A Predictive Model of User Experience M-Learning based on Learning Style
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Abstract
Each M-Learning media type will give a unique user experience (UX). As each individual learner has a different learning style, good selection of M-learning type will build academic achievement. We thus apply UX principles in designing of M-learning to improve suitability. It is called LS-based MLUX ( Learning Style based Mobile Learning User Experience). However, selection of suitable LS-based MLUX for the learner traditionally needs testing to determine the learner’s type, then M-learning types are introduced to the learner to try out and measure their learning achievement. This process usually is complex and time-consuming, so we focus on reduction of process in LS-based MLUX finding while maintaining efficiency using factor analysis to build new components and develop a prediction model using decision tree classifier techniques to reduce LS-based MLUX selection time. High accuracy found during tests show that this prediction model can effectively recommend LS-based MLUX for the learner. Which can measure the accuracy of 94.45% which is considered to be highly accurate.
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