An intelligent framework for activity led learning in network planning and management

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning to engage students with the subject and improve their understanding of the material. We describe the use of a fuzzy linguistic summarisation LS technique for extracting linguistically interpretable rules from student data describing prominent relationships between activity/engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem-based learning tasks. The proposed system is developed as a set of services to cater for data heterogeneity and deployable on a cloud computing platform. We present a case study and experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a group performance model GPM to deploy activity led learning ALL in a master-level module. Results show that the fuzzy rules can identify useful relationships between student engagement and performance.

[1]  S. Gest,et al.  Reading Tutoring for Students at Academic and Behavioral Risk: Effects on Time-on-Task in the Classroom , 2005 .

[2]  Àngela Nebot,et al.  Extraction of logical rules to describe students' learning behavior , 2007 .

[3]  C. Hmelo‐Silver,et al.  Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006) , 2007 .

[4]  Pierre Dillenbourg,et al.  The Evolution of Research on Computer-Supported Collaborative Learning , 2009 .

[5]  Anne E. James,et al.  Collaborative design of computer network using Activity-Led Learning approach , 2011, Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[6]  C. Bonwell,et al.  Active learning : creating excitement in the classroom , 1991 .

[7]  Vijendra Pratap Singh,et al.  Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach , 2011 .

[8]  Tossapon Boongoen,et al.  Fuzzy Qualitative Link Analysis for Academic Performance Evaluation , 2011, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[9]  Li-Xin Wang,et al.  The WM method completed: a flexible fuzzy system approach to data mining , 2003, IEEE Trans. Fuzzy Syst..

[10]  Bandit Suksawat,et al.  Propose of fuzzy logic-based students' learning assessment , 2010, ICCAS 2010.

[11]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[12]  C. Hmelo‐Silver.  Problem-Based Learning: What and How Do Students Learn? , 2004 .

[13]  Hani Hagras,et al.  An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments , 2005 .

[14]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[15]  M. A. Owais.  Subjective decision making using type-2 fuzzy logic advisor , 2009, 2009 International Conference on Information and Communication Technologies.

[16]  B. Tuckman.  DEVELOPMENTAL SEQUENCE IN SMALL GROUPS. , 1965, Psychological bulletin.

[17]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Marion Williams,et al.  Psychology for language teachers : a social constructivist approach , 1997 .

[19]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[20]  E.N. Ogor.  Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[21]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[22]  Z. Sevarac.  Neuro Fuzzy Reasoner for Student Modeling , 2006 .

[23]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[24]  Teknik Informatika,et al.  PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES , 2011 .

[25]  Hisao Ishibuchi,et al.  Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.

[26]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[27]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[28]  S. Grossberg.  Adaptive Resonance Theory , 2006 .

[29]  Margarida Romero,et al.  Gestion du temps dans les activités projet médiatisées à distance , 2010 .

[30]  N. Nguyen,et al.  Time on task mediates the conscientiousness - performance relationship , 2008 .

[31]  Jameela Al-Jaroodi,et al.  Service-oriented middleware: A survey , 2012, J. Netw. Comput. Appl..

[32]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[33]  R. Mayer.  Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. , 2004, The American psychologist.

[34]  Adam Niewiadomski,et al.  A Type-2 Fuzzy Approach to Linguistic Summarization of Data , 2008, IEEE Transactions on Fuzzy Systems.

[35]  George D. Magoulas,et al.  Neural network-based fuzzy modeling of the student in intelligent tutoring systems , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[36]  Jerry M. Mendel,et al.  Linguistic summarization using IF-THEN rules , 2010, International Conference on Fuzzy Systems.

[37]  Claire Howell Major,et al.  Foundations of Problem Based Learning , 2004 .