999
Views
19
CrossRef citations to date
0
Altmetric
Articles

A comprehensive cybersecurity learning platform for elementary education

ORCID Icon, , &
Pages 81-106 | Published online: 30 Aug 2019
 

ABSTRACT

For elementary students, security and privacy education is anticipated to be more joyful when the knowledge is delivered in the form of a digital game-based learning activity. This paper details on the development of a novel learning platform that comprises a web-based Learning Content Management Systems (LCMS) and a mobile client application (app) for educating and raising young learners’ awareness on basic cybersecurity and privacy issues. The app, which comprises a suite of quick games, can be played either in standalone or in client/server mode and it is especially destined to elementary students. Further, due to the anytime and anywhere characteristics of the app, it can be experienced as a classroom or an outdoor learning activity. Contrary to analogous studies found in the literature so far, during the design phase of the app, our focus was not solely on its technological aspects, but we uniformly paid special attention to the educational factor by applying the Attention, Relevance, Confidence, and Satisfaction (ARCS) model of motivation. A preliminary evaluation of the app, including learning effectiveness, usability, and user’s satisfaction was conducted with 52 elementary-aged students. Among others, the results show that the interaction with the app significantly increases the mean performance of the participants by almost 20%.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 101.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.