We want to investigate whether we can use innovative data modeling to find the right development areas and measures in preschool activities.
The requirements for quality and development in preschool are high and the goal is for every child to have the best possible conditions for development and learning.
With the help of machine learning, different types of data can be brought together and analyzed. Organizational data - such as group size and number of educators per child, background data at unit level and the children's groups' knowledge development can be analyzed with the help of machine learning to identify development areas and more accurate measures.
Some of the data needed to carry out the analyzes has already been collected, while others need to be collected. Since the knowledge development in preschool is not assessed at the individual level, artificial intelligence in the form of machine learning can be used to get an indication of the children's knowledge development at a unit level.
For example, we can analyze pictures of the children's work to identify a progression. Analysis of responses to different cases where educators make an assessment of the children's responses using a digital solution is another type of data that can be analyzed using ML.
The curriculum goals regarding quality and development in preschool can thus be realized with the help of more diversified data that is captured and analyzed in a more efficient and flexible way.
- Use innovative data modeling to collect and analyze children's knowledge development at unit level.
- Use in the analysis the indicators of quality criteria that the City of Helsingborg's preschools have identified.
- Triangulate data using machine learning to create device profiles and identify areas for development.
- Test the method in a pilot project where some units are included.
The preschool areas that are part of the pilot project:
- Maria Park
- Västra Ramlösa
At the H22 City Expo: