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A kind of expertise reversal effect

Personalisation effect can depend on domain-specific prior knowledge

Bibliographic Data

ID19514014
AuthorsKlaus D Stiller (0000-0002-9636-4516, University of Regensburg), Rosemarie Jedlicka (University of Regensburg)
Year2010
Volume26
Issue1
Publication date2010-03-07
Open AccessYes
TypeARTICLE
VenueAustralasian Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 1449-3098 • E-ISSN: 1449-5554
PublisherAustralasian Society for Computers in Learning in Tertiary Education (PUBLISHER • NZ)
DOI10.14742/ajet.1107
OpenAlexW2124950624
LanguageEN
Citations received5
References cited10

In instructional multimedia design, it is often recommended that text accompanying pictures be presented in a personalised style to promote learning. The superiority of personalised over formal text may be explained using social agency theory (Mayer, 2005b), but it has not been investigated empirically whether such effects are valid in classroom settings, or depend on learner characteristics such as domain-specific prior knowledge. In this research, 65 pupils from the 10th grade of German grammar schools received computerised instructions about the structure of the human eye, containing static pictures and on screen text. The texts were written either in formal or in personalised style. Personalisation of the formal text was reached by replacing 130 impersonal articles with second person possessive pronouns and 24 third person articles with second person constructions. For learners with low prior knowledge, personalisation improved drawing and labeling performance, as well as the verbalisation of structural knowledge and transfer. For learners with higher prior knowledge, only labeling and drawing performance were improved, whilst structural knowledge was not affected and transfer performance was reduced

Domain knowledge · German · Grammar · Linguistics · Multimedia · Natural language processing · Personalization · Possessive · World Wide Web · Computer Science · Educational Strategies and Epistemologies · Innovative Teaching and Learning Methods · Visual and Cognitive Learning Processes · Artificial Intelligence

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Citations per year0.83
Citation span2020 - 2025 (6)
Highly citedNo

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