Tool-mediated HCI modelling instruction: evidence from three studies
Christos Katsanos,
Michalis Xenos,
Nikolaos Tselios and
Nikos Karousos
Behaviour and Information Technology, 2022, vol. 41, issue 1, 18-31
Abstract:
Effective teaching of concepts related to human computer interaction (HCI) requires introduction of the core paradigms as well as the design and evaluation methodologies to the learners. In this paper, we investigate the learning effectiveness of a tool-mediated learning activity to support instruction of established human performance models, namely the Keystroke Level Model (KLM) and Fitts’ law, and their application in web form design practice. The tool, named Keystroke Level Model Form Analyser (KLM-FA), supports learning through exploration by providing step-by-step tracing of the KLM modelling for any web form filling task. KLM-FA can be used in modelling exercises of either online or offline web forms according to different interaction strategies or users’ characteristics. Three pretest-posttest studies involving 52 students in total enrolled in computer science curricula with different education delivery methods (campus-based, blended learning, distance learning) and levels (undergraduate, postgraduate) provide evidence for the effectiveness of the proposed learning activity. In all three studies, it was found that the KLM-FA activity had a significant positive effect on students’ learning gain. In addition, students rated their perceived educational experience and KLM-FA usability as rather high. Content analysis of students’ comments also found that KLM-FA is an educationally valuable and usable tool.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/0144929X.2020.1790661 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tbitxx:v:41:y:2022:i:1:p:18-31
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tbit20
DOI: 10.1080/0144929X.2020.1790661
Access Statistics for this article
Behaviour and Information Technology is currently edited by Dr Panos P Markopoulos
More articles in Behaviour and Information Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().