New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
Electrical and Computer
Electrical Engineering (EE) programs seeking accreditation from the EAC of ABET must demonstrate that they satisfy eight general accreditation criteria, plus any program specific criteria. Two of the most challenging and debated criteria are: Criterion 3. Student Outcomes (SOs); and Criterion 4. Continuous Improvement. To prepare our EE program for a successful accreditation review, we divided the six-year ABET accreditation cycle into three distinct phases; the years before the Self-Study year (phase one), the Self-Study year (phase two), and the visit year (phase three).
During phase one of the accreditation cycle (2010-2014) a number of direct and indirect assessment methods were used to assess and evaluate Student Outcomes. The results were used to identify program improvements. The program faculty documented the results in annual assessment and evaluation reports. During the Self-Study year (2014-2015), we used the annual reports to prepare the Self-Study report. The annual reports also provide evidence that improvements to our EE program were based on assessment and evaluation of SOs as well as other inputs.
At the heart of our assessment program lies course-embedded assessment. The choice of courses for course-embedded assessment is guided by two principles: (1) each Student Outcome is assessed with student work in a benchmark course, and (2) only required courses, not elective courses, in the curriculum are selected as benchmark courses.
Assessment of a benchmark course is conducted with the following in mind: (1) assessment of student work measures the extent to which SOs are being attained, (2) it is not necessary to use all of the student work to assess an outcome, and (3) outcomes assessment is based upon student work and is guided by the grading of that work.
EGR 360-Analysis of Engineering Data was selected as a Benchmark Course for the EAC Student Outcome b (an ability to design and conduct experiments, as well as to analyze and interpret data.) To determine the degree to which Student Outcome b is attained, the following Performance Indicators were used:
Performance Indicator b.1. Analyze data to determine specified quantities. Exam problems asked students to determine mean and standard deviation for a random sample, and apply the Central Limit Theorem to calculate probabilities.
Performance Indicator b.2. Interpret the results for correctness and precision or apply the results to a pre-assigned problem. Students were asked to specify the value of a test statistic and draw a conclusion based on a statistical hypothesis test.
Performance Indicator b.3. Understand and apply concepts of randomization in experimental design. Students were asked to identify factors that would introduce variability in replicating an experiment, such as the manufacture of a given product.
In this paper, a detailed description of the process, data collection efforts, and analysis of the results in applying course embedded assessment method to the EGR 360-Analysis of Engineering Data course are provided.
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