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Evaluation of an Extendable Context-Aware “Learning Java” App with Personalized User Profiling

Evaluation of an Extendable Context-Aware “Learning Java” App with Personalized User Profiling In recent times, there has been an uptake of mobile learning (hereafter, abbreviated as m-learning), i.e. learning with mobile technologies, especially in the form of mobile apps. Apps are particularly useful as they are small applications usually with a single-purpose and allow the users to view learning content offline from their mobile devices. They can be a good supplement to formal learning/training and can be very efficient informal learning tools, allowing learners to learn anytime and anywhere. In this paper, we present our “Learning Java” app, which was designed based on the theoretical framework “context-aware personalized m-learning application with m-learning preferences” (Yau and Joy 2011). The app utilizes a personalized user profile consisting of location, noise and time of day, as well as the learner’s knowledge level. Additionally, an understanding of different m-learning preferences by learners is represented in our app as their individual user profile, for example, a learner may concentrate the best in a quiet library and the app will select appropriate (more difficult and longer) material based on this information, as opposed to shorter and easier materials. Video materials are also used by learners. This app was tested by 40 volunteers; 10 of which completed a long questionnaire regarding the usage of the app in terms of personalized user profile, context-awareness factors and whether the app helped increase their motivation for learning and their learning effectiveness for the subject. The results highlighted that participants could optimize their spare times for most effective learning (e.g. video-watching with headphones) in busy and/or noisy environments. Findings also showed other chosen learning strategies by learners to make their learning more effective. Future work includes (1) extending the app for other subjects and disseminating it for use by remote learners, for example, those who are situated in developing countries without frequent access to wireless internet and/or educational materials, and (2) including learning analytical support to students to enhance their study success. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Technology, Knowledge and Learning" Springer Journals

Evaluation of an Extendable Context-Aware “Learning Java” App with Personalized User Profiling

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Education; Learning and Instruction; Mathematics Education; Educational Technology; Science Education; Creativity and Arts Education
ISSN
2211-1662
eISSN
2211-1670
DOI
10.1007/s10758-017-9339-7
Publisher site
See Article on Publisher Site

Abstract

In recent times, there has been an uptake of mobile learning (hereafter, abbreviated as m-learning), i.e. learning with mobile technologies, especially in the form of mobile apps. Apps are particularly useful as they are small applications usually with a single-purpose and allow the users to view learning content offline from their mobile devices. They can be a good supplement to formal learning/training and can be very efficient informal learning tools, allowing learners to learn anytime and anywhere. In this paper, we present our “Learning Java” app, which was designed based on the theoretical framework “context-aware personalized m-learning application with m-learning preferences” (Yau and Joy 2011). The app utilizes a personalized user profile consisting of location, noise and time of day, as well as the learner’s knowledge level. Additionally, an understanding of different m-learning preferences by learners is represented in our app as their individual user profile, for example, a learner may concentrate the best in a quiet library and the app will select appropriate (more difficult and longer) material based on this information, as opposed to shorter and easier materials. Video materials are also used by learners. This app was tested by 40 volunteers; 10 of which completed a long questionnaire regarding the usage of the app in terms of personalized user profile, context-awareness factors and whether the app helped increase their motivation for learning and their learning effectiveness for the subject. The results highlighted that participants could optimize their spare times for most effective learning (e.g. video-watching with headphones) in busy and/or noisy environments. Findings also showed other chosen learning strategies by learners to make their learning more effective. Future work includes (1) extending the app for other subjects and disseminating it for use by remote learners, for example, those who are situated in developing countries without frequent access to wireless internet and/or educational materials, and (2) including learning analytical support to students to enhance their study success.

Journal

"Technology, Knowledge and Learning"Springer Journals

Published: Sep 27, 2017

References