DQAMLearn: Device and QoE-Aware Adaptive Multimedia Mobile Learning Framework
暂无分享,去创建一个
[1] Prajakta Diwanji,et al. Success factors of online learning videos , 2014, 2014 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2014).
[2] Tobias Hoßfeld,et al. Active Learning for Crowdsourced QoE Modeling , 2018, IEEE Transactions on Multimedia.
[3] Ioana Ghergulescu,et al. NEWTON Virtual Labs: Introduction and Teacher Perspective , 2017, 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT).
[4] Moushir M. El-Bishouty,et al. A Framework for Automatic Identification and Visualization of Mobile Device Functionalities and Usage , 2013, CHI-KDD.
[5] Gabriel-Miro Muntean,et al. A DASH-Based Adaptive Multiple Sensorial Content Delivery Solution for Improved User Quality of Experience , 2019, IEEE Access.
[6] Jaime Lloret,et al. QoE assesment of MPEG-DASH in polimedia e-learning system , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[7] Yue Dong,et al. Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning , 2018, J. Netw. Comput. Appl..
[8] D. Toher,et al. Why Welch’s test is Type I error robust , 2016 .
[9] Gabriel-Miro Muntean,et al. A LARGE-SCALE PILOT STUDY ON GAME-BASED LEARNING AND BLENDED LEARNING METHODOLOGIES IN UNDERGRADUATE PROGRAMMING COURSES , 2018, EDULEARN18 Proceedings.
[10] Eva Ibarrola,et al. Mulsemedia in Telecommunication and Networking Education: A Novel Teaching Approach that Improves the Learning Process , 2019, IEEE Communications Magazine.
[11] Abdulhadi Shoufan,et al. Estimating the cognitive value of YouTube's educational videos: A learning analytics approach , 2019, Comput. Hum. Behav..
[12] Cristina Hava Muntean,et al. A novel methodology for mapping objective video quality metrics to the subjective MOS scale , 2014, 2014 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.
[13] Kursat Cagiltay,et al. A systematic review of eye tracking research on multimedia learning , 2018, Comput. Educ..
[14] Iraj Sodagar,et al. The MPEG-DASH Standard for Multimedia Streaming Over the Internet , 2011, IEEE MultiMedia.
[15] Nian-Shing Chen,et al. A Classification Framework for Context-aware Mobile Learning Systems , 2017 .
[16] Gabriel-Miro Muntean,et al. An Innovative No-Reference Metric for Real-Time 3D Stereoscopic Video Quality Assessment , 2016, IEEE Transactions on Broadcasting.
[17] Cristina Hava Muntean,et al. QoE-aware video resolution thresholds computation for adaptive multimedia , 2017, 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).
[18] Petros Spachos,et al. A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video , 2018, Comput. Networks.
[19] Helen Crompton,et al. The use of mobile learning in higher education: A systematic review , 2018, Comput. Educ..
[20] Stefan Winkler,et al. Analysis of Public Image and Video Databases for Quality Assessment , 2012, IEEE Journal of Selected Topics in Signal Processing.
[21] Gabriel-Miro Muntean,et al. Final Frontier Game: A Case Study on Learner Experience , 2018, CSEDU.
[22] Mike Joy,et al. Technical Feasibility of a Mobile Context-Aware (Social) Learning Schedule Framework , 2013, Int. J. Distance Educ. Technol..
[23] Bernd Girod,et al. ClassX: an open source interactive lecture StreamingSystem , 2011, ACM Multimedia.
[24] Gabriel-Miro Muntean,et al. Interactive Personalised STEM Virtual Lab Based on Self-Directed Learning and Self-Efficacy , 2019, UMAP.
[25] Gabriel-Miro Muntean,et al. Water Cycle in Nature: Small-Scale STEM Education Pilot , 2018 .
[26] Paula J. Durlach,et al. Adaptive Technologies for Training and Education , 2015 .
[27] Deep Medhi,et al. Study of user QoE improvement for dynamic adaptive streaming over HTTP (MPEG-DASH) , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).
[28] Chuang-Kai Chiou,et al. Design of a personalized navigation support system for context-aware ubiquitous learning environment , 2012, LocalPeMA '12.
[29] Monther M. Elaish,et al. Mobile English Language Learning (MELL): a literature review , 2019 .
[30] Cristina Hava Muntean,et al. Analysis of Learner Interest, QoE and EEG-Based Affective States in Multimedia Mobile Learning , 2017, 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT).
[31] Cristina Hava Muntean,et al. A Novel Sensor-Based Methodology for Learner's Motivation Analysis in Game-Based Learning , 2014, Interact. Comput..
[32] Cristina Hava Muntean,et al. User QoE assessment on mobile devices for natural and non-natural multimedia clips , 2016, 2016 23rd International Conference on Telecommunications (ICT).
[33] Stephen R. Gulliver,et al. Cognitive style and personality: impact on multimedia perception , 2010, Online Inf. Rev..
[34] Guangming Shi,et al. Reduced-Reference Image Quality Assessment With Visual Information Fidelity , 2013, IEEE Transactions on Multimedia.
[35] Gabriel-Miro Muntean,et al. Final Frontier: An Educational Game on Solar System Concepts Acquisition for Primary Schools , 2017, 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT).
[36] Marcus Barkowsky,et al. Subjective quality assessment comparing UHD and HD resolution in HEVC transmission chains , 2015, 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX).
[37] L. Sterling. Intelligent Systems , 1993, Springer US.
[38] Gary J. Sullivan,et al. Introduction to the Special Issue on HEVC Extensions and Efficient HEVC Implementations , 2016, IEEE Trans. Circuits Syst. Video Technol..
[39] X. Dang. Effective Learning Recommendations Powered by AI Engine , 2018 .
[40] Mohammad Reza Nakhai,et al. Green Radio Communication Networks , 2012 .
[41] Alan C. Bovik,et al. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.
[42] Cristina Hava Muntean,et al. User-centered EEG-based multimedia quality assessment , 2013, 2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).
[43] Ioana Ghergulescu,et al. Innovative pedagogies and personalisation in STEM education with NEWTON Atomic Structure Virtual Lab , 2018 .
[44] Bhawna Dhupia,et al. Review of Cross-Platforms for Mobile Learning Application Development , 2015 .
[45] Ning-Han Liu,et al. Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors , 2013, Sensors.
[46] Cristina Hava Muntean,et al. Sensing Learner Interest Through Eye Tracking , 2009 .
[47] Philip J. Corriveau,et al. Study of Rating Scales for Subjective Quality Assessment of High-Definition Video , 2011, IEEE Transactions on Broadcasting.
[48] Huong May Truong. Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities , 2016, Comput. Hum. Behav..
[49] Cristina Hava Muntean,et al. EcoLearn : Battery Power Friendly e-Learning Environment for Mobile Device Users , 2011 .
[50] D. Saupe,et al. KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment , 2019, IEEE Transactions on Image Processing.
[51] Ioana Ghergulescu,et al. Pedagogical based Learner Model Characteristics , 2018 .
[52] Ioana Ghergulescu,et al. OULAD MOOC Dropout and Result Prediction using Ensemble, Deep Learning and Regression Techniques , 2019, CSEDU.
[53] Martin Reisslein,et al. Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison , 2011, IEEE Transactions on Broadcasting.
[54] Wei Song,et al. Acceptability-Based QoE Models for Mobile Video , 2014, IEEE Transactions on Multimedia.
[55] Sebastian Bosse,et al. Toward a Direct Measure of Video Quality Perception Using EEG , 2012, IEEE Transactions on Image Processing.
[56] Cristina Hava Muntean,et al. Energy-aware Adaptive Multimedia for Game-based e-learning , 2014, 2014 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.
[57] Cristina Hava Muntean,et al. Energy-Aware Mobile Learning:Opportunities and Challenges , 2014, IEEE Communications Surveys & Tutorials.
[58] Sangwook Lee,et al. Comparison of subjective video quality assessment methods for multimedia applications , 2007 .
[59] Chih-Kai Chang,et al. A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students , 2013, Comput. Educ..
[60] Phuoc Tran-Gia,et al. Towards QoE Management for Scalable Video Streaming , 2011 .
[61] Marcus Barkowsky,et al. The Influence of Subjects and Environment on Audiovisual Subjective Tests: An International Study , 2012, IEEE Journal of Selected Topics in Signal Processing.
[62] Petra Ritter,et al. State-Dependent Perceptual Learning , 2013, The Journal of Neuroscience.
[63] Cristina Hava Muntean,et al. Learning Assessment for Different Categories ofEducational Multimedia Clips in a Mobile Learning Environment , 2014 .
[64] Cristina Hava Muntean,et al. Educational Multimedia Profiling Recommendations for Device-aware Adaptive Mobile Learning , 2014 .
[65] Gabriel-Miro Muntean,et al. Is Multimedia Multisensorial? - A Review of Mulsemedia Systems , 2018, ACM Comput. Surv..
[66] S. Nandi,et al. M - learning in university campus scenario - Design and implementation issues , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).
[67] Ja-Ling Wu,et al. Adaptive Video Learning by the Interactive E-partner , 2010, 2010 Third IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning.
[68] Gwo-Jen Hwang,et al. Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: a structural equation model analysis , 2016 .
[69] Weisi Lin,et al. Do Personality and Culture Influence Perceived Video Quality and Enjoyment? , 2016, IEEE Transactions on Multimedia.
[70] Hazer Inaltekin,et al. Optimal Network-Assisted Multiuser DASH Video Streaming , 2018, IEEE Transactions on Broadcasting.
[71] Swarun Kumar,et al. piStream: Physical Layer Informed Adaptive Video Streaming over LTE , 2015, MobiCom.
[72] Stefan Winkler,et al. ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors , 2018, IEEE Transactions on Affective Computing.
[73] Carlos Delgado Kloos,et al. Augmented reality for STEM learning: A systematic review , 2018, Comput. Educ..
[74] Gianluca Cornetta,et al. Assessing the Effectiveness of Using Fab Lab-Based Learning in Schools on K–12 Students’ Attitude Toward STEAM , 2020, IEEE Transactions on Education.
[75] Toshio Okamoto,et al. Adaptive multimedia content delivery for context-aware u-learning , 2011, Int. J. Mob. Learn. Organisation.
[76] Cristina Hava Muntean,et al. Subjective Assessment of BitDetect—A Mechanism for Energy-Aware Multimedia Content Adaptation , 2012, IEEE Transactions on Broadcasting.
[77] Alan C. Bovik,et al. Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[78] Muneera Bano,et al. Mobile learning for science and mathematics school education: A systematic review of empirical evidence , 2018, Comput. Educ..
[79] Cristina Hava Muntean,et al. VQAMap: A Novel Mechanism for Mapping Objective Video Quality Metrics to Subjective MOS Scale , 2016, IEEE Transactions on Broadcasting.
[80] Cristina Hava Muntean,et al. Investigating Flipped Classroom and Problem-based Learning in a Programming Module for Computing Conversion Course , 2018, J. Educ. Technol. Soc..
[81] Gabriel-Miro Muntean,et al. Teaching and Learning Physics using 3D Virtual Learning Environment: A Case Study of Combined Virtual Reality and Virtual Laboratory in Secondary School , 2020 .
[82] Cristina Hava Muntean,et al. Motivation Monitoring and Assessment Extension for Input-Process-Outcome Game Model , 2014, Int. J. Game Based Learn..
[83] Cristina Hava Muntean,et al. Performance evaluation of EMOS model for mapping-based Video Quality estimation , 2015, 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA).
[84] Gabriel-Miro Muntean,et al. STEM EDUCATION WITH ATOMIC STRUCTURE VIRTUAL LAB FOR LEARNERS WITH SPECIAL EDUCATION NEEDS , 2018, EDULEARN18 Proceedings.
[85] Cristina Hava Muntean,et al. ToTCompute: A Novel EEG-Based TimeOnTask Threshold Computation Mechanism for Engagement Modelling and Monitoring , 2016, International Journal of Artificial Intelligence in Education.
[86] James P. Davis,et al. M-Learning: Exploring Mobile Technologies for Secondary and Primary School Science Inquiry. , 2019 .
[87] Maria Virvou,et al. Extending Mobile Personalization to Students with Special Needs , 2014 .