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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\u2019 failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M\u2010learning) model that predicts learners\u2019 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\u2010learning models were created for different learners considering their learning features (study behavior) and their weight values. The M\u2010learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners\u2019 performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature\u2019s importance in the creation of the M\u2010learning 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\u2010user 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\u2009=\u200990.77%, model 2\u2009=\u200987.69%, model 3\u2009=\u200983.85%, and model 4\u2009=\u200980.00%. Moreover, the five most important features that significantly affect the students\u2019 final performance were MP3\u2009=\u20090.34, MP1\u2009=\u20090.26, MP2\u2009=\u20090.24, NTAQ\u2009=\u20090.05, and AST\u2009=\u20090.018.<\/jats:p>","DOI":"10.1155\/2021\/5519769","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T17:50:31Z","timestamp":1626285031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context\u2010Aware, and Adaptive M\u2010Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3366-4084","authenticated-orcid":false,"given":"Muhammad","family":"Adnan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8464-7279","authenticated-orcid":false,"given":"Duaa H.","family":"AlSaeed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4590-3399","authenticated-orcid":false,"given":"Heyam H.","family":"Al-Baity","sequence":"additional","affiliation":[]},{"given":"Abdur","family":"Rehman","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2017.09.016"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2017.07.014"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2018.04.007"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40692-019-00139-3"},{"key":"e_1_2_11_5_2","unstructured":"MohamedD. 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