Adam Kirn

Quantitative assessment of student motivation to characterize differences between engineering majors

Student motivation is often undervalued in comparison to academic performance measures for evaluating changes in higher education. There is a need to consider the affective domain in reform, in addition to academic performance. The effect of student motivation toward short- and long-term goals on student actions is not well understood. To assess this need, two research questions are addressed: 1) What elements of a motivation instrument designed for first-year engineering students are valid for upper-level engineering students? 2) How do motivations differ for upper level students in different engineering majors? Students in their major-specific engineering courses were surveyed with the Motivation and Attitudes in Engineering (MAE) instrument, which assesses long-term goal related expectancy, and perceptions of present and future tasks/goals. Short-term task self-efficacy was assessed using items adapted from the Attitudes and Approaches to Problem Solving survey. Results based on comparisons between major, class, and grade point average (GPA) showed: 1) Higher GPA indicates significantly higher expectancies and self-efficacy; 2) Bioengineers have significantly higher expectancies than mechanical engineers; and 3) Juniors have significantly higher GPAs, expectancies, and more positive perceptions of the present than sophomores. Results indicate that students motivations toward short- and long-term goals may influence actions toward learning.

Work in progress: How differences in student motivation characterize differences between engineering disciplines

The limited impact of many engineering education reforms may be due in part to treating engineering as a single monolithic discipline. We are using student motivation to characterize differences in engineering majors in order to inform educational practices and differences in student learning. Here we report on differences in student attitudes towards their engineering majors, elucidated through the use of survey data collected from first year students and longitudinal data on their majors 2 years later. We provide a detailed picture of the complexity of the engineering population, which will help direct more in-depth qualitative research to examine possible correlations between student motivation and learning in different engineering majors.

Assessing student knowledge transfer during group work

Successful group work requires that students transfer relevant prior knowledge to solve problems. This paper establishes a method to assess dynamic knowledge transfer in a group setting through analysis of a group project in a biomechanics class. Transcripts of student-student and student-instructor interactions were coded for evidence of target tools (students identifying relevant problem features), source tools (students activating prior knowledge), answers (stopping points), external inputs (resources and prompts from individuals or the instructor), and workbench explanations (student explanations of connections between source tools and target tools). Knowledge transfer was identified when a source tool and a target tool were coded within a phrase. The frequencies of codes were quantified to provide an overall picture of knowledge transfer for each group member throughout the project. Analysis for one group (a sophomore and junior bioengineering student, and a freshman engineering student) revealed that the junior was the largest contributor in the group, followed by the sophomore and freshman. The group mentioned source tools most frequently, followed by external inputs and target tools. The analysis provided evidence of knowledge transfer within the group through their identification of target tools and use of prior knowledge to explain their observations.