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

Autor(en): Schrumpf, J.
Weber, F.
Schurz, K.
Dettmer, N.
Thelen, T. 
Herausgeber: Cukurova, M.
Rummel, N.
Gillet, D.
McLaren, B.
Uhomoibhi, J.
Stichwörter: Artificial Intelligence; Data extraction; Dataset; Digital Study Assistant; E-learning; Education computing, Data driven; Educational goals; Educational recommendation engine; Educational Recommendation Engines; High educations; Higher Education; Modeling designs; Prototype versions; User study, Students
Erscheinungsdatum: 2022
Herausgeber: Science and Technology Publications, Lda
Journal: International Conference on Computer Supported Education, CSEDU - Proceedings
Volumen: 2
Startseite: 155
Seitenende: 162
Zusammenfassung: 
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. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Beschreibung: 
Conference of 14th International Conference on Computer Supported Education, CSEDU 2022 ; Conference Date: 22 April 2022 Through 24 April 2022; Conference Code:183566
ISBN: 9789897585623
ISSN: 2184-5026
DOI: 10.5220/0011038800003182
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133160914&doi=10.5220%2f0011038800003182&partnerID=40&md5=d98f92009228bd0682d7d42bd1e2139b

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