Modeling a Quality of Experience Aware Distance Educational System

A successful distance educational system has to provide certain commonly accepted educational objectives in order to assure its high acceptability and successful implementation. One of the main objectives of any distance educational system is to bring the educational material and its presentation closer to the individual student’s needs thus delivering high Quality of Experience (QoE). The overall QoE is a subjective perception impacted by factors from objective technical nature and subjective nature. In this paper we propose few educational scenarios that enable dynamical presentation of educational services and content delivery to the students depending on their personal affinities. Students can choose the preferred media presentation and after completion of the course an evaluation and comparison of the student’s performances on the different educational scenarios is made. The aim of the study is to explore the impact of the Quality of Experience (QoE) on the performance and thus the overall Quality of Learning (QoL) of students. For comparative analysis of the educational scenarios, t-test statistic is proposed. In addition, we propose a neuro fuzzy inference system for QoE evaluation, utilizing an ANFIS controller to compare and predict the expected QoE based on the input parameters affecting the QoE. The model should provide an aid to design of educational courses and media presentation in a way that aims toward student satisfaction and educational benefits for the current and future users of distance educational systems. This paper is focused on the description of the proposed QoE aware educational system, leaving the discussion of the experimental results for future work.

[1]  Fernando A. Kuipers,et al.  Techniques for Measuring Quality of Experience , 2010, WWIC.

[2]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[3]  Mladen A. Vouk,et al.  Workflow and End-User Quality of Service Issues in Web-Based Education , 1999, IEEE Trans. Knowl. Data Eng..

[4]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[5]  Brigitte Maier,et al.  Students' expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction , 2010, Comput. Educ..

[6]  George D. Magoulas,et al.  Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia , 2004, AH.

[7]  I. E. Allen,et al.  Going the Distance: Online Education in the United States, 2011. , 2011 .

[8]  Hsiu-Li Liao,et al.  Impact of media richness and flow on e-learning technology acceptance , 2009, Comput. Educ..

[9]  John C. Tang,et al.  Why do users like video? , 1992, Computer Supported Cooperative Work (CSCW).

[10]  Janet C. Moore,et al.  The Sloan Consortium , 2005 .

[11]  Janet C. Moore,et al.  The Sloan Consortium Quality Framework And The Five Pillars , 2005 .

[12]  Pamela A. Havice,et al.  The Impact of Rich Media Presentations on a Distributed Learning Environment: Engagement and Satisfaction of Undergraduate Students , 2010 .

[13]  Lingfen Sun,et al.  An ANFIS-based hybrid quality prediction model for H.264 video over UMTS networks , 2010, 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2010).

[14]  Mickey Shachar,et al.  Meta-Analysis: The preferred method of choice for the assessment of distance learning quality factors , 2008 .

[15]  Klara Nahrstedt,et al.  Quality of experience in distributed interactive multimedia environments: toward a theoretical framework , 2009, ACM Multimedia.

[16]  M. Allen,et al.  Evaluating the Effectiveness of Distance Learning: A Comparison Using Meta-Analysis , 2004 .

[17]  Dowming Yeh,et al.  What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction , 2008, Comput. Educ..