effects of learning style and student background on self-assessment and course performance
This study evaluates a new taxonomy-based self-assessment scale, and examines factors that affect assessment accuracy and course performance. The scale is based on Bloom's Revised Taxonomy and is evaluated by comparing students' self-assessment results with course performance in a programming course. Correlation has been used to reveal possible connections between student information and both self-assessment and course performance. The results show that students can place their knowledge along the taxonomy-based scale quite well and the scale seems to fit engineering students' learning style. Advanced students assess themselves more accurately than novices. The results also show that reflective students were better in programming than active. The scale used in this study gives a more objective picture of students' knowledge than general scales and with modifications it can be used in other classes than programming.
learning style self-assessment bloom's revised taxonomy programming