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A Framework for Applying Educational Psychology Principles to the Design and Assessment of Learning Technology

Published: 07 October 2020 Publication History

Abstract

Nowadays, it is safe to say that technology is involved directly or indirectly in all professions and academic fields. Therefore it is common for software engineers to find themselves in multidisciplinary projects that require knowledge and skills different than their own.
In this paper, a novel framework for designing and evaluating learning applications is proposed. It is intended for software engineers who may not have the assistance of educators to apply the basic principles of educational psychology while developing an edtech solution. This framework is presented as a ready-to-use questionnaire to guide anyone who wants to evaluate a learning technology, thus leveraging collaboration between experts with diverse backgrounds.
In order to assess its suitability, we present a use case showing the application of this framework on an actual educational software.

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cover image ACM Conferences
SIGITE '20: Proceedings of the 21st Annual Conference on Information Technology Education
October 2020
446 pages
ISBN:9781450370455
DOI:10.1145/3368308
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 October 2020

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Author Tags

  1. educational psychology
  2. framework
  3. human factors
  4. learning science
  5. learning technologies
  6. software engineers

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  • Research-article

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  • DGIIP of the Universidad Tecnica Federico Santa Maria

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SIGITE '20
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Overall Acceptance Rate 152 of 371 submissions, 41%

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