Development of a Mechatronics System Design Course

Paper ID #35246

Development of a Mechatronics System Design Course

Dr. Benjamin D McPheron, Anderson University

Benjamin D. McPheron is Chair of the Department of Physical Sciences ans Engineering and AssociateProfessor of Electrical Engineering at Anderson University. Dr. McPheron received his B.S.E.E. inElectrical Engineering at Ohio Northern University in 2010, and his Ph.D, in Electrical Engineering fromthe Department of Electrical Engineering at The Pennsylvania State University in 2014. Dr. McPheronteaches Freshman Engineering and various courses in Electrical Engineering including Circuit Theory,Electronics, Controls, and Mechatronics. His research interests include Engineering Education, ControlSystems, Mechatronics, and Signal Processing. Dr. McPheron is a Senior Member of the IEEE.

Mr. Kenneth M Parson, Thor Motor Coach

Kenneth M. Parson is a 2020 graduate of Anderson University in Electrical Engineering and currentlyholds a position of Electrical Engineer at Thor Motor Coach.

Dr. Matthew Stein, Roger Williams University

Dr. Stein received a BS from Rutgers College of Engineering (1985); MS from the University of Cal-ifornia, Berkley (1987); and, Ph.D. from the University of Pennsylvania (1994). Assistant Professor atWilkes University from 1994-1999, moved to Roger Williams University in 1999, promoted to associate2003 and full professor in 2009. Dr. Stein teaches courses in Dynamics, Mechatronics, Vibrations, FiniteElement Analysis, Dynamic Modeling and Control and Computer Vision.

c©American Society for Engineering Education, 2021

2021 ASEE Illinois-Indiana Section Conference Proceedings | Paper ID 35246

© American Society for Engineering Education, 2021

Development of a Mechatronics System Design Course

Benjamin D. McPheron* Anderson University

[email protected]

Kenneth M. Parson Anderson University

Matthew R. Stein Roger Williams University

Abstract Mechatronics Engineering is a multidisciplinary field that draws from computer, electrical, and mechanical engineering and applies these topics to a variety of applications of electromechanical systems including robotics, control, and automation. Engineers in this field are vitally important in continued development of autonomous vehicles, industrial robotics, and space exploration vehicles. Anderson University has recently been awarded ABET accreditation in Computer Engineering, Electrical Engineering, and Mechanical Engineering. Based on the disciplines represented at Anderson University, mechatronics is a logical choice for further development of upper-level elective courses. Students with experience in mechatronics are increasingly valuable in the automation and manufacturing workforce. Many students who attend Anderson University in STEM related fields had their interest in STEM developed by robotics programs in their youth. Furthermore, recent participation in the Autonomous Division of the evGrandPrix competition has developed a deep interest in Mechatronics System Design by existing students.

This paper details the development of a Mechatronics System Design course, intended to enhance the existing curricular offerings in each of the accredited engineering disciplines. Literature on other mechatronics courses describes wide variations in course design and most lack enough information to fully implement. This work will present, in brief, the details of each project, and the projects created as a result of this work will be made publicly available for use by other institutions. The assessment strategy is presented, as are results from the pilot offering of the course. These results are affected by the change in instruction mode required due to the COVID-19 pandemic.

Introduction Recently, Anderson University approved a new mechatronics engineering major, which integrates courses from EE, CpE, and ME curricula. The mechatronics engineering major is built on the same rigorous background as the ABET accredited engineering majors, and includes upper-level courses in solid mechanics, kinematics and robotics, electronics, control systems, and microcontrollers. This program is intended to prepare students for the in-depth design and analysis of mechatronic systems, which differs from similarly titled programs (Mechatronics Engineering Technology) at technical colleges which tend to be application based. As this major was developed, it was important to develop a course to direct mechatronics specific projects to serve as a keystone to the major. Although students in the major are required to take a course in robotics, that course primarily covers the kinematics and operation of robotic manipulators. Ideally, students would gain some familiarity with integrating electrical and mechanical systems to develop platforms capable of autonomous behavior in response to external stimuli. The resulting course is described in this paper. In addition to serving as the keystone



course for the mechatronics engineering major, it also serves as an elective for upper lever EE, CpE, and ME students. This course was offered for the first time in the spring of 2020, but an unanticipated shift to online instruction impacted the latter half of the course and influenced assessment results. This paper reports on the course design and particular laboratory experiences, as well as the student performance in the (hybrid) pilot of the course. There is existing literature describing similar courses, however, few papers describe these courses in enough detail to replicate, and many do not cover the exact content that is desired of the Mechatronics Systems Design course presented in this work [1]-[9]. Furthermore, few of these papers provide much discussion of assessment or direct assessment techniques employed to assess the efficacy of the course and to drive data-supported continuous improvement. In addition, the structure of the Mechatronics System Design course to include 5 weekly contact hours with faculty is different from many found in the literature. The design of the course described in this work is most closely tied to a similar course entitled Mechatronics which has been developed and delivered by Matthew Stein at Roger Williams University in Bristol, Rhode Island [1], [2]. This course employs a laser cutter for fabrication, use of an Arduino microcontroller, and boasts a semester long project in which students design, build, and program a mobile autonomous robot to collect, sort, and deliver objects on a playfield. This course is an upper-level elective for ME, EE, CpE, and Computer Science students and currently meets for three contact hours a week. Many of the lessons learned by Stein were used in designing the project, milestones, and pedagogical techniques in this work. Stein’s own course design work was built on the foundation of many other studies [3]-[7]. Cherng, et al. [3] provide a detailed discussion of their mechanical engineering senior elective course entitled Principles and Applications of Mechatronics System Design. Particular course objectives were to prepare ME undergraduate students in the area of integrating microcontrollers, provide the student with hands-on experiences, and to challenge student’s innovation abilities. Notably, this course met for six contact hours weekly, including both a lecture and lab. Two course projects were given, one asking students to program a microcontroller for process control applications, and the second being more open-ended. This paper clearly defines assessment strategies, and also provides reasonable detail for replication of the course. Unfortunately, this course is fairly discipline specific to ME, including detailed discussions of mechanical actuators (cam mechanisms, kinematic chains, gears and ratchet mechanisms) and a complete introduction to microcontroller programming, which makes it ill-suited for CpE and EE students. Like Cherng, Riofrio and Northrup [8], and Rabb, et al. [9] write about mechatronics courses for mechanical engineering students. In the work by Riofrio and Northrup, they describe a semester long project in a mechatronics course for ME students in which they were required to build, instrument, and operate a solenoid engine, using the Arduino controller to simplify the software, electronics, and controls aspects of mechatronics. In this course, the class met for 3 contact hours per week, and most of the academic rigor is placed on dynamics and kinematics, rather than control systems or programming. Rabb, et al. report on an ME specific mechatronics course that also



employs the Arduino microcontroller and challenges students to complete an open-ended project. Assessment in this paper is carried out by student survey, asking students to self-assess achievement of specific outcomes. In contrast to the works described so far, Consi [10] discusses the tendency of most mechatronics courses to utilize a project to allow students to integrate their knowledge into a real mechatronic system. As seen in the literature, projects can be free form, or prebuilt systems for specific tasks, and, as in the work of this paper, may take the form of small mobile robots competing at the end of the semester. Consi points out that using a pre-built kit, with no design integration possesses the shortcoming of forcing students to focus on coding and testing, with very little mechanical (or electrical) design. On the other hand, open-ended projects can be difficult to assess. To combat these issues, Consi proposes a generic and flexible platform (kit) that students must use to perform an open-ended task. One reference to mention in the context of mechatronics is a paper by Nandikolla and Durgesh [11] which describes an instrumentation course related to mechatronics content. What is valuable to take from this paper is a discussion of rubric design for assessment, which informs the assessment of the Mechatronics System Design course in this work. The closest recent effort towards making projects and course documentation widely available is found on www.mechatronicseducation.org, which provides a repository of course materials from the Tennessee Technological University Mechatronics Concentration [12]. The site also has a forum community for discussion on mechatronics education. In addition, there are multiple workshops and webinars available, which makes this a valuable resource for those seeking to create Mechatronics Engineering courses. While the literature provides ample background for the formation of the Mechatronics System Design course described in this work, a few key differences (and a few similarities) between previous work and the current work must be highlighted. One distinction is that, with five contact hours per week, a significant depth and breadth of topics can be covered, ranging from programming, to electronics, to mechanical design and more advanced sensors such as IMUs and Computer Vision. This is distinct, as the course is intended not just for Mechanical Engineering students, but also for Electrical and Computer Engineering students. Another difference is that most of the literature does not discuss assessment strategies, and those that do have a wide array of rigor, from the very direct (Cherng, Nandikolla) to the very indirect (Rabb). In this work, we also provide a link to a GitHub repository in several places throughout the paper which contains all course materials, which can be paired with other resources, such as the content that is available at www.mechatronicseducation.org, to create comprehensive courses. One similarity to the literature is that we will employ an open-ended platform to a final project with a narrower scope. This allows the mechanical and electrical design process, programming and debugging to be the primary course focus. This is achieved by asking students to design small mobile robotic platforms.



Lecture Course Content The course is scheduled with three contact hours of lecture per week and two contact hours of laboratory experiences. The lecture content provides a brief treatment of microcontrollers, largely in review, then provides significant coverage of sensors and actuators, with several other topics included throughout the semester, timely to the tasks being completed in lab. Table 1 presents a rough schedule for lecture material. A detailed course syllabus and lecture notes are provided in a GitHub repository, found at: https://github.com/bdmcpheron/Mechatronic-Systems-Design. Table 2 provides the breakdown of graded artifacts in the course.

Table 1: Rough schedule of Mechatronics System Design lecture topics Week Topics

1 Intro to Mechatronics, Programming Review

2 Programming Review, Motor Control

3 DC Motor and Servo Motor Control

4 Analog Inputs

5 CAD Review, Laser Cutter Fabrication

6 Encoders, Mechanical Design, Functions

7 Proximity Sensing

8 Interrupts, Timers

9 Control Systems

10 Control Design

11 Additional Topics in Mechanical Design

12 Printed Circuit Board (PCB) Layout and Fabrication

13 Computer Vision

14 Computer Vision

15 Final Project Competition



Table 2: Graded artifacts for Mechatronics System Design Artifact Value

Programming Homework 10%

Lab 20%

Project Milestones and Reports 40%

Final Project Competition and Report 30%

Total 100%

Project Description This course includes one primary project which is provided to students on the first day of class which is an adaptation of previous work by Matthew Stein at Roger Williams University [1], [2]. The goal of this project is for teams of 2-3 students to design, build, and program an autonomous robot to score as many points as possible in a 4-minute period. The robot will navigate a well-defined playfield and collect objects, sort them by color, and deliver them to specified bases. The playfield is shown below in Figure 1 and is 6 feet in length and 4 feet in width. All parts of the robot must start within a 12x12x12 inch starting cube. Within the object locations, there are 8 one-inch cubes, evenly distributed between white and black in color, but randomized in their configuration. Objects are randomized to encourage students to adopt a strategy of acquiring and sorting objects by color. Robots employing rote motions delivering objects without sorting can only achieve limited scores. In addition, there is exactly one white or black bonus ball located in each area. For an understanding of the layout of these objects, consult Figure 2.

Figure 1: Playfield



Figure 2: Object configuration

Each group receives a starter kit, complete with laser cuttable acrylic and electronics to match a basic robotic platform, shown in Figure 3. Notably, the basic platform contains only the materials required for open loop navigation and has no actuation or sensors beyond two geared DC motors used for propulsion. A complete bill of materials is available on the GitHub repository mentioned earlier. The acrylic used for the final robot design must fit in one 1/8x12x36 inch sheet, and students must provide an AutoCAD drawing demonstrating the group is following the material restriction requirements, as shown in Figure 4. The material for the base robot is pre-cut, but the students are required to produce a completely novel design by the end of the semester. The final platform must employ a PCB that the students design, fabricate, and populate.

Figure 3: Base robotic platform



Figure 4: Demonstration of meeting the material requirements

The final competition takes place over two separate days during which each group will have 12 minutes to complete as many runs as possible. The group order will be chosen at random the week prior to the competition. Teams must go in the order they are assigned and may not swap. Points for objects delivered are outlined below in Table 3. The maximum one-day score is 50 points. The group score for the competition is the high score total of both days (max two-day score is 100 points). Groups scoring 40 points or more receive full credit for the competition. In addition to the robot performance score, groups must submit complete documentation of their project in the form of a lab binder and milestone report, as well as supporting CAD files, annotated C++ code, PCB layout design, and wiring diagrams.

Table 3: Final competition point allocation Category Score

Any object to any base 4 points

Black/White Cube in correct base 2 points

Black/White Cube in wrong base -2 points

Positive net score from both bases 4 points

Bonus Ball in correct base (only counts if cube of same color in base and positive score from base)

5 points

Bonus Ball in wrong base -5 points

This project relates to 5 of the 7 ABET student outcomes (SOs) [13], making this a keystone course for students studying in Mechatronics Engineering. These outcomes are listed in abbreviated form along with their justification below:

• ABET Outcome 2: An ability to apply engineering design to produce solutions that meet specified needs



o Justification: The heart of this project is the design of a mechatronic system to perform a specific task. requiring direct application of the engineering design process

• ABET Outcome 3: An ability to communicate effectively o Justification: Appropriate written documentation is required for the final project,

each lab, and each milestone. • ABET Outcome 5: An ability to function effectively on a team

o Justification: All work is completed in teams of 2-3 students. Responsibility for different parts of the project will be split among team members, making each an integral part of project completion.

• ABET Outcome 6: An ability to develop and conduct appropriate experimentation o Justification: In order to employ sensors, actuators, and mechanical design, students

will be required to conduct lab experiments (described later) and interpret data, using their engineering judgement to draw conclusions about the results. This is included as a necessary step in the engineering design process.

• ABET Outcome 7: An ability to acquire and apply new knowledge as needed o Justification: The lecture and laboratory course is designed to introduce some basic

level knowledge of skills required for completion of the project, but additional study will be necessary to learn advanced programming techniques, software skills, and apply sensors and actuators other than those used in the course.

Milestones To help students stay on task towards achieving the end goal of the project, four milestones have been designed that add core functionality to their robot. Each milestone is 100 points, includes a demonstration component, and requires submission of a milestone report.

• Milestone 1: Basic C++ Programming on an mbed microcontroller • Milestone 2: Navigate the playfield with digital input (buttons) as feedback • Milestone 3: Return an object to base • Milestone 4: Correctly sort and deliver two objects to the correct base

Final Report and Lab Notebook Both the final project and Milestones require a detailed and organized lab notebook. As a requirement for each milestone, the lab notebook will be submitted and checked. To combat ad hoc fabrication each report requires that students demonstrate adherence to material restrictions and must contain a 3D CAD model of their robotic platform and pictures of the actual robot, verifying that the two match, as in [2].

• Assignment print outs (Project, Milestones) • Final project report • Milestone reports • Flow charts (Homework) • Lab reports • Circuit diagrams • Datasheets • Course notes



• C++ Code Lab Exercises In this section, each lab exercise is described with particular learning outcomes. These laboratory exercises are intended to address the following student outcomes:

• ABET Outcome 5: An ability to function effectively on a team • ABET Outcome 6: An ability to develop and conduct appropriate experimentation • ABET Outcome 7: An ability to acquire and apply new knowledge as needed

In addition to lab exercises, lab time is used for demonstration of milestones and the final competition. With the four milestones and final competition, there are a total of fifteen lab sections for this course. The following regular lab exercises are described now:

1. Lab 1: Introduction to mbed microcontroller, digital and analog signals Learning Outcomes: By the completion of this lab exercise, students will:

• Demonstrate the ability to write and compile a C++ program • Demonstrate D/A conversion using a resistor ladder

2. Lab 2: Motor control Learning Outcomes: By the completion of this lab exercise, students will:

• Build an H-bridge using transistors and other discrete components • Use an IC H-bridge and the mbed microcontroller to control two motors to spin

clockwise and counterclockwise 3. Lab 3: Laser cutter

Learning Outcomes: By the completion of this lab exercise, students will: • Employ AutoCAD to design a laser cuttable part • Successfully fabricate parts using a laser cutter

4. Lab 4: Optical encoder Learning Outcomes: By the completion of this lab exercise, students will:

o Construct an optical encoder using reflective photosensors and a circular disc o Program a microcontroller to count rotations of an optical encoder

6. Lab 5: Hall Effect Encoder Learning Outcomes: By the completion of this lab exercise, students will:

• Count rotations using a hall effect encoder 6. Lab 6: Interrupt task execution

Learning Outcomes: By the completion of this lab exercise, students will: • Program a microcontroller to manage robotic platform tasks using interrupts

7. Lab 7: IMU with filtering Learning Outcomes: By the completion of this lab exercise, students will:

• Program a microcontroller to read and filter IMU data 8. Lab 8: Printed circuit board fabrication

Learning Outcomes: By the completion of this lab exercise, students will: • Design PCB layouts for electronic components • Fabricate PCBs • Populate and verify PCBs

9. Lab 9: Stepper motor control Learning Outcomes: By the completion of this lab exercise, students will:



• Interpret a datasheet to make electrical connections to a stepper motor • Program a stepper motor for position control

10. Lab 10: Computer vision Learning Outcomes: By the completion of this lab exercise, students will:

• Program computer vision algorithms for line detection and avoidance Assessment Strategies Achievement of ABET Student Outcomes is assessed by evaluating student results for different performance indicators composed of lab and project assignments in the course. For this direct assessment method, performance indicators were chosen to collect evidence related to each student outcome. Rubrics for each assignment attempted to assess the particular outcome in question from the performance indicator. These rubrics are also provided in the GitHub repository, found at: https://github.com/bdmcpheron/Mechatronic-Systems-Design. Baseline performance for each outcome is a mastery level of 83%. This percentage was chosen because it is the minimum percentage to earn a B in the course. Basic achievement of the SO is set to be 73%, the minimum to earn a ‘C’, as students are required to average at least a 2.0 to graduate. There are two measures that will be employed for testing student success based on this mastery goal. The first measure is to take the average scores course-wide and compare against the mastery target. A higher average than the target suggests that the majority of students exceed the performance goal. The second metric is to evaluate each student individually to see if they exceed the mastery goal. Ideally every graduate of our engineering program should demonstrate mastery in each of the 7 SOs. Both measures contribute to both assessing the efficacy of the course instruction and driving data-supported changes to pedagogy and curriculum.

• ABET Outcome 2: An ability to apply engineering design to produce solutions that meet specified needs

o Performance Indicators: Each milestone demonstration and report, final competition and report

• ABET Outcome 3: An ability to communicate effectively o Performance Indicators: Milestone reports, final report

• ABET Outcome 5: An ability to function effectively on a team o Performance Indicators: Each lab exercise, milestone and project demonstrations

• ABET Outcome 6: An ability to develop and conduct appropriate experimentation o Performance Indicators: Each lab exercise

• ABET Outcome 7: An ability to acquire and apply new knowledge as needed o Performance Indicators: Each lab exercise, milestone and project demonstrations,

notebook check of course notes, code, and datasheets collected for completion of project

Assessment Results and Discussion This course was offered in a pilot run in Spring 2020 to 8 registered students from CpE, EE, and ME disciplines. As a result of the COVID-19 pandemic, there was an unexpected shift to online learning halfway through the course. With the shift to online learning, only about 50% of the



course activities were completed as scheduled, and the course was modified for the second half of the semester. The course was modified after the move to online in the following ways:

• Lecture videos were produced and offered asynchronously with synchronous office hours. • Several labs could not be completed with lack of access to lab equipment. Those that could

be completed after the switch were completely software-based. • For the project, students were required to complete the mechanical, electrical, and program

design in software. However, students were unable to complete the fabrication/prototyping and testing stages of the design process. Some opportunities for this existed prior to the move online.

Prior to the move online, Milestones 1-3 were completed, as were Labs 1-4. Lab 5, 6, 7, and 9 were canceled, and Lab 8 and 10 were completed in software only. Most of the SOs could still be assessed, although the move online affected the results. In some cases, such as the final project and Milestone 4, the assignments became less challenging because the prototype and testing/debugging step was removed. In other cases, such as in lab exercises, the assignments became more challenging due to reduced access to the instructor for help. In the case of ABET Outcome 6: An ability to develop and conduct appropriate experimentation, no assessment data was collected after the move online as in person lab experiences were not possible. Table 4 displays the results for each SO addressed by this course. The results are split to display the average achievement for Performance Indicators prior to online instruction, the achievement after the move online, and the overall average with appropriate weighting. The first observation that can be made from these results is that none of the outcomes have an average achievement above the mastery level of 83%, although they all meet the basic achievement benchmark of 73%. Another notable observation is that SO3 (communicate effectively) and SO7 (acquire and apply new knowledge) were most negatively affected by the move to online. The unexpected change in instruction mode made collaboration and communication more difficult for the students, especially in writing cohesive reports. The change also made faculty more inaccessible to aid in gaining new knowledge. It is likely that these results would have been closer to those from in-person instruction had the change in instruction mode not occurred. The final observation is that SO5 saw a dramatic increase in the switch to online. One reason for this is that teamwork was assessed as a part of Milestones and the Final Project, which has the highest weighting of any single artifact in the course. The requirements for Milestone 4 and the Final project were relaxed with the move to online instruction, and the testing, debugging, and other performative aspects which often challenge teams was removed, resulting in higher achievement in teamwork related tasks. An additional factor to be considered for this SO is that the results prior to the move online were noticeably low because two different groups failed the Milestone 3 and did not have the opportunity to complete after the fact for partial credit.



Table 4: Course Wide Direct Assessment

Student Outcome Pre-Online Instruction

Post-Online Instruction Total

SO2 81.9% 82.5% 82.3% SO3 95% 68.1% 77.1% SO5 68.75% 82.5% 79.1% SO6 78.6% N/A 78.6% SO7 79.25% 58.3% 74.2%

To further refine the information from Table 4, a more specific measure is required to determine if students met mastery. A few outliers in the results can significantly impact the average, especially with only eight students in the course. In order to more clearly evaluate student performance, student by student achievement is assessed, which is shown in Table 5. The average performance for each student by SO is shown, as is the percentage of students that met the mastery level. Of the eight students in the course, three achieved mastery for all five SOs assessed. One student achieved mastery for four SOs. One student achieved mastery for three SOs and basic achievement for one SO. One student achieved mastery for two SO and basic achievement in one SO. Two students did not achieve mastery or basic achievement in any SO. The students who failed to achieve mastery for any SO were in the same group and failed the majority of the Milestones for the project. These results paint do a slightly more encouraging picture: 75% of the students achieved mastery or basic achievement for all or most of the SOs, although the lack of achievement by 25% of the cohort is troubling. Several confounding variables exist which affected the results of assessment, beyond the change in instruction mode. With the move online, a Pass/Fail option was offered by the university and the effort put forth by some students decreased. As mentioned earlier, collaboration between students was challenged, and access to the instructor became more limited. In addition, the modifications to assignments changed the difficulty, making the metrics somewhat inconsistent.

Table 5: Student by Student Direct Assessment Student SO2 SO3 SO5 SO6 SO7

1 55% 35% 46.3% 57.1% 48.4% 2 95.2% 92% 95% 85.7% 69.7% 3 86% 80% 83.8% 71.4% 58.1% 4 55% 40% 46.3% 57.1% 64.5% 5 95.2% 93.7% 95% 85.7% 93.9% 6 92.8% 91.3% 91.3% 100% 88.4% 7 86% 93.3% 83.8% 71.4% 82.3% 8 92.8% 91.3% 91.3% 100% 88.4%

% Mastering 75% 62.5% 75% 50% 37.5% Examples of Student Work Although students were not able to complete their final projects in-person after the move to online learning, two groups successfully completed Milestone 3: Return an object to base. The robotic platforms implemented used a combination of laser cut and 3D printed components designed by the students. Figure 5 shows examples of two platforms used for the successful completion of



Milestone 3. Both robots are fairly similar, as cross-pollination between groups is common for in-person instruction. Both use a scoop, similar to a front-end loader, to pick up blocks. The scoops are actuated by servomotors and apply touch sensing (pushbuttons) to indicate contact has been made with the wall.

Figure 5: Examples of robotic platforms used to complete Milestone 3

Students were still required to complete the design and modeling of their robots for the final project. Mechanical design was performed in Autodesk Inventor and AutoCAD. Figure 6 shows examples of the mechanical design for the final project. In isolation, the designs evolved differently as cross pollination was more difficult remotely. Although both designs retain the scoop, the sorting and object collection bins/delivery methods diverge significantly.

Figure 6: Examples of robotic platforms mechanical designs for the final project.

Conclusions In this work, we present a comprehensive discussion of curricular development for a Mechatronics System Design course. In addition to providing a review of contemporary course design projects, we clarify the key differences between this course and existent courses at other institutions. In addition, a discussion of assessment methods and links to ABET Student Outcomes is provided, and results from the pilot run of the course are presented. These results were impacted by the switch to online learning, with SO3 and SO7 being most negatively affected and SO5 being most



positively affected. Even with these challenges, 37.5% of the enrolled students achieved mastery in all SOs assessed, and 75% achieved mastery or basic achievement in the majority of the SOs. Additional data and increased sample size are necessary to establish meaningful results, but the initial results are promising, despite the unexpected challenges of the COVID-19 pandemic. This course will be offered again in Spring 2022, and new assessment data will be collected at that point for ongoing assessment and continuous improvement purposes. The Mechatronics Engineering program will have its first graduate in Spring 2021. Acknowledgements This work was supported by a 2019-20 Indiana Space Grant Consortium (INSGC) award for the proposal entitled “Course Development for Mechatronic System Design.” References [1] M.R. Stein, “A mechatronics course at Roger Williams University,” presented at the 2011 ASEE Northeast

Section Annual Meeting, 2011. [2] M.R. Stein, “Combatting ad hoc fabrication in a senior level mechatronics course,” 2016 ASEE Northeast

Section Annual Conference, 2016. [3] J.G. Cherng, B. Q. Li and N. Natarajan, “Development of a senior mechatronics course for mechanical

engineering student,” Proceedings of the 2013 ASEE Annual Conference, 2013. [4] S. Shooter and M. McNeil, “Interdisciplinary collaborative learning in mechatronics at Bucknell

University,” Journal of Engineering Education, July 2002. [5] O. Harrison and R. Edwards, “Enhancing engineering problem solving skills in a mechatronics course,”

Proceedings of the 2013 ASEE Annual Conference, 2013. [6] M. Lobaugh and R. Edwards, “Mechatronics for non-electrical engineers,” Proceedings of the 2011 ASEE

Annual Conference, 2011. [7] B. Samanta and Y. Zhu, “Development of a mechatronics studio course in mechanical engineering,”

Proceedings of the 2013 ASEE Annual Conference, 2013. [8] J.A. Riofrio and S.G. Northrup, “Teaching undergraduate introductory course to mechatronics in the

mechanical engineering curriculum using Arduino,” Proceedings of the 2013 ASEE Annual Conference, 2013.

[9] R.J. Rabb, N.J. Washuta, C.D. Floyd, “Using mechatronics to develop self-learners and connect the dots in the curriculum,” Proceedings of the 2018 ASEE Annual Conference, 2018.

[10] T.R. Consi, “ A versatile platform for teaching mechatronics,” Proceedings of the 2012 ASEE Annual Conference, 2012.

[11] V.K. Nandikolla and V. Durgesh, “Integrating instrumentation and mechatronics education in the mechanical engineering curriculum,” Proceedings of the 2016 ASEE Annual Conference, 2016.

[12] Mechatronics Education Shared Resources <https://www.mechatronicseducation.org/> . [13] Criteria for Accrediting Engineering Programs 2019-20, <https://www.abet.org/accreditation/accreditation-

criteria/criteria-for-accrediting-engineering-programs-2019-2020/#GC3> ABET.

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