View in EDS HTML Full Text PDF Full Text

Exploring Time-Use Profiles in Digital Mathematics Assessments for Students with Learning Disabilities

Saved in:
Bibliographic Details
Title: Exploring Time-Use Profiles in Digital Mathematics Assessments for Students with Learning Disabilities
Language: English
Authors: Xin Wei (ORCID 0000-0002-6978-6609)
Source: Journal of Learning Disabilities. 2026 59(3):172-184.
Availability: SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2026
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R324P230002
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Grade 8
Junior High Schools
Middle Schools
Secondary Education
Descriptors: National Competency Tests, Grade 8, Students with Disabilities, Time Factors (Learning), Time Management, Learning Disabilities, Academic Accommodations (Disabilities), Testing Accommodations, Computer Assisted Testing, Mathematics Achievement, Mathematics Tests, Middle School Students
Assessment and Survey Identifiers: National Assessment of Educational Progress
DOI: 10.1177/00222194251347965
ISSN: 0022-2194
1538-4780
Abstract: This study investigates the time-use patterns of students with learning disabilities during digital mathematics assessments and explores the role of extended time accommodations (ETA) in shaping these patterns. Using latent profile analysis, the researcher identified four distinct time-use profiles separately for a group of U.S. 8th-grade students with LD, with and without ETA. "Initial Focusers" spend more time on simpler initial items and less time on later, more difficult items, exhibiting high omission rates and low performance. "Rapid Progressors" complete assessments quickly but exhibit shallow engagement across all items, achieving low performance. "Diligent Time Maximizers" allocate time effortfully across items but often run out of time on the last two items when ETA was not granted, achieving the second-highest scores. "Efficient Prioritizers," excel in strategic time management, score the highest, and report strong persistence and interest in math. The findings reveal that ETA supports students who adopt meticulous strategies, such as Diligent Time Maximizers, but does not universally address the challenges faced by other profiles. This study underscores the need for tailored interventions and accommodations aligned with individual time-use profiles to foster equitable and effective learning and assessment environments.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2026
Accession Number: EJ1502795
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGIFsw4QwCqC3Qx_VjpPOlRAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDI-5P3ySl6jZKkHy6gIBEICBmnqvnvd-Vi1UO3_5tGov_oJ34mVrnAoknQRBcAdKkxDSF48wWP8C8Vo54UFhAkTbfHnnvCDxb9buaPETp7Il3wsqeFOAvk88NDfQuk2aMRQIl3Lkb3UM1yNr6ucQwKodBfjrD_VpFqoS_KNL3SyBJwyyS6J2U1E3I6XD7oUIermwTzfkk01UsOW-R-QucCsFXz5CuEyxIsgj7M8=
Text:
  Availability: 1
  Value: <anid>AN0192937328;led01may.26;2026Apr14.06:50;v2.2.500</anid> <title id="AN0192937328-1">Exploring Time-Use Profiles in Digital Mathematics Assessments for Students With Learning Disabilities </title> <p>This study investigates the time-use patterns of students with learning disabilities during digital mathematics assessments and explores the role of extended time accommodations (ETA) in shaping these patterns. Using latent profile analysis, the researcher identified four distinct time-use profiles separately for a group of U.S. 8th-grade students with LD, with and without ETA. "Initial Focusers" spend more time on simpler initial items and less time on later, more difficult items, exhibiting high omission rates and low performance. "Rapid Progressors" complete assessments quickly but exhibit shallow engagement across all items, achieving low performance. "Diligent Time Maximizers" allocate time effortfully across items but often run out of time on the last two items when ETA was not granted, achieving the second-highest scores. "Efficient Prioritizers," excel in strategic time management, score the highest, and report strong persistence and interest in math. The findings reveal that ETA supports students who adopt meticulous strategies, such as Diligent Time Maximizers, but does not universally address the challenges faced by other profiles. This study underscores the need for tailored interventions and accommodations aligned with individual time-use profiles to foster equitable and effective learning and assessment environments.</p> <p>Keywords: time use patterns; latent profile analysis; NAEP; extended time accommodation; process data; mathematics performance</p> <p>Mathematics proficiency is a critical skill that shapes educational trajectories and future opportunities. However, for students with learning disabilities (LD), the journey through mathematics education is often challenging, as they face unique obstacles that hinder both performance and motivation. The urgency of this issue is underscored by the National Assessment of Educational Progress (NAEP), which reveals a staggering statistic: 74% of eighth-grade students with LD perform below the basic proficiency threshold in mathematics, compared with 21% of their nondisabled peers and 65% of students with other disabilities ([<reflink idref="bib14" id="ref1">14</reflink>]).</p> <p>These challenges are multifaceted, involving cognitive difficulties such as issues with working memory, attention, executive functioning, and comprehension ([<reflink idref="bib3" id="ref2">3</reflink>]; [<reflink idref="bib7" id="ref3">7</reflink>]; [<reflink idref="bib35" id="ref4">35</reflink>]). These cognitive challenges are compounded by heightened levels of testing anxiety and stress, which can further impede academic progress ([<reflink idref="bib12" id="ref5">12</reflink>]; [<reflink idref="bib13" id="ref6">13</reflink>]; [<reflink idref="bib43" id="ref7">43</reflink>]). Extended time accommodations (ETA) have been widely implemented as a potential solution, offering additional time for test completion to mitigate these barriers ([<reflink idref="bib5" id="ref8">5</reflink>]). However, despite their potential benefits, ETAs remain underutilized among students with LD ([<reflink idref="bib2" id="ref9">2</reflink>]; [<reflink idref="bib8" id="ref10">8</reflink>]; [<reflink idref="bib23" id="ref11">23</reflink>]; [<reflink idref="bib36" id="ref12">36</reflink>]; [<reflink idref="bib42" id="ref13">42</reflink>]).</p> <p>Even with ETA, universal approaches may not adequately address the diverse time-management challenges faced by students with LD. Research on how students with LD manage time during assessments or how these patterns differ based on the availability of ETA remains scarce. Addressing this gap is crucial because time management directly influences problem-solving performance. For example, [<reflink idref="bib10" id="ref14">10</reflink>] suggests that the relationship between time allocation and performance is complex and context-dependent. While longer response times (RT) may indicate careful problem-solving, they can also reflect inefficiencies or challenges. For easy items and highly skilled persons, longer RT are often negatively associated with performance, whereas for difficult items and less skilled persons, longer times may show a positive or less negative relationship. Allocating additional time to complex tasks has been shown to improve outcomes for students with LD, who often fall at the lower end of the performance spectrum ([<reflink idref="bib42" id="ref15">42</reflink>]).</p> <p>Analyzing time allocation patterns during assessments provides valuable insights into students' engagement and test-taking behaviors. High-achieving students tend to manage their time effectively, displaying behaviors such as reading all answer choices, eliminating incorrect options, and using problem-solving strategies like drawing diagrams or making educated guesses ([<reflink idref="bib12" id="ref16">12</reflink>]; [<reflink idref="bib18" id="ref17">18</reflink>]; [<reflink idref="bib41" id="ref18">41</reflink>]). In contrast, students with LD are less likely to exhibit effective test-taking behaviors, often struggling with pacing and time allocation, showing higher rates of rapid guessing and omitted items ([<reflink idref="bib15" id="ref19">15</reflink>]; [<reflink idref="bib21" id="ref20">21</reflink>]; [<reflink idref="bib30" id="ref21">30</reflink>]; [<reflink idref="bib34" id="ref22">34</reflink>]). These challenges are compounded by the anxiety associated with time constraints during assessments.</p> <p>Time-related anxiety further exacerbates the performance challenges faced by students with LD. Compared to their general education peers, students with LD often report greater pressure, nervousness, frustration, and feelings of helplessness ([<reflink idref="bib11" id="ref23">11</reflink>]; [<reflink idref="bib24" id="ref24">24</reflink>]). For many, the rigid time limit in standardized testing formats hinders their ability to fully demonstrate their knowledge and skills ([<reflink idref="bib11" id="ref25">11</reflink>]). While general education students may also benefit from extended time, the intensity of stress caused by time constraints is typically more pronounced among students with LD ([<reflink idref="bib11" id="ref26">11</reflink>]). Research demonstrates that providing additional time can reduce anxiety, enhance performance, and promote greater engagement in assessments ([<reflink idref="bib42" id="ref27">42</reflink>]).</p> <p>Despite the critical nature of these issues, there remains a critical gap in understanding how students with LD utilize their time during assessments and how these patterns relate to academic performance, behavior, and motivation. Few, if any, studies have examined time-use patterns at the item level for students with ETA compared with those without ETA. This distinction is crucial, as the allocation of extended time may fundamentally alter how students engage with assessment items, influencing their pacing, strategy use, and overall performance.</p> <p>The 2017 eighth-grade NAEP digital mathematics assessment provides a rich dataset, capturing detailed responses, actions, RT, and self-reported attitudes toward the assessment. By conducting separate analyses for ETA and non-ETA students, this study aims to uncover distinct time-use profiles within each group and explore how these profiles relate to sociodemographic characteristics, attitude, test-taking behaviors, and academic outcomes. This investigation is guided by the following research questions:</p> <p></p> <ulist> <item> <bold> Research Question 1: </bold> Are there distinguishable time-use profiles among students with LD who received ETA and those who did not?</item> <p></p> <item> <bold> Research Question 2: </bold> What sociodemographic, academic, behavioral, and motivational factors are associated with time-use profile membership among students with LD who received ETA and those who did not?</item> </ulist> <p>By examining these subgroup-specific patterns, this study seeks to uncover insights that can inform the development of more inclusive and supportive educational interventions and accommodations, ultimately enhancing the academic experiences and outcomes for students with LD in mathematics.</p> <hd id="AN0192937328-2">Method</hd> <p></p> <hd id="AN0192937328-3">Data and Study Sample</hd> <p>The NAEP is a key tool for evaluating student achievement across the United States. As the largest nationally administered low-stakes assessment, NAEP provides a comprehensive overview of student capabilities in subjects such as reading and mathematics. The assessment utilizes a deeply stratified multistage cluster sampling plan, ensuring a representative sample of schools and students across various states ([<reflink idref="bib27" id="ref28">27</reflink>]). In 2017, the sample included approximately 144,900 eighth graders from around 6,500 schools, all of whom participated in two 30-min blocks of the mathematics test, supplemented by a 15-min survey administered on digital tablets.</p> <p>A significant focus of the NAEP is on students with disabilities (SWDs), who are carefully integrated into the assessment process. Each SWD selected for participation is profiled by school officials through a detailed disability questionnaire, which records the student's specific disability category, their participation in state assessments, and the accommodations they receive, if any ([<reflink idref="bib28" id="ref29">28</reflink>]). Students identified with LD, recognized under the specific learning disability category, are included unless they qualify for state alternate assessments. Accommodations, such as ETA, are determined by the student's Individualized Education Program (IEP) teams, and NAEP testing adheres to these accommodations.</p> <p>Our analysis utilized restricted response process data from the 2017 NAEP Eighth-Grade Math Assessment, which comprised response, process, and survey data from a subsample of participants. This subsample included students who completed a released set of math items. Details for the mathematics items are provided in Table A1 of the Appendix. The analyzed sample consisted of 1,530 students with LD: 930 students in the ETA group (who were granted 90 min) and 600 students in the non-ETA group (who had 30 min to complete the test). Notably, among the 930 students receiving ETA, a majority (<reflink idref="bib680" id="ref30">680</reflink>, or 73%) did not utilize the extended time, whereas 250 students (27%) did.</p> <p>Other accommodations received by students with LD included breaks during tests (17%), cueing (5%), preferential seating (5%), and separate testing sessions (13%). These accommodations were excluded from the analysis due to their low prevalence and data confidentiality policies, which restrict reporting on small subgroups (e.g., fewer than 30 students per category).</p> <hd id="AN0192937328-4">Measures</hd> <p></p> <hd id="AN0192937328-5">Overall Math Performance</hd> <p>The NAEP dataset provided students' total scores for the math block, calculated as the sum of item-level scores across all 15 test items. The items covered topics such as fractions, lines, shapes, rotations, decimals, intercepts, graphs, plots, and geometry. Six items were scored dichotomously (0 = <emph>incorrect</emph>; 1 = <emph>correct</emph>), eight items had a maximum score of 2 (0 = <emph>incorrect</emph>; 1 = <emph>partially correct</emph>; 2 = <emph>correct</emph>), and one item had a maximum score of 4, with partial credit awarded for intermediate levels of correctness.</p> <hd id="AN0192937328-6">Response Time</hd> <p>Total RT and item-level RT were recorded in the NAEP process data. Since the NAEP math test allows students to navigate between items, item RT reflects the cumulative time spent on each item across multiple visits, measured in seconds. Total RT represents the total time a student spent on the 15-item math test.</p> <p>Given the right-skewed distribution of item RT, a log transformation was applied to achieve approximate symmetry, which is important for meeting the assumptions of many statistical analyses. This transformation was conducted using the formula:</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>l</mi><mi>o</mi><mi>g</mi><mi>R</mi><mi>T</mi><mo>=</mo><mi>l</mi><mi>o</mi><mi>g</mi><mrow><mo>(</mo><mrow><mi>R</mi><mi>T</mi><mo>+</mo><mn>1</mn></mrow><mo>)</mo></mrow></mrow></math> </ephtml> </p> <p>Graph</p> <p>The addition of "+1" addressed cases where RT were zero (1.3% of item RT and 2.6% of sequence lengths), which often resulted from omitted or unreached items, particularly toward the end of the test. Because log transformation of zero results in negative infinity, a small constant, one, was added to item RT so that the resulting log-transformed RT for these observations is zero ([<reflink idref="bib6" id="ref31">6</reflink>]).</p> <hd id="AN0192937328-7">Test-Taking Behaviors</hd> <p>Test-taking behaviors serve as indicators of student effort and engagement ([<reflink idref="bib16" id="ref32">16</reflink>]). This study examined several key behaviors to understand how students interacted with test content:</p> <p></p> <ulist> <item> Number of Actions: The total count of actions taken on all 15 items, including responses, entries, eliminations, and the use of scratchwork and other tools. These actions, recorded during both initial and subsequent visits to each item, indicate the level of their engagement and persistence ([<reflink idref="bib16" id="ref33">16</reflink>]).</item> <p></p> <item> Number of Revisits: The total number of revisits to all 15 items, excluding those shorter than 3 seconds. This metric helps distinguish between genuine problem-solving efforts and rapid-guessing behaviors, with longer revisits suggesting a deeper engagement with the content ([<reflink idref="bib19" id="ref34">19</reflink>]).</item> <p></p> <item> Unreached Items and Item Omissions: Items left unanswered or unreached by the end of the test were considered indicators of items' difficulty, lack of engagement, or time constraints ([<reflink idref="bib31" id="ref35">31</reflink>]). Higher rates of unreached items and omissions are linked to lower engagement and performance, a pattern especially pronounced among SWDs, who exhibit higher omission and unreached item rates compared with their general education peers ([<reflink idref="bib17" id="ref36">17</reflink>]).</item> <p></p> <item> Text-to-Speech (TTS) Usage: The usage of the TTS was measured by summing its application across all test items. This metric is particularly relevant for students with reading or visual impairments or those facing language barriers. TTS usage serves as an indicator of engagement and has been shown to correlate with improved math performance among SWDs ([<reflink idref="bib40" id="ref37">40</reflink>]).</item> </ulist> <hd id="AN0192937328-8">Test-Taking Attitude and Math Interest</hd> <p>After the math test, students completed a detailed survey assessing their attitudes and perceptions toward mathematics. From this survey, two key indices were derived.</p> <p></p> <ulist> <item> Math Interest: Comprising six survey items rated on a 5-point scale (1 = <emph>not at all like me</emph>; 5 = <emph>exactly like me</emph>), this index gauges students' interest or enjoyment in solving math problems.</item> <p></p> <item> Perseverance Index: Based on four survey items rated on a same 5-point Likert scale, this index measures students' persistence in problem-solving.</item> </ulist> <p>NAEP employed an item-response theory partial-credit scaling model to calculate both indices, placing the resultant scores on a scale from 0 to 20. Additional survey items assessed perceived time pressure, effort, and test difficulty all utilized a 5-point scale (1 = <emph>not at all</emph>; 5 = <emph>a lot</emph>).</p> <hd id="AN0192937328-9">Student Demographics</hd> <p>Demographic variables included a student's gender; race/ethnicity, coded as four dichotomous variables for African American, Hispanic, White, or other (Asian, American Indian, Pacific Islander, or multiple races); and free or reduced lunch status.</p> <hd id="AN0192937328-10">Statistical Analysis</hd> <p>To examine time-use patterns, separate Latent Profile Analysis (LPA) was conducted for the ETA and non-ETA groups. This approach was chosen because the substantial differences in testing time (90 min vs. 30 min) likely result in distinct patterns of time management that cannot be adequately captured in a combined analysis. Latent Profile Analysis, as outlined by [<reflink idref="bib9" id="ref38">9</reflink>], is a sophisticated statistical technique used for identifying subgroups within a population based on observed variables—in this case, item-level response time variables.</p> <p>The analysis utilized R "mclust" package ([<reflink idref="bib32" id="ref39">32</reflink>]; [<reflink idref="bib33" id="ref40">33</reflink>]), and the Expectation Maximization (EM) algorithm. Initial models explored one to nine profiles across 14 model variants to determine the optimal number of profiles. A focused analysis of one to five profiles (See Table 1) was then conducted. Based on model fit indices and theoretical interpretability, the four-profile solution was selected, as it provided a strong balance between statistical fit and meaningful distinctions in time-use patterns.</p> <p>Table 1. Fit Statistics for Latent Profile Analysis by Sample and Number of Profiles.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Sample group</th><th align="center">Model</th><th align="center">LL</th><th align="center">AIC</th><th align="center">BIC</th><th align="center">Entropy</th></tr></thead><tbody><tr><td rowspan="4">ETA<italic>n</italic> = 930</td><td>2-profile</td><td>−12674</td><td>25652</td><td>26387</td><td>0.81</td></tr><tr><td>3-profile</td><td>−12511</td><td>25360</td><td>26177</td><td>0.72</td></tr><tr><td>4-profile<xref ref-type="table-fn" rid="tfn3">a</xref></td><td>−12481</td><td>25334</td><td>26133</td><td>0.69</td></tr><tr><td>5-profile</td><td>−12454</td><td>25315</td><td>26296</td><td>0.32</td></tr><tr><td rowspan="4">Non-ETA<italic>n</italic> = 600</td><td>2-profile</td><td>−8408</td><td>17120</td><td>17787</td><td>0.91</td></tr><tr><td>3-profile</td><td>−8176</td><td>16689</td><td>17431</td><td>0.93</td></tr><tr><td>4-profile<xref ref-type="table-fn" rid="tfn3">a</xref></td><td>−8007</td><td>16385</td><td>17202</td><td>0.81</td></tr><tr><td>5-profile</td><td>−7893</td><td>16193</td><td>17084</td><td>0.70</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Source</emph>. U.S. Department of Education, National Center for Education Statistics, <emph>Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment.</emph></p> <ulist> <item>2 <emph>Note</emph>. LL = Log-Likelihood; AIC = Akaike information criterion; BIC = Bayesian information criterion. ETA = extended time accommodation.</item> <item>3 Indicates the selected model based on lowest BIC, entropy, and interpretability. While entropy was slightly lower for the four-profile solution in some cases, the profiles provided clearer distinctions and better alignment with theoretical expectations.</item> </ulist> <p>Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy, with values ≥.80 indicating strong classification quality ([<reflink idref="bib26" id="ref41">26</reflink>]). Item profile plots were examined to ensure conceptually meaningful profiles, aligning statistical results with substantive interpretations ([<reflink idref="bib29" id="ref42">29</reflink>]). This approach ensures that the chosen solution aligns with both statistical and substantive considerations.</p> <p>After identifying the best-fitting profiles, multinomial logistic regression was performed separately for ETA and non-ETA groups to examine the factors associated with membership in each profile, as the outcome variable is categorical with four distinct time-use profiles. The Efficient Prioritizers, the highest-achieving group, served as the reference category, allowing us to compare how each of the other profiles differed from this benchmark in terms of predictor variables.</p> <hd id="AN0192937328-11">Handling of Missing Data</hd> <p>The NAEP team's diligent data collection efforts ensured that missing rates were low across all variables, allowing for robust analysis without the need for imputation. Item RT had no missing data, as "not reached" items were coded as zero, aligning with NAEP's standards to preserve dataset validity. Similarly, item scores were complete, with NAEP assigning fractional correctness or the lowest score to missing responses, depending on the item type ([<reflink idref="bib22" id="ref43">22</reflink>]). Missing rates for demographic variables were exceptionally low (<1%), and process data on test-taking behaviors had no missing observations. Although survey data had missing rates between 8% and 17%, no imputation methods were applied to maintain methodological transparency and avoid introducing biases.</p> <hd id="AN0192937328-12">Results</hd> <p></p> <hd id="AN0192937328-13">Model Selection</hd> <p>The fit statistics (See Table 1) indicate that the four-profile solution provides the optimal balance of fit and interpretability for both the ETA and non-ETA groups. For the ETA group (<emph>n</emph> = 930), the four-profile solution minimized the BIC (<reflink idref="bib26" id="ref44">26</reflink>,<reflink idref="bib133" id="ref45">133</reflink>) and offered clear distinctions among profiles despite moderate entropy (0.69). A sensitivity analysis was conducted by removing the 250 students who utilized extra time, reducing the ETA group to a sample size of 680. The LPA results, including entropy values, remained stable. Since this exclusion did not meaningfully improve classification accuracy or alter the profile structures, we retained the full ETA sample (<emph>n</emph> = 930) for the primary analysis to ensure consistency in comparisons between ETA and non-ETA groups. For the non-ETA group (<emph>n</emph> = 600), the four-profile solution exhibited both a low BIC (<reflink idref="bib17" id="ref46">17</reflink>,<reflink idref="bib202" id="ref47">202</reflink>) and strong entropy (0.81).</p> <hd id="AN0192937328-14">Profile Descriptions and Predictive Insights</hd> <p>The four distinct time-use profiles—Initial Focusers, Rapid Progressors, Diligent Time Maximizers, and Efficient Prioritizers—emerged consistently across both ETA and non-ETA groups. Descriptive statistics (See Tables 2 and 3) provide an overview of demographic, performance, attitudinal, and behavioral characteristics, whereas multinomial regression results (See Tables 4 and 5) offer insights into predictors of profile membership.</p> <p>Table 2. Demographic, Performance, and Behavioral Characteristics of Students by Time Usage Profile: ETA Group.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Characteristic</th><th align="center">Initial focusers(<italic>n</italic> = 40, 4%)</th><th align="center">Rapid progressors(<italic>n</italic> = 160, 17%)</th><th align="center">Diligent time maximizers(<italic>n</italic> = 200, 22%)</th><th align="center">Efficient prioritizers(<italic>n</italic> = 530, 57%)</th><th align="center">Total</th></tr></thead><tbody><tr><td colspan="6">Demographic (%)</td></tr><tr><td> Male</td><td>70</td><td>66</td><td>66</td><td>56</td><td>60</td></tr><tr><td> African American</td><td>20</td><td>20</td><td>17</td><td>12</td><td>15</td></tr><tr><td> Hispanic</td><td>30</td><td>49</td><td>41</td><td>29</td><td>35</td></tr><tr><td> White</td><td>40</td><td>23</td><td>34</td><td>50</td><td>42</td></tr><tr><td> Other</td><td>10</td><td>8</td><td>8</td><td>9</td><td>9</td></tr><tr><td>Free or reduced-price lunch status</td><td>78</td><td>76</td><td>71</td><td>57</td><td>64</td></tr><tr><td colspan="6">Performance</td></tr><tr><td> Total mathematics score<xref ref-type="table-fn" rid="tfn6">a</xref></td><td>4.04 (2.15)</td><td>3.25 (2.19)</td><td>4.98 (2.65)</td><td>5.66 (2.97)</td><td>5.02 (2.86)</td></tr><tr><td colspan="6">Attitude and Interest</td></tr><tr><td> Mathematics interest index</td><td>8.98 (2.25)</td><td>9.56 (2.17)</td><td>9.90 (2.26)</td><td>9.82 (2.07)</td><td>9.77 (2.13)</td></tr><tr><td> Persistence in learning index</td><td>8.80 (2.07)</td><td>9.40 (1.98)</td><td>9.64 (1.78)</td><td>9.75 (1.73)</td><td>9.62 (1.84)</td></tr><tr><td> Time pressure</td><td>2.92 (1.31)</td><td>2.65 (1.36)</td><td>2.67 (1.43)</td><td>2.51 (1.35)</td><td>2.59 (1.40)</td></tr><tr><td> Effort in taking the test</td><td>3.79 (1.03)</td><td>3.51 (1.07)</td><td>3.90 (0.94)</td><td>3.96 (0.84)</td><td>3.86 (0.98)</td></tr><tr><td> Perceived difficulty level</td><td>3.46 (1.15)</td><td>3.14 (1.09)</td><td>3.16 (1.08)</td><td>3.18 (0.99)</td><td>3.18 (1.07)</td></tr><tr><td colspan="6">Test-taking behaviors</td></tr><tr><td> Total time (seconds)</td><td>1,426 (830.65)</td><td>898 (836.18)</td><td>1,991 (910)</td><td>1,600 (565)</td><td>1,551 (734)</td></tr><tr><td> Number of items not reached</td><td>2.15 (3.77)</td><td>0.03 (0.20)</td><td>0.06 (0.25)</td><td>0.02 (0.15)</td><td>0.12 (0.92)</td></tr><tr><td> Number of items omitted</td><td>1.95 (3.46)</td><td>0.35 (0.84)</td><td>0.16 (0.52)</td><td>0.03 (0.27)</td><td>0.17 (0.74)</td></tr><tr><td> Number of actions</td><td>178.64 (144.38)</td><td>124.48 (112.57)</td><td>202.49 (148.50)</td><td>163.24 (112.57)</td><td>164.87 (118.07)</td></tr><tr><td> Number of revisits</td><td>22.41 (9.55)</td><td>22.48 (9.04)</td><td>24.39 (10.08)</td><td>20.71 (7.42)</td><td>21.84 (8.33)</td></tr><tr><td> Number of text-to-speech usage</td><td>1.63 (23.81)</td><td>1.04 (34.21)</td><td>2.32 (3.68)</td><td>2.98 (4.85)</td><td>2.44 (4.25)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Source</emph>. U.S. Department of Education, National Center for Education Statistics, <emph>Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment.</emph></item> <item>5 <emph>Note. N =</emph> 930. ETA= extended time accommodation.</item> <item>6 The mean and standard deviation of total mathematics score of general education students is 9.16 and 4.68, respectively.</item> </ulist> <p>Table 3. Demographic, Performance, and Behavioral Characteristics of Students by Time Usage Profile: Non-ETA Group.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Characteristic</th><th align="center">Initial focusers(<italic>n</italic> = 30, 5%)</th><th align="center">Rapid progressors(<italic>n</italic> = 140, 23%)</th><th align="center">Diligent time maximizers(<italic>n</italic> = 70, 12%)</th><th align="center">Efficient prioritizers(<italic>n</italic> = 360, 60%)</th><th align="center">Total</th></tr></thead><tbody><tr><td colspan="6">Demographic (%)</td></tr><tr><td> Male</td><td>77</td><td>68</td><td>62</td><td>62</td><td>64</td></tr><tr><td> African American</td><td>12</td><td>12</td><td>16</td><td>11</td><td>12</td></tr><tr><td> Hispanic</td><td>31</td><td>31</td><td>21</td><td>24</td><td>26</td></tr><tr><td> White</td><td>42</td><td>43</td><td>68</td><td>50</td><td>47</td></tr><tr><td> Other</td><td>15</td><td>15</td><td>25</td><td>15</td><td>16</td></tr><tr><td> Free or reduced-price lunch status</td><td>54</td><td>67</td><td>63</td><td>60</td><td>61</td></tr><tr><td colspan="6">Performance</td></tr><tr><td> Total mathematics score<xref ref-type="table-fn" rid="tfn9">a</xref></td><td>3.11 (1.88)</td><td>4.25 (2.10)</td><td>4.99 (2.34)</td><td>5.86 (3.04)</td><td>5.26 (2.84)</td></tr><tr><td colspan="6"> Attitude and Interest</td></tr><tr><td> Mathematics interest index</td><td>9.53 (1.75)</td><td>8.99 (1.95)</td><td>9.45 (2.27)</td><td>9.74 (2.05)</td><td>9.42 (2.03)</td></tr><tr><td> Persistence in learning index</td><td>9.58 (1.73)</td><td>9.38 (2.15)</td><td>9.56 (1.64)</td><td>9.77 (1.79)</td><td>9.63 (1.87)</td></tr><tr><td> Time pressure</td><td>3.35 (1.23)</td><td>2.62 (1.36)</td><td>3.21 (1.29)</td><td>2.83 (1.36)</td><td>2.84 (1.35)</td></tr><tr><td> Effort in taking the test</td><td>3.78 (0.95)</td><td>3.57 (1.09)</td><td>4.02 (0.84)</td><td>3.90 (0.90)</td><td>3.82 (0.96)</td></tr><tr><td> Perceived difficulty level</td><td>3.35 (1.30)</td><td>3.25 (1.15)</td><td>3.17 (0.97)</td><td>3.11 (1.02)</td><td>3.18 (1.06)</td></tr><tr><td colspan="6">Test-taking behaviors</td></tr><tr><td> Total time (seconds)</td><td>1,528 (496)</td><td>1,151 (464)</td><td>1,788 (43)</td><td>1,374 (310)</td><td>1,377 (388)</td></tr><tr><td> Number of items not reached</td><td>3.23 (3.23)</td><td>0.03 (0.15)</td><td>1.38 (0.73)</td><td>0.008 (0.09)</td><td>0.31 (1.04)</td></tr><tr><td> Number of items omitted</td><td>5.33 (4.56)</td><td>0.32 (0.74)</td><td>1.50 (0.53)</td><td>0.15 (0.51)</td><td>0.31 (1.07)</td></tr><tr><td> Number of actions</td><td>228.33 (197.2)</td><td>149.25 (84.72)</td><td>195.5 (120.36)</td><td>144.59 (83.75)</td><td>148.27 (88.38)</td></tr><tr><td> Number of revisits</td><td>24.89 (10.11)</td><td>22.81 (8.98)</td><td>19.4 (2.12)</td><td>20.00 (5.82)</td><td>20.82 (6.95)</td></tr><tr><td> Number of text-to-speech usage</td><td>1.54 (2.89)</td><td>1.29 (2.91)</td><td>2.88 (4.27)</td><td>2.28 (4.27)</td><td>2.09 (3.96)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 <emph>Source</emph>: U.S. Department of Education, National Center for Education Statistics, <emph>Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment.</emph></item> <item>8 <emph>Note. N</emph> = 600. ETA = extended time accommodation.</item> <item>9 The mean and standard deviation of total mathematics score of general education students is 9.16 and 4.68, respectively.</item> </ulist> <p>Table 4. Multinomial Logistic Regression Predicting Time Usage Profile: ETA Group.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Variables</th><th align="center" colspan="3">IF vs. EP</th><th align="center" colspan="3">RP vs. EP</th><th align="center" colspan="3">DTM vs. EP</th></tr><tr><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th></tr></thead><tbody><tr><td>Male</td><td>1.18<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.03</td><td>3.26</td><td>0.44</td><td>0.28</td><td>1.55</td><td>0.49<xref ref-type="table-fn" rid="tfn12">*</xref></td><td>0.21</td><td>1.63</td></tr><tr><td>African American</td><td>−0.48<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.05</td><td>0.62</td><td>1.17<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.32</td><td>3.23</td><td>0.60<xref ref-type="table-fn" rid="tfn12">*</xref></td><td>0.26</td><td>1.83</td></tr><tr><td>Hispanic</td><td>−0.26<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.04</td><td>0.77</td><td>1.12<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.26</td><td>3.07</td><td>0.51<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.21</td><td>1.67</td></tr><tr><td>Other</td><td>−0.80<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.03</td><td>0.45</td><td>1.00<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.25</td><td>2.73</td><td>−0.06</td><td>0.17</td><td>0.94</td></tr><tr><td>Free or reduced-price lunch status</td><td>3.25<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.03</td><td>25.78</td><td>0.28</td><td>0.30</td><td>1.33</td><td>0.60<xref ref-type="table-fn" rid="tfn12">**</xref></td><td>0.23</td><td>1.82</td></tr><tr><td>Total mathematics score</td><td>−0.07</td><td>0.14</td><td>0.94</td><td>−0.26<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.06</td><td>0.77</td><td>−0.08</td><td>0.04</td><td>0.93</td></tr><tr><td>Math interest index</td><td>−0.14</td><td>0.18</td><td>0.87</td><td>0.002</td><td>0.08</td><td>1.00</td><td>0.06</td><td>0.06</td><td>1.06</td></tr><tr><td>Persistence in learning index</td><td>−0.25</td><td>0.19</td><td>0.78</td><td>0.10</td><td>0.09</td><td>1.10</td><td>−0.006</td><td>0.07</td><td>0.99</td></tr><tr><td>Time pressure</td><td>0.16</td><td>0.26</td><td>1.18</td><td>0.04</td><td>0.12</td><td>1.14</td><td>0.007</td><td>0.09</td><td>1.01</td></tr><tr><td>Effort in taking the test</td><td>−0.99<xref ref-type="table-fn" rid="tfn12">**</xref></td><td>0.32</td><td>0.37</td><td>−0.48<xref ref-type="table-fn" rid="tfn12">**</xref></td><td>0.17</td><td>0.62</td><td>−0.20</td><td>0.13</td><td>0.82</td></tr><tr><td>Perceived difficulty level</td><td>−0.07</td><td>0.30</td><td>0.92</td><td>0.07</td><td>0.14</td><td>1.07</td><td>−0.04</td><td>0.12</td><td>0.97</td></tr><tr><td>Total time (seconds)</td><td>0.0001</td><td>0.0005</td><td>1.00</td><td>−0.003<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.0004</td><td>1.00</td><td>0.0007<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.0002</td><td>1.00</td></tr><tr><td>Number of items omitted</td><td>2.19<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.14</td><td>8.94</td><td>0.78<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.16</td><td>2.17</td><td>0.89<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.18</td><td>2.42</td></tr><tr><td>Number of actions</td><td>0.007<xref ref-type="table-fn" rid="tfn12">**</xref></td><td>0.002</td><td>1.01</td><td>−0.0008</td><td>0.002</td><td>1.00</td><td>0.002</td><td>0.001</td><td>1.00</td></tr><tr><td>Number of revisits</td><td>0.01</td><td>0.04</td><td>1.01</td><td>0.07<xref ref-type="table-fn" rid="tfn12">***</xref></td><td>0.02</td><td>1.07</td><td>0.02</td><td>0.01</td><td>1.02</td></tr><tr><td>Number of text-to-speech usage</td><td>−0.34<xref ref-type="table-fn" rid="tfn12">*</xref></td><td>0.14</td><td>0.71</td><td>−0.07</td><td>0.05</td><td>0.93</td><td>−0.09<xref ref-type="table-fn" rid="tfn12">**</xref></td><td>0.03</td><td>0.91</td></tr></tbody></table> </ephtml> </p> <ulist> <item>10 <emph>Source</emph>. U.S. Department of Education, National Center for Education Statistics, <emph>Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment</emph>.</item> <item>11 <emph>Note. N</emph> = 930. ETA = Extended Time Accommodation; IF = Initial Focusers; EP = Efficient Prioritizers; RP = Rapid Progressors; DTM = Diligent Time Maximizers.</item> <item>12 <emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>Table 5. Multinomial Logistic Regression Predicting Time Usage Profile: Non-ETA Group.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Variables</th><th align="center" colspan="3">IF vs. EP</th><th align="center" colspan="3">RP vs. EP</th><th align="center" colspan="3">DTM vs. EP</th></tr><tr><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th><th align="center">β</th><th align="center"><italic>SE</italic></th><th align="center"><italic>OR</italic></th></tr></thead><tbody><tr><td>Male</td><td>0.29</td><td>0.72</td><td>1.34</td><td>0.21</td><td>0.27</td><td>1.23</td><td>0.24</td><td>0.44</td><td>1.27</td></tr><tr><td>African American</td><td>1.05<xref ref-type="table-fn" rid="tfn15">**</xref></td><td>0.35</td><td>2.85</td><td>−0.12</td><td>0.43</td><td>0.88</td><td>0.31</td><td>0.36</td><td>1.36</td></tr><tr><td>Hispanic</td><td>1.30<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>0.55</td><td>3.67</td><td>0.55</td><td>0.33</td><td>1.74</td><td>−0.17</td><td>0.41</td><td>0.85</td></tr><tr><td>Other</td><td>0.10</td><td>0.20</td><td>1.11</td><td>0.02</td><td>0.41</td><td>1.02</td><td>0.46</td><td>0.45</td><td>1.58</td></tr><tr><td>Free or reduced-price lunch status</td><td>−1.52<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>0.73</td><td>0.22</td><td>0.21</td><td>0.30</td><td>1.23</td><td>−0.58</td><td>0.48</td><td>0.56</td></tr><tr><td>Total mathematics score</td><td>−0.55<xref ref-type="table-fn" rid="tfn15">**</xref></td><td>0.21</td><td>0.58</td><td>−0.19<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.06</td><td>0.83</td><td>−0.14</td><td>0.08</td><td>0.87</td></tr><tr><td>Math interest index</td><td>−0.01</td><td>0.22</td><td>0.99</td><td>−0.09</td><td>0.08</td><td>0.92</td><td>−0.08</td><td>0.13</td><td>0.93</td></tr><tr><td>Persistence in learning index</td><td>−0.21</td><td>0.24</td><td>0.81</td><td>−0.05</td><td>0.08</td><td>0.95</td><td>−0.17</td><td>0.15</td><td>0.84</td></tr><tr><td>Time pressure</td><td>0.37</td><td>0.31</td><td>1.44</td><td>−0.03</td><td>0.10</td><td>0.97</td><td>−0.03</td><td>0.19</td><td>0.97</td></tr><tr><td>Effort in taking the test</td><td>−0.38<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.39</td><td>0.69</td><td>−0.16</td><td>0.15</td><td>0.85</td><td>0.25<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.27</td><td>1.29</td></tr><tr><td>Perceived difficulty level</td><td>0.01</td><td>0.37</td><td>1.01</td><td>−0.08</td><td>0.12</td><td>0.92</td><td>−0.04</td><td>0.24</td><td>0.97</td></tr><tr><td>Total time (seconds)</td><td>0.007<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.0001</td><td>1.01</td><td>−0.002<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.0004</td><td>1.00</td><td>0.02<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.0009</td><td>1.02</td></tr><tr><td>Number of items omitted</td><td>1.83<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.32</td><td>6.21</td><td>0.50<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>0.17</td><td>1.64</td><td>1.44<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.28</td><td>4.23</td></tr><tr><td>Number of actions</td><td>0.01<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>0.004</td><td>1.01</td><td>0.002<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.001</td><td>1.00</td><td>0.009<xref ref-type="table-fn" rid="tfn15">**</xref></td><td>0.003</td><td>1.01</td></tr><tr><td>Number of revisits</td><td>0.02</td><td>0.07</td><td>1.02</td><td>0.05<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.01</td><td>1.05</td><td>−0.06</td><td>0.08</td><td>0.94</td></tr><tr><td>Number of text-to-speech usage</td><td>−0.13<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>0.14</td><td>0.88</td><td>−0.10<xref ref-type="table-fn" rid="tfn15">**</xref></td><td>0.04</td><td>0.91</td><td>−0.14</td><td>0.11</td><td>0.87</td></tr></tbody></table> </ephtml> </p> <ulist> <item>13 <emph>Source.</emph> U.S. Department of Education, National Center for Education Statistics, <emph>Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment</emph>.</item> <item>14 <emph>Note. N</emph> = 600. ETA = Extended Time Accommodation; IF = Initial Focusers; EP = Efficient Prioritizers; RP = Rapid Progressors; DTM = Diligent Time Maximizers.</item> <item>15 <emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <hd id="AN0192937328-15">Initial Focusers</hd> <p>Initial Focusers represented the smallest proportion of students in both groups (4% in ETA and 5% in non-ETA). These students devoted more time to overly simpler initial items while neglecting later, more difficult ones. This unbalanced time allocation was more pronounced in the non-ETA group (see Figure 1). As shown in Tables 2 and 3, they had the highest rates of not-reached (ETA: 2.15; non-ETA: 3.23) and omitted items (ETA: 1.95; non-ETA: 5.33) and achieved the second-lowest math performance in the ETA group (4.04) and the lowest in the non-ETA group (3.11). Initial Focusers reported the second-lowest test-taking effort (ETA: 3.79; non-ETA: 3.78) and Text-to-Speech (TTS) usage (ETA: 1.63 uses; non-ETA: 1.54 uses), along with the highest perceived test difficulty (ETA: 3.46; non-ETA: 3.35) and time pressure (ETA: 2.92; non-ETA: 3.35).</p> <p>Graph: Figure 1. Time Utilization Profiles by Item Response Times for the Extended Time Accommodation (ETA) Group (A, n = 930) and Non-ETA Group (B, n = 600). Source. U.S. Department of Education, National Center for Education Statistics, Response Process Data from the NAEP 2017 Grade 8 Mathematics Assessment.</p> <p>Compared to Efficient Prioritizers, Initial Focusers in both groups had significantly more omitted items (ETA: <emph>OR</emph> = 8.94, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 6.21, <emph>p</emph> <.001), reported lower test-taking effort (ETA: <emph>OR</emph> = 0.37, <emph>p</emph> <.01; non-ETA: <emph>OR</emph> = 0.69, <emph>p</emph> <.001), and were less likely to use TTS (ETA: <emph>OR</emph> = 0.71, <emph>p</emph> <.05; non-ETA: <emph>OR</emph> = 0.88, <emph>p</emph> <.001) (See Tables 4 and 5).</p> <p>However, Initial Focusers in the ETA and non-ETA groups differed: In the ETA group, Initial Focusers were significantly more likely to be male (odds ratio [<emph>OR</emph>] = 3.26, <emph>p</emph> <.001) and qualify for free and reduced-price lunch (FRL) (<emph>OR</emph> = 25.78, <emph>p</emph> <.001) but less likely to be minority students compared with Efficient Prioritizers (African American: <emph>OR</emph> = 0.62, <emph>p</emph> <.001; Hispanic: <emph>OR</emph> = 0.77, <emph>p</emph> <.001). In the non-ETA group, Initial Focusers were more likely to be minority students (African American: <emph>OR</emph> = 2.85, <emph>p</emph> <.01; Hispanic: <emph>OR</emph> = 3.67, <emph>p</emph> <.05) and less likely to receive FRL (<emph>OR</emph> = 0.22, <emph>p</emph> <.05). They also scored lower in mathematics (<emph>OR</emph> = 0.58, <emph>p</emph> <.01) but spent more time on the test (<emph>OR</emph> = 1.01, <emph>p</emph> <.001) compared with Efficient Prioritizers.</p> <hd id="AN0192937328-16">Rapid Progressors</hd> <p>Rapid Progressors accounted for 17% of the ETA group and 23% of the non-ETA group. These students completed the test in the shortest time (ETA: 898 s, non-ETA: 1,151 s) and scored the lowest or second-lowest in math (ETA: 3.25; non-ETA: 4.25). They reported the lowest test-taking effort among all profiles (ETA: 3.51; non-ETA = 3.57).</p> <p>Compared to Efficient Prioritizers, these students scored lower (ETA: <emph>OR</emph> = 0.77, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 0.83, <emph>p</emph> <.001), completed the test faster (ETA: <emph>OR</emph> = 1.00, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 1.00, <emph>p</emph> <.001), omitted more items (ETA: <emph>OR</emph> = 2.17, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 1.64, <emph>p</emph> <.05), and revisited items more frequently (ETA: <emph>OR</emph> = 1.07, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 1.05, <emph>p</emph> <.001).</p> <p>In the ETA group, Rapid Progressors were more likely to be African American (<emph>OR</emph> = 3.23, <emph>p</emph> <.001) and Hispanic (<emph>OR</emph> = 3.07, <emph>p</emph> <.001) and reported lower test-taking effort (<emph>OR</emph> = 0.62, <emph>p</emph> <.01) than Efficient Prioritizers. These demographic differences were not observed in the non-ETA group.</p> <hd id="AN0192937328-17">Diligent Time Maximizers</hd> <p>Diligent Time Maximizers comprised 22% of the ETA group and 12% of the non-ETA group. These students spent the most time on the test (ETA: 1,991 s; non-ETA: 1,788 s), achieved the second-highest math scores (ETA: 4.98; non-ETA: 4.99), reported the greatest effort (ETA: 3.90; non-ETA: 4.02), and used TTS most frequently (ETA: 2.32; non-ETA: 2.88).</p> <p>Compared to Efficient Prioritizers, Diligent Time Maximizers spent more time on the test (ETA: <emph>OR</emph> = 1.00, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 1.02, <emph>p</emph> <.001) and omitted more items (ETA: <emph>OR</emph> = 2.42, <emph>p</emph> <.001; non-ETA: <emph>OR</emph> = 4.23, <emph>p</emph> <.001). In the ETA group, they were more likely to be male (<emph>OR</emph> = 1.63, <emph>p</emph> <.05), African American (<emph>OR</emph> = 1.83, <emph>p</emph> <.05), Hispanic (<emph>OR</emph> = 1.67, <emph>p</emph> <.001), and qualify for FRL (<emph>OR</emph> = 1.82, <emph>p</emph> <.01) but showed lower TTS usage (<emph>OR</emph> = 0.91, <emph>p</emph> <.01). These relationships were not observed in the non-ETA group. In addition, Diligent Time Maximizers in the non-ETA group reported higher effort than Efficient Prioritizers (<emph>OR</emph> = 1.29, <emph>p</emph> <.001).</p> <hd id="AN0192937328-18">Efficient Prioritizers</hd> <p>Efficient Prioritizers were the most prevalent profile in both groups (ETA: 57%, non-ETA: 61%). These students demonstrated strong time management, allocating less time to simpler items and more time on complex items (See Figure 1). While their time-use patterns for earlier items resembled those of Rapid Progressors, they diverged significantly on challenging later items (e.g., Items 13–15). Efficient Prioritizers achieved the highest math scores (ETA: 5.66; non-ETA: 5.86), with minimal omitted (ETA: 0.03; non-ETA: 0.15) or not-reached items (ETA: 0.02; non-ETA: 0.008). They also reported the highest persistence (ETA: 9.75; non-ETA: 9.77) and had the lowest proportion of students receiving FRL (ETA: 57%; non-ETA: 60%) and minority students (ETA: 12% African American, 29% Hispanic; non-ETA: 11% African American, 24% Hispanic).</p> <hd id="AN0192937328-19">Group Comparisons</hd> <p>A chi-square test confirmed significant differences in profile distributions between ETA and non-ETA groups (<emph>χ²</emph> = 27.95, <emph>p</emph> <.001). The prevalence of Diligent Time Maximizers nearly doubled in the ETA group (22%) compared with the non-ETA group (12%), underscoring the role of ETA in supporting thorough, effortful test-taking approaches. The fact that Rapid Progressors spent less time in the ETA condition than in the non-ETA condition (ETA: 898 s; non-ETA: 1,151 s) highlights the limited impact of ETA on this profile.</p> <hd id="AN0192937328-20">Discussion</hd> <p>This study identifies four distinct time-use profiles among students with LD during digital mathematics assessments, revealing how ETA shape these patterns. The findings highlight the diversity of strategies students employ and underscore the need for tailored interventions that address individual time-management challenges. Below, we discuss the implications of each profile, the role of ETA, and the broader implications for practice, policy, and future research.</p> <hd id="AN0192937328-21">Initial Focusers: Overfocus on Simpler Tasks at the Expense of Later Performance</hd> <p>Initial Focusers, although a small subset in both groups, consistently struggled with ineffective pacing, leading to the highest rates of omitted and not-reached items at the end of the test and low performance. This behavior aligns with prior research showing that students with LD often struggle with effective pacing strategies ([<reflink idref="bib21" id="ref48">21</reflink>]). However, this study extends the literature by pinpointing their challenges in overly engagement with simple items and lack of engagement toward the end of test, likely due to fatigue or disengagement. Despite ETA, these students reported high perceived test difficulty and time pressure, suggesting that additional time alone is insufficient to address their pacing challenges.</p> <p>Demographic contrasts between ETA and non-ETA groups further complicate this picture. In the ETA group, Initial Focusers were more likely to be male and qualify for FRL but less likely to be minority students. In the non-ETA group, this profile was more prevalent among minority students who were less likely to qualify for FRL. These findings point to a complex interplay of socioeconomic and demographic factors influencing time management strategies, highlighting the need for culturally responsive interventions.</p> <p>This behavior reflects a need for explicit support in pacing strategies that balance time allocation across tasks of varying difficulty. Targeted supports should focus on explicit pacing instruction and sustained engagement strategies. Tools such as progress monitors ([<reflink idref="bib4" id="ref49">4</reflink>]) and gamified elements ([<reflink idref="bib25" id="ref50">25</reflink>]; [<reflink idref="bib37" id="ref51">37</reflink>]) could help students optimize time use and maintain motivation throughout assessments.</p> <hd id="AN0192937328-22">Rapid Progressors: Speed Over Accuracy</hd> <p>Rapid Progressors prioritized speed over accuracy, completing assessments quickly but with frequent revisits and low scores. Unlike Initial Focusers who overly engaged on simple items, this group shows shallow engagement across all items. This profile aligns with research identifying a higher percentage of rapid-guessing behaviors and disengagement on a large-scale state assessment among special education students than their general education peers ([<reflink idref="bib39" id="ref52">39</reflink>]). Notably, Rapid Progressors underutilized assistive tools like TTS and neglected the extra time provided by ETA, indicating deeper issues of disengagement. This disengagement may stem from several factors, including low self-efficacy, lack of interest in the subject matter, learned helplessness due to repeated academic struggles, or a general indifference toward the assessment and its consequences. In addition, some students may have developed a "get it done quickly" approach to testing that prioritizes speed over accuracy, regardless of available resources or time. Their higher prevalence in the non-ETA group suggests that time constraints exacerbate these tendencies. This may be because students who feel pressured by time limits are more likely to resort to rapid guessing or skip items altogether, prioritizing completion over accuracy and potentially hindering their overall performance.</p> <p>Interventions for Rapid Progressors should focus on foundational math skills and metacognitive training to enhance deliberate engagement with test items. For example, explicit instruction in problem-solving strategies, such as breaking down complex problems into smaller steps or using visual representations, could improve their accuracy and reduce impulsive guessing. Metacognitive training could involve prompting students to reflect on their thinking processes, identify potential errors, and self-regulate their pace during the assessment. In addition, providing feedback on accuracy and encouraging self-evaluation could help them shift their focus from speed to precision. Future research could explore the role of interventions targeting executive function deficits to support this profile, as these deficits may contribute to impulsivity and shallow engagement.</p> <hd id="AN0192937328-23">Diligent Time Maximizers: Thoroughness Enabled by ETA</hd> <p>Diligent Time Maximizers benefited significantly from ETA, with their prevalence nearly doubling in the ETA group compared with the non-ETA group. These students allocated time effortfully across all items but often ran out of time for the last questions without ETA. This finding highlights the utility of ETA in reducing time pressure and enabling students with thorough, effortful strategies to demonstrate their full potential. In the non-ETA condition, Diligent Time Maximizers spent only 40 s and 0 s on the last two items, whereas their counterparts in the ETA group allocated 110 and 170 s, respectively. These results align with prior research demonstrating that ETA can alleviate performance barriers for students who require more time to process and solve complex problems ([<reflink idref="bib42" id="ref53">42</reflink>]).</p> <p>Building on these findings, integrating behavioral insights, such as time-use profiles, into accommodation policies could ensure that students with meticulous but slower strategies receive the support they need. Policymakers and educators should consider combining ETA with other interventions, such as adaptive pacing alerts or structured breaks, to enhance performance and reduce stress. These accommodations could further promote equitable access to resources for students who employ strategic, effortful approaches to testing.</p> <hd id="AN0192937328-24">Efficient Prioritizers: A Model of Balanced Strategies</hd> <p>Efficient Prioritizers, the majority profile in both groups, demonstrated exceptional time management, allocating more time to complex items while completing simpler ones quickly. This strategic approach enabled them to achieve the highest scores, report the least time pressure, and demonstrate the greatest persistence and interest in mathematics. The demographic composition of this profile, with lower proportions of minority and FRL students, underscores structural advantages in developing effective test-taking strategies, such as access to quality instruction and preparatory resources. These findings align with self-regulation theories, which emphasize the importance of goal-setting, self-monitoring, and adaptive strategy use in academic performance ([<reflink idref="bib44" id="ref54">44</reflink>]).</p> <p>The strong alignment of Efficient Prioritizers with performance outcomes highlights the importance of fostering self-regulation and metacognitive strategies in all students. Integrating these approaches into classroom instruction could enable students from other profiles, particularly Initial Focusers and Rapid Progressors, to adopt more effective behaviors. In addition, the success of this profile underscores the need for systemic efforts to address inequities in resource access and preparatory opportunities.</p> <hd id="AN0192937328-25">Cross-Profile Insights: The Role of ETA and Broader Implications</hd> <p>The comparison between ETA and non-ETA groups reveals critical insights into how ETA influences time-use patterns. The increased prevalence of Diligent Time Maximizers in the ETA group underscores the potential of ETA to support thorough and effortful strategies. In contrast, the higher proportion of Rapid Progressors in the non-ETA group suggests that time pressure exacerbates tendencies toward rapid item completion and shallow engagement. However, ETA alone does not appear sufficient to address the challenges faced by all profiles. For instance, Initial Focusers and Rapid Progressors may require additional scaffolds, such as real-time pacing feedback or explicit strategy training, to complement the benefits of ETA. These findings underscore the importance of tailoring accommodations to individual time-use profiles rather than adopting a one-size-fits-all approach.</p> <p>The overrepresentation of minority and low-SES students in lower-performing profiles highlights systemic inequities in access to resources and support. Addressing these disparities requires systemic efforts, such as increasing access to high-quality instruction, assistive technologies, and accommodations. In addition, integrating time-management strategies into the curriculum—beyond simple time-keeping techniques to include higher-order skills such as task prioritization and self-monitoring—could help all students develop essential skills for academic success ([<reflink idref="bib20" id="ref55">20</reflink>]).</p> <hd id="AN0192937328-26">Limitations and Future Directions</hd> <p>Despite the valuable insights gained from this study, several limitations must be acknowledged. First, the findings, derived from a low-stakes assessment, may not generalize to high-stakes testing environments, where external pressures differ significantly. Second, the observational nature of this study precludes definitive causal inferences. While the multinomial regression results identify significant predictors of profile membership, they do not establish direct causal links between time-use patterns and performance outcomes. Third, a key limitation lies in the small sample size of students who fully utilized ETA (<emph>n</emph> = 250), which constrained the robustness of the LPA for this subgroup. Larger datasets are needed to examine time-use patterns in greater detail. Fourth, while process data provide rich insights, they are subject to interpretative flexibility. Complementary data sources, such as observational studies or self-reports, may validate and enrich these findings. Finally, the LPA approach, while commonly used, may introduce bias in the associations between profiles and auxiliary variables, particularly given the moderate entropy value for the ETA group. Future research could employ methods such as the three-step approach ([<reflink idref="bib1" id="ref56">1</reflink>]) to account for classification uncertainty and further validate these findings.</p> <hd id="AN0192937328-27">Conclusion</hd> <p>This study highlights the diversity of time-use patterns among students with LD during digital mathematics assessments, revealing distinct profiles with unique strengths and challenges. The findings emphasize the importance of tailoring accommodations to individual time-use profiles instead of applying a universal approach. By integrating insights from this study into assessment policies, instructional practices, and systemic reforms, educators and policymakers can create more equitable and effective learning environments that foster academic achievement and confidence for all students with LD.</p> <hd id="AN0192937328-28">Appendix</hd> <p>Table A1. Item Characteristics.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Item</th><th align="center">Content</th><th align="center">Item score range</th><th align="center">Item type</th><th align="center">Content classification</th><th align="center">Difficulty</th></tr></thead><tbody><tr><td><p> 1</p></td><td><p>Translate a percent to a fraction</p></td><td><p>0–1</p></td><td><p>MC</p></td><td><p>Number properties and operations</p></td><td><p>Easy</p></td></tr><tr><td><p>2</p></td><td><p>Complete a circle graph to represent data</p></td><td><p>0–1</p></td><td><p>SR</p></td><td><p>Data analysis, statistics, and probability</p></td><td><p>Easy</p></td></tr><tr><td><p>3</p></td><td><p>Multiplication of two two-digit decimals</p></td><td><p>0–1</p></td><td><p>SCR</p></td><td><p>Number properties and operations</p></td><td><p>Medium</p></td></tr><tr><td><p>4</p></td><td><p>Determine x and y intercept of a given line</p></td><td><p>0–1</p></td><td><p>SR</p></td><td><p>Algebra</p></td><td><p>Easy</p></td></tr><tr><td><p> 5</p></td><td><p>Compare measurement using unit conversions</p></td><td><p>0–2</p></td><td><p>SR</p></td><td><p>Measurement</p></td><td><p>Easy</p></td></tr><tr><td><p>6</p></td><td><p>Extend a numerical pattern</p></td><td><p>0–1</p></td><td><p>SR</p></td><td><p>Algebra</p></td><td><p>Medium</p></td></tr><tr><td><p>7</p></td><td><p>Calculate diameter of a circle from a given circumference</p></td><td><p>0–1</p></td><td><p>SCR</p></td><td><p>Measurement</p></td><td><p>Hard</p></td></tr><tr><td><p>8</p></td><td><p>Rotation of a triangle</p></td><td><p>0–1</p></td><td><p>MC</p></td><td><p>Geometry</p></td><td><p>Hard</p></td></tr><tr><td><p>9</p></td><td><p>Create a proportion to find distance on a map</p></td><td><p>0–1</p></td><td><p>SR</p></td><td><p>Measurement</p></td><td><p>Easy</p></td></tr><tr><td><p>10</p></td><td><p>Identify characteristics of lines</p></td><td><p>0–2</p></td><td><p>SR</p></td><td><p>Geometry</p></td><td><p>Hard</p></td></tr><tr><td><p>11</p></td><td><p>Make and explain a conclusion about linear equations</p></td><td><p>0–2</p></td><td><p>SCR</p></td><td><p>Algebra</p></td><td><p>Hard</p></td></tr><tr><td><p>12</p></td><td><p>Identify figures that are composites of 2 given shapes</p></td><td><p>0–2</p></td><td><p>SR</p></td><td><p>Geometry</p></td><td><p>Hard</p></td></tr><tr><td><p>13</p></td><td><p>Evaluate circle graph and bar graph to determine possible data sets</p></td><td><p>0–4</p></td><td><p>ECR</p></td><td><p>Data analysis, statistics, and probability</p></td><td><p>Hard</p></td></tr><tr><td><p>14</p></td><td><p>Match box-plots to stem-and-leaf plots</p></td><td><p>0–2</p></td><td><p>SR</p></td><td><p>Data analysis, statistics, and probability</p></td><td><p>Hard</p></td></tr><tr><td><p>15</p></td><td><p>Write expression for polygon area using conjecture</p></td><td><p>0–2</p></td><td><p>SCR</p></td><td><p>Geometry</p></td><td><p>Hard</p></td></tr></tbody></table> </ephtml> </p> <p>16 <emph>Note.</emph> MC = Multiple choice; SR = Selected response; SCR = Short constructed response; ECR = Extended constructed response. All 15 mathematics test items can be found at https://nces.ed.gov/NationsReportCard/nqt/Search.</p> <ref id="AN0192937328-29"> <title> References </title> <blist> <bibl id="bib1" idref="ref56" type="bt">1</bibl> <bibtext> Asparouhov T., Muthén B. (2014). Auxiliary variables in mixture modeling: Using the three-step approach for distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181</bibtext> </blist> <blist> <bibl id="bib2" idref="ref9" type="bt">2</bibl> <bibtext> Cahalan-Laitusis C., King T. C., Cline F., Bridgeman B. (2006). Observational timing study on the SAT reasoning test™ for test-takers with learning disabilities and/or AD/HD [ETS Research Report Series]. https://doi.org/10.1002/j.2333-8504.2006.tb02029.x</bibtext> </blist> <blist> <bibl id="bib3" idref="ref2" type="bt">3</bibl> <bibtext> Church J. A., Cirino P. T., Miciak J., Juranek J., Vaughn S., Fletcher J. (2019). Cognitive, intervention, and neuroimaging perspectives on executive function in children with reading disabilities. In Fuchs L. S., Compton D. L. (Eds.), Models for innovation: Advancing approaches to higher-risk and higher-impact learning disabilities science. New directions for child and adolescent development (Vol. 165, pp. 25–54). Wiley.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref49" type="bt">4</bibl> <bibtext> Conrad F. G., Couper M. P., Tourangeau R., Peytchev A. (2010). The impact of progress indicators on task completion. Interacting with Computers, 22(5), 417–427. https://doi.org/10.1016/j.intcom.2010.03.001</bibtext> </blist> <blist> <bibl id="bib5" idref="ref8" type="bt">5</bibl> <bibtext> Duncan H., Purcell C. (2020). Consensus or contradiction? A review of the current research into the impact of granting extra time in exams to students with specific learning difficulties (SpLD). Journal of Further and Higher Education, 44(4), 439–453. https://doi.org/10.1080/0309877X.2019.1578341</bibtext> </blist> <blist> <bibl id="bib6" idref="ref31" type="bt">6</bibl> <bibtext> Feng C., Wang H., Lu N., Tu X. M. (2013). Log transformation: Application and interpretation in biomedical research. Statistics in Medicine, 32(2), 230–239. https://doi.org/10.1002/sim.5486</bibtext> </blist> <blist> <bibl id="bib7" idref="ref3" type="bt">7</bibl> <bibtext> Fuchs L., Fuchs D., Capizzi A. (2005). Identifying appropriate test accommodations for students with learning disabilities. Focus on Exceptional Children, 37, 1–8. https://doi.org/10.17161/fec.v37i6.6812</bibtext> </blist> <blist> <bibl id="bib8" idref="ref10" type="bt">8</bibl> <bibtext> Gelbar N., Madaus J. (2021). Factors related to extended time use by college students with disabilities. Remedial and Special Education, 42(6), 374–383. https://doi.org/10.1177/0741932520972787</bibtext> </blist> <blist> <bibl id="bib9" idref="ref38" type="bt">9</bibl> <bibtext> Gibson W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis and latent profile analysis. Psychometrika, 24, 229–252. https://doi.org/10.1007/BF02289845</bibtext> </blist> <blist> <bibtext> Goldhammer F., Naumann J., Greiff S. (2015). More is not always better: The relation between item response and item response time in Raven's matrices. Journal of Intelligence, 3(1), 21–40. https://doi.org/10.3390/jintelligence3010021</bibtext> </blist> <blist> <bibtext> Heiman T., Precel K. (2003). Students with learning disabilities in higher education: Academic strategies profile. Journal of Learning Disabilities, 36(3), 248–258. https://doi.org/10.1177/002221940303600304</bibtext> </blist> <blist> <bibtext> Holzer M. L., Madaus J. W., Bray M. A., Kehle T. J. (2009). The test-taking strategy intervention for college students with learning disabilities. Learning Disabilities Research & Practice, 24(1), 44–56. https://doi.org/10.1111/j.1540-5826.2008.01276.x</bibtext> </blist> <blist> <bibtext> Hong E. (1999). Test anxiety, perceived test difficulty, and test performance: Temporal patterns of their effects. Learning and Individual Differences, 11, 431–447. https://doi.org/10.1016/S1041-6080(99)80012-0</bibtext> </blist> <blist> <bibtext> Horowitz S. H., Rawe J., Whittaker M. C. (2017). The state of learning disabilities: Understanding the 1 in 5. National Center for Learning Disabilities.</bibtext> </blist> <blist> <bibtext> Hughes C. A., Schumaker J. B., Deshler D. D., Mercer C. D. (1993). The test-taking strategy. Edge Enterprises, Inc.</bibtext> </blist> <blist> <bibtext> Ivanova M., Michaelides M., Eklöf H. (2020). How does the number of actions on constructed-response items relate to test-taking effort and performance? Educational Research and Evaluation, 26(5–6), 252–274. https://doi.org/10.1080/13803611.2021.1963939</bibtext> </blist> <blist> <bibtext> Kato K., Moen R., Thurlow M. (2007). Examining DIF, DDF, and omit rate by discrete disability categories. <ulink href="http://www.cehd.umn.edu/nceo/onlinepubs/PARA/examingdifddfomitrate/PARADIFDDFandOmitRateReport.pdf">http://www.cehd.umn.edu/nceo/onlinepubs/PARA/examingdifddfomitrate/PARADIFDDFandOmitRateReport.pdf</ulink></bibtext> </blist> <blist> <bibtext> Kern C. W., Fagley N. S., Miller P. M. (1998). Correlates of college retention and GPA: Learning and Strategies, testwiseness, attitudes, and ACT. Journal of College Counseling, 1, 26–34. https://doi.org/10.1002/j.2161-1882.1998.tb00121.x</bibtext> </blist> <blist> <bibtext> Kong X. J., Wise S. L., Bhola D. S. (2007). Setting the response time threshold parameter to differentiate solution behavior from rapid-guessing behavior. Educational and Psychological Measurement, 67(4), 606–619. https://doi.org/10.1177/0013164406294779</bibtext> </blist> <blist> <bibtext> Koriat A. (2015). Metacognition. In Keren G., Wu G. (Eds.), The Wiley Blackwell handbook of judgment and decision making (pp. 356–379). Wiley-Blackwell. https://doi.org/10.1002/9781118468333.ch12</bibtext> </blist> <blist> <bibtext> Laitusis C. C., Buzick H. M., Cook L., Stone E. (2011). Adaptive testing options for accountability assessments. In Russell M., Kavanaugh M. (Eds.), Assessing students in the margins: Challenges, strategies, and techniques. Information Age Publishing.</bibtext> </blist> <blist> <bibtext> Liaw Y.-L. (2023). Reached or not reached: A tale of two data sources. Educational Measurement: Issues and Practice, 42, 4–4. https://doi.org/10.1111/emip.12574</bibtext> </blist> <blist> <bibtext> Lindstrom W., Lindstrom J. H., Barefield T. T., Slaughter M. H., Benson E. W. (2021). Examination of extended time use among postsecondary students with non-apparent disabilities. Journal of Postsecondary Education and Disability, 34(4), 297–309.</bibtext> </blist> <blist> <bibtext> Lufi D., Okasha S., Cohen A. (2004). Test anxiety and its effect on the personality of students with learning disabilities. Learning Disability Quarterly, 27(3), 176–184. https://doi.org/10.2307/1593667</bibtext> </blist> <blist> <bibtext> Malone M. (2023). Improving student enjoyment, time management, and performance using a secure, immersive gamified learning platform (Order No. 30417993). Available from ProQuest Dissertations & Theses Global Closed Collection; ProQuest One Academic. (2817225975). https://<ulink href="http://www.proquest.com/dissertations-theses/improving-student-enjoyment-time-management/docview/2817225975/se-2">www.proquest.com/dissertations-theses/improving-student-enjoyment-time-management/docview/2817225975/se-2</ulink></bibtext> </blist> <blist> <bibtext> Muthén B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In Kaplan D. (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345–368). Sage. https://doi.org/10.4135/9781412986311.n19</bibtext> </blist> <blist> <bibtext> National Assessment of Educational Progress. (2018). NAEP technical documentation. https://nces.ed.gov/nationsreportcard/tdw/sample_design/</bibtext> </blist> <blist> <bibtext> National Assessment of Educational Progress. (2020). NAEP students with disabilities questionnaire. https://nces.ed.gov/nationsreportcard/subject/experience/pdfs/sd_2020.pdf</bibtext> </blist> <blist> <bibtext> Nylund K. L., Asparouhov T., Muthén B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701575396</bibtext> </blist> <blist> <bibtext> Pohl S., Südkamp A., Hardt K., Carstensen C. H., Weinert S. (2016). Testing students with special educational needs in large-scale assessments: Psychometric properties of test scores and associations with test taking behavior. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00154</bibtext> </blist> <blist> <bibtext> Pohl S., Ulitzsch E., von Davier M. (2019). Using response times to model not-reached items due to time limits. Psychometrika, 84, 892–920. https://doi.org/10.1007/s11336-019-09669-2</bibtext> </blist> <blist> <bibtext> Scrucca L., Fop M., Murphy T. B., Raftery A. E. (2023). Mclust (Version 6.1) [Software]. https://cran.r-project.org/package=mclust</bibtext> </blist> <blist> <bibtext> Scrucca L., Fraley C., Murphy T. B., Raftery A. E. (2023). Model-based clustering, classification, and density estimation using mclust in R. Chapman and Hall/CRC. https://doi.org/10.1201/9781003277965</bibtext> </blist> <blist> <bibtext> Scruggs T. E., Mastropieri M. (1988). Are learning disabled students "test-wise?" A review of the recent research. Learning Disabilities Focus, 3, 87–97. https://doi.org/10.1177/093889828800300205</bibtext> </blist> <blist> <bibtext> Semrud-Clikeman M. (2005). Neuropsychological aspects for evaluating learning disabilities. Journal of Learning Disabilities, 38(6), 563–568. https://doi.org/10.1177/00222194050380061301</bibtext> </blist> <blist> <bibtext> Spenceley L. M., Wheeler S. (2016). The use of extended time by college students with disabilities. Journal of Postsecondary Education and Disability, 29(2), 141–150.</bibtext> </blist> <blist> <bibtext> Tabuenca B., Greller W., Verpoorten D. (2022). Mind the gap: Smoothing the transition to higher education fostering time management skills. Universal Access in the Information Society, 21, 367–379. https://doi.org/10.1007/s10209-021-00833-z</bibtext> </blist> <blist> <bibtext> U.S. Department of Education, National Center for Education Statistics. (2020). Process data from the NAEP 2017 Grade 8 mathematics assessment (NCES 2020068). https://nces.ed.gov/use-work/resource-library/report/statistical-analysis-report/process-data-2017-naep-grade-8-mathematics-assessment?pubid=2020068</bibtext> </blist> <blist> <bibtext> Valdivia D. S., Rutkowski L., Rutkowski D., Canbolat Y., Underhill S. (2023). Test engagement and rapid guessing: Evidence from a large-scale state assessment. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1127644</bibtext> </blist> <blist> <bibtext> Wei X. (2024a). Text-to-speech technology and math performance: A comparative study of students with disabilities, English Language Learners, and their general education peers. Educational Researcher. https://doi.org/10.3102/0013189X241232995</bibtext> </blist> <blist> <bibtext> Wei X. (2024b). Universal design element utilization and mathematics performance: Implications for diverse student populations. Journal of Special Education Technology. https://doi.org/10.1177/01626434241289951</bibtext> </blist> <blist> <bibtext> Wei X., Zhang S. (2024). Extended time accommodation and the academic, behavioral, and psychological outcomes of students with learning disabilities. Journal of Learning Disabilities, 57(4), 242–254. https://doi.org/10.1177/00222194231195624</bibtext> </blist> <blist> <bibtext> Whitaker Sena J. D., Lowe P. A., Lee S. W. (2007). Significant predictors of test anxiety among students with and without learning disabilities. Journal of Learning Disabilities, 40(4), 360–376. https://doi.org/10.1177/00222194070400040601</bibtext> </blist> <blist> <bibtext> Zimmerman B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2</bibtext> </blist> </ref> <ref id="AN0192937328-30"> <title> Footnotes </title> <blist> <bibtext> Xin Wei: Conceptualization, Data analysis, Methodology, Writing</bibtext> </blist> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R324P230002 to Digital Promise. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.</bibtext> </blist> <blist> <bibtext> Xin Wei</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-6978-6609</bibtext> </blist> </ref> <aug> <p>By Xin Wei</p> <p>Reported by Author</p> </aug> <nolink nlid="nl1" bibid="bib14" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib35" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib12" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib13" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib43" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib23" firstref="ref11"></nolink> <nolink nlid="nl7" bibid="bib36" firstref="ref12"></nolink> <nolink nlid="nl8" bibid="bib42" firstref="ref13"></nolink> <nolink nlid="nl9" bibid="bib10" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib18" firstref="ref17"></nolink> <nolink nlid="nl11" bibid="bib41" firstref="ref18"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref19"></nolink> <nolink nlid="nl13" bibid="bib21" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib30" firstref="ref21"></nolink> <nolink nlid="nl15" bibid="bib34" firstref="ref22"></nolink> <nolink nlid="nl16" bibid="bib11" firstref="ref23"></nolink> <nolink nlid="nl17" bibid="bib24" firstref="ref24"></nolink> <nolink nlid="nl18" bibid="bib27" firstref="ref28"></nolink> <nolink nlid="nl19" bibid="bib28" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib680" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib16" firstref="ref32"></nolink> <nolink nlid="nl22" bibid="bib19" firstref="ref34"></nolink> <nolink nlid="nl23" bibid="bib31" firstref="ref35"></nolink> <nolink nlid="nl24" bibid="bib17" firstref="ref36"></nolink> <nolink nlid="nl25" bibid="bib40" firstref="ref37"></nolink> <nolink nlid="nl26" bibid="bib32" firstref="ref39"></nolink> <nolink nlid="nl27" bibid="bib33" firstref="ref40"></nolink> <nolink nlid="nl28" bibid="bib26" firstref="ref41"></nolink> <nolink nlid="nl29" bibid="bib29" firstref="ref42"></nolink> <nolink nlid="nl30" bibid="bib22" firstref="ref43"></nolink> <nolink nlid="nl31" bibid="bib133" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib202" firstref="ref47"></nolink> <nolink nlid="nl33" bibid="bib25" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib37" firstref="ref51"></nolink> <nolink nlid="nl35" bibid="bib39" firstref="ref52"></nolink> <nolink nlid="nl36" bibid="bib44" firstref="ref54"></nolink> <nolink nlid="nl37" bibid="bib20" firstref="ref55"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1502795
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring Time-Use Profiles in Digital Mathematics Assessments for Students with Learning Disabilities
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xin+Wei%22">Xin Wei</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6978-6609">0000-0002-6978-6609</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Disabilities%22"><i>Journal of Learning Disabilities</i></searchLink>. 2026 59(3):172-184.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Institute of Education Sciences (ED)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R324P230002
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+8%22">Grade 8</searchLink><br /><searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22National+Competency+Tests%22">National Competency Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+8%22">Grade 8</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Factors+%28Learning%29%22">Time Factors (Learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Management%22">Time Management</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Disabilities%22">Learning Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Accommodations+%28Disabilities%29%22">Academic Accommodations (Disabilities)</searchLink><br /><searchLink fieldCode="DE" term="%22Testing+Accommodations%22">Testing Accommodations</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Tests%22">Mathematics Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22National+Assessment+of+Educational+Progress%22">National Assessment of Educational Progress</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/00222194251347965
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0022-2194<br />1538-4780
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study investigates the time-use patterns of students with learning disabilities during digital mathematics assessments and explores the role of extended time accommodations (ETA) in shaping these patterns. Using latent profile analysis, the researcher identified four distinct time-use profiles separately for a group of U.S. 8th-grade students with LD, with and without ETA. "Initial Focusers" spend more time on simpler initial items and less time on later, more difficult items, exhibiting high omission rates and low performance. "Rapid Progressors" complete assessments quickly but exhibit shallow engagement across all items, achieving low performance. "Diligent Time Maximizers" allocate time effortfully across items but often run out of time on the last two items when ETA was not granted, achieving the second-highest scores. "Efficient Prioritizers," excel in strategic time management, score the highest, and report strong persistence and interest in math. The findings reveal that ETA supports students who adopt meticulous strategies, such as Diligent Time Maximizers, but does not universally address the challenges faced by other profiles. This study underscores the need for tailored interventions and accommodations aligned with individual time-use profiles to foster equitable and effective learning and assessment environments.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
  Label: IES Funded
  Group: SrcInfo
  Data: Yes
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1502795
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1502795
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/00222194251347965
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 172
    Subjects:
      – SubjectFull: National Competency Tests
        Type: general
      – SubjectFull: Grade 8
        Type: general
      – SubjectFull: Students with Disabilities
        Type: general
      – SubjectFull: Time Factors (Learning)
        Type: general
      – SubjectFull: Time Management
        Type: general
      – SubjectFull: Learning Disabilities
        Type: general
      – SubjectFull: Academic Accommodations (Disabilities)
        Type: general
      – SubjectFull: Testing Accommodations
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
      – SubjectFull: Mathematics Achievement
        Type: general
      – SubjectFull: Mathematics Tests
        Type: general
      – SubjectFull: Middle School Students
        Type: general
      – SubjectFull: National Assessment of Educational Progress
        Type: general
    Titles:
      – TitleFull: Exploring Time-Use Profiles in Digital Mathematics Assessments for Students with Learning Disabilities
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xin Wei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0022-2194
            – Type: issn-electronic
              Value: 1538-4780
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Journal of Learning Disabilities
              Type: main
ResultId 1