Blended or Distance Learning? Comparing Student Performance Between University and Open University

Erkki Tapio Kaila, Henri Kajasilta

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Sammanfattning

Programming and computer science are nowadays taught in various institutions to a very heterogeneous group of people. Open universities are a typical example of non-traditional educational institutes. Online learning and blended learning models are often utilized in open universities because the students rarely study full time. In this paper, we present a study where four programming and computer science courses were taught in the Open University and in the university at the same time. A blended learning methodology was used to teach the courses in the university. The students in the open university could decide freely whether they wanted to take the courses fully online or to participate into classroom sessions as well. Moreover, no lectures were given in the open university. Instead, the students could download lecture handouts and other material online. The results from continuous assessment and the final exam of four shared IT/CS courses were analyzed. We found out that although there are some statistical differences in the results of individual sections, in general the course results are quite similar in both universities. However, the incomplete data of chosen methodologies prevents us from fully answering the research questions.
Originalspråkengelska
Titel på gästpublikationWeb Engineering: 20th International Conference ICWE 2020, : Helsinki, Finland, June 9–12, 2020, Proceedings
Antal sidor8
UtgivningsortCham
FörlagSpringer
Utgivningsdatum10 jun 2020
Sidor477-484
ISBN (tryckt)978-3-030-50577-6
ISBN (elektroniskt)978-3-030-50578-3
DOI
StatusPublicerad - 10 jun 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational conference on web engineering - Helsinki, Finland
Varaktighet: 9 jun 202012 jun 2020
Konferensnummer: 20
https://icwe2020.webengineering.org/

Publikationsserier

NamnLecture Notes in Computer Science
FörlagSpringer
Volym12128
ISSN (tryckt)0302-9743

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  • 113 Data- och informationsvetenskap

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