Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment

Abstract

Empirical directions to innovate e-assessments and to support the theoretical development of e-learning are discussed by presenting a new learning assessment system based on cognitive technology. Specifically, this system encompassing trained neural nets that can discriminate between students who successfully integrated new knowledge course content from students who did not successfully integrate this new knowledge (either because they tried short-term retention or did not acquire new knowledge). This neural network discrimination capacity is based on the idea that once a student has integrated new knowledge into long-term memory, this knowledge will be detected by computer-implemented semantic priming studies (before and after a course) containing schemata-related words from course content (which are obtained using a natural semantic network technique). The research results demonstrate the possibility of innovating e-assessments by implementing mutually constrained responsive and constructive cognitive techniques to evaluate online knowledge acquisition

Keywords

• E-assessment, learning, knowledge representation, connectionism, educational technology, innovation, neural nets

Kaynakça

1. Arieli-Attali, M. (2013, October). Formative assessment with cognition in mind: The cognitively based assessment of, for and as Learning (CBALTM) research initiative at educational testing service. Proceeding of the 39th annual conference on Educational Assessment 2.0: Technology in Educational Assessment. Paper retrieved from http://www.iaea.info/papers.aspx?id=81
2. Asogwa, O. C., & Oladugba, V. A. (2015). Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs). American Journal of Applied Mathematics and Statistics, 3(4), 151-155. doi: 10.12691/ajams-3-4-3
3. Becker, S., Moscovitch M., Behrman, M., & Joordens, S. (1997). Long-term semantic priming: A computational account and empirical evidence. Journal of experimental psychology: Learning, Memory and Cognition, 23(5), 1059-1082. Retrieved from http://eds.a.ebscohost.com.pbidi.unam.mx:8080/eds/pdfviewer/pdfviewer?vid=2&sid=43a5506e-c6c0-4352-a5c1-810767cfc0dd@sessionmgr120
4. Bersano-Méndez, N. I., Schaefer, S. E., & Bustos-Jimenez, J. (2012). Metrics and models for social networks. In A. Abraham, A. E. Hassanien (Eds), Computational social networks: Tools, perspectives and applications (pp. 115-142). London: Springer Verlag. doi: 10.1007/978-1-4471-4048-1
5. Black, P. & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles Policy and Practice, 5(1), 7-73. doi: 10.1080/0969595980050102
6. Buscema, M. (2013). Theory of constraint satisfaction neural net. In M. Buscema, & W. J. Tastle (Eds.), Intelligent data mining in law enforcement analytics: New neural networks applied to real problems (pp. 215-229). Netherlands: Springer. doi: 10.1007/978-94-007-4914-6
7. Cheng, Y. M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21, 269- 299. doi: 10.1111/j.1365-2575.2010.00356.x
8. Clark, R. C., & Mayer, R. E. (2011). E-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (3th ed.). San Francisco, CA, USA: Pfeiffer. doi: 10.1002/9781118255971

9. Conway, M. A., Cohen, G., & Stanhope, N. (1991). On the very long-term retention of knowledge acquired through formal education: Twelve years of cognitive psychology. Journal of Experimental Psychology: General, 120(4), 395-409.
10. Conway, M. A., Cohen, G., & Stanhope, N. (1992). Very long-term memory for knowledge acquired at school and university. Applied Cognitive Psychology, 6, 467-482.
11. Farrell, T. & Rushby, N. (2016). Assessment and learning technologies: An overview. British Journal of Educational Technology, 47(1), 106-120. doi:10.1111/bjet.12348
12. Figueroa, J. G., Gonzales, G. E. & Solis, V. M. (1975). An approach to the problem of meaning: Semantic networks. Journal of Psycholinguistic Research, 5(2), 107-115.
13. Flinders, D. J. (2005). The failings of NCLB. Curriculum and Teaching Dialogue, 7(1/2), 1-9.
14. Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice (2nd ed.). London: Taylor & Francis.
15. GEPHI (2017). Force Atlas method. Retrieved from http://gephi.github.io
16. Gonzalez, C. J., López, E. O., & Morales, G. E. (2013). Evaluating moral schemata learning. International Journal of Advances in Psychology, 2(2), 130-136. Retrieved from http://www.seipub.org/ijap/AllIssues.aspx?PublicationID=282
17. Green, R, G., & William, M.B. (1998). Using neural network and traditional psychometric procedures in the analysis of test scores: An exploratory study. Educational Research Quarterly, 22(2), 52-61.
18. Holley, C. D., & Danserau, D. F. (1984). Networking: The technique and the empirical evidence. In A C. D. Holley, & D. F. Danserau (Eds.), Spatial learning strategies: Techniques, applications and related issues (pp. 81-108). New York: Academic Press.
19. Ibrahim, Z., & Rusli, D. (2007, September). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. Paper presented at the 21st Annual SAS Malaysia Forum, Kuala Lumpur, Malaysia. Paper retrieved from https://www.researchgate.net/publication/ 228894873_Predicting_Students'_Academic_Performance_Comparing_ Artificial_Neural_Network_Decision_Tree_and_Linear_Regression
20. Itoyama, K., Nita, T., & Fujiki, T. (2007). On the Relation between Semantic Network and Association Map for the Assessment of Class Work. In M. Iskander (Ed.), Innovations in E-learning, Instruction Technology, Assessment, and Engineering Education (pp. 199-204). Netherlands: Springer. doi: 10.1007/978-1-4020-6262-9
21. Karamouzis, S. T., & Vrettos, A. (2008, October). An artificial neural network for predicting student graduation outcomes. Paper presented at the World Congress on Engineering and Computer Science, San Francisco, USA. Retrieved from http://www.iaeng.org/publication/WCECS2008/WCECS2008_pp991-994.pdf
22. Kirkwood, A., & Price, L. (2013). Examining some assumptions and limitations of research on the effects of emerging technologies for teaching and learning in higher education. British Journal of Educational Technology, 44(4), 536-543. doi: 10.1111/bjet.12049
23. Lin, Y. L., Chang, Y. C., Liew, K. H., & Chu, C. P. (2015). Effects of concept map extraction and a test-based diagnostic environment on learning achievement and learners’ perceptions. British Journal of Educational Technology, 47(4), 649-664. doi: 10.1111/bjet.12250
24. Lindem, W. J., & Glas, C. W. (2010). Elements of adaptive testing. New York: Springer. doi: 10.1007/978-0-387-85461-8
25. Lopez, R. E. O., Morales, M. G. E., Hedlefs, A.M.I., Gonzalez, T. C. J. (2014). New empirical directions to evaluate online learning. International Journal of Advances in Psychology, 3, 40-47. doi: 10.14355/ijap.2014.0302.03
26. Lopez, R. E. O., & Theios, J. (1992). Semantic analyzer of schemata organization (SASO). Behavior Research Methods, Instruments, & Computers, 24(2), 277-285.
27. Lopez, E. O. (1996). Schematically related word recognition (Order No. 9613356). Available from ProQuest Dissertations & Theses Global. (304292488). Retrieved from https://search.proquest.com/docview/304292488?accountid=14598
28. Lopez, R.E.O. & Theios, J. (1996). Single word schemata priming: a connectionist approach. Paper presented at the 69th Annual Meeting of the Midwestern Psychological Association, Chicago, IL.
29. Mazinani, S. M., & Abolghasempur, S. A. (2013). Prediction of success or fail of students on different educational majors at the end of the high school with artificial neural networks methods. International Journal of Innovation, Management and Technology, 4(5), 461-465. Retrieved from http://files.eric.ed.gov/fulltext/EJ1094642.pdf
30. McCombs, B. L. (2013). The Learner-Centered Model: Implications for Research Approaches. In J. H. D. Cornelius-White, R. Motschnig-Pitrik, & M. Lux (Eds.), Interdisciplinary Handbook of the Person-Centered Approach: Research and Theory (pp. 335-352). New York: Springer. doi: 10.1007/978-1-4614-7141-7
31. Mcnamara, T. P. (2005). Semantic Priming: Perspectives from Memory and Word Recognition (Essays in Cognitive Psychology). New York: Psychology Press, Taylor & Francis Group.
32. Morales-Martinez, G. E., Lopez-Ramirez, E. O., & Lopez-Gonzalez A. E. (2015). New Approaches to e-cognitive assessment of e-learning. International Journal for e-Learning Security (IJeLS), 5(2), 449-453. doi: 10.20533/ijels.2046.4568.2015.0057
33. Morales-Martinez, G. E., & Santos-Alcantara, M.G. (2015). Alternative Empirical Directions to Evaluate Schemata Organization and Meaning. Advances in Social Sciences Research Journal, 2(9), 51-58. doi: http://dx.doi.org/10.14738/assrj.29.2015
34. Morales-Martinez, G. E., Lopez-Ramirez, E. O., & Velasco-Moreno, D. (2016). Alternative e-learning assessment by mutual constrain of responsive and constructive techniques of knowledge acquisition evaluation. International Journal for Infonomics (IJI), 9(3), 1195-1200. doi: 10.20533/iji.1742.4712.2016.0145
35. Morales-Martinez, G. E., & Lopez-Ramirez, E. O. (2016). Cognitive responsive e-assessment of constructive e-learning. Journal of e-Learning and Knowledge Society, 12(4), 10-19. doi: 10.20368/1971-8829/1187
36. Nichols, S. L. N. (2007). High-Stakes Testing: Does It Increase Achievement? Journal of Applied School Psychology, 23(2), 47-64. doi: http://dx.doi.org/10.1300/J370v23n02_04
37. Padilla, M. V. M., Lopez, R. E. O., & Rodriguez, N. M. C. (2006, July). Evidence for schemata priming. Paper presented at the 4th International Conference on Memory. University of New South Wales, Sydney, Australia.
38. Padilla, M. V. M., Peña, M. V. G., Lopez, R. E. O., & Rodriguez, N. M. C. (2006, July). Using natural semantic networks to evaluate student’s performance on school courses. Paper presented at the 4th International Conference on Memory. University of New South Wales, Sydney, Australia.
39. Paiva, J., Morais, C., Costa, L., & Pinheiro, A. (2015). The shift from “e-learning” to “learning”: Invisible technology and the dropping of the “e”. British Journal of Educational Technology, 47(2), 226-238. doi: doi:10.1111/bjet.12242
40. Park, S. Y., Nam, M. W., & Cha S. B. (2011). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605. doi: 10.1111/j.1467-8535.2011.01229.x
41. Price, R. V. (1989). An historical perspective on the design of computer- assisted instruction. Computers in the schools, 6(1-2), 145-158. doi: http://dx.doi.org/10.1300/J025v06n01_12
42. Rainer, L. (2005, July). Using semantic networks for assessment of learners´ answers. Paper presented at the Sixth IEEE international conference on advanced learning technologies (ICALT – 06), (pp. 1070-1072), Kerkrade, Netherlands. doi: 10.1109/ICALT.2006.1652631
43. Rogers, T. T., & McClelland, J. J. (2004) Semantic cognition: A parallel distributed approach. Cambridge, Massachussets: MIT Press.
44. Rubin, D. I., & Kazaniian, C.J. (2011). "Just another brick in the wall”: Standardization and the devaluing of education. Journal of Curriculum and Instruction (JoCI), 5(2), 94-108. Retrieved from http://www.joci.ecu.edu/index.php/JoCI/article/view/101/pdf
45. Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart & the PDP research group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Psychological and biological models (Vol. 2, pp. 7-57): Massachusetts: MIT Press.
46. Sahay, S. (2004). Beyond utopian and nostalgic views of information technology and education: Implications for research and practice. Journal of the Association for Information Systems, 5(7), 282-313. Retrieved from http://aisel.aisnet.org/jais/vol5/iss7/9/
47. Scalise, K., Bernbaum, D. J. Timms, M., Harrell, S. V., Burmester, K., Kennedy, C. A., & Willson, M. (2007). Adaptive technology for e-learning: Principles and case studies of an emerging field. Journal of the American Society for Information Science and Technology, 58(14), 2295–2309.
48. Sorgenfrei, C., & Smolnik, S. (2016). The effectiveness of e-learning systems: A review of the empirical literature on learner control. Decision Sciences Journal of Innovative Education, 14(2), 154-184. doi: 10.1111/dsji.12095
49. Stufflebeam, D. L., Madaus, G. F., & Kellaghan, T. (2002). Evaluation models: Viewpoints on Educational and Human Services Evaluation (2nd ed.). New York, USA: Kluwer Academic Publishers.
50. Turham, K., Kurt, B., & Engin, Y. Z. (2013). Estimation of student success with artificial neural networks. Education and Science, 8(170), 112-120. Retrieved from file:///C:/Users/Lupita/Downloads/1360-25632-1-PB.pdf
51. Yen, J. C., Lee, C. Y., & Chen, I. J. (2012). The effects of image-based concept mapping on the learning outcomes and cognitive processes of mobile learners. British Journal of Educational Technology, 43(2), 307-320. doi: 10.1111/j.1467-8535.2011.01189.x