On assessment of students´ academic achievement considering categorized individual differences at engineering education (Neural Networks Approach)

This work introduces analysis and evaluation of an interesting, challenging, and interdisciplinary, pedagogical issue. That´s originated from categorization of the achievement diversity of students´ (individual differences), equivalently students´ Structure of the Observed Learning Outcome (SOLO). This students´ academic diversity affected in classrooms by three interactive learning/teaching approaches (orientations) namely: surface, deep, and strategic. Assessment of these approaches has been performed via realistic simulation adopting Artificial Neural Networks (ANN^s) modeling considering Hebbian rule for coincidence detection learning. That modeling results in interesting mathematical analogy of two effective learning performance factors with students´ achievement individual differences. Firstly, the effect of two brain functional phenomena; namely long term Potentiation (LTP) and depression (LTD). That´s in accordance with opening time for crossing N-methyl-D-aspartate NMDA observed at hippocampus brain area. Secondly, the effect of neurons´ number associated with diverse learning/teaching environments comprise the dichotomy (extroversion/introversion). This dichotomy has been investigated as the external and internal environmental learning conditions. The obtained simulation results concerned with student´s diversity attitudes (extroversion/introversion). They shown to be in well agreement with recently published results after performing a case study at an engineering institution in Egypt. Finally, introduced study, aims mainly to present interesting analysis of brain´s functional development based students´ individual differences, and learning abilities.