0%)	6 (<1%)	35 (4%)	184 (20%)	669 (74%)	7 (<1%)
Rural SPED	3.55 (0.69)	0 (0%)	0 (0%)	6 (11%)	13 (24%)	36 (65.5%)	0 (0%)
Error correction^a
Full sample	3.68 (0.60)	3 (<1%)	5 (<1%)	32 (4%)	190 (21%)	658 (73%)	13 (1%)
Rural SPED	3.78 (0.42)	0 (0%)	0 (0%)	0 (0%)	12 (22%)	43 (78%)	0 (0%)
Learning styles^b
Full sample	3.65 (0.59)	0 (0%)	7 (<1%)	34 (4%)	221 (24.5%)	634 (70%)	5 (<1%)
Rural SPED	3.67 (0.61)	0 (0%)	1 (2%)	1 (2%)	13 (24%)	40 (73%)	0 (0%)
Small group^a
Full sample	3.57 (0.655)	0 (0%)	6 (<1%)	64 (7%)	238 (26%)	588 (65%)	5 (<1%)
Rural SPED	3.76 (0.58)	0 (0%)	0 (05)	4 (7%)	5 (9%)	46 (84%)	0 (0%)
Worked examples^a
Full sample	3.48 (0.85)	11 (1%)	29 (3%)	59 (6.5%)	213 (24%)	582 (65%)	7 (<1%)
Rural SPED	3.49 (0.81)	1 (2%)	0 (0%)	5 (9%)	14 (25.5%)	35 (63.6%)	0 (0%)
Peer learning^a
Full sample	3.37 (0.69)	0 (0%)	10 (1%)	80 (9%)	378 (42%)	428 (47.5%)	5 (<1%)
Rural SPED	3.13 (0.86)	0 (0%)	3 (5.5%)	8 (14.5%)	23 (42%)	21 (38%)	0 (0%)
Explicit instruction^a
Full sample	3.35 (1.00)	45 (5%)	11 (1%)	41 (5%)	283 (31%)	512 (57%)	9 (1%)
Rural SPED	3.29 (1.01)	3 (5.5%)	0 (0%)	4 (7%)	19 (34.5%)	29 (53%)	0 (0%)
Math centers^b
Full sample	3.30 (0.90)	0 (0%)	49 (5%)	124 (14%)	229 (25%)	494 (55%)	5 (<1%)
Rural SPED	2.84 (1.10)	0 (0%)	9 (16%)	11 (20%)	15 (27%)	20 (36%)	0 (0%)
CBMs^a
Full sample	3.21 (0.88)	5 (<1%)	38 (4%)	127 (14%)	316 (35%)	404 (45%)	11 (1%)
Rural SPED	3.13 (0.93)	0 (0%)	4 (7%)	8 (14.5%)	19 (34.5%)	23 (41.8%)	1 (2%)
5 and 10 frames^a
Full sample	3.13 (1.00)	12 (<1%)	61 (7%)	128 (14%)	288 (32%)	406 (45%)	6 (<1%)
Rural SPED	3.00 (0.86)	1 (2%)	1 (2%)	11 (20%)	26 (47%)	16 (29%)	0 (0%)
Math crafts^b
Full sample	2.74 (0.875)	22 (2%)	30 (3%)	263 (29%)	417 (46%)	160 (18%)	9 (1%)
Rural SPED	2.75 (0.97)	1 (25)	5 (9%)	13 (24%)	24 (44%)	12 (22%)	0 (0%)
Left/right brain^b
Full sample	2.67 (1.11)	43 (5%)	91 (10%)	210 (23%)	320 (35.5%)	229 (25%)	8 (<1%)
Rural SPED	2.55 (1.15)	3 (5.5%)	7 (13%)	15 (27%)	17 (31%)	13 (24%)	0 (0%)
CRA framework^a
Full sample	2.63 (1.45)	160 (18%)	30 (3%)	107 (12%)	276 (30%)	319 (35%)	9 (1%)
Rural SPED	2.60 (1.24)	7 (13%)	0 (0%)	15 (27%)	19 (34.5%)	14 (25.5%)	0 (0%)
Constructivism^b
Full sample	2.56 (1.39)	164 (18%)	17 (2%)	100 (11%)	372 (41%)	236 (26%)	12 (1%)
Rural SPED	2.36 (1.43)	12 (22%)	0 (0%)	12 (22%)	18 (33%)	13 (24%)	0 (0%)

Note. Ordered from largest to smallest mean response. Full sample includes data from all 901 retained respondents. Data for rural special education teachers included 55 respondents. SPED = special education; CBMs = curriculum-based measures; CRA = concrete-representational-abstract.

Practices identified as research-supported for students with a disability. ^bUnsubstantiated practices for students with a disability.

Do Rural SETs Differ From Urban and Suburban SETs in Their Reported Use of Practices During Mathematics Instruction?

When comparing rural and urban SETs’ reported use of practices, no statistically significant differences were identified. When comparing rural and suburban SETs’ reported use of practices, a statistically significant difference was identified for one practice, namely, explicit instruction. Suburban SETs reported using explicit instruction more often than rural SETs, F(1, 98) = 4.35, p = .04, η² = .04. This can be interpreted as 4% of the variance in teachers’ self-reported use of explicit instruction can be explained by group membership. Although benchmark systems are imperfect, the obtained effect size falls between a small and medium effect size according to Cohen’s (1988) general benchmark system.

Do Rural SETs Differ in Their Reported Use of Practices During Mathematics Instruction From Rural General Education Teachers?

When comparing rural SETs’ and rural general education teachers’ reported use of mathematical practices, we identified statistically significant differences for four practices. Rural general education teachers rated using peer learning at a higher frequency than rural SETs, F(1, 366) = 8.55, p = .004, η² = .02. This can be interpreted as 2% of the variance in teachers’ self-reported use of peer learning can be explained by group membership. For small-group instruction, rural SETs rated a higher frequency of use than rural general education teachers, F(1, 366) = 10.73, p = .001, η² = .03. This can be interpreted as 3% of the variance in teachers’ self-reported use of peer learning can be explained by group membership. For math centers, rural SETs rated a lower frequency of use than rural general education teachers, F(1, 365) = 10.73, p = .001, η² = .02. This can be interpreted as 2% of the variance in teachers’ self-reported use of peer learning can be explained by group membership. Finally, rural general education teachers reported using independent practice more frequently than rural SETs, F(1, 366) = 4.295, p = .04, η² = .01. This can be interpreted as 1% of the variance in teachers’ self-reported use of peer learning can be explained by group membership. The effect sizes can all be categorized as a small effect size according to Cohen’s (1988) benchmark system.

Discussion

The goal of this study was to identify the frequency at which rural SETs serving students in prekindergarten through third grade reported use of research-supported practices and unsubstantiated practices. A secondary focus was to investigate whether rural SETs differed in their reported use of practices compared with suburban SETs, urban SETs, and rural general education teachers. Teachers self-reported using a variety of research-supported practices at high frequencies, with modeling, guided practice, and independent practice as the top three. Of concern was the high frequency of self-reported use of unsubstantiated practices: 70% of teachers reported frequently using learning styles to guide mathematics instruction, and 60% of teachers reported occasionally or frequently using left/right brain activities to guide mathematics instruction. The learning style mythology is entrenched in many areas of education; however, less is known about how this belief actually impacts teacher behaviors. For example, if a teacher believes in learning styles and plans whole-class instruction that includes auditory input along with visual input, this aligns with the theory of dual coding—which has empirical research support (Clark & Paivio, 1991). More problematic would be teachers differentiating instruction, or providing “accommodations,” by catering activities or materials to a student’s “learning style.”

Except for one practice, rural SETs reported using research-supported practices at the same frequency as urban SETs and suburban SETs. When compared with rural general education teachers, rural SETs self-reported using two research-supported practices at a lower frequency (i.e., peer learning and independent practice). However, rural SETs reported using small-group instruction more frequently and using an unsubstantiated practice (i.e., math centers) less frequently than rural general education teachers.

Our findings parallel the results reported by Hott et al. (2019). Teachers reported using research-supported practices frequently across our study (Hott et al., 2019). An interesting similarity across both studies was the number of teachers unfamiliar with the CRA framework, 33% in Hott et al. (2019) and 18% in our data set. In Hott et al. (2019), 50% of teachers reported using learning styles routinely. We obtained a higher percentage, with 70% of the full sample reportedly using learning styles frequently and 73% of rural SETs reportedly using learning styles frequently.

For the full sample, modeling (i.e., 94%), guided practice (i.e., 93%), independent practice (i.e., 82.5%), and error correction (i.e., 73%) were among the top-reported practices used. These are four of the five core components of explicit instruction (see Hughes et al., 2017), yet teachers reported using explicit instruction less frequently (i.e., 57%). This stark difference between ratings on the components and the term “explicit instruction” were more extreme for rural SETs: (a) modeling–98%, (b) guided practice–96%, (c) independent practice–76%, and (d) error correction–78%, compared with explicit instruction (i.e., 53%). We believe there are three plausible hypotheses: (a) teachers have a different operational definition for explicit instruction; (b) explicit instruction has become a dirty word in certain circles, much like direct instruction; or (c) teachers are unfamiliar with the term. Hughes and colleagues (2017) synthesized the research literature and identified five essential components used to describe explicit instruction included in more than 75% of reviewed articles (i.e., segment complex skills, modeling, fading supports through guided practice, opportunities to respond with feedback, and independent practice) and additional components included between 50% and 74% of articles (i.e., critical content, sequence skills logically, assess prerequisite skills, clear statement of goals and expectations, using examples and nonexamples, brisk pace, and present information in ways to organize knowledge). Depending on the training educators received, they may associate some or all of these components with explicit instruction. This would impact the frequency in which teachers self-report using explicit instruction versus just some of the components. Another possible hypothesis is that educators have a negative connotation of the term “explicit instruction” due to initiatives focused on inquiry-based approaches, specifically in science and mathematics instruction (e.g., Therrien et al., 2017). Finally, educators may not have been taught the term “explicit instruction” and thus are unaware of the components. We believe if this were the case, more educators would have selected “do not know”, which we provided as an option. Future research may consider delving into this issue to better inform pre-service and in-service teacher training.

One of the most promising findings was that rural SETs reported using research-supported practices with the same frequency as urban and suburban SETs. It has been highlighted that an inequitable distribution of resources and continued professional development is provided to rural locales; however, in this sample, rural SETs reported using practices with similar frequency. We did identify differences in the reported use of research-based practices between rural SETs and general education teachers. Rural SETs reported using independent practice less frequently. We find this concerning, particularly when thinking through Haring et al.’s (1978) instructional hierarchy. Students with disabilities are receiving services for specific skill deficits. In Haring and Eaton’s (1978) instructional hierarchy, independent practice is critical to acquire the skill and then ultimately build fluency. There are a host of research-supported mathematics interventions focused on fluency building (i.e., cover-copy-compare, taped problems, and incremental rehearsal) and fluency is critical for further mathematical development (Geary, 2011).

Limitations

When interpreting findings, several limitations should be considered. First, due to the unique contextual factors in the state of Oklahoma, findings from this study may not generalize to other states. Second, teachers only provided information on their self-reported usage of the practice. These data provide no information on teachers’ actual implementation of practices, although prior research does suggest teachers likely overreport their frequency of use (Ross et al., 2003). Third, the selection of practices to be included on the survey was not exhaustive. Finally, teachers were not provided definitions of the practice when rating their frequency of use. Some practices included on the survey encompass multiple components or could include varying types of implementation (e.g., explicit instruction, math centers). Teachers may have a different operational definition, or conceptualization, of the practice and rate their usage accordingly. Future research may consider providing brief video models of the practice, or further description of the implementation of the practice, to ensure that educators rate their usage off of a cohesive definition.

Implications for Practice

Increasing the use of research-based practices and reducing the use of unsubstantiated practices is essential to increase outcomes for students with disabilities. Teacher preparation (Darling-Hammond et al., 2005), access to training and quality professional development (Kennedy, 2016), and funding allocations (Baker & Albert Shanker Institute, 2016) each have the potential to influence rural teachers’ ability to implement quality, research-aligned instructional practices while progressively eliminating unsupported practices from their repertoire of instruction.

A strong foundation in research-supported practices for those who are unsubstantiated must be laid during preservice teacher preparation programs and should include a comprehensive program focused on building strong content knowledge in the science of teaching and learning mathematics. In addition to content knowledge, preservice teachers need support in obtaining the pedagogical skills in the provision of instruction. Furthermore, it is important that preservice teachers are provided with experiences and knowledge on how to address the unique needs of rural students with disabilities.

To accomplish these objectives, teacher preparation programs may consider numerous training opportunities. These opportunities may incorporate case scenarios addressing the needs specific to rural localities including both challenges (e.g., lack of access to specialists, poverty, and geographic isolation) and strengths (strong school–family partnerships). Case scenarios can be adjusted to address precise community challenges or be more global (Butera & Dunn, 2005). The use of virtual learning opportunities, such as Second Life, offer the potential to remove barriers of distance and geographic isolation while providing authentic learning opportunities (Hartley et al., 2015; Williams et al., 2010). Such digital tools can enable preservice teachers to practice skills, with a range of supports provided by their instructors, using avatars. Students are able to observe consequences of their decisions in an authentic classroom setting. Capitalizing on the family partnerships has the potential to improve both Individualized Education Program (IEP) development and implementation as well as investment in educational outcomes (Hott et al., 2019; Williams-Diehm et al., 2014).

After having exposure to quality preservice training, teachers need access to ongoing professional development. School–university partnerships are one viable option to increase both the quality and quantity of professional development and the likelihood of effective translation of research to practice (Rude & Brewer, 2003). Maheady et al. (2016) offered three components of effective school–university partnerships: (a) improving educators’ understanding and use of research-supported practices, (b) capturing practitioners’ professional wisdom, and (c) changing practice through instructional coaching.

Embedding research-supported practices in all coursework and teaching them explicitly creates an understanding and ability to use practices fluently. University instructors taking time to review syllabi and course objectives in collaboration with school districts can increase the likelihood that coursework meets the needs of local districts and schools. Frequently, educators have developed “professional wisdom” based on experience serving students with exceptionalities. These experiences can be captured, and validated, through partnerships with researchers (Cook & Cook, 2013; Maheady et al., 2016). Finally, and perhaps most important, is the need for coaching and support to ensure that practices are implemented with fidelity and meet the needs of the students served. Coaching in rural areas includes a variety of face-to-face and e-coaching options. As technology continues to advance, new doors open and additional support can be provided (Randolph et al., 2020; Rock et al., 2009).

Given that over half of Oklahoma schools are classified by the state as rural, it is concerning that rural schools receive only 31% of an already low state education funding allotment (Showalter et al., 2019). This lack of funding has the potential to disproportionally impact under-resourced, underserved, and under-researched localities. Yet, simply increasing funding will likely not change outcomes. Rather, shifts in both funding priorities and how money is invested will more likely lead to the greatest impact. For example, investing in grow your own programs, which support preservice and in-service teachers who return to rural communities, will provide access to comprehensive training and promote longevity in the profession (Sutton et al., 2014).

Implications for Future Research

This study raises several questions to investigate in the future. First, conducting replication research will allow stakeholders to have more confidence in understanding what mathematics instruction may look like in their context. Second, although observational research is more expensive and time-consuming, it will increase the accuracy in determining which practices teachers are actually using and establish the frequency of their usage. Third, investigating whether self-reported, or actual observed, practice usage differs across grade levels (i.e., pre-K through third grade) is warranted. Finally, research directed toward in-service and preservice teacher training is needed to aid in inoculation against the use of practices that lack scientific credibility. Most critical of all is that training leverages the most effective and efficient ways to support teachers in implementing research-aligned practices.

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.

ORCID iDs

Corey Peltier

Tiffany K. Peltier

Brittany L. Hott

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