Defining the Needs for Study Data Analysis in Finnish Higher Educational Institutions: Case Peppi Study Management System Users
Rahkonen-Ellä, Päivi (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023091425801
https://urn.fi/URN:NBN:fi:amk-2023091425801
Tiivistelmä
The thesis aimed to find data analysis and reporting needs of study data at Finnish higher education institutions using the Peppi education management system. The thesis was done for Eduix Oy which is a Finnish-owned software company that maintains the Peppi system and other related products to higher education institutions in Finland.
The goal was to condense the large amount of study data into specific views that serve different data consumer roles. To identify the needs a round of semi-structured interviews were conducted to specialists in higher education institutions. The results of the interviews were analysed with thematic analysis method.
As a result five common perspectives for all data consumers were identified. The sector of higher education institutions (university or university of applied sciences) was not found to be relevant, while the size of institution, the field of education and the data consumer role had more influence.
In addition to common perspectives, seven topics for data analysis and reporting needs were discovered. Aggregated and comparable view for study data was most desired and would be used when examining the success offered courses or programmes and when giving student counselling. The functional and reliable presentations are needed to help everyday work at higher education institutions as well as for the matter of data quality assurance. The data analysis needs have been described through user stories in accordance with the methods of agile development.
For further actions it is recommended to start producing basic, reduced views together with Eduix Oy and one or two higher education institutions. When a limited number of actors are involved, it would be easier to ensure that the data consumer's point of view is taken into account in a better way, but at the same time the development process will not become too complex. After this, it would be advisable to move on to more sophisticated data analysis methods and utilise artificial intelligence and machine learning. Along with the local analysis development work it is important to closely follow the similar work done nationally.
The goal was to condense the large amount of study data into specific views that serve different data consumer roles. To identify the needs a round of semi-structured interviews were conducted to specialists in higher education institutions. The results of the interviews were analysed with thematic analysis method.
As a result five common perspectives for all data consumers were identified. The sector of higher education institutions (university or university of applied sciences) was not found to be relevant, while the size of institution, the field of education and the data consumer role had more influence.
In addition to common perspectives, seven topics for data analysis and reporting needs were discovered. Aggregated and comparable view for study data was most desired and would be used when examining the success offered courses or programmes and when giving student counselling. The functional and reliable presentations are needed to help everyday work at higher education institutions as well as for the matter of data quality assurance. The data analysis needs have been described through user stories in accordance with the methods of agile development.
For further actions it is recommended to start producing basic, reduced views together with Eduix Oy and one or two higher education institutions. When a limited number of actors are involved, it would be easier to ensure that the data consumer's point of view is taken into account in a better way, but at the same time the development process will not become too complex. After this, it would be advisable to move on to more sophisticated data analysis methods and utilise artificial intelligence and machine learning. Along with the local analysis development work it is important to closely follow the similar work done nationally.