Data-Based Decision Making for Struggling Readers in the Secondary Grades

Abstract

The process of implementing intensive reading interventions using data-based decision-making (DBDM) becomes increasingly challenging as students move into the secondary grades and reading tasks correspondingly become more complex. This article provides teachers with guidelines to support effective implementation of DBDM for students with or at risk for reading disabilities in the secondary grades. Specifically, this article presents four steps for secondary teachers to follow within the context of a reading intervention to decide when instructional changes are needed based on progress-monitoring data. Diagnostic assessment is explained to determine students’ strengths and weaknesses in order to target instruction accordingly. A case study is included throughout to demonstrate application of the steps as well as supplemental materials to help teachers implement this practice in their classrooms.

Keywords

reading secondary intensive intervention data-based decision making curriculum-based measurement diagnostic assessment

As students progress into the secondary grades, they are expected to learn increasingly complex subject-area material through reading (Edmonds et al., 2009; Leach, Scarborough, & Rescorla, 2003; Wendt, 2013). Specifically, the Common Core State Standards suggest that students must read and comprehend complex literary and informational texts proficiently and independently (National Governors Association Center for Best Practices, Council of Chief State School Officers, 2010). However, the texts that secondary students are required to access in order to learn new material are more challenging in length, vocabulary level, complexity, and conceptual demands (Leach et al., 2003). This presents a particular problem for students with or at risk for reading disabilities at the secondary level, as they often lack proficiency in word reading and comprehension skills (Fuchs, Fuchs, & Compton, 2010). Perhaps not surprisingly, the literacy gap widens significantly into the secondary years, particularly for students with a learning disability (LD) in reading. In fact, national data suggest that by eighth grade, 66% of students—and an alarming 92% of students with disabilities—are reading below grade-level standards (National Center for Education Statistics, 2015).

Research-based multicomponent interventions can help some struggling readers at the secondary level (Edmonds et al., 2009). However, up to 50% of students with LD do not respond to this level of intervention and require more intensive intervention to remediate skill deficits (Fuchs & Fuchs, 2015). These students have reading difficulties that are persistent, severe, and often considered intractable by the middle and high school years (Vaughn et al., 2012). Data-based decision making (DBDM), or using data to make individual instructional decisions, is one way to intensify intervention that has been suggested to improve reading outcomes (Filderman, Toste, Didion, Peng, & Clemens, 2018). DBDM is an iterative process that consists of (a) implementing intensive intervention, (b) administering ongoing progress monitoring, (c) determining student progress, and (d) continuing with or adjusting intervention based on student needs (National Center on Intensive Intervention [NCII], 2014). Although the data collected during DBDM may be useful in the development of an individualized education program (IEP), DBDM is not reliant upon the IEP, nor is it the same as the process of collecting data to track a student’s progress toward IEP goals. Rather, DBDM provides a tool for teachers to determine whether their students are making progress and, if not, how to adjust instruction accordingly.

Although the process of DBDM has been used successfully for secondary students (Fuchs, Fuchs, Hamlett, & Ferguson, 1992; Vaughn et al., 2012), making instructional decisions based on data presents a particular challenge for teachers of secondary students. Secondary teachers must rely on multiple sources of data (e.g., curriculum-based measurement, benchmark assessments, state testing, diagnostic assessment, classroom assessment) to glean an understanding of student performance, which teachers report to be a particular challenge (Means, Chen, DeBarger, & Padilla, 2011). To support teachers in using DBDM with secondary students, this article describes how to make instructional decisions based on multiple sources of data. Specifically, this article presents guidelines for (a) using the appropriate tools to monitor student progress and make instructional decisions and (b) conducting follow-up diagnostic assessments to support individualization through identification of specific student strengths and needs.

Progress Monitoring to Make Instructional Decisions

The process for making instructional decisions for secondary students follows the same format as for primary students (Filderman & Toste, 2017). Table 1 outlines four key steps that teachers should follow in order to make data-based instructional decisions as well as specific considerations and corresponding questions specifically for teachers of secondary students. Each of the following steps will be described in the sections that follow:

Select an assessment tool

Determine the frequency of data collection

Set a performance goal

Analyze the data to make decisions

Table 1.

Steps for Data-Based Decision Making.

Step	Considerations for Secondary Students	Questions and Recommendations
Select an assessment tool	• Fewer tools available for progress monitoring of secondary students. • Combination of tools needed to gain a holistic perspective due to subject-area focus at the secondary level.	1) Is my student reading below an eighth grade instructional level? If so, consider CBM. If not, consider CAT or mastery measures. 2) Do I need general or specific information? If you need general information, including instructional level or overall strengths and weaknesses, choose a CAT. If you need specific information on a particular area of instructional focus, choose a mastery measure.
Determine frequency of data collection	• Secondary students take longer to demonstrate growth. • Collect data for longer, with less frequency.	3) Will more frequent assessment demotivate the student? If so, collect data less frequently. 4) Does your student demonstrate slow growth based on previous observation? If so, collect data less frequently over a longer period of time. 5) What tool are you using? If CBM or CAT, track over a longer period of time. If mastery measure, tracking for a shorter amount of time may be appropriate.
Set a performance goal	• For some CBM, preexisting goals for students above a sixth grade level are not available. The intraindividual framework can be used to set goals. • For CAT, grade level equivalents can be used to set goals. • For mastery measures, goals can be based on set criterion.	6) Is my student reading below an eighth grade level? If so, setting a goal based on CBM using an intraindividual framework may be appropriate. 7) If using CAT, what grade level is my student currently on, and where would I like them to be by the end of my tracking period? Is this feasible based on my student’s past performance? 8) If using a mastery measure, what criterion would a student need to reach to demonstrate mastery?
Analyze data	• Due to varied progress monitoring tools at this level, multiple sources of data will be used to make decisions.	9) If using CBM or CAT, consider using the points below rule to determine whether students have met their goal. 10) If using mastery measures, consider adding a self-monitoring component to track progress and determine mastery.

Step 1: Select an Assessment Tool

Monitoring student progress at the secondary level presents a particular challenge. One major reason for this is that normed progress monitoring measures are less readily available at this level, as students are expected to be proficient readers by the time they reach the secondary grades (Espin, Wallace, Lembke, Campbell, & Long, 2010). Another reason is that research most often focuses on content-area growth as opposed to general reading growth when considering secondary students (Espin et al., 2010). Although there is less availability of progress monitoring materials to be used with secondary students, there are some tools available at this level that can be used for the purposes of DBDM: (a) curriculum-based measurement, (b) computer-adaptive testing, and (c) mastery measures. For students at the secondary level, using a combination of these tools will provide the most holistic picture of student growth and needs. Once tools are selected, the teacher should administer three baseline assessments, taking the median of the baseline assessments as the student’s starting point (Stecker & Lembke, 2011).

Curriculum-based measurement

Curriculum-based measurement (CBM) is a norm-referenced set of assessments used to determine student progress over time (Stecker & Lembke, 2011). CBM provides information about a student’s overall academic performance and can be used to indicate if a student is on track or is not demonstrating adequate progress (Stecker & Lembke, 2011). Importantly, CBM is used to determine overall progress in reading skills; therefore, it does not necessarily provide information about the specific skills with which a student is struggling. Two reliable and valid CBM that are frequently used for secondary readers are oral reading fluency (ORF) and maze comprehension (Espin et al., 2010).

The ORF CBM consists of standardized reading passages that measure the number of words read correctly per minute (WCPM). The maze CBM consists of a 150- to 400-word silent reading passage with a multiple-choice cloze task. In each passage, every seventh word is replaced with three word choices inside parenthesis. Only one word choice makes sense within the context of the sentence and passage. Students have 2.5 minutes to read the passage silently and select as many correct word choices as possible (Deno, Fuchs, Marston, & Shin, 2001).

In addition to these frequently administered CBM, some CBM are delivered on the computer. A list of CBM assessments for secondary students can be found in Table 2.

Table 2.

Standardized Progress Monitoring Tools for Secondary Students.

Program	Progress Monitoring Type and Skill	Grade	Description
AIMSweb	CBM; oral reading fluency	6–8	Measures words correct per minute with a 1-minute reading passage; provides raw scores, national percentiles, and normed performance levels to monitor progress toward goals.
DIBELS	CBM; oral reading fluency, comprehension	6	Provides raw scores and benchmarks based on words correct per minute on a 1-minute passage (ORF) or words correctly selected on a 3-minute silent reading passage (Daze).
easyCBM	CBM; comprehension	6–8	40-minute comprehension assessment delivered on computer that provides raw scores and percentiles based on reading a narrative story and answering response questions.
HMH Reading Inventory	CAT; comprehension	6–12	20- to 30-minute assessment that provides raw, standard, percentile, and composite scores, as well as Lexile reading level, based on reading passages that include fill-in-the-blank, inferential, and literal questions.
iReady	CAT; reading	6–8	15-minute assessment of multiple choice and technology-enhanced comprehension questions that identifies strengths and challenges as well as an overall performance level.
istation	CAT; word analysis, text fluency, vocabulary, comprehension	6–8	30-minute assessment that provides benchmark, subtest, and ability scale scores to determine risk. Includes error analysis to target instruction.
Star	CAT; reading skills, comprehension	6–12	15-minute assessment that provides grade level equivalent, percentile rank, normal curve equivalent, growth percentile, and scale scores in reading skill areas and comprehension.
Yearly Progress Pro	CBM; word analysis, spelling, vocabulary, passage comprehension, language	6–8	15-minute assessments delivered on computer that provide raw score and mastery status of subskills.

Source: Adapted from the National Center on Intensive Intervention Resources, http://intensiveintervention.org.

Note: CAT = computer-adaptive testing; CBM = curriculum-based measurement

CBM is beneficial because it provides general growth measurement that teachers can continually track over time, as well as a way to compare students to grade level norms (Filderman & Toste, 2017). If a student is reading below an eighth grade level, CBM may be useful to measure fluency and/or comprehension based on normed levels of performance and growth. Alternatively, if a student is reading above this instructional level, computer-adaptive testing may provide a useful tool for monitoring progress.

Computer-adaptive testing

Computer-adaptive testing (CAT) is designed to provide teachers with student strengths and weaknesses by adjusting the skill levels of questions being asked as students provide responses (Shapiro & Gebhardt, 2012). Questions are designed to address key skills that students need to master, with mastery of all skills demonstrating student strength in a specific area of reading. Together, this information can tell overall progress in each domain as well as strengths and weaknesses in specific subskills. A list of CAT assessments for secondary students can be found in Table 2.

Mastery measures

Unlike CBM that provides a reliable and valid measure of a student’s progress overall, mastery measures provide information on a student’s mastery of specific skills that have been taught. As students enter secondary grade levels, mastery measures become more important, as these tools can assess the higher-level skills that students are working on more readily than CBM (Zimmerman & Dibenedetto, 2008). Mastery measures are highly aligned with instruction and provide teachers with more sensitive real-time feedback on progress being made on skills (Zimmerman & Dibenedetto, 2008).

To design or select a mastery measure, consider the skill being targeted. For instance, if a teacher is working on vocabulary, a mastery measure may assess student knowledge of specific vocabulary before and during intervention. This may be available as part of the curriculum or may be something that is designed by the teacher. When designing a mastery measure, make sure to consider the following.

How many items to include. For example, if the student loses attention more frequently, consider including fewer questions on the tool to get more accurate information.

What items to include. For instance, if working on inference skills, make sure to only include questions that assess this particular skill, and do not encompass other skills.

The presentation or format. For example, if the student struggles with oral reading fluency, consider reading out loud to get an accurate measure of the skill being targeted.

Ms. Prather (see Note 1) is a ninth-grade special education teacher. She knew that her newly assigned student, Devon, was struggling with reading comprehension based on benchmark assessment data. She administered three baseline probes of the 1-minute ORF and 2.5-minute maze CBM during the reading intervention block over several days to determine whether Devon’s fluency was impacting his comprehension, or whether his struggles were specifically with comprehension. She noticed that Devon could read fluently, but was comprehending well below grade level based on the data collected. She now knew that Devon’s struggle was primarily with comprehension, so she decided to monitor his progress using the maze CBM. As Ms. Prather had already been working with Devon to increase his vocabulary in order to support his background knowledge when interacting with new text, she also decided to track his mastery of vocabulary. She created lists of 10 Tier 2 words (e.g., high-utility academic vocabulary words used across contexts) and planned to have Devon write the definition and use the word in a sentence to track his progress each week.

Step 2: Determine Frequency of Data Collection

Once a tool is selected, decide on the frequency with which to track growth and make decisions. Research recommends anywhere from 5 to 14 weeks of data collection, administering assessments one to three times per week (Ardoin, Christ, Morena, Cormier, & Klingbeil, 2013; Christ, Zopluoglu, Long, & Monaghen, 2012). At the secondary level, growth is more stable—that is, it will take longer to see changes in performance for students struggling at this level (Scammacca, Roberts, Vaughn, & Stuebing, 2015). For this reason, less frequent assessments can be administered over longer periods of time.

When considering the frequency with which to collect progress monitoring data, consider the following.

Student motivation: Will more frequent assessment deter the student and decrease the accuracy of their scores? If so, consider less frequent assessment.

Expected rates of growth: Does the student demonstrate slow or no growth based on classroom observations? If so, consider giving assessments once a week and collecting data over a longer period of time to allow time to see a change.

Which tool is being used? For CBM, tracking progress over a longer period of time is recommended. For mastery measures, depending on the skill being measured, mastery may be met earlier than tracking with a CBM. For a combination, consider concurrent goals, with a longer term CBM goal and a shorter term mastery measure goal.

Ms. Prather thought about Devon’s progress thus far on benchmark assessments and class assignments. As Devon previously demonstrated slow growth, she decided to track his progress once per week for 12 weeks using the maze CBM. She felt 12 weeks would allow ample time to accurately determine whether he was making progress toward his goal. Ms. Prather decided to administer mastery measures once per week as well so that she could target vocabulary instruction based on Devon’s progress. Ms. Prather pulled Devon once per week from reading intervention to take the 2.5-minute maze and 5-minute mastery measure.

Step 3: Set a Performance Goal

Once frequency of collection and decision-making has been decided, the teacher should determine an individual performance goal for the student—this allows for monitoring growth toward the goal over time. Setting a goal depends on the tool used for progress monitoring: (a) CBM, (b) CAT, and (c) mastery measures.

Curriculum-based measurement

For secondary students, using an intraindividual framework is most appropriate (Fuchs & Fuchs, 2007). To employ this method, collect at least eight data points to identify an average weekly rate of growth. To identify the average rate of growth, subtract the highest score from the lowest score, and divide by the number of scores. Then, multiply the baseline by 1.5. Multiply this product by the number of weeks of intervention. Finally, add this to the baseline median score to get the outcome goal.

Computer-adaptive testing

Many CATs include specific goal-setting tools based on students’ areas of strength and need (intensiveintervention.org). Many CATs also provide additional information, such as grade level equivalents, which can be useful for goal-setting purposes. To use a grade-level equivalent to set goals, simply add expected growth to their present level. For instance, if a student is reading at a 7.2 (seventh grade, second month) grade level, they should be reading at a 7.4 (seventh grade, fourth month) grade level after 10 weeks of progress monitoring. Using the same intraindividual framework as with CBM can also provide teachers with a useful way of setting goals. To employ this method, take the raw score provided by the CAT and use the same calculations previously noted.

Mastery measures

Mastery goals can be set to inform teachers about what specific skills students have mastered. Thus, they can be used in conjunction with CBM or CAT to measure progress toward a more global goal. Mastery goals can be long term, such as mastering a list of 100 sight words on a list, or short term, such as mastering 10 sight words each week. They can also be a combination, such as mastering 100 sight words over a 10-week period and mastering 10 words each week. Mastery is met when students reach a predetermined criterion level; for instance, 85% to 90% accuracy or 9 out of 10 words correct on a list.

Ms. Prather laid the groundwork for setting a goal by administering eight baseline probes of the maze CBM over several weeks. Devon scored 40, 36, 37, 36, 32, 42, 37, and 33 on these baseline probes. During the first 10 minutes of her planning period, Ms. Prather subtracted his highest score from his lowest score (42 − 32 = 10), then divided the sum by the number of baseline probes (10 ÷ 8 = 1.25), representing 1.25 words per minute of growth per week on the maze CBM. Next, she multiplied this by number by 1.5 (1.25 × 1.5 = 1.875), and then by 12 weeks of intervention (1.875 × 12 = 22.5). Finally she added this number to the median baseline score, 36.5 (36.5 + 22.5 = 59). By the end of the 12 weeks of intervention, Devon should be able to correctly select 59 words in the maze passage. Ms. Prather then set a goal for mastery measures. She decided that she wanted Devon to learn 100 vocabulary words by the end of the twelve weeks of intervention. To meet this long-term goal, she set a goal of 85% accuracy on 10 newly taught words each week.

Step 4: Analyze Data

After setting a goal and collecting data for the determined length of time, the performance goal can be compared to the student’s data to determine whether to continue or change instruction. Again, the method of analysis used for this purpose differs based on the type of tool used to collect data.

Curriculum-based measurement and computer-adaptive testing

There are several procedures that can be used when making a decision based on CBM or CAT, including the points below rule and the slope rule (Jenkins & Terjeson, 2011). The slope rule entails comparing a student’s trend line to their goal line, while the points below rule compares the last three data points to the goal line. The points below rule is best to use with students who have stable trends in their data (Filderman & Toste, 2017). For this reason, the points below method lends itself to tracking progress for secondary students who have more stable trends in their data.

To make a decision based on a CBM the teacher should (a) draw a line from the student’s average baseline score to the outcome goal, (b) plot the student’s data collected over the determined period, and (c) look at the three latest data points. If the points are above the outcome goal line, continue with instruction and/or increase the goal. If the points are above and below the outcome goal line, instruction is on target and can be continued. If the points are below the outcome goal line, an instructional change needs to be made (Filderman & Toste, 2017).

Mastery measures

For students at the secondary level, mastery measures are increasingly important to track progress toward goals (Zimmerman & Dibenedetto, 2008). Many methods can be used to track progress toward a mastery goal, including bar graphs, line graphs, or charts. Another method that may be particularly useful at the secondary level is self-monitoring, or having students track their own progress toward a goal (Zimmerman, 2002). This is especially useful for secondary students as they become increasingly responsible for their own outcomes as they progress in their education.

Ms. Prather analyzed the data she collected over 12 weeks during the first 10 minutes of her planning period (see Figure 1). She began by looking at Devon’s performance on maze CBM. Although Devon was demonstrating some growth, his last three data points were below the goal line. Ms. Prather knew she needed to intensify instruction in order for Devon to make adequate growth. She then reviewed Devon’s progress toward mastery of the word list (see Figure 2). Devon was learning 7 to 8 of the 10 words on the list each week, and therefore was on track toward this goal. Ms. Prather thought about the conflicting sources of information. On the one hand, Devon was making progress toward his mastery goal; however, he continued to struggle with reading comprehension. Ms. Prather wondered if there was another skill she should target that she was potentially missing instructionally. She wanted to intensify his intervention by aligning instruction to meet his needs; however, she knew she would need additional information in order to target instruction. She decided to administer a diagnostic assessment in order to collect additional information to help her intensify Devon’s intervention.

Figure 1.

Devon’s curriculum-based measurement progress monitoring data.

Figure 2.

Devon’s mastery measure progress monitoring data.

Diagnostic Assessment to Inform Instruction

After a decision is made that instruction needs to be adjusted, teachers need to decide how to adjust instruction to meet student needs. For this purpose, diagnostic assessments can be used to identify student strengths and needs in order to target instruction toward specific skill areas (Masterson & Apel, 2010). There are various methods of collecting diagnostic assessment data—two commonly used methods that provide ample information about instructional skill deficits are (a) standardized diagnostic tools and/or (b) error analysis.

Standardized Diagnostic Assessment

Standardized diagnostic assessments are beneficial because they include a wide range of skills, allowing teachers to pinpoint areas of weakness to address instructionally (e.g., decoding, fluency, vocabulary, comprehension; NCII, 2014). An additional benefit is that they provide teachers with an instructional level as well as a percentile of student performance compared to grade-level norms. These are valuable pieces of information for designing instruction at a student’s level. Table 3 provides a list of several frequently used diagnostic assessments, subtests, and the corresponding skill for each subtest. If the school does not have a standardized assessment available or the cost is prohibitive, holistic diagnostic information can still be gathered across each of the skill areas included in Table 3 (e.g., classroom work, unit tests). Although these assessments provide a good starting point for instruction, they do not necessarily allow teachers to determine what subskills or specific items students may be struggling with.

Table 3.

Common Standardized Diagnostic Measures and Skills Assessed.

Diagnostic measure	Subtests	Skills Assessed
Woodcock–Johnson IV Tests of Achievement^a	Letter-Word ID Reading Fluency Passage Comprehension Word Attack Reading Vocabulary	Decoding Reading speed, semantic processing speed Reading comprehension, cloze ability Decoding Word structure, reading comprehension
Gates-MacGinitie Reading Test^b	Word Decoding Word Knowledge Vocabulary Comprehension	Decoding Word structure for meaning, comprehension Vocabulary, comprehension Reading comprehension
Developmental Reading Assessment^c	Reading Engagement Oral Reading Comprehension Word Analysis	Reading behaviors Reading speed, semantic processing speed Reading comprehension Phonological awareness, letter and word recognition, phonics, decoding, word structure
Test of Word Reading Efficiency–2^d	Sight Word Efficiency Phonemic Decoding Efficiency	Word recognition Decoding fluency
Test of Silent Reading Efficiency and Comprehension^e	Passage Comprehension	Reading comprehension
Woodcock Reading Mastery Tests^f	Rapid Automatic Naming Word Identification Word Attack Word Comprehension Oral Reading Fluency Passage Comprehension	Phonics Decoding Decoding Vocabulary Reading speed Reading comprehension

Schrank, Mather, and McGrew (2014).

MacGinitie, MacGinitie, Maria, Dreyer, and Hughes (2000).

Beaver (2006).

Torgesen, Wagner, and Rashotte (1999).

Wagner, Torgesen, Rashotte, and Pearson (2010).

Woodcock (1998).

Error Analysis

Error analysis can be used to determine strengths and needs in reading. This includes the specific subskills with which the students may be struggling (NCII, 2014).

Oral reading fluency

The errors students make while reading orally can be analyzed for patterns to help identify skill deficits and guide instruction (NCII, 2014). There are three types of errors students may make: (a) graphophonetic errors, which preserve sounds, (b) syntactic errors, which preserve grammar, and (c) semantic errors, which preserve meaning (National Reading Panel, National Institute of Child Health and Human Development, 2000). See Table 4 for a description and example of each type of error. In addition to categorizing errors by type, teachers can analyze errors to see what specific grapheme-phoneme (letter-sound) correspondences students are struggling with. For instance, students may struggle with certain long vowel teams (e.g., “ee” or “a_e”) but not with short vowel sounds, which is essential knowledge when planning targeted instruction.

Table 4.

Oral Reading Fluency Miscue Error Types.

Type of Error	Description of Error Type	Common Errors	Example of Error Type
Graphophonetic errors	Preserve some important phonetics of the written word	Incorrectly guessing a word based on initial sound(s) Substituting a known word that is spelled similarly for an unknown word Correctly reading the base word but omitting suffixes	Sentence: By collecting weather information over time at many places, scientists have discovered that Earth has many different climate patterns. Student error: Replaced the word “discovered” with the word “developed.” Both words start with the same initial /d/ sound. Sentence: The equator is an imaginary line that circles the middle of Earth like a belt. Student error: Replaced the word “equator” with the word “equal.” The word equator is a new, and unknown, vocabulary word in science. The student replaces this unknown word with a similarly spelled familiar word. Sentence: When the U.S. Constitution was adopted in 1787, it did not address voting rights. Student error: Omitted the “ed” ending in the word “adopted.”
Syntactic errors	Preserve the grammar of the written word	Substitute a word with similar grammar based on syntax knowledge	Sentence: The cheetah is the fastest land animal on Earth and can run up to 70–75 miles per hour. Student error: Replaced the word “run” with the word “walk.”
Semantic errors	Preserve the meaning of the sentence	Substitute a word with similar meaning, but different phonetic patterns	Sentence: After the arduous hike, she settled by the campfire and stared in awe at the beautiful sun setting over the mountains. Student error: Replaced the word “beautiful” with “pretty.”

To engage in miscue analysis the teacher should (a) have the student read an instructional level passage out loud, (b) indicate errors by drawing a slash through the word and writing the word the student said in place of the correct word, (c) identify the type of error (see Table 4) and record the error type (Figure S1), marking each time an error occurs to determine frequency, and (d) indicate any additional information that could be helpful in analyzing patterns in the target student’s errors (i.e., particular sounds with which the student struggles). Once a student’s areas of weakness are known, instruction can be targeted to these needs.

Comprehension

Teachers can analyze errors in classroom assessments, anecdotal records, and mastery measures to determine student strengths and weaknesses. This can be done by conducting an item analysis of the questions being asked.

Consider the type of questions the student consistently gets correct and incorrect. Are the questions literal or inferential?

Consider the skills required to answer the question. Do the questions require students to summarize, provide evidence, or apply knowledge to a new context?

Note the format of the question. Is the question multiple choice, fill in the blank, verbal, or open response? Noting where students are consistently struggling can help the teacher to align instruction to these skills. For instance, if a mastery measure of inferencing skills includes open response and multiple-choice questions, and the student consistently misses the open response questions, consider scaffolding with sentence stems or with a cloze passage.

During her planning period, Ms. Prather looked through the results of Devon’s diagnostic assessments in his case file to determine his strengths and needs. Based on the results of the diagnostic assessments, Ms. Prather confirmed that Devon had deficits in vocabulary and comprehension. Ms. Prather decided to look closer at the mastery measure next. She conducted an item analysis of the questions on the mastery measure. The questions were all literal, but some required summarization and some required application to a new context (e.g., writing the word in a sentence). She noticed that Devon was doing well with writing definitions but struggled with applying words to a new context based on his percentage correct in each category of question. Based on her assessment, Ms. Prather knew that Devon needed additional support applying vocabulary to new contexts. She decided to increase the time spent on this skill and to provide Devon with scaffolding in his written responses. She continued with intervention adapted in these ways and made a plan to check back in after 12 additional weeks of intervention.

Putting It All Together

Using data to inform instructional decisions and align instruction based on student strengths and needs can help address the reading deficits of secondary students; however, in order to be effective, teachers need to use multiple sources of data. To assist teachers with this process, this article discussed how to make instructional decisions using various sources of data and how to use diagnostic assessment to determine student strengths and needs. It should be noted that the process of DBDM can be time intensive, particularly when initiating the process. This can be especially challenging for secondary teachers who have other responsibilities such as providing subject-area supports for students. However, as DBDM is a recommended practice for students who have demonstrated previous minimal response to intervention, it is a worthwhile and necessary endeavor to help students to become successful readers.

Supplemental Material

FIGURE_3 – Supplemental material for Data-Based Decision Making for Struggling Readers in the Secondary Grades

Supplemental material, FIGURE_3 for Data-Based Decision Making for Struggling Readers in the Secondary Grades by Marissa J. Filderman, Christy R. Austin and Jessica R. Toste in Intervention in School and Clinic

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by Grants H325D150056 and H325H140001 from the Office of Special Education Programs, U.S. Department of Education. These contents do not necessarily represent the policy of this agency, and one should not assume endorsement by the federal government.

Notes

Supplemental Material

Supplemental material for this article is available online.

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