A Free and Open Dataset from a Prototypical Data-driven Study Assistant in Higher Education

Digital study assistants (DSAs) are an as of yet sparsely explored method to build bridges between classical, on-campus higher education and novel digital education opportunities. The DSA we present in this paper (SIDDATA) aims at supporting students to identify, reflect upon and follow their personal educational goals. Over the course of 11 months, students interacted with a prototype version 2.0 of the software, generating data about what features were interacted with, users' study-related data, and which features were deemed as useful. In this data paper, we present a preprocessed version of the DSA database for research in the domain of digital higher education. We present the data model design of the DSA and its relation to its' features. We further expand on the data extraction method used to generate the present dataset from the DSA's database. We discuss potential research paths that can be explored based on the dataset as well as its limitations