Abstract
Machine learning (ML) and deep learning (DL) algorithms work well where future estimations and predictions are required. Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. Furthermore, the prediction of the learning performance of learners can assist instructors and intelligent learning systems (ILSs) in taking preemptive measures (i.e., early engagement or early intervention measures) so that the learning performance of weak learners could be increased thus reducing learners’ failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M-learning) model that predicts learners’ performance and classify them into various performance groups. Subsequently, adaptive feedback and support are provided to those learners who struggle in their studies. Four M-learning models were created for different learners considering their learning features (study behavior) and their weight values. The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. In the last stage of this study, we performed an early intervention/engagement experiment on those learners who showed weak performance in their study. End-user computing satisfaction (EUCS) model questionnaire was adopted to measure the attitude of learners towards using an ILS. As compared to traditional machine learning algorithms, ANN achieved the highest prediction accuracy for all four learning models, i.e., model 1 = 90.77%, model 2 = 87.69%, model 3 = 83.85%, and model 4 = 80.00%. Moreover, the five most important features that significantly affect the students’ final performance were MP3 = 0.34, MP1 = 0.26, MP2 = 0.24, NTAQ = 0.05, and AST = 0.018.
Highlights
IntroductionDespite the numerous advantages of mobile learning (Mlearning), the practicality of M-learning had been limited due to not knowing about the exact behavior or learning features of M-learners and how these learning features have different meanings and values for different M-learners [1]
Despite the numerous advantages of mobile learning (Mlearning), the practicality of M-learning had been limited due to not knowing about the exact behavior or learning features of M-learners and how these learning features have different meanings and values for different M-learners [1].e use of mobile phones for E-learning, U-learning, and M-learning creates additional challenges for the delivery of the adaptive learning content [2]
We have developed the M-learning model powered by the artificial neural network (ANN)
Summary
Despite the numerous advantages of mobile learning (Mlearning), the practicality of M-learning had been limited due to not knowing about the exact behavior or learning features of M-learners and how these learning features have different meanings and values for different M-learners [1]. Adaptive learning (AL) is an educational method that uses computer algorithms to orchestrate the interaction with the learner and delivers tailored learning content according to the needs and performance of each learner [4]. E learning features determine the learning behavior of various learners which when collected and processed carefully can help adaptive learning systems to guide learners properly. Machine learning (ML) and deep learning (DL) algorithms can model the learning behavior of learners when the corresponding learning features are properly provided. Machine learning algorithms are commonly used for learner modeling because of their ability to represent complex feature relationships, feature weights, and combined features’ effect on the learning behavior of individual learners in the learning environment [14]. (5) Performing early intervention/engagement experiment: the objective of this experiment was to determine whether early intervention by ILSs motivates M-learners to improve their performance. e result of this experiment revealed that early intervention could be a very effective technique in encouraging weak learners to improve their learning behavior
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