7 Reporting and Results

Chapter 7 of the Dynamic Learning Maps® (DLM®) Alternate Assessment System 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017) describes assessment results for the 2015–2016 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 spring 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 2023–2024 and a study related to educators’ ratings of student mastery in science.

For a complete description of score reports and interpretive guides, see Chapter 7 of the 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017).

7.1 Student Participation

During spring 2024, assessments were administered to 44,139 students in 20 states. Table 7.1 displays counts of students tested in each state. The assessments were administered by 18,286 educators in 10,963 schools and 4,118 school districts. A total of 388,150 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 = 44,139)
State Students (n)
Alaska    217
Arkansas 2,587
Delaware    317
District of Columbia    201
Illinois 4,360
Iowa    960
Kansas    905
Maryland 2,332
Missouri 2,575
New Hampshire    259
New Jersey 4,513
New Mexico    870
New York 7,498
North Dakota    248
Oklahoma 1,996
Pennsylvania 7,064
Rhode Island    391
Utah 3,745
West Virginia    576
Wisconsin 2,525

Table 7.2 summarizes the number of students assessed in each grade and course. More than 14,430 students participated in the DLM science assessment at each of the elementary and the middle school grade bands. In an effort to increase science instruction beyond the tested grades, several states promoted participation in the science assessment at all grade levels (i.e., did not restrict participation to the grade levels required for accountability purposes). In high school, almost 15,100 students participated in the DLM assessment. 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 or Course in 2023–2024 (N = 44,139)
Grade Students (n)
  3      497
  4   4,110
  5   9,829
  6   1,035
  7      971
  8 12,663
  9   3,965
10   1,941
11   8,064
12       92
Biology      972

Table 7.3 summarizes the demographic characteristics of the students who participated in the spring 2024 administration. The majority of participants were male (67%), White (58%), and non-Hispanic (78%). About 6% of students were monitored or eligible for English learning services.

Table 7.3: Demographic Characteristics of Participants in 2023–2024 (N = 44,139)
Subgroup n %
Gender
Male 29,513 66.9
Female 14,608 33.1
Nonbinary/undesignated       17   
Other          1   
Race
White 25,568 57.9
African American   9,411 21.3
Two or more races   5,482 12.4
Asian   2,297   5.2
American Indian   1,051   2.4
Native Hawaiian or Pacific Islander      246   0.6
Alaska Native       84   0.2
Hispanic ethnicity
Non-Hispanic 34,607 78.4
Hispanic   9,532 21.6
English learning (EL) participation
Not EL eligible or monitored 41,280 93.5
EL eligible or monitored   2,859   6.5

In addition to the spring assessment window, instructionally embedded science 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 3,121 students in 14 states took at least one instructionally embedded testlet during the 2023–2024 academic year.

Table 7.4: Students Completing Instructionally Embedded Science Testlets by State (N = 3,121)
State n
Arkansas    410
Delaware      93
Illinois        1
Iowa    233
Kansas    260
Maryland      35
Missouri 1,455
New Jersey    244
New Mexico        1
New York        7
North Dakota      20
Oklahoma    169
Utah    190
West Virginia        3

Table 7.5 summarizes the number of instructionally embedded testlets taken in science. Across all states, students took 21,941 science testlets during the instructionally embedded window.

Table 7.5: Number of Instructionally Embedded Science Testlets by Grade (N = 21,941)
Grade n
  3   1,084
  4   1,187
  5   4,595
  6   1,378
  7   1,488
  8   5,197
  9   1,259
10   2,048
11   3,551
12      154
Total 21,941

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 the 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017). 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 the same four performance levels approved by the DLM Governance Board for previous years:

  • 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 science assessment administration by grade. At the elementary level, the percentage of students who achieved at the At Target or Advanced levels (i.e., proficient) was approximately 17%; in middle school, the percentage of students who achieved at the At Target or Advanced levels was approximately 26%; in high school, the percentage of students who achieved at the At Target or Advanced levels was approximately 21%; in end-of-instruction biology, the percentage of students who achieved at the At Target or Advanced levels was approximately 14%.

Table 7.6: Percentage of Students by Grade and Performance Level
Grade n Emerging (%) Approaching (%) At Target (%) Advanced (%) At Target + Advanced (%)
  3      497 68.2 14.7   9.1 8.0 17.1
  4   4,110 63.3 17.5 14.7 4.5 19.2
  5   9,829 62.8 20.5 15.9 0.9 16.8
  6   1,035 57.5 22.4 13.0 7.1 20.1
  7      971 55.2 21.1 16.5 7.2 23.7
  8 12,663 53.8 20.1 19.7 6.5 26.1
  9   3,965 50.8 25.8 17.5 6.0 23.4
10   1,941 56.0 27.4 13.0 3.6 16.6
11   8,064 52.2 27.1 16.1 4.6 20.8
12       92 50.0 22.8 18.5 8.7 27.2
—      972 64.2 21.9 10.7 3.2 13.9

7.2.2 Subgroup Performance

Data collection for DLM assessments includes demographic data on gender, race, ethnicity, and English learning status. Table 7.7 summarizes the disaggregated frequency distributions for science performance levels, 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: Science Performance Level Distributions by Demographic Subgroup in 2023–2024 (N = 44,139)
Emerging
Approaching
At Target
Advanced
At Target +
Advanced
Subgroup n % n % n % n % n %
Gender
Male 16,644 56.4 6,400 21.7 5,026 17.0 1,443     4.9 6,469   21.9
Female   8,370 57.3 3,352 22.9 2,333 16.0    553     3.8 2,886   19.8
Nonbinary/undesignated       14 82.4        1   5.9        2 11.8        0     0.0        2   11.8
Other          0   0.0        0   0.0        0   0.0        1 >99.9          1 >99.9  
Race
White 14,306 56.0 5,605 21.9 4,414 17.3 1,243     4.9 5,657   22.1
African American   5,264 55.9 2,154 22.9 1,592 16.9    401     4.3 1,993   21.2
Two or more races   3,214 58.6 1,240 22.6    812 14.8    216     3.9 1,028   18.8
Asian   1,546 67.3    412 17.9    277 12.1      62     2.7    339   14.8
American Indian      518 49.3    264 25.1    209 19.9      60     5.7    269   25.6
Native Hawaiian or Pacific Islander      143 58.1      54 22.0      39 15.9      10     4.1      49   19.9
Alaska Native       37 44.0      24 28.6      18 21.4        5     6.0      23   27.4
Hispanic ethnicity
Non-Hispanic 19,600 56.6 7,623 22.0 5,792 16.7 1,592     4.6 7,384   21.3
Hispanic   5,428 56.9 2,130 22.3 1,569 16.5    405     4.2 1,974   20.7
English learning (EL) participation
Not EL eligible or monitored 23,418 56.7 9,107 22.1 6,887 16.7 1,868     4.5 8,755   21.2
EL eligible or monitored   1,610 56.3    646 22.6    474 16.6    129     4.5    603   21.1

7.3 Mastery Results

As previously described, student performance levels are determined by applying cut points to the total number of linkage levels mastered. 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 Update—Science (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, and Target. A student can be a master of zero, one, two, or all three linkage levels, within the order constraints. For example, if a student masters the Precursor level, they also master the Initial linkage level. 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. Theoretical evidence for the use of the two-down rule based on DLM content structures is presented in Chapter 2 of the 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017).

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 Initial), 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 and 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, approximately 71%–84% of mastered linkage levels were derived from the posterior probability obtained from the modeling procedure. Approximately 4%–9% of linkage levels were assigned mastery status by the percentage correct rule. The remaining 11%–21% of mastered linkage levels were determined by the two-down rule.

Figure 7.1: Linkage Level Mastery Assignment by Mastery Rule for Each Grade Band or Course

A set of stacked bar charts. 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 48% 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. 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 in science. For example, across all elementary science EEs, the Target level was the highest level that 41% of students mastered. The percentage of students who mastered as high as the Target linkage level ranged from approximately 30% to 46%.

Figure 7.2: Students’ Highest Linkage Level Mastered Across Science Essential Elements by Grade in 2023–2024

A set of stacked bar charts. 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 Additional Scoring Evidence

This section describes additional scoring evidence for DLM assessments. In 2023–2024, DLM staff examined the relationship between educators’ ratings of student mastery and linkage level mastery on DLM assessments to evaluate the extent to which DLM mastery results in science are consistent with educators’ perceptions of student mastery.

7.4.1 Relationships Between Educators’ Ratings of Student Mastery and Linkage Level Mastery

Educators completing the spring 2024 test administrator survey were asked to rate student mastery on EEs and linkage levels. The items asked test administrators to indicate if the student mastered or did not master each EE and linkage level in the grade and subject, or whether that EE was not taught. Survey responses were matched to student linkage level mastery data.

Based on educators’ ratings at the EE level, a student was considered a master at the highest linkage level rated as mastered by the educator, regardless of the pattern of ratings at lower linkage levels. This aligns to the DLM mastery assumption that mastering a linkage level implies mastery of lower linkage levels. If a student was rated as not mastering at least one linkage level for an EE, and all other linkage levels for that EE were missing or rated as not taught, then the entire EE was rated as not mastered.

Table 7.8 shows the relationship between educator ratings and DLM scoring on the highest linkage level mastered for EEs. There was a significant association between educator ratings and DLM scoring on highest linkage level mastered [χ2 (9)= 2,447, p < .001, V = .17] with a small effect size. The polychoric correlation of educator ratings and DLM mastery was moderate and positive [r(27,068) = .36].

Table 7.8: Percentage of Essential Elements by Educator Rating and Highest Linkage Level Mastered (N = 27,070)
Educator rating
DLM mastery No evidence of mastery Initial Precursor Target
No evidence of mastery 27.4 4.7 1.4 3.1
Initial 19.9 7.7 2.4 5.1
Precursor   4.9 1.7 1.1 2.4
Target   7.7 3.0 1.8 5.9
Note. Each Essential Element is included in only one cell representing the highest linkage level mastered.

Table 7.9 shows the percentage of agreement and polychoric correlations between the educator ratings and DLM scoring on the highest linkage level mastered for EEs by grade band. Exact agreement ranged from 38% to 44%, and near agreement (i.e., highest linkage level in educator ratings and DLM scoring were within one linkage level of each other) ranged from 73% to 77%. The polychoric correlations were all positive and ranged from .34 to .38.

Table 7.9: Percentage and Polychoric Correlations for Educator Rating and DLM Scoring of Highest Linkage Level Mastered by Grade Band (N = 27,070)
Grade band % Exact agreement % Near agreement r
Elementary 44.4 76.5 .380
Middle school 38.3 72.5 .340
High school 41.2 73.4 .370
Note. Near agreement = highest linkage level mastered in educator rating and DLM scoring within one linkage level of each other (exact and adjacent).

7.5 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 science 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.

7.6 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. 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 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017).

7.6.1 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 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017).

7.7 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 2015–2016 Technical Manual—Science (Dynamic Learning Maps Consortium, 2017).

7.8 Conclusion

Results for DLM assessments include students’ overall performance levels and mastery decisions for each assessed EE and linkage level. During spring 2024, science assessments were administered to 44,139 students in 20 states. Between 14% and 27% of students achieved at the At Target or Advanced levels across all grades. Of the three scoring rules, linkage level mastery status was most frequently assigned by the posterior probability of mastery. In 2024, a new study on relationships between educator ratings of student mastery and linkage level mastery based on DLM assessments found a significant association between the two mastery ratings.

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.