Multi-disciplinary engineering courses present certain instructional challenges that stem directly from having students from many different programs in one classroom. Challenges include but are not limited to developing meaningful course materials that resonate across the disciplines and finding and applying the appropriate level of rigor for individual topics and for the course as a whole. These difficulties are compounded when the course involves large lecture sections taught by faculty and smaller lab-based sections that are taught by multiple teaching assistants. In such cases it can be difficult to assess the effectiveness both of instruction and student learning.
In this paper, we present the results of an effort to establish a methodology for assessing the quality of instruction and student learning in a multi-disciplinary engineering statistics course at a large, regional university. The introductory statistics course is offered through the Industrial Engineering department and serves approximately 25% of the college’s undergraduate student population. The lab-based course is comprised of two lecture sections (2 credit hours, ~100 students) and multiple lab sections (3 contact hours, ~25 students). Lecture sections are taught by faculty and focus on concepts, theory, and application. Lab sections are taught by graduate teaching assistants and focus on reinforcing lecture content and applying concepts with software. The objectives of the work are to: 1) develop a methodology to determine factors that contribute to variation in classroom performance such as students’ major and their knowledge of and sentiment toward statistics, and 2) to utilize those factors in developing a model to assess the quality of instruction and student learning across lecture and lab sessions.
Two semesters of performance data are analyzed in development of the regression-based statistical model. Factors explored during model development included major, class level, lab session characteristics (time of day, day of week), lecture section characteristics, lab instructor, measures of student engagement, and student sentiment toward statistics. The final model will serve as a basis for assessing instructional effectiveness as the course undergoes a major redesign between the Fall 2019 and Fall 2020 semesters.
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