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PurposeThe purpose of this study is to evaluate the sustainability of the proposed mobile learning framework for higher education. Most sustainability evaluation studies use quantitative and qualitative methods with statistical approaches. Sometimes, in previous studies, machine learning models were utilized conventionally.Design/methodology/approachIn the proposed method, the authors use a novel machine learning-based ensemble approach with severity indexes to evaluate the sustainability of the proposed mobile learning system. In this severity indexes, consider the cause-and-effect relationship to identify the hidden correlation among sustainability factors. Also, the proposed novel sustainability evaluation algorithm helps to evaluate and improve sustainability iteratively to have an optimal sustainable mobile learning system. In total, 150 learners and 150 teachers in the university community engaged in the study by taking the sustainability questionnaire. The questionnaire consists of 20 questions that represent 20 sustainable factors in five sustainability dimensions, i.e. economic, social, political, technological and pedagogical.FindingsThe results reveal that the proposed system has achieved its economic and pedagogical sustainability. However, the results further reveal that the proposed system needs to be improved on technological, social and political sustainability.Originality/valueThe study focused novel machine learning approach and technique for evaluating sustainability of the proposed mobile learning framework.

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In successful mobile learning (ML) integration, factors associated with live-ware, software, and infrastructure are important. The main objective of this study is to investigate and model the influencing factors for learners and teachers at once to adopt ML in higher education. The proposed model consists of five impact factors: teacher, learner, mobile devices, ML tools, ML contents, communication technologies, and higher education institutes. Then the proposed               model was implemented using a modified Moodle mobile application and evaluated using                      60 teachers and 60 learners attached to the University of Kelaniya, Sri Lanka in 2021. According   to the experimental research design approach, the proposed impact model was assessed as pre-test and post-test surveys using seven questionnaires. According to the Pearson correlation coefficient test, the most significant factor for learners and teachers to adopt ML is the mobile device. Learning content and communication technology were elected as the second most significant adoption factors for teachers and learners consecutively. However, higher correlation values were obtained for all factors denoting that they are greatly influenced the participant to adopt ML. The significant influencing factor of each impact factor was also investigated. In conclusion, it was recommended that featured smart devices, quality learning content, user-satisfied communication technology, academic enriched ML tools and higher education institutes with sound educational facilities are crucial for the university community to adopt ML in higher education. These findings help design academic community acceptable ML environments for higher education context.