Intellectual outputs


Intellectual output 7 applies learning analytics methods on the data gathered during the large-scale trialling phase of the project. The trialling phase will have provided the project consortium with a large dataset on empirical data of user interaction with the online LSP teacher training course. The data will include the users’ nationality, affiliation, status (e.g. student, early career, experienced practitioner), their choice of their learning content, the dedication time to the different course contents, the correctness of the quizzes, to name but a few. In this intellectual output, typical groups of users shall be identified with the aim to develop individualised pathways through the online course. These individualised pathways will clearly improve the user experience and increase the survival rate. The learning analytics included in this intellectual output will make use of descriptive and inferential statistics and highly innovate machine learning algorithms to classify the different LSP practitioners into separate pathway groups. The machine learning algorithms may include supervised and unsupervised methods, clustering methods, etc.
The analysis of the user groups will be made publicly available (by academic publications, social media, etc.) so that other interested parties may replicate the methodology in other online learning settings as well. A brochure will be prepared (in paper form and for download online) which includes the different identified pathways and the reasoning behind the classification.