Effectiveness of a Semi-Mastery-Based Learning Course Design

Galen I. Papkov; Jiehong Liao

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: DSA Technical Session 4
Tagged Topics: Diversity and Data Science & Analytics Constituent Committee (DSA)
Permanent URL: https://peer.asee.org/47219

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Paper Authors

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Galen I. Papkov Florida Gulf Coast University

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Dr. Galen Papkov is a Professor of Statistics at Florida Gulf Coast University where he founded the minor in statistics and currently serves as the Graduate Program Coordinator for the M.S. Program in Applied Mathematics. His collaborations have resulted in publications in engineering education, agriculture, and health sciences. Originally from New York, he earned his Ph.D. in Statistics from Rice University. Galen's research interests include experimental design, survey design and data analysis, nonparametric density estimation and inference, multivariate analysis, and regression modeling. His adventurous spirit led him to complete two marathons, earn a black belt in tae kwon do, and live in Zhuhai, China, for two years, where he taught statistics and improved his Mandarin-speaking skills.

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Jiehong Liao Florida Gulf Coast University orcid.org/0000-0003-4083-9663

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Dr. Jiehong Liao is an Assistant Professor of Bioengineering at Florida Gulf Coast University (FGCU). She earned a Ph.D. in Bioengineering from Rice University and a B.S. in Biomedical Engineering from Rensselaer Polytechnic Institute (RPI). Originally from Hawaii, her journey into academia began with the Rensselaer Medalist award and being selected into the inaugural class of Gates Millennium Scholars. Before joining FGCU, she was a visiting Assistant Professor of Biotechnology in the Division of Science and Technology at the United International College (UIC) in Zhuhai China. She has trained with ASCE’s Excellence in Civil Engineering Education (ExCEEd) initiative, been exploring and applying evidence-based strategies for instruction, and is a proponent of Learning Assistants (LAs). Her scholarship of teaching and learning interests are in motivation and mindset, teamwork and collaboration, and learning through failure and reflection. Her bioengineering research interests and collaborations are in the areas of biomaterials, cellular microenvironments, and tissue engineering and regenerative medicine. She serves on leadership teams for the Whitaker Center of STEM Education and the Lucas Center for Faculty Development at FGCU, and is a member of the Biomedical Engineering Society (BMES) and the KEEN Engineering Unleashed Network as an Engineering Unleashed Fellow.

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Abstract

Course content is typically covered at the same pace for all students; however, some students take longer to grasp concepts than others. Mastery-based learning allows for learners to progress at a pace that is comfortable for them. Faculty at a mid-size, primarily undergraduate institution, investigated the effectiveness of a flipped classroom combined with a semi-mastery-based design for a Probability and Statistics course that met twice a week for 105 minutes per session.

Content was split into seven modules, where Modules 1-6 represents the typical content of the course. Module 7 is an add-on for further depth, whereas up through Module 5 is the basic minimum level of expected proficiency. A hierarchical grading scheme was employed such that students completing Modules 1-6 could earn the typical ‘A-F’ range of letter grades on a standard scoring scale. Students completing Modules 1-5 could only earn a maximum of a ‘B’ letter grade with scores in the 90s. However, students completing Modules 1-7 could earn an ‘A’ even with a numerical score in the 80s. Modules had to be completed in chronological order, with up to three weeks allotted for each of the required modules. The modules contained topic handouts with example problems, worksheets with additional practice problems, and were available on the Canvas learning management system. Each module contained 1-2 quizzes that were administered in class once the instructor was notified by the student, at least 24-hours in advance of the next class meeting, that they were ready to take a quiz.

Students had to cover material at a faster pace if they wanted or needed to complete more than the minimum number of modules. Class meetings were structured so that students could ask questions of the Learning Assistant (LA) or instructor, collaborate in small groups, work through problems provided in topic handouts, worksheets, and online homework, or take assessments. The online homework site WebWork was another venue where students could practice course content; however, the homework was optional. That is, students did not have to do the homework problems. If they attempted the homework and their average was poorer than their quiz average, then their final grade was purely based on their quiz average. On the other hand, if their homework grade was better than their quiz average, then their final grade was a weighted average of their homework (20%) and their quizzes (80%). Final letter grades were based on how many modules a student completed and their final numerical grade.

Preliminary data suggests that students appreciate the flexible learning schedule and are more likely to pass with this design compared to a traditional one. Learning analytics, data visualizations, and statistical analysis will be used to answer the following questions: (a) Do students primarily do the minimum number of modules? (b) Why do some students go beyond the minimum number of modules? (c) Is the passing rate superior to past sections conducted with a traditional lecture-based design? (d) How do students feel about this flexible design?

Citation
Format

Papkov, G. I., & Liao, J. (2024, June), Effectiveness of a Semi-Mastery-Based Learning Course Design Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47219

TY - CPAPER
AB - Course content is typically covered at the same pace for all students; however, some students take longer to grasp concepts than others. Mastery-based learning allows for learners to progress at a pace that is comfortable for them. Faculty at a mid-size, primarily undergraduate institution, investigated the effectiveness of a flipped classroom combined with a semi-mastery-based design for a Probability and Statistics course that met twice a week for 105 minutes per session.

Content was split into seven modules, where Modules 1-6 represents the typical content of the course. Module 7 is an add-on for further depth, whereas up through Module 5 is the basic minimum level of expected proficiency. A hierarchical grading scheme was employed such that students completing Modules 1-6 could earn the typical ‘A-F’ range of letter grades on a standard scoring scale. Students completing Modules 1-5 could only earn a maximum of a ‘B’ letter grade with scores in the 90s. However, students completing Modules 1-7 could earn an ‘A’ even with a numerical score in the 80s. Modules had to be completed in chronological order, with up to three weeks allotted for each of the required modules. The modules contained topic handouts with example problems, worksheets with additional practice problems, and were available on the Canvas learning management system. Each module contained 1-2 quizzes that were administered in class once the instructor was notified by the student, at least 24-hours in advance of the next class meeting, that they were ready to take a quiz.

Students had to cover material at a faster pace if they wanted or needed to complete more than the minimum number of modules. Class meetings were structured so that students could ask questions of the Learning Assistant (LA) or instructor, collaborate in small groups, work through problems provided in topic handouts, worksheets, and online homework, or take assessments. The online homework site WebWork was another venue where students could practice course content; however, the homework was optional. That is, students did not have to do the homework problems. If they attempted the homework and their average was poorer than their quiz average, then their final grade was purely based on their quiz average. On the other hand, if their homework grade was better than their quiz average, then their final grade was a weighted average of their homework (20%) and their quizzes (80%). Final letter grades were based on how many modules a student completed and their final numerical grade.

Preliminary data suggests that students appreciate the flexible learning schedule and are more likely to pass with this design compared to a traditional one. Learning analytics, data visualizations, and statistical analysis will be used to answer the following questions: (a) Do students primarily do the minimum number of modules? (b) Why do some students go beyond the minimum number of modules? (c) Is the passing rate superior to past sections conducted with a traditional lecture-based design? (d) How do students feel about this flexible design?

AU - Galen I. Papkov
AU - Jiehong Liao
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Effectiveness of a Semi-Mastery-Based Learning Course Design
UR - https://peer.asee.org/47219
ER -