Abstract
The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners? performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.
Highlights
The aim of this research study is to develop and deploy an m-learning model that can deliver adapted learning contents to individual learners taking into account their preferences, performance, learning contexts, and background knowledge.For creating an efficient and comprehensive m-learning model, it is important to carefully observe what learning features are generated during the learning process and how these features change for diversified learners
M-learning features can be categorized into different groups which include learning content, learning context, social interaction, target learning object, number of revision for target object, number of clicks, login frequency, and the learner performance
Conclusions and future work The contribution of this research is the generation of the m-learning model from learners’ features that can be used in an adaptive learning environment
Summary
The aim of this research study is to develop and deploy an m-learning model that can deliver adapted learning contents to individual learners taking into account their preferences, performance, learning contexts, and background knowledge. Proper and right learning features are important for efficient modeling of learner knowledge and providing exclusive information in the adaptive learning process. In the evaluation process of user data, mostly learning classifier systems, rule-based inference systems, machine learning algorithms and information engineering methods are used. Based on the TripAdvisor service case study, they compared the performances of naïve Bayes, JRip, and J48 classification algorithms using features dataset derived from hotel reviews Those reviews were considered helpful for which at least 75% of the classified opinions were positive. The weight-tuning approach improves user/learner modeling estimation, prediction, and classification results
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