Characteristic of classroom instruction in Engineering curricula is highly uni-directional and passive, and subsequent assessment of student learning effectiveness depends on specific instruction delivery mechanism and /or level of learning readiness by the recipient, students. Also, conventional assessment methodology is largely based on discretized quantification via test and assignment scores, which the very assessment efforts become reduced to mere proportional comparisons of posterity of events at a sample level that do not guarantee much needed reproducibility of desired effects or improvements under similar or the same measure implemented to the course in interest. The motivation of this research is to introduce a new pedagogical assessment framework based on statistical Randomized Factorial Design (RFD) Assessment Framework concept to capture true student feedbacks at system-level so that assessment findings can be correctly used to reproduce gains in student learning effectiveness in the future. Thus this new pedagogical assessment framework is based on an approach of logical deduction with clarity, compared to conventional assessment framework of an additive and qualitative reasoning approach, to identify what works and what does not.
Civil and Environmental Engineering
Old Dominion University