An Adaptive Learning System based on Cloud Computing: Implementation and Evaluation of BDS

In this paper, we report the design and implementation of an adaptive individualized elearning environment based on cloud computing named BDS (Beijing Digital School). With its five-layer architecture and its use of cloud computing technology, BDS possesses the characteristics of both a massive access system and an adaptive e-learning environment. This paper focuses on investigating three distributed solutions proposed for load balancing: an active clustering algorithm, a random algorithm and a honey bee foraging algorithm. An experiment comparing 2 groups of students was conducted to evaluate the impact of the proposed environment on learning performance, and the results were analyzed to determine the most effective strategy.

[1]  Sebastián Ventura,et al.  Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems , 2009, Comput. Educ..

[2]  Vatcharaporn Esichaikul,et al.  Student Modelling in Adaptive E-Learning Systems , 2011 .

[3]  Pei-wei Tsai,et al.  Interactive Artificial Bee Colony Supported Passive Continuous Authentication System , 2014, IEEE Systems Journal.

[4]  Peter Brusilovsky,et al.  Adaptive and Intelligent Web-based Educational Systems , 2003, Int. J. Artif. Intell. Educ..

[5]  Curtis A. Carver,et al.  Enhancing student learning through hypermedia courseware and incorporation of student learning styles , 1999 .

[6]  Christian Wolf,et al.  iWeaver: Towards 'Learning Style'-based e-Learning in Computer Science Education , 2003, ACE.

[7]  Jean-Louis Deneubourg,et al.  Aggregation Dynamics in Overlay Networks and Their Implications for Self-Organized Distributed Applications , 2009, Comput. J..

[8]  Dustin Owens,et al.  Securing elasticity in the cloud , 2010, Commun. ACM.

[9]  Adnan Baki,et al.  Learning objects in high school mathematics classrooms: Implementation and evaluation , 2010, Comput. Educ..

[10]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem , 2011, Inf. Sci..

[11]  L. Youseff,et al.  Toward a Unified Ontology of Cloud Computing , 2008, 2008 Grid Computing Environments Workshop.

[12]  Philippe Trigano,et al.  An Adaptive Hypermedia System Integrating Thinking Style (AHS-TS) Model and Experiment , 2012 .

[13]  Andreas Papasalouros,et al.  Designing and Generating Educational Adaptive Hypermedia Applications , 2005, J. Educ. Technol. Soc..

[14]  Farhad Saba.  Student Attrition: How To Keep Your Online Learner Focused. , 2000 .

[15]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[16]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[17]  Adnan Baki,et al.  Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT , 2013, Comput. Hum. Behav..

[18]  Adam Moore,et al.  WHURLE-an adaptive remote learning framework , 2003 .

[19]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[20]  Rosa M. Carro,et al.  Sentiment analysis in Facebook and its application to e-learning , 2014, Comput. Hum. Behav..

[21]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[22]  Z. Berge.  Active, Interactive, and Reflective eLearning. , 2002 .

[23]  A. Taleb-Bendiab,et al.  A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing , 2010, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.

[24]  Tony Fisher,et al.  Evaluating Learning Style Personalization in Adaptive Systems: Quantitative Methods and Approaches , 2009, IEEE Transactions on Learning Technologies.

[25]  Jeng-Shyang Pan,et al.  Enhanced Artificial Bee Colony Optimization , 2022 .

[26]  Juan E. Gilbert,et al.  Adapting instruction in search of 'a significant difference' , 1999, J. Netw. Comput. Appl..

[27]  Junjie Li,et al.  Structural inverse analysis by hybrid simplex artificial bee colony algorithms , 2009 .

[28]  Robin S. Groff Alarcon.  Distance learning: making connections across virtual space and time , 2002 .

[29]  Tony Fisher,et al.  Real users, real results: examining the limitations of learning styles within AEH , 2007, SIGWEB Newsl..

[30]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[31]  Princy Johnson,et al.  A Dynamic Biased Random Sampling Scheme for Scalable and Reliable Grid Networks , 2008 .

[32]  Boris Aberšek,et al.  TECH8 intelligent and adaptive e-learning system: Integration into Technology and Science classrooms in lower secondary schools , 2015, Comput. Educ..

[33]  R. Srinivasa Rao,et al.  Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm , 2008 .

[34]  Elizabeth J. Brown,et al.  The use of learning styles in adaptive hypermedia , 2007 .

[35]  Ali Mustafa,et al.  An approach to Adaptive E-learning Hypermedia System based on Learning Styles (AEHS-LS): Implementation and evaluation , 2011 .

[36]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[37]  Mehmet Bahadır Çetinkaya,et al.  A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm , 2011, Turkish Journal of Electrical Engineering and Computer Sciences.

[38]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.