Research Article

Measuring Behavioral Evaluation Performance Using Neural Network and Regression Analysis

Dimitrios Varveris
International Hellenic University of Greece campus Kavala, Greece.
* Corresponding author
Vasilios Tsiantos
International Hellenic University of Greece campus Kavala, Greece.
Vasilios Saltas
International Hellenic University of Greece campus Kavala, Greece.
DOI icon 10.24018/ejedu.2022.3.6.498

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Submitted 2022-10-26
Published 2022-11-21

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Article Main Content

Our era is characterized by information overload. Low skills are a demand in the world of work, such as salespeople in brick-and-mortar stores, customer reception, fast-food restaurant clerks, call center operators, order dispatchers, order packers, etc. The required educational background shrinks to the requirement for adaptability and few “basic skills”: text comprehension, basic communication in one or two foreign languages, some math, science and technology concepts, a good dose of familiarity with digital as well as interpersonal and social skills (OECD, 2001). Assessment in secondary education has not remained unaffected; the holistic nature, originality, and innovation are incompatible with everyday teaching practice. With the scientific tools of statistics and neural networks, the divergence between formal assessment and diagnostic and feedback assessment has been quantified. Finally, the evolution of behavioral learning at a public high school level during the academic year 2021-2022 has been studied.

Keywords: formative evaluation, neural networks, regression analysis, summative evaluation.

References

  1. Broadfoot, P., & Black, P. (2004). Redefining assessment? The first ten years of assessment in education. Assessment in Education: Principles, Policy & Practice, 11(1), 7–26. https://doi.org/10.1080/0969594042000208976.
     Google Scholar
  2. Delandshere, G. (2002). Assessment as Inquiry. Teachers College Record, 104(7), 1461–1484. https://doi.org/10.1111/1467-9620.00210.
     Google Scholar
  3. Gagne, R. M. (1965). The conditions of learning. New York: Rinehart &Winston.
     Google Scholar
  4. Gipps, C. (1994). Developments in educational assessment: what makes a good test? Assessment in Education: Principles, Policy & Practice, 1(3), 283–292. https://doi.org/10.1080/0969594940010304.
     Google Scholar
  5. Graue, B. (1993). Integrating theory and practice through instructional assessment. Educational Assessment, 1(4), 283–309. https://doi:10.1207/s15326977ea0104_1.
     Google Scholar
  6. Hamilton, L. S., Stecher, B. M., Marsh, J. A., McCombs, J. S., & Robyn, A. (2007). Standards-based accountability under no child left behind: Experiences of teachers and administrators in three states. Rand Corporation.
     Google Scholar
  7. Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory. New York: Appleton-Century.
     Google Scholar
  8. Kaarstein, H. (2014). Norwegian mathematics teachers’ and educational researchers’ perception of MPCK items used in the TEDS-M study. Nordic Studies in Mathematics Education, 19(3-4), 57–82.
     Google Scholar
  9. Lange, J. (2007). Freudenthal institute. Large-scale assessment of mathematics education. Second Handbook of Research on Mathematics Teaching and Learning.
     Google Scholar
  10. OECD (2001). The school of the future. What future for our schools? https://doi.org/10.1787/9789264195004-en.
     Google Scholar
  11. Palm, T., Boesen, J., & Lithner, J. (2011). Mathematical reasoning requirements in Swedish upper secondary level assessments. Mathematical Thinking and Learning, 13(3), 221–246.
     Google Scholar
  12. Peña-López, I. (2012). Pisa 2012 assessment and analytical framework Mathematics, reading, science, problem-solving and financial literacy.
     Google Scholar
  13. Skinner, B.F. (1954). The science of learning and the art of teaching. Harvard Educational Review, 24, 86–97.
     Google Scholar
  14. Shepard, L. A. (2000). The role of assessment in a learning culture. Educational Researcher, 29(7), 4–14. https://doi.org/10.3102/0013189X029007004.
     Google Scholar
  15. Suurtamm, C., Thompson, D. R., Kim, R. Y., Moreno, L. D., Sayac, N., Schukajlow, S, &Vos, P. (2016). Assessment in mathematics education: Large-scale assessment and classroom assessment. Springer Nature.
     Google Scholar
  16. Thorndike, E. L. (1922). The psychology of arithmetic. New York: Macmillan.
     Google Scholar
  17. Wang, S., Jiao, H., Young, M. J., Brooks, T., & Olson, J. (2007). A meta-analysis of testing mode effects in grade K-12 mathematics tests. Educational and Psychological Measurement, 67(2), 219–238.
     Google Scholar
  18. Wiliam, D. (2007). Keeping learning on track: Classroom assessment and the regulation of learning. Information Age Publishing.
     Google Scholar
  19. Wiliam, D. (2007). Classroom assessment and the regulation of learning. Second Handbook of Research on Mathematics Teaching and Learning. University of London.
     Google Scholar