An Adaptive Learning Engineering Mechanics Curricular Sequence

Katherine Saul; Anna Howard; Zo Webster; Dan Spencer

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Statics and Dynamics Topics
Page Count: 13
DOI: 10.18260/1-2--40509
Permanent URL: https://peer.asee.org/40509
Download Count: 389

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Paper Authors

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Katherine Saul North Carolina State University at Raleigh

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Dr. Saul is a Professor of Mechanical and Aerospace Engineering at North Carolina State University in Raleigh, NC, having joined NCSU in 2013. The research performed in her Movement Biomechanics Laboratory aims to improve treatment for upper limb neuromusculoskeletal conditions by providing biomechanical insight to clinicians regarding the effects of neuromuscular and orthopaedic injury, predicting outcomes of surgical interventions, and understanding healthy and impaired motor control. Dr. Saul has served as an 2019-2021 UNC System Academic Affairs Faculty Fellow exploring digital learning initiatives, OpenSim Fellow and Scientific Advisory Board member for the National Center for Simulation in Rehabilitation Research, on the Executive Board of the American Society of Biomechanics, and as Associate Editor of the Journal of Applied Biomechanics and PLOS ONE. Other honors include: American Society of Biomechanics Predoctoral Young Scientist (2005), Medtronic Foundation Graduate Fellow, Whitaker Foundation Graduate Fellow, NCAA Woman of the Year (Rhode Island, 2000), Outstanding Teaching Award at NCSU at the department, college, university, and Alumni Association levels. Dr. Saul received her ScB in Engineering from Brown University in 2000, and her MS and PhD in Mechanical Engineering from Stanford University. She was previously an Assistant Professor of Biomedical Engineering and Orthopaedic Surgery at Wake Forest School of Medicine.

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Anna Howard North Carolina State University at Raleigh

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Dr. Anna Howard graduated from Penn State University having worked with the Rotorcraft Center of Excellence there. Her research investigated the aeromechanical stability of tiltrotors. She works at NC State as a Teaching Professor researching ways to provide active learning to large numbers of students and investigating the role technology can play in improving student learning and retention. Her newest research focus is on entrepreneurially-minded learning in the classroom with the goal of having NC State become a KEEN partner school.

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Zo Webster North Carolina State University at Raleigh

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Dan Spencer

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Abstract

Adaptive learning (AL) is a personalized learning approach that dynamically adjusts content, assessment, and feedback based on algorithms that monitor student progress, pace, or performance. The engineering mechanics introductory sequence (Physics I, Statics, Dynamics) is a gateway sequence that requires strong foundational knowledge, but students present with variable prerequisite knowledge and skills. Our goal was to develop AL materials that can elucidate conceptual connections across a sequence and provide just-in-time support for prerequisite remediation to enable individualized support that can be challenging to provide in large introductory courses.

We created AL courseware for Physics I, Statics, Dynamics, and prerequisite math concepts, implemented in Realizeit, an adaptive learning platform. AL courseware included learning content, quizzes, and algorithmic multi-part capstone homework problems that allowed each student to receive different numerical scenarios; overall, 190 lessons and 1900 interactive problems were implemented. We deployed these materials in Spring 2021 to 1224 students at [University] in the 3 sequence courses. Evaluation of the sequence development experience was achieved through pre- and post-course surveys delivered to the 3 faculty leads. Faculty surveys addressed perceptions of and interactions with the courseware during development and deployment. Detailed evaluation of student perceptions and platform usage was performed for the Dynamics course (130 students) using student pre/post surveys and AL analytics; in Dynamics, weekly AL modules were required for a grade, and students had access to AL prerequisite materials as part of the sequence design. The student survey addressed comfort with engineering and AL technology, and perceptions of the content and platform.

Evaluation revealed overall positive student perceptions in Dynamics. Student comfort with engineering improved from 81.1% to 87.5% pre- to post-course. Post-course perceptions revealed satisfaction with the technology was expressed by 70.8% of students, and 83.3% found it helped them master skills. Importantly, relevant to implementation within a course sequence, 75.0% found it helped make connections between prior and new knowledge, and 79.2% found it somewhat, very or extremely helpful to transition into the dynamics class. Analytics revealed that students spent a substantial 2946.7 hours in the AL platform and completed 39,000 total questions (22.7 hours and 300 questions per student on average). In Dynamics, scores on the learning activities in Realizeit were significant predictors of a student’s project and exam grades (p<0.0001). Notably, 48% and 20% of the variability in project and exam grades, respectively, were explained by the Realizeit score. This was a marked improvement over the relationship of homeworks and quizzes to exams and projects in an earlier semester without AL elements. Student feedback exposed the need for more examples and practice questions. Faculty reported students were more aware of concepts requiring support and asked more pertinent questions. In addition, faculty perceptions were more positive when AL materials were graded elements that substantially replaced non-AL course material compared to when they were used to supplement existing course materials. These results suggest that AL can enhance connections in the introductory mechanics sequence, but emphasize that adaptive content and assessments must be carefully integrated in course design and reveal the need for a large scope of practice questions to enhance student learning.

Citation
Format

Saul, K., & Howard, A., & Webster, Z., & Spencer, D. (2022, August), An Adaptive Learning Engineering Mechanics Curricular Sequence Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40509

TY - CPAPER
AB - Adaptive learning (AL) is a personalized learning approach that dynamically adjusts content, assessment, and feedback based on algorithms that monitor student progress, pace, or performance. The engineering mechanics introductory sequence (Physics I, Statics, Dynamics) is a gateway sequence that requires strong foundational knowledge, but students present with variable prerequisite knowledge and skills. Our goal was to develop AL materials that can elucidate conceptual connections across a sequence and provide just-in-time support for prerequisite remediation to enable individualized support that can be challenging to provide in large introductory courses.

We created AL courseware for Physics I, Statics, Dynamics, and prerequisite math concepts, implemented in Realizeit, an adaptive learning platform. AL courseware included learning content, quizzes, and algorithmic multi-part capstone homework problems that allowed each student to receive different numerical scenarios; overall, 190 lessons and 1900 interactive problems were implemented. We deployed these materials in Spring 2021 to 1224 students at [University] in the 3 sequence courses. Evaluation of the sequence development experience was achieved through pre- and post-course surveys delivered to the 3 faculty leads. Faculty surveys addressed perceptions of and interactions with the courseware during development and deployment. Detailed evaluation of student perceptions and platform usage was performed for the Dynamics course (130 students) using student pre/post surveys and AL analytics; in Dynamics, weekly AL modules were required for a grade, and students had access to AL prerequisite materials as part of the sequence design. The student survey addressed comfort with engineering and AL technology, and perceptions of the content and platform.

Evaluation revealed overall positive student perceptions in Dynamics. Student comfort with engineering improved from 81.1% to 87.5% pre- to post-course. Post-course perceptions revealed satisfaction with the technology was expressed by 70.8% of students, and 83.3% found it helped them master skills. Importantly, relevant to implementation within a course sequence, 75.0% found it helped make connections between prior and new knowledge, and 79.2% found it somewhat, very or extremely helpful to transition into the dynamics class. Analytics revealed that students spent a substantial 2946.7 hours in the AL platform and completed 39,000 total questions (22.7 hours and 300 questions per student on average). In Dynamics, scores on the learning activities in Realizeit were significant predictors of a student’s project and exam grades (p<0.0001). Notably, 48% and 20% of the variability in project and exam grades, respectively, were explained by the Realizeit score. This was a marked improvement over the relationship of homeworks and quizzes to exams and projects in an earlier semester without AL elements. Student feedback exposed the need for more examples and practice questions. Faculty reported students were more aware of concepts requiring support and asked more pertinent questions. In addition, faculty perceptions were more positive when AL materials were graded elements that substantially replaced non-AL course material compared to when they were used to supplement existing course materials. These results suggest that AL can enhance connections in the introductory mechanics sequence, but emphasize that adaptive content and assessments must be carefully integrated in course design and reveal the need for a large scope of practice questions to enhance student learning.

AU - Katherine Saul
AU - Anna Howard
AU - Zo Webster
AU - Dan Spencer
CY - Minneapolis, MN
DA - 2022/08/23
PB - ASEE Conferences
TI - An Adaptive Learning Engineering Mechanics Curricular Sequence
UR - https://peer.asee.org/40509
DO - 10.18260/1-2--40509
ER -