The Discriminant Analysis Approach for Evaluating Effectiveness of Learning in an Instructor-Led Virtual Classroom
References
- AI-Radaideh, Q. A., AI-Shawakfa, E. M. and AI-Najjar, M. I. 2006. “Mining student data using decision trees”, International Arab Conference on Information Technology Yarmouk University, Jordan, pp. 1–5.
- Angel, B., Candice, B. and Jennifer, H. 2011. “Evaluating the effectiveness of the North Carolina Virtual Public Schools System”. North Carolina Virtual Public School, pp. 1–12.
- Bower, M. and Richards, D. 2005. The impact of virtual classroom laboratories in computer science education. Thirty-Sixth SIGCSE Technical Symposium of Computer Science Education, St. Louis, Missouri, pp. 292–296.
- Brown, M. T. and Wicker, L. R. 2000. “Discriminant Analysis”, Handbook of Applied Multivariate Statistics and Mathematical Modeling, pp. 209–235.
- Etchells, A. T., Nebot, A., Vellido, A., Lisboa, P. J. and Mugica, F. 2006. “Learning what is important: feature selection and rule extraction in a virtual course”, The 14th European Symposium on Artificial Neural Networks ESANN, Bruges, pp. 401–406.
- Ferm, S. R. and Naughton, N. l. 2002. Collaborative virtual environments to support communication and community in internet-based distance education. Journal of Information Technology Education 1(3): 201–211.
- Gagne, R. 1985. “The Conditions of Learning and Theory of Instruction” 4th ed., Holt, Rinehart and Winston, New York, NY.
- Galit, B. Z., Hershkovitz, A., Mintz, R. and Nachmias, R. 2007. “Examining online learning processes based on log files analysis: a case study”. Research, Reflection and Innovations in Integrating ICT in Education 1: 55–59.
- Han, J. and Kamber, M. 2006. Data mining: concepts and techniques 2nd ed., The Morgan Kaufmann Series in Data Management Systems Jim Gray, Series Editor, Morgan Kaufmann Publishers, San Francisco.
- Hiltz, S. R. 1994. The Virtual Classroom: Learning without Limits via Computer Networks Ablex Publishing Corporation, Norwood, MA.
- Hwang, G. J., Tsai, C. C. and Yang, S. J. H. 2008. “Criteria, strategies and research issues of context-aware ubiquitous learning”. Journal of Educational Technology and Society 11(2): 81–91.
- Kankaanranta, M. and Makela, T. 2014. “Valuation of emerging learning solutions. Proceedings World Conference on Educational Multimedia, Hypermedia and Telecommunications, Tampere Association for the Advancement of Computing in Education, pp. 168–172.
- Karacapilidis, N. 2010. “Novel Developments in Web-based Learning Technologies: Tools for Modern Teaching” IGI Global, Hershey, PA.
- Keller, J. and Knopp, T. 1987. “Instructional Theories in Action: Lessons Illustrating Theories and Models”, Associates Hillsdale, Erlbaum, NJ.
- Kotsiantis, S. B., Pierrakeas, C. J. and Pintelas, P. E. 2004. “Predicting students performance in distance learning using machine learning techniques”. Applied Artificial Intelligence 18(5): 411–426.
- Magdalene Delighta Angeline, D., Ramasubramanian, P. and Samuel Peter James, I. 2015. “Learner’s prognostic analysis using class association rule”. International Journal of Advanced Research in Computer Science and Software Engineering 5(9): 314–318.
- Magdalene Delighta Angeline, D., Ramasubramanian, P. and Samuel Peter James, I. 2017. “Increased success outcome of a learner with ensemble teaching and analysis with Naive Bayes Algorithm”. Ciencia E Tecnica Vitivinicola 32(6): 59–73.
- Marks, R. B., Sibley, S. D. and Arbaugh, J. B. 2005. “A structural equation model of predictors for effective online learning”. Journal of Management Education 29(4): 531–563.
- McManus, D. A. 2001. “The two paradigms of education and the peer review of teaching”. Journal of Geosciences Education 49(5): 423–434.
- Milheim, W. D. and Martin, B. L. 1991. “Theoretical bases for the use of learner control: three different perspectives”. Journal of Computer-based Instruction, Association for the Development of Computer-Based Instructional Systems, West Woodruff Columbus OH United States, 18(3): 99–105.
- Minaei, B. and Punch, B. 2003. “Using genetic algorithms for data mining optimization in an educational web-based system”. Genetic and Evolutionary Computation 2: 2252–2263.
- Nebot, A., Castro, F., Vellido, A. and Mugica, F. 2006. Identification of fuzzy models to predict students performance in an e-learning environment. In The Fifth IASTED International Conference on Web-Based Education, Puerto Vallarta, pp. 74–79.
- New York Smart Schools Commission Report 2014. New York Smart Schools, Available at: http://www.governor. ny.gov/sites/governor.ny.gov/files/archive/governor_files/SmartSchoolsReport.pdf (accessed November 2017).
- Nunez, R. E., Edward, L. D. and Matos, J. F. 1999. “Embodied cognition as grounding for situatedness and context in mathematics education”. Educational Studies in Mathematics 39: 45–65.
- Oliver, K., Osborne, J., Patel, R. and Kleiman, G. 2009. “Issues surrounding the development of a New State wide Virtual Public Schools”. The Quarterly Review of Distance Education 10(1): 37–49.
- Reid, J. M. 1987. “The learning style preferences of ESL students” TESOL Quarterly, 21(1): 87–8711.
DOI: https://doi.org/10.21307/ijssis-2020-035 | Journal eISSN: 1178-5608
Language: English
Page range: 1 - 15
Submitted on: Jan 4, 2018
Published on: Dec 30, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2020 D. Magdalene Delighta Angeline, P. Ramasubramanian, I. Samuel Peter James, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.