7 Reporting and Results | 2023–2024 Technical Manual Update

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.¹⁰ 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.