Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model
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 learnin...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5519769 |
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| author | Muhammad Adnan Duaa H. AlSaeed Heyam H. Al-Baity Abdur Rehman |
| author_facet | Muhammad Adnan Duaa H. AlSaeed Heyam H. Al-Baity Abdur Rehman |
| author_sort | Muhammad Adnan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6b96843db0c048a69899fcf2a08b6aa9 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6b96843db0c048a69899fcf2a08b6aa92025-08-20T02:06:07ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55197695519769Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning ModelMuhammad Adnan0Duaa H. AlSaeed1Heyam H. Al-Baity2Abdur Rehman3Institute of Computing, Kohat University of Science and Technology, Kohat 26000, PakistanInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat 26000, PakistanMachine 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.http://dx.doi.org/10.1155/2021/5519769 |
| spellingShingle | Muhammad Adnan Duaa H. AlSaeed Heyam H. Al-Baity Abdur Rehman Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model Complexity |
| title | Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model |
| title_full | Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model |
| title_fullStr | Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model |
| title_full_unstemmed | Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model |
| title_short | Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model |
| title_sort | leveraging the power of deep learning technique for creating an intelligent context aware and adaptive m learning model |
| url | http://dx.doi.org/10.1155/2021/5519769 |
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