Effects of Shared Story Reading in Mathematics for Students With Academic Difficulty and Challenging Behaviors

Abstract

The primary focus of this study was to examine the effects of Shared Story Reading (SSR) during mathematics instruction on the behavioral outcomes of four elementary students with academic difficulty and challenging behaviors. In addition, the study examined the effect of implementing SSR during mathematics instruction on the teacher’s use of opportunities to respond (OTR). A multiple baseline design across participants was implemented to examine the effects of this curricular approach on increasing the teacher’s OTR, increasing student engagement, and reducing disruptive behavior. Results indicated there was an increase in OTR during the SSR lessons and suggest implementing SSR during mathematics instruction was effective for increasing engagement for students with academic difficulty and challenging behaviors. Results were not definitive regarding the effects of SSR decreasing disruptive behavior.

Keywords

mathematics instruction behavior single subject research methodology

The benefits of reading aloud to students to improve reading have been known for years. In 1985, the U.S. Department of Education’s Commission on Reading stated, “the single most important activity for building the knowledge required for eventual success in reading is reading aloud to children” (Anderson, 1985, p. 33). The commission also stated the benefits of reading aloud are greater when students are active participants and engaged in the discussion (Anderson, 1985). To accomplish this, the read aloud must be interactive and foster communication. Shared Story Reading (SSR) is an interactive read aloud that involves students purposefully and strategically interacting with both the content of the book and the teacher before, during, and after the read aloud (Courtade, Lingo, Karp, & Whitney, 2013). SSR promotes active student engagement rather than relegating them as passive listeners. Student engagement is imperative for the learning and success of all students, especially for students with academic difficulty and challenging behaviors, because it is a strong predictor of academic achievement (Dotterer & Lowe, 2011).

SSR has become increasingly popular for supporting children’s mathematical understanding by developing their mathematical processes while also motivating them to learn (Flevares & Schiff, 2014). Students often have difficulty learning mathematical concepts when taught in isolation, and SSR provides a context in which mathematical concepts can be interrelated with opportunities to think critically, solve problems, and apply to real-world situations (Columba, Kim, & Moe, 2009). Furthermore, previous studies have suggested that using SSR to teach mathematics concepts had positive effects on students in the general education setting. Although a relatively small body of research, specific benefits included greater pre–post improvement on tests measuring general mathematics abilities (Jennings, Jennings, Richey, & Dixon-Krauss, 1992; van den Heuvel-Panhuizen, Elia, & Robitzsch, 2016), significantly greater improvement in numeracy scores (Hong, 1996; Young-Loveridge, 2004), and improved performance in geometry tasks (Capraro & Capraro, 2006; Casey, Erkut, Ceder, & Young, 2008; Skoumpourdi & Mpakopoulou, 2011). Using SSR to teach mathematics concepts has also shown positive effects on factors such as increasing student interest in mathematics (Jennings et al., 1992), increasing student positive disposition toward mathematics (Hong, 1996), and increasing student mathematical communication (Capraro & Capraro, 2006).

SSR differs from a straight-through storybook reading because it gives the opportunity for the teacher to incorporate practices identified by research as effective for students struggling in mathematics. These practices include modeling or demonstrating processes or steps to solve problems, verbalizing thought processes through student and teacher “think alouds,” providing multiple opportunities to practice skills and concepts through guided practice, and opportunities for corrective and specific feedback (Gersten et al., 2009). Because SSR gives the opportunity to incorporate effective teaching practices before, during, and after the read aloud, recent research has examined the effects of this practice on adult behaviors as well. Hojnoski, Polignano, and Columba (2016) investigated whether implementing SSR during whole-group mathematics instruction had an effect on teacher mathematical talk (i.e., any verbal behavior between teacher and student referencing mathematical content outside of the printed text). During the intervention, teachers conducted the read aloud by following a reader’s guide that consisted of a brief summary of the book, objectives, key concepts, vocabulary, recommended questions, and prompts to provide feedback. Results of the study indicated that the percentage of mathematical talk increased when the SSR lesson was implemented compared with reading the book alone. These findings are important because increasing mathematical talk encourages communication and discourse in the mathematics classroom. According to the National Council of Teachers of Mathematics’ (NCTM; 2000) Principles and Standards for School Mathematics, communication plays a critical role in the learning of mathematics by nurturing the interaction and exploration of thoughts and ideas. When students are prompted to communicate their thinking through mathematical talk, either to justify their reasoning or to formulate a question, it requires reflection instead of relying on responses based on rote memorization (Columba et al., 2009).

A necessary component of mathematical talk is teachers providing students sufficient opportunities to respond (OTR). An OTR can be defined as the interaction between a teacher’s academic prompt (i.e., verbal, visual, or written) and a student’s response (i.e., verbal, written, gestural, or action). Providing sufficient OTR is an effective instructional practice with emerging empirical support to positively affect both academic and behavioral outcomes for students with and without disabilities (MacSuga-Gage & Simonsen, 2015). When specifically targeting mathematics instruction, research has suggested that increasing the rate of OTR not only improves students’ mathematics performance (Lambert, Cartledge, Heward, & Lo, 2006; Sutherland, Alder, & Gunter, 2003) but also increases student engagement (Haydon, Mancil, & Van Loan, 2009; Sutherland et al., 2003) and decreases disruptive behavior (Haydon et al., 2009; Lambert et al., 2006; Sutherland et al., 2003) during mathematics instruction.

Although specific optimal rates of OTR per minute have not been determined, recommendations have been suggested. The Council for Exceptional Children (1987) suggested that the optimal rate of OTR for students with high-incidence disabilities is four to six responses per minute with 80% accuracy for new material and between eight to 12 responses per minute with 90% accuracy for material being reviewed. Because these guidelines referred to teaching functional communication skills or basic fact concepts in a drill format, this may not be applicable for other types of instruction. A systematic literature review of OTR for students with and without disabilities by MacSuga-Gage and Simonsen (2015) indicated that approximately three to five OTR per minute are associated with positive student outcomes.

Given the potential benefits of implementing SSR during mathematics instruction on student and teacher outcomes, the purpose of this study was to contribute to the growing body of research in two ways. First, although there has been research conducted on the effects of using SSR during mathematics instruction on effective teacher practices such as mathematical talk, how implementing SSR affects the teaching practice of providing students with OTR has not been examined. Second, although previous research has suggested implementing SSR during mathematics instruction has positive effects on the academic and behavioral outcomes of general education students, what has not been demonstrated is whether SSR can be effective in increasing student engagement while decreasing disruptive behavior for students with academic difficulty and challenging behaviors. The current study attempted to answer the following research questions:

Research Question 1: What is the effect of using SSR in mathematics with fidelity on increasing teachers’ use of OTR?

Research Question 2: Will implementing SSR during mathematics instruction increase student academic engagement for students with academic difficulties and challenging behaviors?

Research Question 3: Will implementing SSR during mathematics instruction reduce disruptive behaviors for students with academic difficulties and challenging behaviors?

Method

Participants

Participants were four elementary-aged students (referred to as S1 through S4) attending the same school. The teacher participating in the study recommended students who were participating in a Tier II mathematics intervention, continuing to demonstrate mathematics difficulties, and exhibiting challenging behaviors. The referrals were then confirmed using the following criteria: performing at least one grade level below in mathematics determined by the KeyMath-3 Diagnostic Assessment (KeyMath-3 DA; Connolly, 2007) and receiving three or more office discipline referrals (ODR). The KeyMath-3 DA measures student academic achievement on three general mathematics content areas: basic concepts (conceptual knowledge), operations (computational skills), and applications (problem solving). It has been previously validated on a sample of 3,630 students, ages 4 to 21 (Connolly, 2007). Using the mean split-half reliability method, they reported the internal consistency reliability coefficient was .95 for Grades Kindergarten through 5.

S1 was an 11-year-old, fifth grade, Black female who had been participating in a Tier II mathematics intervention since the beginning of the school year. Her standard score of 81 on the KeyMath-3 DA indicated a grade-level equivalency of 3.6 in mathematics. In addition, both the general education teacher and Tier II intervention teachers reported S1’s inattention, talking out, and defiance in small- and large-group settings also adversely affected her learning in mathematics. She had received three ODRs in the current school year.

S2 was a 9-year-old, third grade, Black male who had been participating in a Tier II mathematics intervention since the beginning of the school year. His standard score of 71 on the KeyMath-3 DA indicated a grade-level equivalency of 1.5 in mathematics. S2 had been referred for special education services earlier in the school year for a specific learning disability in reading and mathematics but did not meet the state’s intelligence quotient (IQ)-Achievement discrepancy criteria. Furthermore, the general education and the Tier II intervention teacher reported that S2’s verbal and/or physical outbursts and refusal to participate or complete work adversely affected his learning. He had received four ODRs in the current school year.

S3 was a 9-year-old, third grade, White male who had been participating in a Tier II mathematics intervention since the middle of the school year. His standard score of 85 on the KeyMath-3 DA indicated a grade-level equivalency of 2.5 in mathematics. Both the general education and Tier II intervention teacher reported that S3’s behavior, which included verbal and/or physical aggression toward peers and adults and defiance, was adversely affecting his learning. He had received 15 ODRs in the current school year and, at the time of the study, was being referred for special education services for an emotional disturbance (ED).

S4 was a 9-year-old, third grade, White male who had been participating in a Tier II mathematics intervention since the beginning of the school year. His standard score of 82 on the KeyMath-3 DA indicated a grade-level equivalency of 2.7 in mathematics. S4 had just been diagnosed with an attention-deficit/hyperactivity disorder by the family’s physician and was being evaluated for special education services. Both the general education teacher and Tier II intervention teachers reported S4’s inattention and impulsivity in small- and large-group settings was adversely affecting his learning. In addition, he had received three ODRs in the current school year.

The teacher was identified through personal contact with the elementary school’s principal and agreed to participate after the first author provided a general overview of the study. She was a White female responsible for the Tier II mathematics instruction of the student participants, had 8 years teaching experience at the elementary level, and had been teaching at the current school for the last 6 years. At the time of the study, she was working toward her master’s degree in special education with emphasis in learning and behavior disorders.

Setting

The setting for the study was a Tier II mathematics intervention classroom in a public elementary school in a large Midwestern city during the last quarter of the school year. The school had an enrollment of 418 students, and 90% of the population were eligible for free and reduced lunch. Students receiving Tier II instruction were identified by the school as performing at least one grade level below their peers in mathematics, based on a universal screener given at the beginning and middle of the school year. Tier II instruction was delivered in same-skill, small instructional groups consisting of three to five students across grade levels and classes and was supplemental to the core mathematics instruction delivered in the general education classroom. In addition, Tier II instruction was delivered daily for 30 min in duration, and content was based on designated skill areas that included number sense and operations, data analysis/place value, geometry, and measurement.

Dependent Variables and Data Collection Procedures

The dependent variables for this study were teacher behavior and student behavior. The teacher behavior being observed was the frequency of providing an OTR to the target student. OTR was defined as the teacher providing an opportunity to respond that is directed to the group or individual target student. This included any instance the teacher asked a question (e.g., “Who can tell me how many sides a triangle has?”) or made a request (“Explain to me how you came up with that answer”) to prompt a student response that could be verbal, gestural, or an action (Scott, Alter, & Hirn, 2011). To be considered an OTR, the question or request had to be related to the lesson content and not for behavioral issues (e.g., “Why are you out of your seat?”) or directions not related to the lesson content (e.g., “Get out paper and pencil, please”).

The observed student behaviors were twofold: duration of engagement and frequency of disruptions. For the student to be considered engaged in the instructional lesson, he or she had to be responding to teacher prompts or instruction, which included choral responding, verbally answering a teacher-directed question, raising a hand, writing, reading (e.g., eyes oriented on page), manipulating objects when prompted by teacher, or looking in the direction of teacher or another student who is called on to speak by the teacher. A disruptive behavior was defined as the student displaying a behavior that interrupts, or potentially interrupts, instruction for the entire class or an individual peer. Examples of disruptive behavior included a student being out of seat without permission, talking to another student without permission, making noises either verbally or through action (e.g., tapping loudly on desk and crumbling or ripping paper), arguing or threatening a student or teacher, or verbally refusing to complete an assignment.

Behavioral data were collected continuously throughout the study using the MOOSES™ (Multi-Option Observation System for Experimental Studies) software program (Tapp, 2004) on a handheld personal digital assistant (PDA) device. The system allows for the collection and analysis of continuous data on both the frequency and duration of teacher and student behaviors through allowing the user to produce code sets that are specific to the individual’s research questions. For this study, the code sets included the dependent measures OTR, disruption, and engagement.

Because the SSR lesson was being implemented during the first 10 min of the 30 min Tier II session, both baseline and intervention data were collected during the first 10 min of all sessions. To ensure accuracy of the observations, the following rules were applied: (a) for duration recording (i.e., engagement), the observer silently counted 5 s before coding the event; (b) for frequency recording (i.e., OTR, disruption), the observer waited until the end of the question/prompt before coding. After the coding sessions, the data from the handheld PDA computer were synced and transferred to a password-protected computer containing the MOOSES™ software program for analysis.

The first author collected primary data, and one of two trained graduate students collected data on 26% of the observations for purposes of interobserver reliability. Prior to data collection, the first author met with the two data collectors, reviewed the definitions of each observational code, and conducted a practice session in an actual classroom setting. A data collector was considered ready for observations once 80% reliability agreement for all variables was achieved in the classroom observation training session, which is considered an acceptable standard (Tapp, 2004). Furthermore, the researcher met with the data collector 10 min before each observation to review the observational codes in an attempt to address observer drift.

Experimental Design and Analysis

A multiple baseline design across participants (Gast & Ledford, 2014) was used to assess teacher and student behavior. When baseline data of the first participant were stable for at least three consecutive sessions (i.e., less than 10% variability of the data points) for the dependent variable student academic engagement, the intervention was introduced to the first participant’s group only, while continuous data were collected on all the other participants’ groups under pre-intervention conditions. When the first participant reached the specified criterion of at least three data points of an increasing level or trend, and the baseline data were stable for the second participant for the dependent variable student academic engagement, the intervention was applied to the second participant’s group while continuous data collection under pre-intervention conditions continued to be collected on the third and fourth participant’s groups. These criteria continued until all participants’ groups were receiving the intervention.

Data were regularly plotted on graphs to aid in the evaluation of level, trend, and variability of the student academic engagement data, which determined each phase of the design. Level stability was determined by using the “80%–20%” criteria of the stability envelope (Gast & Ledford, 2014). If 80% of the data points fell on or within the 20% of the stability envelope (e.g., median value), the data would be considered stable. Trend lines were created by using regression trend lines in Microsoft Excel. In addition, the percentages of nonoverlapping data-point values (PND) were calculated to compare the data of the baseline and intervention conditions. The PND values were calculated by dividing the number of data points that fell outside the range of data-point values of the baseline condition by the number of data points in the intervention condition and multiplying by 100 (Gast & Ledford, 2014).

Intervention

Prior to the start of the study, the first author reviewed the mathematics content and concepts for the upcoming 6-week period using the intervention program being implemented by the teacher and selected children’s books related to the concepts of number sense (specifically, place value), addition, geometry and measurement, and data analysis and applications. Once the books were selected, semi-scripted lesson plans were created that supplemented the existing mathematics program with the appropriate children’s books (see Table 1 for a list of books used in the study). The lesson plans were designed for students to purposefully and strategically interact with both the content of the book and the teacher before, during, and after the read aloud. Lessons associated with each book varied in length, ranging from two to five sessions, and were created to encourage the use of the following research-based practices: modeling or demonstrating processes or steps to solve problems, verbalizing thought processes through student and teacher “think alouds,” providing multiple opportunities to practice skills and concepts through guided practice, and opportunities for corrective and specific feedback (Gersten et al., 2009). In addition, the children’s books and lessons were reviewed by the fourth author, a full professor of mathematics education at a research university, as an attempt to address the mathematical validity of the materials. The semi-scripted lessons were created to ensure consistency across lessons and groups; however, the lessons were studied by the teacher before implementing the lesson instead of being read during instruction to allow for natural interactions between the teacher and students (e.g., additional positive and corrective feedback, modeling, and follow-up OTR) rather than strict reliance on the script.

Table 1.

Books Included in Study.

Group	Content topic	Books	Lesson length (sessions)
S1 Group	Data Analysis/Place Value	Lemonade for Sale (Murphy, S. J., 1998)	3
		Fair Is Fair (Dussling, J., 2003)	3
		Alexander, Who Used to Be Rich Last Sunday (Viorst, J., 2003)	5
		Earth Day—Hooray! (Murphy, S. J., 2004)	2
S2 Group	Addition	“Band-Aids” from Where the Sidewalk Ends (Silverstein, S., 1974)	2
		12 Ways to Get to 11 (Merriam, E., 1993)	3
		How Many Feet in the Bed (Hamm, D. J., 1991)	3
		Cats Add Up (Ochiltree, D., 1998)	2
S3 Group	Geometry and Measurement	Chickens on the Move (Pollack, P., 2002)	3
		Carrie Measures Up (Aber, L. W., 2001)	3
		The Greedy Triangle (Burns, M., 1994)	4
S4 Group	Addition	“Band-Aids” from Where the Sidewalk Ends (Silverstein, S., 1974)	2
		512 Ants on Sullivan Street (Losi, C. A., 1997)	2
		Guinea Pigs Add Up (Cuyler, M., 2010)	2

Intervention training

Prior to implementation, the first author trained the teacher on implementing the SSR lessons. The 90 min training consisted of (a) reviewing the procedural guidelines of the SSR lesson, (b) modeling how to implement an SSR lesson, followed by (c) the teacher practicing the SSR lesson until the performance criterion of at least 100% accuracy was met. See Table 2 for procedural guidelines and examples of an SSR lesson. After the training session, the teacher was given the SSR lesson materials (i.e., lesson plan, children’s book, manipulatives) that would be used in the first intervention session and asked to review. Once implementation of the intervention began, the first author met with the teacher at the end of each day to address any implementation issues as well as to give the SSR lesson materials for the next session. It is important to note that although the teacher was made aware of the dependent variables of the study when requesting participation, there was no direct instruction or training on providing OTR. Furthermore, although semi-scripted lesson plans were given to the teacher, specific criteria were not discussed (e.g., at least 15 OTR per lesson, model at least three strategies per lesson).

Table 2.

Shared Story Reading Guidelines and Examples.

Stage	Procedures	Example from lesson
Before Read Aloud	1. If beginning read aloud of a new book, read the title of book and give students an opportunity to predict what the story will be about. If continuing read aloud of a book from previous day, give review of previous session.	Teacher: “What do you think this story will be about? Will there be any math? Why do you think this? Let’s start reading to see if our predictions are correct.” Teacher: “Yesterday we learned about the properties of a triangle. How many sides does a triangle have? How many angles? Can the figure be open or closed? Why?”
Before Read Aloud	2. If beginning read aloud of a new book, model how to find “how old the book is” or the book’s birthday. This is done by showing the students the year of publication and subtracting this number from the present year using strategies other than the standard algorithm as well as soliciting students’ ideas and strategies (e.g., skip count by tens and ones).	Teacher: “We are now going to find out how old this book is. I know it was published in 1994 and it is now 2015. What would be the best way to do this? To find the age, I could just start with 1994 and count up to 2000. 1995, 1996, 1997, 1998, 1999, 2000 . . . That is 6. And I know that 2000 is 15 away from 2015, so I would add 6 + 15. This book is 21 years old. Is there another way we could do this?”
During Read Aloud	3. Ask students questions related to mathematical concepts in the book during the SSR lesson.	Teacher: “If we started with 10 guinea pigs and then added four more, how many guinea pigs would we have in all?”
	4. Model a strategy for answering a mathematically related question in the book during the SSR lesson.	Teacher models finding the perimeter of the fence using the following steps on the whiteboard: Draw picture of fence, label sides, write a number sentence below the picture (e.g., 9 + 3 + 9 + 3 = 24 feet). Students will write on their paper what the teacher is writing on the whiteboard.
	5. Provide students with manipulatives to work out problems associated with the book during the SSR lesson, if applicable.	Teacher gives each student a bag of manipulatives (i.e., base ten blocks) and place value mats and says: “Using your manipulatives, show me the number 1,483.”
After Read Aloud	6. If ending a read aloud of a book, conclude the SSR lesson by reviewing mathematical concepts discussed in the book. If the read aloud will continue on the next day, review mathematical concepts discussed in the book.	Teacher: “Now that we have finished this story, what can you tell me about what you learned? Why is knowing place value so important?” Teacher: “So now we know the properties of a triangle. A triangle is a closed figure with three sides and three angles. We will find out what the new shape will be next time.”

Note. SSR = Shared Story Reading.

Baseline phase

During the baseline phase, the teacher taught as she typically would, implementing a Tier II mathematics intervention program called SRA Number Worlds: A Prevention/Intervention Math Program (Griffin, 2007). Number Worlds is an intensive intervention program that focuses on elementary students that are one or more grade levels behind in mathematics. The core content topics covered during the study included number and operations, geometry and measurement, and data analysis and applications. Data were collected during the Warm Up and Engage sections of the Number Worlds program. Typical instruction during this time included cumulative review and practice, providing instructional models, hands-on activities, discussion, and strategy building exercises (Griffin, 2007). A total of 29 observations were conducted during the baseline phase.

Intervention phase

The teacher implemented the SSR lessons during an instructional 6-week time period, and a total of 38 observations across participants were conducted during the intervention phase. The SSR lessons occurred daily during the first 10 min of the 30 min Tier II session. During the SSR lesson, the teacher was expected to show fidelity to the lesson design by following the guidelines provided in Table 2. Following the SSR lesson, the teacher implemented the existing mathematics program for the remainder of the period.

Assessment of Interobserver Agreement and Procedural Fidelity

Interobserver agreement (IOA) was conducted for 27% of baseline condition observations, 26% intervention condition observations, and 26% overall observations. This meets the recommended criteria of a minimum of 20% up to 33% IOA observation sessions (Gast & Ledford, 2014). The MOOSES™ software program provided an estimate of agreement using two methods: second-by-second comparisons for duration recording (agreements divided by total seconds) and time window analysis for frequency recording (agreements divided by the sum of agreements plus disagreements). For frequency recording, an agreement was defined as two independent observers scoring the same code within a 5 s window. The percentage of agreement across all variables using frequency recording was 94% during baseline condition, 95% during intervention condition, and 95% overall. The percentage of agreement across all variables using duration recording was 96% during baseline condition, 98% during intervention condition, and 97% overall.

Procedural fidelity data were collected for 39% of the intervention sessions using a teacher fidelity checklist that was aligned with the six steps of the SSR Guidelines (see Table 2), which exceed the recommended criteria of a minimum of 20% up to 33% of sessions (Gast & Ledford, 2014). During the procedural fidelity observation, the observer marked if each step was completed (+), not completed (−), or not applicable (N/A). Results of the procedural fidelity data showed the teacher completed 71 of 76 possible observed teacher behaviors, which is an average of 93%. Furthermore, in an effort to provide evidence that procedural fidelity data were accurate, IOA was conducted for 26% of the sessions. The percentage of IOA on the fidelity checklist was 91%.

Social Validity

Both the teacher who implemented the intervention and the student participants completed a researcher-created questionnaire following the completion of the study to give feedback regarding their experience with the SSR lessons. The teacher questionnaire consisted of eight multiple-choice questions that included an option for additional comments (e.g., Will you use children’s literature to teach mathematics concepts in the future? Yes, No, Not sure) and three open-response questions (e.g., What did you like best about using children’s literature to teach mathematics?). The student questionnaire consisted of three multiple-choice questions (e.g., Would you like your teacher to continue reading books during mathematics? Yes, No, Don’t care) and three open-response questions (e.g., What did you like best about reading books during mathematics?).

Results

Teacher Behavior

The mean frequency and rate of teacher providing students OTR is presented for each participant in Table 3, and the rate of OTR for each participant during the baseline and intervention sessions can be seen in Figure 1. There was an immediate increase in the rate of OTR per minute from the last data point of the baseline condition to the first data point of the intervention condition for all students, and the teacher demonstrated higher rates of OTR during the intervention condition (M = 1.62; SD = .18; range = 1.00–2.50) for all students when compared with the baseline condition (M = 0.58; SD = .03; range = 0.10–1.00). Furthermore, data for three students (S2, S3, and S4) had no overlapping data points between conditions and data, for one student (S1) had 15% overlapping data points between conditions. The variability of the data was moderate during the intervention condition, with a range of 46% to 60% falling on or within the stability envelope.

Table 3.

Mean Percentage (and Range) of Student Academic Engagement, Frequency of Student Disruptive Behavior, and Frequency and Rate per Minute of Teacher Providing Opportunities for Students to Respond.

	Academic engagement		Disruptions		OTR
	Academic engagement		Disruptions		Frequency		Rate per minute
	BL	INT	BL	INT	BL	INT	BL	INT
Participants	M % (range)	M % (range)	M (range)	M (range)	M (range)	M (range)	M (range)	M (range)
S1	74.92 (70.70–79.50)	96.00 (92.30–100.00)	1.25 (0–3)	0.31 (0–1)	5.50 (3–10)	14.85 (10–24)	0.55 (0.30–1.00)	1.49 (1.0–2.40)
S2	64.24 (37.00–69.80)	91.24 (88.50–94.40)	6.38 (1–11)	2.60 (1–5)	5.75 (2–10)	15.20 (13–20)	0.58 (0.20–1.00)	1.52 (1.30–2.00)
S3	72.63 (63.50–77.50)	94.60 (91.80–96.00)	7.38 (2–16)	2.33 (0–5)	5.88 (2–9)	15.78 (13–18)	0.59 (0.20–0.90)	1.58 (1.30–1.80)
S4	70.54 (61.30–77.20)	92.77 (90.00–94.60)	4.89 (0–13)	2.83 (1–6)	6.11 (1–10)	18.83 (14–25)	0.61 (0.10–1.00)	1.89 (1.40–2.50)

Note. OTR = opportunities to respond; BL = baseline condition; INT = intervention condition.

Figure 1.

Rate of teacher OTR per minute.

Student Behavior

Student academic engagement

The mean percentages of student academic engagement are presented for each participant in Table 3, and the percentage of student academic engagement for each participant during the baseline and intervention sessions can be seen in Figure 2. There was an immediate increase in student academic engagement from the last data point of the baseline condition to the first data point of the intervention for all students. Four out of four students demonstrated a higher percentage of engagement during the intervention condition (M = 93.91; SD = 2.93; range = 88.50–100.00) when compared with the baseline condition (M = 69.98; SD = 8.20; range = 37.00–79.50). Furthermore, there was an accelerating trend for three participants (S1, S2, and S4) during the intervention condition, and the data remained stable during the intervention condition for all four students, with 100% falling on or within the stability envelope. In addition, there were no overlapping data points between conditions (PND = 100%).

Figure 2.

Percentage of student academic engagement.

Disruptions

The mean frequency of student disruptive behavior is presented for each participant in Table 3, and the number of disruptions for each participant during the baseline and intervention sessions can be seen in Figure 3. There was an immediate drop in the number of disruptions from the last data point of the baseline condition to the first data point of the intervention for each participant, and the mean number of disruptions was 5.48 during baseline condition and 1.79 during intervention condition. Although there was a drop in mean number of disruptions, S1 and S3 had a small decelerating trend while S2 and S4 had a small accelerating trend. Furthermore, the nonoverlapping data points between conditions ranged from 0% to 33%, and the variability of the data was moderate to high during the intervention condition, with a range of 0% to 69% falling on or within the stability envelope.

Figure 3.

Number of student disruptions.

Social Validity Results

Both teacher and students gave positive comments regarding the SSR lessons. The teacher indicated that she enjoyed using SSR to teach mathematics, the lessons were easy to implement, and the SSR lessons helped her students learn mathematics concepts better. In response to what she liked best, the teacher replied “the level of engagement and hands-on activities.” Student responses were also positive, with four out of four students indicating they liked participating in the SSR lessons. Student responses included that the SSR lessons “help you more,” helped them “learn more math,” and “made math more easier.” Furthermore, all four students wanted their teacher to continue using the SSR lessons, and two students suggested that they would like to use this curricular approach for the learning of other mathematics content areas.

Discussion

Results of this study indicate that using SSR during mathematics instruction had a positive effect on the teacher’s use of OTR. The teacher gave an average of 10 more OTR during the SSR lessons when compared with the typical instruction. These results are promising because increasing the rate of OTR has been shown to improve student mathematics performance (Lambert et al., 2006; Sutherland et al., 2003), increase student engagement (Haydon et al., 2009; Sutherland et al., 2003), and decrease disruptive behavior (Haydon et al., 2009; Lambert et al., 2006; Sutherland et al., 2003). Although it is encouraging that the teacher increased her rate of OTR to 1.62 (1 every .61 min) during the SSR lessons, it is important to note that this is still under the suggested optimal rate of at least three OTR per minute to positively affect student academic and behavioral outcomes (MacSuga-Gage & Simonsen, 2015).

Results also indicate implementing SSR during mathematics instruction had a positive effect on increasing the academic engagement of students with academic difficulty and challenging behaviors. Experimental control was demonstrated when students exhibited increasing percentages of engagement only after the intervention was introduced. Replicating the positive results across each of the four students in a time-lagged manner strengthened the external validity of the results. The lack of overlap between data collected during baseline and intervention provides additional evidence of the effectiveness of this approach in increasing student engagement. The positive results are promising for students with academic difficulty and challenging behaviors because students who are engaged in the learning process are less likely to exhibit inappropriate behaviors and more likely to achieve academic success (Simonsen, Fairbanks, Briesch, Myers, & Sugai, 2008).

These results also add to the growing body of research in two ways. First, although previous studies suggested that integrating children’s literature in mathematics had a positive impact on factors related to student engagement in mathematics such as student interest (Jennings et al., 1992), student disposition (Hong, 1996), and student mathematical communication (Capraro & Capraro, 2006), there was no direct measurement of student academic engagement. Furthermore, no prior study investigated the effects of SSR on increasing student academic engagement with students with mathematics difficulties and challenging behaviors.

Results were not definitive regarding the effectiveness of increasing students’ OTR through SSR on decreasing disruptive behavior for students with academic difficulty and challenging behaviors. Although student participants in this study showed a decrease in disruptions, the nonoverlapping data points between conditions and variability of the data were high for all four students, which makes it difficult to infer a functional relation. It is important to note that baseline disruptive behavior levels were low, ranging from 1.25 (S1) to 7.38 (S3). In addition, individual baseline data points of disruptive behavior during baseline condition show two students had at least one session of zero disruptions (S1, S4), one participant had at least one session of one disruption (S3), and one participant had at least one session of two disruptions (S2). Based on this information, the high overlapping data are not surprising. Furthermore, although the data during the intervention condition did not meet the criteria of being stable, they showed more stability than data in the baseline condition.

Limitations and Future Research

Limitations of this study have been identified and should be considered when interpreting current findings as well as conducting future research pertaining to integrating children’s literature in mathematics through SSR. The most serious limitation of this study concerns the increase of OTR. Although the teacher was not given direct instruction or training on providing OTR, the semi-scripted lessons included possible questions and prompts that could have artificially increased the frequency of OTR. In addition, because the semi-scripted lessons incorporated OTR, separating the effect of increased OTR and the SSR lessons cannot be determined. Future studies should take additional measures to control for these confounding variables. This could include modifying the design to include a phase in which the teacher was provided the children’s books only, followed by a phase in which the teacher was provided both the children’s book and semi-scripted lesson.

Another limitation to this study is the measurement procedure used for the dependent variable disruptive behavior. A disruptive behavior was defined as the student displaying a behavior that interrupts, or potentially interrupts, instruction for the entire class or an individual peer and measured using frequency recording. Because disruptive behavior includes nonuniform behaviors that can vary in duration and topography (e.g., out of seat, talking to other students, refusing teacher direction), a time-based measurement system may have been a better measurement procedure. Future research should consider using duration or interval recording when measuring disruptive behavior.

Due to the small number of participants inherent in single subject designs, external validity is restricted. To increase the external validity and reliability of the effects of SSR during mathematics instruction on students with academic difficulties and challenging behaviors, future direct and systematic replications will be needed. In addition, research is also needed across both diverse groups of students and settings. Students could include those in middle or high school as well as those already identified with a disability, and settings could include other small group settings such as self-contained classrooms for students with learning disabilities or emotional disorders and Tier III mathematics intervention groups. Results from these studies could not only add to the existing research, but also suggest modifications needed to implement SSR lessons for the specific group or setting being examined.

Last, this study examined the effects implementing SSR during mathematics instruction on only behavioral outcomes of students with academic difficulty and challenging behaviors. Although student engagement has been shown to be a strong predictor of academic achievement (Dotterer & Lowe, 2011), direct measurement of academic outcomes is needed. Future research should examine the effects of SSR during mathematics instruction on the mathematics performance of this population through either standardized assessments or curriculum-based measures.

Implications for Practice

Given the identified benefits of increasing student engagement, the current study has implications for educators and researchers. As the research literature base on the positive effects of increasing student engagement continues to grow, it is critical that researchers empirically validate effective teaching practices that result in increased engagement. The findings of this study suggest the use of SSR during mathematics instruction can be implemented in a Tier II intervention setting and provide additional supports for students with academic difficulties and challenging behaviors. Currently, most research focusing on effective teaching practices for students with challenging behaviors has been focused on reading interventions instead of mathematics intervention (Vaughn & Bos, 2012). Although further research in this area is warranted, the results of this study suggest that using SSR can be a beneficial practice for teachers during mathematics instruction to not only increase student engagement in the classroom, but also increasing teacher delivered OTR.

Although using SSR during mathematics instruction has promise, there are practical implications that need to be addressed. For teachers to effectively incorporate SSR lessons during mathematics instruction, there will be extensive preparation and planning involved. Children’s books will need to be selected and reviewed for content and their relation to mathematical concepts. Furthermore, lesson plans will need to be created that incorporate meaningful and engaging contexts, provide real-world connections, and promote student communication of mathematical practices. Fortunately, there are many books pertaining to teaching mathematics and literature that can be of resource, including Math Through Children’s Literature: Making the NCTM Standards Come Alive (Braddon, Hall, & Taylor, 1993), How to Use Children’s Literature to Teach Mathematics (Welchman-Tischler, 1992), Exploring Mathematics Through Literature: Articles and Lessons for Prekindergarten Through Grade 8 (Thiessen, 2004), Math and Literature: Grades K–1 (Burns & Sheffield, 2004a), and Math and Literature: Grades 2–3 (Burns & Sheffield, 2004b).

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.

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