Mastery Learning and Assessment Approach in Operations Research Course

Omar Ashour

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: IED Technical Session: Preparing Courses for the Future
Tagged Division: Industrial Engineering
Page Count: 10
DOI: 10.18260/1-2--30791
Permanent URL: https://peer.asee.org/30791
Download Count: 604

Paper Authors

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Omar Ashour Pennsylvania State University orcid.org/0000-0003-3775-6445

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Dr. Omar Ashour is Assistant Professor of Industrial Engineering at Pennsylvania State University, The Behrend College. Dr. Ashour received the B.S. degree in Industrial Engineering/Manufacturing Engineering and the M.S. degree in Industrial Engineering from Jordan University of Science and Technology (JUST) in 2005 and 2007, respectively. He received his M.Eng. degree in Industrial Engineering/Human Factors and Ergonomics and the Ph.D. degree in Industrial Engineering and Operations Research from Pennsylvania State University (PSU) in 2010 and 2012, respectively. Dr. Ashour was the inaugural recipient of William and Wendy Korb Early Career Professorship in Industrial Engineering in 2016. Dr. Ashour's research areas include applied decision making, modeling and simulation, and process improvement. He contributed to research directed to improve engineering education.

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Abstract

For decades, mastery learning and assessment has been used to improve students learning and outcomes. Mastery learning can be divided into two main approaches: Bloom’s Learning for Mastery (instructor-controlled pace) and Keller’s Personalized System of Instruction (student-controlled pace). In both approaches, the course material is divided into a sequence of modules. Each module represents a concept or a group of similar concepts. The student has to master each module before he/she can continue to the next module in the sequence. The mastery of a module is measured by a set of test, assignment, or any assessment tool that is focused on a certain concept. At this University, mastery learning and assessment approach has been successfully used in many fundamental courses within the School of Engineering, i.e., Statics, Strength of Materials, Thermodynamics, and Engineering Economy. The approach has shown improvement in students’ learning and outcomes. There was a clear evidence that students who passed mastery courses with a grade of C or more could solve engineering problems correctly and were more prepared for advanced courses when compared to students who were taught by the conventional learning and assessment approach. The approach has never been used in any operations research (OR) course before. In Fall 2017, the approach is implemented in a second course in OR (Stochastic Models in OR). This study implements a variation of Bloon’s learning for mastery approach at this large U.S. public University. The instructor controls the teaching pace and not all the assignments in the course are mastery based. Online tests are used as formative tests. These tests offer three trials based on the student’s performance. If the student passes the test with a grade of B or more on a trial, he/she does not have to take the next trial. Each new trial involves new questions that have similar difficulty levels to the previous trial. Therefore, if the student retake a trial, his/her grade will be reduced in the successive trials. The student will not receive a grade until he/she successfully solve the trial or exhaust the trials. The complete detail of the grade breakdown is explained in the paper. In this study, mastery learning and assessment approach is implemented in a second course in OR. The study tests the main hypothesis of the impact of mastery learning and assessment approach on students’ learning and outcomes compared to the conventional learning and assessment approach. Data is being collected this semester. The preliminary data analysis shows that the implementation of mastery learning and assessment approach has improved the students’ performance in the first midterm exam when compared to conventional learning and assessment group from last year. A two-sample t-test was performed. The two samples tested are statistically different. The average of grades on the first midterm exam were 40.8% and 69.6% for the mastery learning and assessment approach cohort and traditional approach cohort, respectively. This paper will present the detail background of the mastery learning and assessment approach along with the experimental setup in the OR course. Future research should focus on collecting data related to self-efficacy and motivation, and more data to confirm the presented results.

Citation
Format

Ashour, O. (2018, June), Mastery Learning and Assessment Approach in Operations Research Course Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30791

TY - CPAPER
AB - For decades, mastery learning and assessment has been used to improve students learning and outcomes. Mastery learning can be divided into two main approaches: Bloom’s Learning for Mastery (instructor-controlled pace) and Keller’s Personalized System of Instruction (student-controlled pace). In both approaches, the course material is divided into a sequence of modules. Each module represents a concept or a group of similar concepts. The student has to master each module before he/she can continue to the next module in the sequence. The mastery of a module is measured by a set of test, assignment, or any assessment tool that is focused on a certain concept.
At this University, mastery learning and assessment approach has been successfully used in many fundamental courses within the School of Engineering, i.e., Statics, Strength of Materials, Thermodynamics, and Engineering Economy. The approach has shown improvement in students’ learning and outcomes. There was a clear evidence that students who passed mastery courses with a grade of C or more could solve engineering problems correctly and were more prepared for advanced courses when compared to students who were taught by the conventional learning and assessment approach.
The approach has never been used in any operations research (OR) course before. In Fall 2017, the approach is implemented in a second course in OR (Stochastic Models in OR). This study implements a variation of Bloon’s learning for mastery approach at this large U.S. public University. The instructor controls the teaching pace and not all the assignments in the course are mastery based. Online tests are used as formative tests. These tests offer three trials based on the student’s performance. If the student passes the test with a grade of B or more on a trial, he/she does not have to take the next trial. Each new trial involves new questions that have similar difficulty levels to the previous trial. Therefore, if the student retake a trial, his/her grade will be reduced in the successive trials. The student will not receive a grade until he/she successfully solve the trial or exhaust the trials. The complete detail of the grade breakdown is explained in the paper.
In this study, mastery learning and assessment approach is implemented in a second course in OR. The study tests the main hypothesis of the impact of mastery learning and assessment approach on students’ learning and outcomes compared to the conventional learning and assessment approach. Data is being collected this semester. The preliminary data analysis shows that the implementation of mastery learning and assessment approach has improved the students’ performance in the first midterm exam when compared to conventional learning and assessment group from last year. A two-sample t-test was performed. The two samples tested are statistically different. The average of grades on the first midterm exam were 40.8% and 69.6% for the mastery learning and assessment approach cohort and traditional approach cohort, respectively. This paper will present the detail background of the mastery learning and assessment approach along with the experimental setup in the OR course. Future research should focus on collecting data related to self-efficacy and motivation, and more data to confirm the presented results.
AU - Omar Ashour
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Mastery Learning and Assessment Approach in Operations Research Course
UR - https://peer.asee.org/30791
DO - 10.18260/1-2--30791
ER -