Video Quality Adaptation for Extended Playback Time on Mobile Devices With Limited Energy

Jo, Sung-Woong; Yoo, Wonsuk; Chung, Jong-Moon

doi:10.1109/lcomm.2018.2820685

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…The mobile device is one of the dominant factors for ML adoption. Researches highlighted that the device features such as screen size [9], [10], supportive software [11], [12], [13], device's hardware functionalities [14], [15], [16], etc. are essential for ML adoption.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Impact Model for Mobile Learning Adoption in Higher Education

Dolawattha

Premadasa

2023

AJRCoS

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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.

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