7 Reporting and Results

Chapter 7 of the Dynamic Learning Maps® (DLM®) Alternate Assessment System 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022) describes assessment results for the 2021–2022 academic year, including student participation and performance summaries and an overview of data files and score reports delivered to state education agencies.

This chapter presents 2023–2024 student participation data; the percentage of students achieving at each performance level; and subgroup performance by gender, race, ethnicity, and English learner status. This chapter also reports the distribution of students by the highest linkage level mastered during spring 2024.

For a complete description of score reports and interpretive guides, see Chapter 7 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022).

7.1 Student Participation

During spring 2024, assessments were administered to 89,338 students in 14 states. Table 7.1 displays counts of students tested in each state. The assessments were administered by 23,212 educators in 12,140 schools and 3,831 school districts. A total of 1,444,880 test sessions were administered during the spring assessment window. One test session is one testlet taken by one student. Only test sessions that were complete at the close of the spring assessment window counted toward the total sessions.

Table 7.1: Student Participation by State in 2023–2024 (N = 89,338)
State Students (n)
Alaska      487
Colorado   4,001
Illinois 14,420
Maryland   5,255
New Hampshire      651
New Jersey 11,676
New Mexico   2,034
New York 18,045
Oklahoma   4,805
Pennsylvania 16,891
Rhode Island      892
Utah   4,211
West Virginia   1,388
Wisconsin   4,582

Table 7.2 summarizes the number of students assessed in each grade. In grades 3–8, over 11,690 students participated in the DLM assessment at each grade. In high school, the largest number of students participated in grade 11, and the smallest number participated in grade 12. The differences in high school grade-level participation can be traced to differing state-level policies about the grade(s) in which students are assessed.

Table 7.2: Student Participation by Grade in 2023–2024 (N = 89,338)
Grade Students (n)
  3 11,949
  4 12,047
  5 11,932
  6 12,039
  7 11,896
  8 11,696
  9   5,795
10   3,072
11   8,674
12      238

Table 7.3 summarizes the demographic characteristics of the students who participated in the spring 2024 administration. The majority of participants were male (68%), White (54%), and non-Hispanic (74%). About 7% of students were monitored or eligible for English learning services.

Table 7.3: Demographic Characteristics of Participants in 2023–2024 (N = 89,338)
Subgroup n %
Gender
Male 60,731 68.0
Female 28,574 32.0
Nonbinary/undesignated       32   <0.1  
Other          1   <0.1  
Race
White 48,489 54.3
African American 19,371 21.7
Two or more races 12,713 14.2
Asian   5,827   6.5
American Indian   2,317   2.6
Native Hawaiian or Pacific Islander      443   0.5
Alaska Native      178   0.2
Hispanic ethnicity
Non-Hispanic 66,464 74.4
Hispanic 22,874 25.6
English learning (EL) participation
Not EL eligible or monitored 83,428 93.4
EL eligible or monitored   5,910   6.6

In addition to the spring assessment window, instructionally embedded assessments are also made available for educators to optionally administer to students during the year. Results from the instructionally embedded assessments do not contribute to final summative scoring but can be used to guide instructional decision-making. Table 7.4 summarizes the number of students who completed at least one instructionally embedded assessment by state. State education agencies are allowed to set their own policies regarding requirements for participation in the instructionally embedded window. A total of 2,103 students in 11 states took at least one instructionally embedded testlet during the 2023–2024 academic year.

Table 7.4: Students Completing Instructionally Embedded Testlets by State (N = 2,103)
State n
Colorado    115
Illinois        7
Maryland    125
New Jersey 1,181
New Mexico        1
New York      49
Oklahoma    372
Pennsylvania        6
Utah    230
West Virginia        5
Wisconsin      12

Table 7.5 summarizes the number of instructionally embedded testlets taken in ELA and mathematics. Across all states, students took 13,993 ELA testlets and 13,093 mathematics testlets during the instructionally embedded window.

Table 7.5: Number of Instructionally Embedded Testlets by Grade and Subject (N = 27,086)
Grade English language arts Mathematics
  3   1,786   1,379
  4   2,036   1,959
  5   2,188   2,000
  6   2,080   1,749
  7   1,859   1,761
  8   1,864   1,834
  9      162      159
10       86       91
11   1,844   2,060
12       88      101
Total 13,993 13,093

7.2 Student Performance

Student performance on DLM assessments is interpreted using cut points determined by a standard setting study. For a description of the standard setting process used to determine the cut points, see Chapter 6 of this manual. Student achievement is described using four performance levels. A student’s performance level is determined by the total number of linkage levels mastered across the assessed Essential Elements (EEs).

For the spring 2024 administration, student performance was reported using four performance levels:

  • The student demonstrates Emerging understanding of and ability to apply content knowledge and skills represented by the EEs.
  • The student’s understanding of and ability to apply targeted content knowledge and skills represented by the EEs is Approaching the Target.
  • The student’s understanding of and ability to apply content knowledge and skills represented by the EEs is At Target. This performance level is considered meeting achievement expectations.
  • The student demonstrates Advanced understanding of and ability to apply targeted content knowledge and skills represented by the EEs.

7.2.1 Overall Performance

Table 7.6 reports the percentage of students achieving at each performance level on the spring 2024 ELA and mathematics assessment administration by grade and subject. In ELA, the percentage of students who achieved at the At Target or Advanced levels (i.e., proficient) ranged from approximately 15% to 32%. In mathematics, the percentage of students who achieved at the At Target or Advanced levels ranged from approximately 9% to 33%.

Table 7.6: Percentage of Students by Grade and Performance Level
Grade n Emerging (%) Approaching (%) At Target (%) Advanced (%) At Target + Advanced (%)
English language arts
  3 11,932 65.2 15.2 18.1   1.6 19.6
  4 12,036 63.6 21.2 13.7   1.5 15.2
  5 11,903 55.8 17.4 22.5   4.3 26.7
  6 12,010 51.8 21.7 19.3   7.3 26.5
  7 11,875 43.3 27.4 23.0   6.2 29.2
  8 11,669 42.9 30.1 25.5   1.5 27.0
  9   5,785 37.9 36.3 22.6   3.2 25.8
10   3,062 38.0 38.1 23.4   0.5 23.9
11   8,615 37.0 31.3 25.9   5.8 31.7
12      233 40.3 33.5 22.7   3.4 26.2
Mathematics
  3 11,915 66.5 11.6 15.2   6.8 21.9
  4 12,007 57.8   8.8 23.3 10.2 33.5
  5 11,891 51.2 23.5 11.0 14.4 25.3
  6 12,003 65.3 18.4   8.4   7.9 16.3
  7 11,858 63.4 21.4   9.8   5.4 15.2
  8 11,657 55.4 33.4   8.1   3.0 11.1
  9   5,776 45.3 36.9 14.0   3.9 17.8
10   3,045 57.1 34.1   8.2   0.6   8.8
11   8,630 43.6 27.1 28.0   1.3 29.2
12      237 45.6 26.6 27.4   0.4 27.8

7.2.2 Subgroup Performance

Data collection for DLM assessments includes demographic data on gender, race, ethnicity, and English learning status. Table 7.7 and Table 7.8 summarize the disaggregated frequency distributions for ELA and mathematics performance levels, respectively, collapsed across all assessed grade levels. Although state education agencies each have their own rules for minimum student counts needed to support public reporting of results, small counts are not suppressed here because results are aggregated across states and individual students cannot be identified.

Table 7.7: ELA Performance Level Distributions by Demographic Subgroup in 2023–2024 (N = 89,120)
Emerging
Approaching
At Target
Advanced
At Target +
Advanced
Subgroup n % n % n % n % n %
Gender
Male 30,863 50.9 14,771 24.4 12,699   21.0 2,250 3.7 14,949   24.7
Female 14,216 49.9   7,072 24.8   6,111   21.4 1,105 3.9   7,216   25.3
Nonbinary/undesignated       20 62.5          4 12.5          6   18.8        2 6.2          8   25.0
Other          0   0.0          0   0.0          1 >99.9          0 0.0          1 >99.9  
Race
White 24,070 49.8 11,658 24.1 10,649   22.0 1,995 4.1 12,644   26.1
African American   9,416 48.7   5,078 26.3   4,119   21.3    703 3.6   4,822   25.0
Two or more races   6,688 52.7   3,092 24.4   2,490   19.6    418 3.3   2,908   22.9
Asian   3,518 60.5   1,234 21.2      908   15.6    152 2.6   1,060   18.2
American Indian   1,069 46.2      634 27.4      535   23.1      74 3.2      609   26.3
Native Hawaiian or Pacific Islander      244 55.1      103 23.3       83   18.7      13 2.9       96   21.7
Alaska Native       94 53.1       48 27.1       33   18.6        2 1.1       35   19.8
Hispanic ethnicity
Non-Hispanic 33,401 50.4 16,223 24.5 14,170   21.4 2,521 3.8 16,691   25.2
Hispanic 11,698 51.3   5,624 24.7   4,647   20.4    836 3.7   5,483   24.0
English learning (EL) participation
Not EL eligible or monitored 42,006 50.5 20,379 24.5 17,694   21.3 3,156 3.8 20,850   25.0
EL eligible or monitored   3,093 52.6   1,468 24.9   1,123   19.1    201 3.4   1,324   22.5
Table 7.8: Mathematics Performance Level Distributions by Demographic Subgroup in 2023–2024 (N = 89,019)
Emerging
Approaching
At Target
Advanced
At Target +
Advanced
Subgroup n % n % n % n % n %
Gender
Male 34,214 56.5 12,906 21.3   8,889   14.7 4,498 7.4 13,387   22.1
Female 16,754 58.8   6,524 22.9   3,668   12.9 1,533 5.4   5,201   18.3
Nonbinary/undesignated       21 65.6          8 25.0          2     6.2        1 3.1          3     9.4
Other          0   0.0          0   0.0          1 >99.9          0 0.0          1 >99.9  
Race
White 27,484 56.9 10,735 22.2   6,946   14.4 3,162 6.5 10,108   20.9
African American 10,780 55.9   4,289 22.2   2,808   14.6 1,419 7.4   4,227   21.9
Two or more races   7,446 58.8   2,786 22.0   1,650   13.0    779 6.2   2,429   19.2
Asian   3,652 63.0      982 16.9      721   12.4    445 7.7   1,166   20.1
American Indian   1,243 53.6      510 22.0      366   15.8    198 8.5      564   24.3
Native Hawaiian or Pacific Islander      270 61.4       98 22.3       50   11.4      22 5.0       72   16.4
Alaska Native      114 64.0       38 21.3       19   10.7        7 3.9       26   14.6
Hispanic ethnicity
Non-Hispanic 38,018 57.4 14,425 21.8   9,427   14.2 4,347 6.6 13,774   20.8
Hispanic 12,971 56.9   5,013 22.0   3,133   13.7 1,685 7.4   4,818   21.1
English learning (EL) participation
Not EL eligible or monitored 47,623 57.3 18,222 21.9 11,758   14.1 5,511 6.6 17,269   20.8
EL eligible or monitored   3,366 57.0   1,216 20.6      802   13.6    521 8.8   1,323   22.4

7.3 Mastery Results

As previously described, student performance levels are determined by applying cut points to the total number of linkage levels mastered in each subject. This section summarizes student mastery of assessed EEs and linkage levels, including how students demonstrated mastery from among three scoring rules and the highest linkage level students tended to master.

7.3.1 Mastery Status Assignment

As described in Chapter 5 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022), student responses to assessment items are used to estimate the posterior probability that the student mastered each of the assessed linkage levels using diagnostic classification modeling. The linkage levels, in order, are Initial Precursor, Distal Precursor, Proximal Precursor, Target, and Successor. A student can be a master of zero, one, two, three, four, or all five linkage levels, within the order constraints. For example, if a student masters the Proximal Precursor level, they also master all linkage levels lower in the order (i.e., Initial Precursor and Distal Precursor). Students with a posterior probability of mastery greater than or equal to .80 are assigned a linkage level mastery status of 1, or mastered. Students with a posterior probability of mastery less than .80 are assigned a linkage level mastery status of 0, or not mastered. Maximum uncertainty in the mastery status occurs when the probability is .5, and maximum certainty occurs when the probability approaches 0 or 1. In addition to the calculated probability of mastery, students could be assigned mastery of linkage levels within an EE in two other ways: correctly answering 80% of all items administered at the linkage level or through the two-down scoring rule. The two-down scoring rule was implemented to guard against students assessed at the highest linkage levels being overly penalized for incorrect responses. When a student did not demonstrate mastery of the assessed linkage level, mastery was assigned at two linkage levels below the level that was assessed. The two-down rule is based on linkage level ordering evidence and the underlying learning map structure which is presented in Chapter 2 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022).

As an example of the two-down scoring rule, take a student who tested only on the Target linkage level of an EE. If the student demonstrated mastery of the Target linkage level, as defined by the .80 posterior probability of mastery cutoff or the 80% correct rule, then all linkage levels below and including the Target level would be categorized as mastered. If the student did not demonstrate mastery on the tested Target linkage level, then mastery would be assigned at two linkage levels below the tested linkage level (i.e., mastery of the Distal Precursor), rather than showing no evidence of EE mastery at all.

The percentage of mastery statuses obtained by each scoring rule was calculated to evaluate how each mastery assignment rule contributed to students’ linkage level mastery statuses during the 2023–2024 administration of DLM assessments (see Figure 7.1). Posterior probability was given first priority. That is, if scoring rules agreed on the highest linkage level mastered within an EE (i.e., the posterior probability and 80% correct rule both indicate the Target linkage level as the highest mastered), the mastery status was counted as obtained via the posterior probability. If mastery was not demonstrated by meeting the posterior probability threshold, the 80% scoring rule was imposed, followed by the two-down rule. This means that EEs that were assessed by a student at the lowest two linkage levels (i.e., Initial Precursor and Distal Precursor) are never categorized as having mastery assigned by the two-down rule. This is because the student would either master the assessed linkage level and have the EE counted under the posterior probability or 80% correct scoring rule, or all three scoring rules would agree on the score (i.e., no evidence of mastery), in which case preference would be given to the posterior probability. Across grades and subjects, approximately 66%–79% of mastered linkage levels were derived from the posterior probability obtained from the modeling procedure. Approximately 2%–9% of linkage levels were assigned mastery status by the percentage correct rule. The remaining 17%–29% of mastered linkage levels were determined by the two-down rule.

Figure 7.1: Linkage Level Mastery Assignment by Mastery Rule for Each Subject and Grade

Two sets of stacked bar charts for ELA and mathematics. There is a bar chart for each grade, and the stacks within each bar chart represent a mastery rule and the percentage of mastery statuses obtained by each scoring rule. The highest percentage of linkage level mastery assignment across all grades is for the posterior probability mastery rule.

Because correct responses to all items measuring the linkage level are often necessary to achieve a posterior probability above the .80 threshold, the percentage correct rule overlaps considerably with the posterior probabilities (but is second in priority). The percentage correct rule did provide mastery status in instances where correctly responding to all or most items still resulted in a posterior probability below the mastery threshold. The agreement between the posterior probability and percentage correct rules was quantified by examining the rate of agreement between the highest linkage level mastered for each EE for each student using each method. For the 2023–2024 operational year, the rate of agreement between the two methods was 86%. When the two methods disagreed, the posterior probability method indicated a higher level of mastery (and therefore was implemented for scoring) in 62% of cases. Thus, in some instances, the posterior probabilities allowed students to demonstrate mastery when the percentage correct was lower than 80% (e.g., a student completed a four-item testlet and answered three of four items correctly).

7.3.2 Linkage Level Mastery

Scoring for DLM assessments determines the highest linkage level mastered for each EE. This section summarizes the distribution of students by highest linkage level mastered across all EEs. For each student, the highest linkage level mastered across all tested EEs was calculated. Then, for each grade, the number of students with each linkage level as their highest mastered linkage level across all EEs was summed and then divided by the total number of students who tested in the grade and subject. This resulted in the proportion of students for whom each level was the highest linkage level mastered.

Figure 7.2 displays the percentage of students who mastered each linkage level as the highest linkage level across all assessed EEs for ELA and mathematics. For example, across all grade 3 mathematics EEs, the Initial Precursor level was the highest level that 40% of students mastered. The percentage of students who mastered the Target or Successor linkage level as their highest level ranged from approximately 38% to 57% in ELA and from approximately 13% to 37% in mathematics.

Figure 7.2: Students’ Highest Linkage Level Mastered Across English Language Arts and Mathematics Essential Elements by Grade in 2023–2024

Two sets of stacked bar charts for ELA and mathematics. There is a bar chart for each grade, and the stacks within each bar chart represent a linkage level and the percentage of students who mastered that linkage level as their highest level. The highest linkage level for most students was below the Target level.

7.4 Data Files

DLM assessment results were made available to DLM state education agencies following the spring 2024 administration. Similar to previous years, the General Research File (GRF) contained student results, including each student’s highest linkage level mastered for each EE and final performance level for the subject for all students who completed any testlets. In addition to the GRF, the states received several supplemental files. Consistent with previous years, the special circumstances file provided information about which students and EEs were affected by extenuating circumstances (e.g., chronic absences), as defined by each state. State education agencies also received a supplemental file to identify exited students. The exited students file included all students who exited at any point during the academic year. In the event of observed incidents during assessment delivery, state education agencies are provided with an incident file describing students affected; however, no incidents occurred during 2023–2024.

Consistent with previous delivery cycles, state education agencies were provided with a 2-week window following data file delivery to review the files and invalidate student records in the GRF. Decisions about whether to invalidate student records are informed by individual state policy. If changes were made to the GRF, state education agencies submitted final GRFs via Educator Portal. The final GRF was used to generate score reports.

7.5 Score Reports

Assessment results were provided to state education agencies to report to parents/guardians, educators, and local education agencies. Individual Student Score Reports summarized student performance on the assessment by subject. Several aggregated reports were provided to state and local education agencies, including reports for the classroom, school, district, and state.

No changes were made to the structure of individual or aggregated reports during spring 2024. For a complete description of score reports, including aggregated reports, see Chapter 7 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022).

7.5.1 Individual Student Score Reports

Similar to previous years, Individual Student Score Reports included two sections: a Performance Profile, which describes student performance in the subject overall, and a Learning Profile, which provides detailed reporting of student mastery of individual skills. During 2023–2024, a new helplet video was created to support interpretation of score reports. For a description of the new score report interpretation video, see Chapter 9 of this manual. Further information on evidence related to the development, interpretation, and use of Individual Student Score Reports and sample pages of the Performance Profile and Learning Profile can be found in Chapter 7 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022).

7.6 Quality-Control Procedures for Data Files and Score Reports

No changes were made to the quality-control procedures for data files and score reports for 2023–2024. For a complete description of quality-control procedures, see Chapter 7 of the 2021–2022 Technical Manual—Year-End Model (Dynamic Learning Maps Consortium, 2022).

7.7 Conclusion

Results for DLM assessments include students’ overall performance levels and mastery decisions for each assessed EE and linkage level. During spring 2024, ELA and mathematics assessments were administered to 89,338 students in 14 states adopting the Year-End model. Between 9% and 33% of students achieved at the At Target or Advanced levels across all grades and subjects. Of the three scoring rules, linkage level mastery status was most frequently assigned by the posterior probability of mastery, and students tended to demonstrate mastery of the Target or Successor level at higher rates in English language arts than in mathematics.

Following the spring 2024 administration, three data files were delivered to state education agencies: the GRF, the special circumstance code file, and the exited students file. No changes were made to the structure of data files, score reports, or quality-control procedures during 2023–2024.