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Statistical processing

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Population and Education, Education
Eva Lotti Hansen
+45 21 59 99 06

ebr@dst.dk

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Teacher-student register for primary school

The data for this registry is collected weekly from the schools' digital login system, Unilogin, and the communication platform, AULA, as automatic system-to-system reporting. The weekly updates are compiled into chronological tracks, which undergo a broad validation process followed by and enrichment of the data, also on a weekly basis. Once a year data from the two sources are integrated and combined with data from the Student Registry.

Source data

The data sources for MED-Elementary are the two administrative systems, AULA and Unilogin, which are used in Danish public schools, and also Statistics Denmark's Student Registry. Unilogin is a digital ID, which all students and teachers in the elementary schools are assigned. It can follow a student all the way from daycare through high school. Data from Unilogin contains information about the persons associations to an institution, grade, and groups. AULA is a digital communication platform, which provides information about scheduled activities, and which groups and employees are associated to the activities. The Student Registry, which is an existing registry over all students in the ordinary educational system, is used to consolidate the student population in MED. It is required that all student records are consistent with the Student Registry on institution, period and grade level.

Frequency of data collection

The statistics are based on data collected weekly from AULA and Unilogin.

Data collection

Data is collected via system-to-system report from AULA and Unilogin.

Data validation

When data is received by Statistics Denmark, they go through an overall quality assurance process, where improbable fluctuations in the number of reported institutions, groups, students, employees and activities are searched for. Various types of potential errors are counted and reported, such as invalid CPR numbers, overlapping student courses, students without a group and employees without a user ID. Finally, calculations are made of how many of the mandatory activities that are associated with teachers and students, how many activities lie outside the normal school day, and the distribution of the length of the activities. The statements are used to monitor data quality, so that the schools or system suppliers can be contacted in the event of unexplained fluctuations. The ongoing monitoring of the data flow also contributes to improving and testing the automated data processing, which is described in section 3.05.

Data compilation

Information from the two data sources is integrated with the Student Register once a year and then published.

In AULA the activities are only described by a free text field, which may or may not refer to an ordinary school subject. At Statistics Denmark, the reported activity descriptions are translated via a text recognition algorithm into one of 28 mandatory subjects, which are described in the Proclamation of the Law on Basic Schools. If the activity cannot be translated into one of the 28 subject, it is labeled "Other subject".

The weekly obtained data contain information about the student population, the teacher population, the groups and their schedules as they appear on the date when the data is retrieved. The data is then added to all previous weeks' data, such that a chronology is built, where duplicate information is deleted, changes to the existing information are corrected, and tracks are merged or terminated.

Once a year, the information from Unilogin and AULA are integrated and subjected to some logical rules for their mutual relations. For example, a teacher cannot be listed as teaching a lesson, if the teacher is not associated with the institution on the date of the lesson. The student population is then replaced with the primary school students from the Student Register, which is then supplemented with information and group relations from Unilogin to the extent that there is consistency between the two sources in terms of institution, grade level, start and end dates.

Adjustment

No corrections are made to data other than what is described under Data Validation and Data Processing