Faculty of Computing

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Subject: search.filters.subject.Mobile learning

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    Group-based mobile learning: Do group size and sharing mobile devices matter?
    (Pergamon, 2015-03-01) Melero, J; Hernández-Leo, D; Manatunga, K
    Within the field of Game-based Learning (GBL) location-based games are based on pervasive and mobile learning to allow the creation of in situ learning activities considering gamification mechanisms. In these learning activities collaboration often plays an important role. Usually, groups of students have to perform different tasks with single mobile device. This paper studies the effects of sharing a mobile device within groups and the size of groups in students’ engagement and their activity performance in an indoor location-based learning activity. In particular, the paper focuses on a game designed by a secondary education teacher to support a learning activity in a contemporary art museum. The teacher’s design has been implemented using “QuesTInSitu: The Game” technology. A total of 76 students played the game during a 3-h activity in the museum. The analysis of the data shows that while there are not important differences in the satisfaction with the activity of the students carrying and not carrying the mobile device within their groups, carrying the device does have a significant (positive) impact in their performance. Group size (4 vs. 5 members) does not seem to be a variable affecting individuals’ performance but students in 4-member groups express higher levels of engagement.
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    Learning Assistant To Acquire The Fundamental Language Skills for Non-Native Learners Using AI
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Srikanthan, P.; Nizar, R.; Ravikumar, A.; Lalitharan, K.; Harshanath, S.M.B.; Alosius, J.
    The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.