Measuring Behavioral Evaluation Performance Using Neural Network and Regression Analysis


  •   Dimitrios Varveris

  •   Vasilios Tsiantos

  •   Vasilios Saltas


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.


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How to Cite
Varveris, D., Tsiantos, V., & Saltas, V. (2022). Measuring Behavioral Evaluation Performance Using Neural Network and Regression Analysis. European Journal of Education and Pedagogy, 3(6), 62–67.