Month: May 2020

Michigan Tech Team Places 4th Overall in TiM$10K Challenge

A team of five Michigan Tech students received Honorable Mention honors in the second annual SICK Inc. TiM$10K Challenge, a national innovation and design competition. University students from around the country participated in the event designed to support innovation and student achievement in automation and technology.

The Michigan Tech team members — Brian Parvin (ME), Paul Allen (EE), David Brushaber (CompEng), Kurtis Alessi (CompEng) and Alex Kirchner (CompEng) — earned Honorable Mention (fourth place overall) for their project, “Evaluating Road Markings (the Road Stripe Evaluator). Their project was sponsored by SICK Inc. Watch a video about the project below.

SICK’s TiM$10K Challenge 2020 – Evaluating Road Markings (The Road Stripe Evaluator)

For the competition, teams were supplied with a 270-foot SICK LiDAR sensor and accessories, and challenged to solve a problem, create a solution, or bring a new application to any industry that utilizes the SICK LiDAR.

Each team was asked to submit a video and paper for judging upon completion of its project. A panel of judges decided the winning submissions based on creativity and innovation, ability to solve a customer problem, commercial potential, entrepreneurship of the team, and reporting.

The Tech team developed an innovative product to help resolve issues caused by poor road markings, while reducing maintenance costs and improving motorist safety. Their new software uses reflectivity values obtained using a SICK LiDAR unit to identify deterioration of road stripes and recommend timely repainting, also aiding in the safety and reliability of self-driving vehicles on roadways.

The Michigan Tech Team

They constructed a prototype to demonstrate functionality, in the form of a pushable cart that evaluates road markings. An intuitive user interface displays the markings being evaluated, and indicates if they meet necessary levels of reflectivity.

Pinar said the team was well organized and demonstrated an excellent work ethic from day one. “It was exciting to watch them identify a salient problem and develop a functional proof-of-concept solution despite the setbacks that affected us all after spring break,” he said.

“This was a unique project in that the team was required to identify a problem and develop a solution to it that is based on SICK’s TiM LiDAR, while most teams are handed a problem and asked to create a solution,” Pinar noted. “I think this format allowed the team to exercise even more innovation than on a ‘typical’ project.”

The same team of students was awarded Honorable Mention honors at this spring’s Senior Design competition for their project, “Road Marking Reflectivity Evaluator.”

SICK, Inc. is one of the world’s leading manufacturers of sensors, safety systems, machine vision, encoders and automatic identification products for industrial applications.


Jason Hiebel, The College of Computing’s First Graduate

By Karen S. Johnson, Communications Director, College of Computing and ICC
This is the first part of a two-part article about Jason Hiebel, Ph.D., the college of Computings first graduate. Watch this blog and College of Computing social media channels for “Part II, A Supportive and Wise Network.”

PART I | THE FAST TRACK

A Profile of Dr. Jason Hiebel: The College of Computing’s First Graduate

In fall 2007, Jason Hiebel enrolled in his first semester at Michigan Tech. He’s been studying and teaching computer science and mathematics at Tech ever since, participating in December 2019 Commencement ceremonies. Shortly after, he successfully defended his dissertation and was awarded his Doctor of Philosophy in Computer Science, the very first from the College of Computing.

“Graduating when I did, and becoming the first Ph.D. for the College of Computing, was really a fluke of timing,” Hiebel says. “But after all my time here in Houghton and with the Computer Science department, I am pleased to have the honor of being the college’s first Ph.D. It’s something that can be mine and mine alone, and I’m okay with being a little bit greedy about it!”

Husky Tenacity

Hiebel grew up north of Green Bay, Wis., and attended Bay Port High School, where he was active in the chess club and the marching band.

He says that during high school, “I was also the kind of person to push for opportunities far beyond those normally available. For example, while our school did offer some computer science courses, they were mostly self-taught in a small computer lab. But I wanted to be a computer scientist, and I wanted to be a professor—even if I lacked an understanding of what that truly entailed at the time.”

So, he pushed himself to complete the entire computer science curriculum before the end of his sophomore year, then took the AP exam. Following, Hiebel continued with the curriculum thanks to the State of Wisconsin, which paid for him to complete several computer science courses at the University of Wisconsin Green Bay.

“These were not opportunities that were typically available to me or anyone else at Bay Port,” Hiebel notes. “I had to fight for these opportunities, with the school and the state. But that Tenacity is exactly what Huskies are all about, right?”

All in all, Hiebel says he started at Michigan Tech as a junior in the Computer Science department and as a sophomore overall.

The Fast Track to Teaching and Research

During his first few weeks at Michigan Tech, Hiebel’s sole major was Computer Science. But soon, he began to pursue a double major in another field he enjoys: mathematics.

Hiebel completed his B.S. in Computer Science in summer 2010, and while he was finishing his B.S. in Mathematics, he dual-enrolled as a graduate student in Computer Science. He completed the Mathematics B.S. a year later, and in spring 2012 he received his Master of Science in Computer Science.

In pursuit of his Ph.D., Hiebel was supported by graduate teaching assistantships (GTA), teaching assistantships, and graduate research stipends (GRA). He spent several summers interning at MIT Lincoln Labs, the Department of Defense, and the Michigan Tech Research Institute (MTRI).

“As a GTA, I did my fair share of grading and also led the lab sections for the introductory courses for three semesters,” Hiebel explains. For two semesters, as an instructor, he taught the accelerated introductory programming course (CS1131) and the undergraduate AI course (CS4811). Some semesters he was both a GTA and an instructor. Finally, with the support of his advisors, Hiebel was able to advance his research full time with a GRA stipend.

As a master’s student, Hiebel worked on developing tools for AI education with Laura Brown and another graduate student. As a Ph.D. student, his focus was on building his dissertation research under graduate advisors Laura Brown and Zhenlin Wang.

“Jason’s research applies machine learning to computer system optimization. He has become an expert in both fields,” says Professor Zhenlin Wang, Computer Science.

“The nature of this type of research makes it very challenging for a student to focus, as it requires continual effort and extended skills,” Wang adds. “Throughout his Ph.D. study, Jason consistently demonstrated diligence, perseverance, and creativity. For these reasons and others, Jason has always been a stand-out among our graduate students.”

In his dissertation, Hiebel investigates the application of online machine learning methods, particularly multi-armed bandit methods, to performance optimization problems in computer systems.

“Computers offer a myriad of configurations for customizing how the system performs. Depending on what you run on the system, different configurations can have a drastic effect on performance,” Hiebel explains. “Ideally, we would like to match the configuration to the workload, but doing so requires a broad expertise of how different components of an individual system interact.”

“My work focuses on modeling this type of configuration problem and uses artificial intelligence to automatically, without human intervention, select the best-performing configurations for a given workload.”

Looking Ahead

Hiebel signed on to instruct some spring 2020 semester courses for the Computer Science department while he waited on his paperwork to process for a job with the Department of Defense. “With the growing pains of the new college, there was a need for a few more people to teach and I was happy to lend my experience,” he says.

But like many in the wake of the global pandemic, Hiebel’s plans are in flux right now. He’s teaching a Summer Track A course at Michigan Tech, and advising an undergraduate research project, as well.

“Mainly, I’ll just be waiting for things to open back up so I can get processed for the job I’m waiting for,” Heibel says. “During that limbo, I hope to tackle some research problems and continue to keep myself busy.”
Hiebel says that in the short term, he is hoping to lend his expertise to government research. But in the long term, his aspirations are to return to academia.

“Only time will tell where I end up,” he muses. “But wherever I do end up, I think I will be happy if I’m working on interesting problems and using the skills and knowledge I gained as a Ph.D. student here at Tech.”

In the meantime, Hiebel enjoys living in the Houghton community. He’s a big fan of winter, and even Houghton summers are far too warm for his tastes. “Small town life suits my sensibilities better,” he confirms.


Computing Awards COVID-19 Research Seed Grants

The College of Computing is pleased to announce that it has awarded five faculty seed grants, which will provide immediate funding in support of research projects addressing critical needs during the current global pandemic.

Tim Havens, College of Computing associate dean for research, said that the faculty seed grants will enable progress in new research that has the potential to make an impact on the current research. Additional details will be shared soon.


Congratulations to the winning teams!

Guy Hembroff (AC, HI): “Development of a Novel Hospital Use Resource Prediction Model to Improve Local Community Pandemic Disaster Planning”

Leo Ureel (CS) and Charles Wallace (CS): “Classroom Cyber-Physical Simulation of Disease Transmission”

Bo Chen (CS): “Mobile Devices Can Help Mitigate Spreading of Coronavirus”

Nathir Rawashdeh (AC, MERET): “A Tele-Operated Mobile Robot for Sterilizing Indoor Space Using UV Light” (A special thanks to Paul Williams, who’s generous gift to support AI and robotics research made this grant possible)

Weihua Zhou (AC, HI) and Jinshan Tang (AC, MERET): “KD4COVID19: An Open Research Platform Using Feature Engineering and Machine Learning for Knowledge Discovery and Risk Stratification of COVID-19″


Weihua Zhou

Nathir Rawashdeh

Jinshan Tang

Guy Hembroff

Leo Ureel

Bo Chen

Chuck Wallace


Meet Bonnie Henderson, Data Science Master’s Student and CCLC Coach

By Karen S. Johnson, Communications Director, College of Computing

Data Science graduate student Bonnie Henderson began her master’s degree at Michigan Tech in fall 2019. From Jarrell, Texas, Henderson earned a B.A. in mathematics and French at Southwestern University, Georgetown, Texas.

Henderson is a recipient of Michigan Tech’s David House Family Fellowship, which she describes as a great honor. Her research interests are in artificial intelligence and machine learning.

“The fellowship has made an incredible difference in my life,” Henderson says. “As the first person in my family to go to college, it is an amazing opportunity to pursue my graduate studies fully funded.”

This January, Henderson began managing the College of Computing Learning Center (CCLC), an undergraduate learning lab staffed entirely by student coaches and available to all Michigan Tech students in Computing classes.

“I mostly work to help manage the CCLC,” Henderson says. “I help tutor students in undergraduate computer science courses during CCLC walk-in hours, help run CCLC staff meetings, and when the time comes, I’ll help manage the interviewing and hiring process for new tutors.”

Many Opportunities for Learning

Henderson says her work with the CCLC often presents computer science issues and computing problems that are not always common in data science, providing her with many opportunities for learning.

“Studying data science, I work a lot with programming,” Henderson says. “However, I often work with problems related to mathematics in programming and not always the typical undergraduate programming issues.”

What Henderson likes best about tutoring is what she learns along the way. “Since I did not complete my undergraduate degree at MTU, I’m not always familiar with the problems that students are facing when they come in for tutoring. Everyone looks at a problem a little differently, and I get the opportunity to be exposed to many different thought processes and unique solutions.”

New Methods of Virtual Support

Not surprisingly, the plans for the CCLC have changed a lot since the COVID-19 pandemic began earlier this year. “Before this news, we were planning on hosting workshops and other events for students in the College of Computing and other departments, such as guided study groups, exam review sessions, and specialized support for individual classes,” Henderson says.

But since the CCLC cannot offer conventional face-to-face tutoring right now, these plans are changing and Henderson and the CCLC are responding with new methods of virtual support. “We have started sending out a weekly CCLC email to students, which shares coding tips and tricks, quizzes, and news, and we are working to encourage more student involvement, especially with the current difficulties we are all facing,” Henderson explains.

So, instead of hosting face-to-face events, CCLC walk-in hours are now being hosted through Zoom, and the Learning Center is maintaining a Canvas page where students can find help and find information on their own. They also hope to host some virtual workshops soon. Students can sign up for the CCLC Canvas page here: https://mtu.instructure.com/enroll/KWFTJ9.

Balancing Life, Work and School

Henderson says the most challenging thing about balancing life, work, and school is finding a separate time and place for each one.

“I’ll often be looking at one thing, and something in it reminds me of a problem from something else. I have a tendency to hop around a lot, and sometimes things may get lost,” she says. “It has become increasingly difficult working and studying from home, as everything is now sharing the same physical space.”

To help with that, Henderson says it’s helpful to try to have different spaces for the things she has to do. “Like one chair for working and another chair for schoolwork, even if they are in the same room. Some sort of distancing between everything is definitely needed.”

Learn More About the CCLC

Visit the CCLC website here. Visit the CCLC’s Infinite Loop: Resources to Explore, Learn, Code, Repeat.

The Dave House graduate student assistantships provide $30,000 annually for three years to each of three graduate assistants in Michigan Tech’s Master of Science in Data Science program.

About Dave House ’65, University Friend and Donor

Dave House ’65 (EE) is a longtime friend and generous donor to Michigan Tech.

“I support Michigan Tech because I believe in the critical importance of higher education, not only for the state and the nation, but most importantly for our graduates, House says in an EE department alumni profile.

“The Fourth Industrial Revolution changes everything, and Michigan Tech is perfectly positioned to prepare our students for these changes. I support fellowships in data science because of the role that sensing, networking, big data, artificial intelligence and human/machine interfacing has in the Fourth Industrial Revolution. Supporting graduate and research activities is critical to keeping Michigan Tech agile and at the cutting edge of this revolution.”

The Data Science Master of Science

The Data Science Master of Science degree is offered jointly by the College of Engineering and the Department of Computer Science. Associate Professor Laura Brown, Computer Science, is director of the program.


Tim Havens Quote in Enterprisers Project Article

Tim Havens, associate dean for research, College of Computing, and director of the Institute of Computing and Cybersystems, was quoted in the article, “Artificial intelligence (AI) vs. machine learning (ML): 8 common misunderstandings,” published May 19, 2020, in the online publication, The Enterprisers Project.

In there article, Havens likens the way AI works to learning to ride a bike: “You don’t tell a child to move their left foot in a circle on the left pedal in the forward direction while moving your right foot in a circle… You give them a push and tell them to keep the bike upright and pointed forward: the overall objective. They fall a few times, honing their skills each time they fail,” Havens says. “That’s AI in a nutshell.”

Link to the article here.

The Enterprisers Project is a community and online publication built to discuss the evolving role of the CIO and how IT leaders drive business value in a digital world. It is a collaborative effort between Harvard Business Review and Red Hat that delivers daily analysis and advice on topics ranging from emerging technologies to IT talent. Articles in the publication are written by CIOs, for CIOs and other IT executives, who share lessons learned from innovating in true partnership with the business.


MTU’s Adrienne Minerick Elected to Lead Engineering Educators

by Allison Mills, University Marketing and Communications

Adrienne Minerick, dean of the College of Computing, is president-elect of the American Society for Engineering Education (ASEE). She will serve as president-elect from June 2020 to June 2021, a year that will surely be shaped by COVID-19 response efforts and their impacts on education, engineering industries and student lives. She will serve as president from June 2021 to June 2022, and as past-president the following year.

“ASEE is the place where engineering and engineering technology educators plan for the futures our students will encounter,” Minerick said. “I am able, willing and ready to help seed conversations that enable engineering professionals to leverage the rapid growth in computing and cybertechnologies to ensure our students engineer a bright future.”

Diversity in engineering education is key, she added. “Study after study, many by ASEE authors, has shown that increasing diversity of teams decreases engineering failures. We are in an exciting time when traditional engineering and educational practices are being re-examined from additional — and different — perspectives.”

Drawing on her research experience in microfluidics, her leadership in the College of Computing and championship of the ADVANCE program, Minerick plans to shift the governance mindset to encourage engineering access and mobility of ideas.

“I am thrilled that Adrienne will be following me as president-elect and then president of ASEE. Two women from Michigan Tech for two years in a leadership role at ASEE is fantastic,” said Sheryl Sorby, ASEE’s next president and professor in the Engineering Education Innovation Center at Ohio State University, who formerly taught in Michigan Tech’s Engineering Fundamentals program. “Adrienne shows steady, solid leadership and is insightful and visionary. She is someone who gets things done!”

Read the full story on mtu.edu/news and learn more about Michigan Tech’s contributions to ASEE.


Michigan Tech Ranks 22nd in “Cyber Power” Top 100

NCL Logo

Twenty-one Michigan Tech students on three teams finished the spring 2020 semester with impressive success at a recent National Cybersecurity League (NCL) competition. All three teams ranked in the top 100, out of 925 teams nationwide. Assistant Professor Bo Chen, Computer Science, is the faculty advisor to the teams.

Michigan Tech’s overall “Cyber Power Ranking” is 22nd nationally and 6th in the central region, as of Spring 2020. Schools are ranked based on their top team performance, their top student’s individual performance, and the aggregate individual performance of their students.

Team 1 ranked 16th in a field of 925 teams; with Alex Larkin (CS), Jack Bergman (CS), Jon Preuth (CS), Trevor Hornsby (Software), Shane Hoppe, Dakoda Patterson (CS), and Matthew Chau (Cyber).

Team 2 ranked 45th among 925 teams; with Sophia Kraus (EE), Sam Breuer (EE), Ian Hughes (Cyber/CS), Austin Doorlag (CS), Sankalp Shastry, Hunter Indermuehle (EE), and Samantha Christie (CS).

Team 3 ranked 78th of 925; with John Claassen (CS), Stu Kernstock (Cyber), Jacson Ott (Cyber), Bradley Gipson (CNSA), Ethan Frenza (CNSA), Tim Lucero (Cyber), and Anders Jacobsen (EE).

Shane Hoppe was ranked 95th among 5,357 participants in the NCL individual game.

The National Cyber League (NCL) is a biannual cybersecurity competition. Open to U.S. high school and college students, the competition consists of a series of challenges that allow students to demonstrate their ability to identify hackers from forensic data, pentest and audit vulnerable websites, recover from ransomware attacks, and more.

Every year, over 10,000 students from more than 300 colleges and universities across the U.S. participate in the NCL competitions. Student players compete in the NCL to build their skills, leverage the NCL Scouting Reports for career and professional development, and to represent their school in the national Cyber Power Rankings.

Powered by Cyber Skyline, NCL provides a platform on which students can prepare and test themselves against practical cybersecurity challenges that they will likely face in the workforce, such as identifying hackers from forensic data, pentesting and audit vulnerable websites, recovering from ransomware attacks, and more.

The Cyber Power Rankings were created by Cyber Skyline in partnership with the National Cyber League (NCL). The rankings represent the ability of student competitors to perform real-world cybersecurity tasks on the Cyber Skyline platform.

Cyber Skyline logo

Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

An article by Audrey Yazdanparast (2019, PhD, Electrical Engineering) and Dr. Timothy Havens, “Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation,” has been accepted for publication in the journal, IEEE Transactions on Big Data.

The paper presents an efficient approach for detecting self-similar communities in weighted graphs, with applications in social network analysis, online commodity recommendation systems, user clustering, biology, communications network analysis, etc.

Paper Abstract: Community detection is an important problem in complex network analysis. Among numerous approaches for community detection, label propagation (LP) has attracted a lot of attention. LP selects the optimum community (i.e., label) of a network vertex by optimizing an objective function (e.g., Newman’s modularity) subject to the available labels in the vicinity of the vertex. In this paper, a novel analysis of Newman’s modularity gain with respect to label transitions in graphs is presented. Here, we propose a new form of Newman’s modularity gain calculation that quantifies available label transitions for any LP based community detection.

The proposed approach is called Modularity Gain Acceleration (MGA) and is simplified and divided into two components, the local and global sum-weights. The Local Sum-Weight (LSW) is the component with lower complexity and is calculated for each candidate label transition. The General Sum-Weight (GSW) is more computationally complex, and is calculated only once per each label. GSW is updated by leveraging a simple process for each node-label transition, instead of for all available labels. The MGA approach leads to significant efficiency improvements by reducing time consumption up to 85% relative to the original algorithms with the exact same quality in terms of modularity value which is highly valuable in analyses of big data sets.

Timothy Havens is director of Michigan Tech’s Institute of Computing and Cybersystems (ICC), the associate dean for research for the College of Computing , and the William and Gloria Jackson Associate Professor of Computer Systems.

View the article abstract here.


Ford Mobility Funds A.I., Acoustics Research

Imagine if your car could tell you when you are passing by an area occupied by rare migratory birds, or if it could listen to roads and bridges to determine when infrastructure repairs need to be made.

A recent gift of $149,518 from Mobility Research at Ford Motor Company is funding research that could make this possible.

Dr. Timothy Havens, Institute of Computing and Cybersystems, and Dr. Andrew Barnard, Great Lakes Research Center, will lead an exploration of how future connected vehicles could use AI and acoustics to detect, classify, and localize external sound events, and evaluate and monitor transportation infrastructure.

The gift will fund two graduate fellowships, a team of undergraduate students in the SENSE Enterprise, and build and develop a mobile acoustics test bed that will allow students, Havens, and Barnard to conduct cutting-edge research in AI and acoustics.

Michigan Tech would like to thank Chad Esselink (’94, Computer Science) and Tavan Eftekhar at Ford Mobility Research for making this possible.

The Institute of Computing and Cybersystems (ICC) is the research arm of the College of Computing at Michigan Tech. The ICC provides faculty and students the opportunity to work across organizational boundaries to create an environment that is a reflection of contemporary technological innovation. This collaboration allows for a convergence in communication, control and computing that mirrors today’s industry and society.

The Great Lakes Research Center (GLRC) provides state-of-the-art laboratories to support research on a broad array of topics. Faculty members from many departments across Michigan Technological University’s campus collaborate on interdisciplinary research, ranging from air–water interactions to biogeochemistry to food web relationships.


Elijah Cobb Awarded Undergraduate Research Fellowship

In November, Michigan Tech undergraduates begin submitting research proposals to the annual competition for Summer Undergraduate Research Fellowships—SURF—which are due the following February and awarded that spring.

Computer Science major Elijah Cobb was one of the many students to submit a SURF proposal this academic year. His was excellent, but so were many others, and the SURF funds were limited, so unfortunately Cobb missed out.

Enter Associate Professor Dr. Charles Wallace (Computer Science) and the College of Computing, which is providing additional funding to allow Cobb to join this summer’s cohort of SURF recipients.

Open to all Michigan Tech undergraduates who have at least one semester remaining after the summer, SURF recipients conduct a research project with a faculty mentor, prepare periodic progress reports, attend a series of professional development seminars over the summer, then present their research at Michigan Tech’s Undergraduate Research Symposium, or at a professional conference in their field. A modest stipend is also awarded.

Cobb will conduct his research project, “Designing Scaffolded Interactive Instruction in Discrete Mathematics,” with Dr. Charles Wallace, Associate Professor of Computer Science.

Watch the College of Computing blog, website, and social media channels for updates from Cobb and Dr. Wallace as their research moves forward.

Elijah Cobb is a fourth year Computer Science student at Michigan Tech. He describes himself as a passionate learner, and throughout his education he has challenged himself to explore learning opportunities outside of the classroom.

Cobb says he is thrilled to have the opportunity to participate in a research project with Professor Wallace. “Together, we will research and develop a new tool for undergraduate Computer Science students to use at Michigan Tech. Our research will focus on Alloy, a programming language used for modeling real-world systems.”

Because Alloy is so complex, Cobb explains that he and Wallace aim to produce an interactive drag and drop interface that will make this powerful programming language more accessible to undergraduate computer science students.

The Project: Designing Scaffolded Interactive Instruction

Discrete mathematics is a foundational Computer Science topic, and undergraduate computer science students enroll in a course at some point in their college career. The course focuses on topics including set theory, relational algebra, and predicate logic, extremely important concepts for beginning students as they form the basis of analysis in the field.

But Computer Science students typically do not get the kind of interactive practice with discrete math that they do with programming in languages like Java. As a result, misconceptions can persist, and students can develop an attitude that math has little relevance to their field.

To introduce the mathematical languages of relational algebra and predicate logic to undergraduate Computer Science students, Dr. Wallace and his colleagues have developed curriculum that uses the Alloy language and modeling tool. He notes that many progressive Computer Science departments across the nation have included programming-oriented exercises like these in their discrete math curricula.

“When I came to Michigan Tech and took discrete structures as a second-year student, the interactive element of the course with Alloy greatly benefited me in learning the course material,” confirms Cobb. “But it was also the most difficult part of the course for me. I knew what I wanted to do, but I could not replicate the thoughts in my head into the code required to run simulations.”

With his SURF project, Cobb aims to help other students with their learning of discrete math and eliminate some of the unnecessary confusion that may develop. He’ll develop a simple, easy to use graphical application that will allow undergraduate students to develop mathematical simulations without the need to fully understand the underlying programming language involved.

To introduce discrete mathematics–the mathematical languages of relational algebra and predicate logic–to undergraduate CS students, Dr. Wallace and his colleagues have developed curriculum that uses the interactive Alloy modeling tool. Wallace notes that many progressive CS departments across the nation have included interactive tools like these in their curriculum.

Alloy is both a programming language based on the mathematical languages of relational algebra and predicate logic, and an application for finding instances (situations) that follow (or break) the requirements set by an Alloy program. It is a powerful tool for “lightweight” modeling of complex systems, allowing designers to explore and gain insights early in the design process. In the classroom, Alloy can give students real-time feedback on their developing understanding of discrete math, and provide them with an authentic sense of how math is applied in the CS field.

“Alloy provides more feedback to students than traditional pencil-and-paper exercises, however the experience could be better for first-time students,” Cobb says. “When students work with Alloy, it can be daunting because as they are trying to develop models, they may encounter small, unfamiliar syntactical issues, and the error messages provided by Alloy can be confusing because they are written for experts, not first time users.”

So, first-time students need careful scaffolding to navigate the powerful Alloy tool. Cobb explains that scaffolding can be thought of as creating a base structure on which students can focus on the task at hand, without overloading them with distracting content.

Cobb’s SURF project will implement automated scaffolding to assist the student new to Alloy by:
(1) Providing a visual, block-based interface for the language, similar to introductory programming languages like Scratch and Snap! This has the potential to keep students focused on learning fundamental concepts of logic and relations, without distracting syntax problems.
(2) Developing automated detection and response for common problems in novice code. This follows from successful work in automated critique of Java programs using the WebTA tool, developed by Assistant Professor Dr. Leo Ureel for use in Michigan Tech’s introductory programming courses.

Cobb says his personal experiences have provided him with much expertise and motivation for a project like this. He notes, “After graduating high school I worked for start-up called Ampel Feedback. It was then that I learned Electron, ReactJS, Typescript, and many other technologies I aim to use for this research program.”

Cobb will begin his work in the second half of summer 2020, and continue its development in the 2020-2021 academic year, including pilot studies of the scaffolded tool. Feedback from the pilot studies will inform further development in summer 2021; Cobb and Wallace expect to introduce the new learning tool in the Michigan Tech Discrete Structures course in fall 2021.

Wallace is looking forward to the collaboration, noting that “it is gratifying to have a student like Elijah who is able to reflect on both what has been valuable and what has been challenging in his own learning experience, and act on those reflections to help future students.”