Annual Mandatory Collection of Elementary and Secondary
Education Data through EDFacts
October 2018
Attachment D
EDFacts Data Set for School Years 2019-20, 2020-21, and 2021-22:
Directed Questions
OMB No. 1850-0925 v.4
Adjusted Cohort Graduation Rate 6
Academic Achievement Performance Levels 7
Rural Education Achivement Program (REAP) 8
Children with Disabilities (IDEA) 9
This attachment contains specific topics for which ED would like to obtain input from data submitters and stakeholders. Please note that in addition to these specific questions, public comments are encouraged on all of the changes proposed. While many of these questions are directed to SEA data submitters, comments from all stakeholders on these topics are welcome.
Magnet Curriculum: The federal Magnet Schools Assistance Program defines a magnet school as a public elementary school, public secondary school, public elementary education center, or public secondary education center that offers a special curriculum capable of attracting substantial numbers of students of different racial backgrounds. The package proposes the collection of an additional data group about magnet schools (Magnet Curriculum) to align the current data collection to the definition of magnet schools in the authorizing legislation for the Magnet Assistance Program.
Do SEAs have this additional information about the magnet schools?
Are there other ways for ED to obtain this information?
Name |
Magnet curriculum New! |
DG |
TBD |
Levels |
SCH |
Definition |
An indication of whether a magnet school offers a special curriculum capable of attracting substantial numbers of students of different racial backgrounds. |
Permitted Values |
|
Charter schools and their relationships are more numerous and varied than are currently described in EDFacts. To better describe and identify charter schools, we are proposing the collection of several new data groups.
Enrollment – This proposed data group would allow users of EDFacts to distinguish among charter schools based on their enrollment policies.
Is distinguishing among charter schools based on their enrollment policies useful in better describing charter schools?
Are data on enrollment policies of charter schools currently available in SEA data systems?
Are there other ways for ED to obtain this information?
State Appropriation – This proposed data group would allow users of EDFacts to distinguish among charter schools by how state appropriations are provided to the charter school.
Is distinguishing among charter schools based on how state appropriations are provided useful to better describe charter schools?
Can SEAs use state financial system or other SEA data systems to identify how charter schools receive state appropriations?
Are there other ways for ED to obtain this information?
Name |
State appropriations for charter New! |
DG |
TBD |
Levels |
SCH |
Definition |
How charter schools receive state appropriations. |
Comment |
State appropriations consist of funds provided by the state government only and do not include allocations of funds from federal grants. |
Permitted Values |
|
Charter Application Initiation – This proposed data group would allow users of EDFacts to distinguish among charter schools based on how the charter school application is initiated.
Is distinguishing among charter schools based on how the charter school application is initiated useful?
Are data on the initiation of a charter school’s application available in SEA data systems? If the data are not in the SEA data systems currently, what is the burden to the SEA to obtain these data from state charter authorizers?
Are there other ways for ED to obtain this information?
Name |
Initiation of charter application New! |
DG |
TBD |
Levels |
SCH |
Notes |
This data group is reported for new charter schools only (i.e., as charter schools are reported into EDFacts). |
Definition |
The individual or entity that submitted or solicited the application for a charter. |
Permitted Values |
|
Charter Holder – This proposed data group would allow users of EDFacts to distinguish among charter schools based on the organization that holds the charter for the school.
Is distinguishing among organizations that hold the charter useful?
Are these data available in SEA data systems?
Are there other ways for ED to obtain this information?
Name |
Charter holder New! |
DG |
TBD |
Levels |
SCH |
Definition |
The organization that holds the charter for the school. |
Permitted Values |
|
Are there other characteristics of charter schools that should be used to distinguish charter schools?
Management Organization Types – The types of management organization have been revised.
Are the types unique?
Is the list of types complete?
Name |
Management organization type Revised! |
DG |
829 |
Definition |
The type of management organization. |
Permitted Values Revised! |
|
Charter schools that received federal grants – The Charter Schools Program Grant provides SEAs and some LEAs with funds to establish charter schools. The CSP Data Collection Form (Expanding Opportunity through Quality Charter Schools Program: Technical Assistance to Support Monitoring, Evaluation, Data Collection, and Dissemination of Best Practices) obtains the list of schools that received these grants annually. ED is considering merging that collection into EDFacts.
Would merging that collection into EDFacts reduce burden on SEAs?
Would merging that collection into EDFacts increase data quality?
The Carl D. Perkins Career and Technical Education Act of 2006 was reauthorized on July 31, 2018 as the Strengthening Career and Technical Education for the 21st Century Act. ED is proposing moving the collection of all the Perkins V enrollment and performance data to EDFacts for both secondary and postsecondary. For SY 2019-20, only enrollment data would be collected. For SY 2020-21, both enrollment and Perkins V performance data would be collected. Under Perkins IV, the performance data for secondary programs are collected using EDFacts.
Students served: The membership data (FS052, DG 39) are the unduplicated counts of students in a school or LEA. Shared-time schools, by definition, provide instruction on a part-time basis to students who are counted in membership at other schools. Consequently, the membership reported by shared-time schools may significantly understate the number of students actually served by the school, misrepresenting the school to casual users of the data and creating invalid indicators where membership is used as a denominator (e.g., student/teacher or expenditures per pupil). A count of students served would help address these concerns while being less burdensome to collect than full-time equivalency (FTE) student counts. NCES would not substitute the count of students served for membership in its reporting of per-pupil ratios, however the data would provide users with additional contextual information to better understand these ratios. The students served data would be collected only by grade level.
Would using students served improve users’ understanding of the data?
Do SEAs have information on students served?
What data quality concerns are associated with these data?
Pre K Enrollment: Prekindergarten (PK) enrollment has been getting more attention recently. Ideally, ED would like to capture enrollment in all publicly-funded PK programs. However, ED has noted that reporting of these data can vary from state to state and even between reporting levels within a state. Therefore, to better understand what is being reported, ED is soliciting input from the State Education Agencies.
The current guidance for reporting PK membership (FS 052, DG 39) says to “Include all groups or classes that are … administered by a public school, local education agency, or SEA ….” However, many states have PK programs that are administered by agencies other than the SEA. ED is considering changing this guidance to require that SEAs include programs run by other state agencies in their reporting of PK membership.”
Does your state’s current reporting of PK enrollment include publicly-funded programs run by agencies other than the SEA?
Do data exist for these other PK programs in the SEA data systems?
Can the SEA access data for these other PK programs?
Do the data for these programs meet the EDFacts reporting guidelines (i.e., an October 1 headcount disaggregated by race/ethnicity and sex)?
Would your state be able to include these data in their reporting for school year 2019-20?
Dual Enrollment: Interest in understanding and tracking dual enrollment has increased. ED would like to determine the availability of these data.
Does your state track dual enrollment with postsecondary?
How is dual enrollment defined in the state?
Sex/Gender: EDFacts currently includes Sex as a data category (The concept describing the biological traits that distinguish the males and females of a species) for many data groups with female and male as the permitted values.
Will a change in the definition of sex, taking out “biological traits”, work for your SEA? Do you use a different definition?
Are the two permitted values useful to SEAs? Do you have trouble reporting all your students in these two permitted values?
Would a change from sex to gender (i.e., allowing for more permitted values) increase your burden or decrease your burden in reporting?
If your state recommends a change to gender, what permitted values should be included?
Homeless Students Enrolled: The federal office responsible for the Education for Homeless Children and Youths Program (McKinney-Vento) needs more information on the demographics of homeless children. The proposal is to add a category set for race/ethnicity to the Homeless Students Enrolled table (FS 118, DG 655) at the SEA and LEA level.
Does your SEA currently collect data on the race/ethnicity of homeless students enrolled in a way that can be reported in this data group?
ED is considering expanding the staff category used for the Common Core of Data to include a new staff category of school psychologist. Currently school psychologists are included in the student support staff.
Does your state data collection differentiate school psychologist from other student support staff?
If so, how does your state define school psychologist?
If not, what would be the burden to differentiate school psychologists?
Diploma Pathways and Regular High School Diploma Definition: With the passage of ESSA, language was introduced to the graduation rate reporting requirements indicating that states may only include the regular high school diploma “awarded to a preponderance of students in the states.” The adjusted-cohort graduation rate data group definitions are changing to include this clause.
How will this change in the definition of regular diplomas change the number of students reported as receiving a regular diploma in your state’s adjusted-cohort graduation rate?
ED is proposing to collect the completion pathways of students included in the adjusted-cohort graduation rate. Increased transparency on how states are awarding diplomas and other completions could provide better information to use in conversations regarding moving more students towards college and career ready pathways. The proposed data group and categories are presented below. The pathways would be described by the state in a metadata survey.
Can your state distinguish students among the different pathways?
How many pathways does your state currently have?
Are there challenges with reporting this data group anticipated in your state? If so, please explain.
Given ED’s proposal to collect the cohort graduation rate data by graduation pathway, what metadata does your state expect ED would need to collect in order to accurately interpret and use the data?
Name |
Pathways to Completion New! |
DG |
TBD |
Definition |
The number of students in the cohort. |
Categories
|
|
Category Name: Cohort Pathway New! |
|
DG |
Pathways to Completion |
Definition |
The pathways of the students in the cohort. |
Permitted Values
|
|
Category Name: Cohort Outcome New! |
|
DG |
Pathways to Completion |
Definition |
The outcome of the pathway of the students in the cohort. |
Permitted Values
|
|
The School Year 2018-19 collection will include the new accountability indicators.
What types of metadata are important for ED to know from your state in order to correctly interpret the different indicators?
ED is proposing to collapse the current performance levels used in FS 175, 178, and 179 into two levels: proficient and not proficient. This would apply to all reporting levels, assessment types, and subgroups. In the current format, states report over 1,000 data points to describe proficiency by subgroup, for one subject, for a single LEA. This proposed change would bring the data points down closer to 400. ED does not use detailed proficiency level data; ED reports and uses data about students proficient and not proficient. ED believes overall burden reduction will be evident in data files, as well as by eliminating the burden associated with reconciling discrepancies between EMAPS responses and data files reported to ED. With the change, States can continue to publish detailed performance level data and the opportunity for disclosure avoidance conflicts between states and ED will be eliminated.
To illustrate the proposed change, the following is a scenario for a state with 5 performance levels (levels 3 and higher are considered proficient) that tested 100 students.
Current reporting using State Performance Levels |
Proposed reporting using only two Performance Levels |
Level 1 = 15 |
Not proficient = 40 |
Level 2 = 25 |
|
Level 3 = 10 |
Proficient = 70 |
Level 4 = 30 |
|
Level 5 = 30 |
Will this change reduce reporting burden for SEAs over the next three school years?
Will this change reduce burden for the SEA during ED’s data quality process of assessment results with the SEA?
Alternate Assessments: With the passage of ESSA, states are required to make available an alternate ELP assessment. Understanding the degree to which students are being offered an alternate ELP assessment, and how they are performing relative to their peers taking the general ELP assessment will be an important policy question to be able to answer as ED implements ESSA. A new data category is being proposed to capture use of the alternate assessment in the ELP assessment data groups (151, 674, 675, and 676).
Is your state able to disaggregate your ELP results by regular and alternate assessments?
Are there challenges with this data group anticipated in your state? If so, please explain.
Category Name: Assessment Administered (ELP) New! |
|
DGs |
Title III English language proficiency results table, English language proficiency test table, Title III English language proficiency test table, English language proficiency results table |
Definition |
The types of English language proficiency assessments administered. |
Permitted Values
|
|
Exits: The last package did not include all the data needed for ED to calculate percentages of Title III exiting and percentages not attaining proficiency after five years. New data categories have been added to data groups 840 (Title III English learners not proficient within five years) and 841 (Title III English learners exited) so that both the numerator and denominator for these data groups are reported.
Can your state report the denominator for both of these data groups?
Are there challenges with this data group anticipated in your state? If so, please explain.
Currently REAP collects data from each SEA to help in the determination of eligibility for all districts via max.gov, a government web portal for collecting data and communication with the Federal Program Office. ED would like to know if it would be easier and more efficient to collect these data through the EDFacts system. This would also allow for these data to be made more available across ED. Data collected include: average daily attendance and Title IIA and Title IVA allocation amounts.
Educational Environments: ED would like to understand the usefulness of the educational environments data.
How are the current educational environments data for children with disabilities, ages 6-21, useful to your state?
How could ED modify the current data categories (educational environments) in a manner that makes the data more useful for States, districts, and schools?
Are there other data categories that should be included?
To what extent, if any, does the current measure of 80 percent of the day in a general education environment impact individualized decision-making on behalf of kids or affect school-level decisions regarding placements that will best meet the needs of individual children?
Pre-school Educational Environments: ED would like to understand the usefulness of the preschool educational environments data.
How are the current educational environments data for children with disabilities, ages 3-5, useful to your state?
How would changing the term “attending” to “enrolled” in a regular early childhood program affect the reporting requirement or affect how your state uses the data?
Children with Disabilities Age 5 and in Kindergarten: ED is proposing to add an optional data category to measure the number of Children with Disabilities who are both Age 5 and in Kindergarten. This would allow states to distinguish these students from their pre-school Children with Disabilities and in educational environment. Would your state find this additional detail useful, why or why not?
Special Education and Paraprofessional and grade span: ED is proposing to change the reporting by age group for special education and paraprofessional personnel to grade spans (pre-school, elementary, middle, and high school).
Would disaggregated personnel data better enable your state to identify and address projecting personnel demand?
These questions are related to the Directory which is explained, in detail, in Attachment B. Each state has a separate and independent education system. ED is looking for information on how to consolidate that information into a national data set of education units.
Use: As explained in Attachment B, ED has many uses for the Directory data.
How do you use the CCD Directory file on LEAs?
How do you use the CCD Directory file on Schools?
Data Quality: The directory data submitted are reviewed by ED.
What data quality issues have you observed in the CCD Directory file on LEAs?
What data quality issues have you observed in the CCD Directory file on Schools?
As explained in Attachment B, each LEA is assigned to a type. The type is an important element of the directory information because it describes the entity and its expected reporting. ED is concerned that LEAs are not properly described by the existing types.
LEA types: ED is concerned that the types are not sufficient to properly classify LEAs in the nation.
Are the types provided sufficient to classify the LEAs in each state?
Are there additional types of LEAs that should be added to the list?
Changes from the last clearance: As part of the last clearance, a new type for specialized district was added.
How did your state incorporate this change?
Did this change improve the usability of the data?
Did this change improve data quality?
ED is also concerned that LEAs are not always assigned to the correct type.
Assigning LEAs types: ED is considering providing additional guidance on how to assign types.
How are LEA types assigned by the state?
Do LEAs self-classify?
LEA Types 1 and 2 Regular School Districts: The majority of LEAs are “Regular public school districts.” Most of the regular school districts are “Regular School Districts that are not part of a supervisory union” (Type 1). However, not every LEA submitted as Type 1 or 2 submits the data expected for a regular public school district. Therefore, to help clarify which entities are submitted as regular public school districts, ED added a set of key elements in the last package to describe regular public school districts. Those elements are listed in the table below.
Organization Principle |
Element |
Legitimation/Authorization |
|
Purpose |
|
Function |
|
Bureaucratic Organization |
|
Membership |
|
Governance |
|
Funding |
|
Do the above principles and key elements properly define a “Regular public school district” (Type 1 or Type 2)?
What principles or key elements are missing to properly define and classify agencies as a “Regular public school district” (Type 1 or Type 2)?
Specifically:
For Legitimation/Authorization principle, should the key elements be expanded to “the LEA is accredited” or “the LEA is included in the state’s accountability system”?
For the Bureaucratic Organization principle, should the key element be modified from “superintendent” to “official (usually called superintendent)”?
For the Membership principle, should a key element be added that LEA has staff?
Should a principle for Boundaries be added? The principle element would be “Regular school district is defined by a geographic boundary.”?
LEA Type 9 Specialized School Districts: The specialized school district type was added in the last package. The elements for a specialized school district are listed in the table below. The differences from a regular school district are bold and underlined.
Organization Principle |
Element |
Legitimation/Authorization |
|
Purpose |
|
Function |
|
Bureaucratic Organization |
|
Membership |
|
Governance |
|
Funding |
|
Do the above principles and key elements properly define a “Specialized School District” (Type 9)?
What principles or key elements are missing to properly define and classify agencies as a “Specialized School District” (Type 9)?
Other LEA Types: The remaining LEA types do not have key elements for the organization principles. Instead, the remaining LEA types are defined based on a primary characteristic as listed in the table below.
LEA type |
Primary characteristic |
Supervisory Union (Type 3) |
Is an administrative center for one or more regular public school districts |
Service Agency (Type 4) |
Provides educational services to other education agencies that agencies cannot readily provide for themselves |
Independent Charter District (Type 7) |
|
State Operated Agency (Type 5) |
Is overseen by a state agency |
Federal Operated Agency (Type 6) |
Is overseen by a federal agency |
Are the primary characteristics sufficient to define the other LEA types?
Would organization principles and key elements be useful to define the remaining LEA types? Is so, what would the key elements be?
As explained in Attachment B, each school is assigned to a type. The type is an important element of the directory information because it describes the entity and its expected reporting. ED is concerned that schools are not properly described by the existing types.
Assigning school types: ED is considering providing additional guidance on how to assign types.
How are school types assigned by the state?
Do schools self-classify?
Physical Location of Schools: A single physical location, as part of the Directory, is an expectation for defining a school. As explained in Attachment B, the directory of schools from the Common Core of Data (CCD) is used for the National Assessment of Educational Progress (NAEP) and other sample studies as a sampling frame. The sampling frames depend on each school being at one physical location. In some cases, NAEP has found that a single school reported by an SEA has multiple physical locations. When that occurs, NAEP must revise the sampling frame which costs additional money. In most cases, the schools with multiple physical locations were charter schools. Answers to the following questions would assist ED in understanding this issue and whether there are any data items that could be proposed to provide clarity.
When would a single school have multiple physical locations?
When a single school has multiple physical locations, do SEAs maintain the multiple physical locations of the school in their data systems?
For states with charter schools that operate at multiple physical locations, can the SEA identify those charter schools?
| File Type | application/vnd.openxmlformats-officedocument.wordprocessingml.document |
| Author | Beth - QIP |
| File Modified | 0000-00-00 |
| File Created | 2021-01-15 |