About

Mattox Beckman is a Teaching Associate Professor at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. He holds a PhD in Computer Science from the University of Illinois at Urbana (2003) and a BS in Math and Computer Science from the same institution (1993). His research interests include programming languages and computer science education, with a focus on formal methods and software engineering. Beckman has been actively involved in coaching teams for the International Collegiate Programming Competition (ICPC), leading Illinois teams to regional and world finals, and has received recognition such as the ICPC Coach’s Award for his contributions to the competition.

Research topics

• Political Science
• Medical education
• Medicine
• Engineering management
• Pedagogy
• Psychology
• Engineering

Selected publications

• ILDBug: A New Approach to Teaching Debugging

  2025-02-18

  article

  ILDBug is a novel debugging approach inspired by the pedagogical technique Interactive Lecture Demonstrations (ILDs). During ILDs, students predict a demonstration's result, experience the demonstration, and then reflect on their experience. We adapted this process to teach debugging (ILDBug) by having students engage in detecting the bug for prediction, then locating and correcting the bug for experience, and then reflecting on the debugging process. The ILDBug approach is designed to be a lightweight technique to create a debugging exercise that is adaptable to many contexts. This demo will cover the high-level pieces the audience needs to create their own ILDBug using our approach, an example ILDBug in an introductory programming context so they can see it in action, and finally tips on how to adapt our technique to fit their own classroom context.

  PublisherDOI

• A Complete Redesign of CS1 for Engineering Students

  2025-08-21 · 1 citations

  article
  PublisherDOI

• ILDBug: A New Approach to Teaching Debugging

  2025-11-02

  article

  This innovative practice category full paper describes a novel approach to teaching debugging. Debugging is an essential skill in programming, yet there are few evidence-based techniques to improve students' debugging abilities. We developed a debugging exercise inspired by the pedagogical technique, Interactive Lecture Demonstrations (ILDs). During ILDs, students predict a demonstration's result, experience the demonstration, and then reflect on their experience. We adapted this process for debugging (ILDBug) by having students look at example program outputs and predict the bug, then they experience debugging by tracing the code and trying to fix the bug, and finally students reflect on their debugging process. We engaged students in a series of three ILDBug exercises during a lab section of an introductory programming course for nonCS, engineering majors. To evaluate whether our exercises were improving students' debugging skills, we introduced a cross-over study design with three populations: each group of students completed the same three exercises but in a different order. We then compared how students performed on each exercise when it was their first exercise or their last exercise. We graded students' predictions for accuracy using a binary score and measured how long students took to fix the bugs using clickstream logs. Students generally fixed bugs faster after completing two ILDBug exercises. Students improved at predicting bugs that were moderately difficult to identify. The ILDBug exercises are a promising, light-weight debugging exercise that can be adapted to many contexts and merit future research.

  PublisherDOI

• Inform Track: Integrated Teaching and Leadership Development Program for Graduate Teaching Assistants

  2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024-02-20 · 3 citations

  articleOpen access

  measurement science, and engineering education.He oversees undergraduate laboratories in fluid mechanics and heat transfer.Pedagogically, Dr

  PublisherOA PDFDOI

• Board 85: Integrated Engineering Leadership Initiative for Teaching Excellence (iELITE) Year Two: Assessment of Intermediate-Term Outcome for Graduate Teaching Assistant Training

  2020-09-10 · 1 citations

  article

  Abstract Since the spring of 2017, the Integrated Engineering Leadership Initiative for Teaching Excellence (iELITE) team has been developing and offering a course that seeks to train graduate teaching assistants (GTAs) in the College of Engineering. The training is to be applicable to all types of GTA contexts: lab, discussion, and lecture. Because many of our engineering students' career goals are within non-academic settings, students often have little natural motivation to develop effective pedagogical skills. As explained in our previous paper, the team made a strategic decision to combine the teaching of leadership skills and pedagogical skills in order to appeal to GTAs who plan to go into non-academic careers. In this paper, we will present our logic model for the iELITE program, which has four categories of inputs: GTAs, Engineering Faculty, Administration (College and Departments), and External Partners (industry sponsors). The logic model will lay out corresponding short-term, intermediate-term and long-term outcomes for each of the categories. The External Partners category is a new addition to the program this year. After collecting feedback from the teaching community in the college, we think that it is crucial to connect our content to what is being done in the professional workplace to make the learning experience more realistic. Intermediate-term outcomes in the GTAs category will be our main focus for the moment. Furthermore, we will discuss faculty feedback from those who have worked with past GTA participants in the program.

  PublisherDOI

• Integrative Engineering Leadership Initiative for Teaching Excellence (iELITE)

  2020 · 7 citations

  • Political Science
  • Medical education
  • Pedagogy

  Abstract A team of engineering faculty, in collaboration with professionals around campus, designed a new teaching and leadership program and successfully offered it as a pilot course for two semesters, beginning in the spring of 2017. Motivated to prepare graduate students for careers in both academia and industry, this program aims to enhance the teaching skills of graduate teaching assistants (GTAs) while simultaneously augmenting their professional skills. Our goal is to train the next generation of leaders who will possess technical and academic expertise as well as critical skills such as communication, organization, and relationship-building. The majority of GTAs do not have prior teaching experience when they start their appointments. Although workshops offered by a campus-level teaching center are a quick and efficient way to introduce new GTAs to their role, follow-up programs are needed to further develop their teaching effectiveness and to properly train them in the specific teaching requirements of their disciplines. Teaching can play a prominent role in the professional development of GTAs. Nearly half of graduate students will take up careers outside of academia, but a typical PhD program provides little direct training for leadership in a non-academic setting. However, by learning to teach well, GTAs develop many leadership and communication skills that will transfer well to their future careers. Activities such as organizing and presenting material, working effectively with instructors and fellow GTAs, and communicating effectively with students present an opportunity to develop communication and leadership skills that will be highly valued whether the GTA goes into academia or industry. For the first iteration, we focused on the design of the program, based on literature and in collaboration with various university education professionals. In the second iteration, we initiated strategic partnerships with various engineering departments, resulting in a dramatic increase in enrollment. We have also deepened the integrative components between teaching skills and leadership skills in the course based on our reflection and feedback from the first iteration. Our program evaluation uses two surveys: the STEM GTA-Teaching Self-Efficacy Scale and a modified version of Alpay and Walsh's skill-perception inventory. The STEM GTA-Teaching Self-Efficacy Scale evaluates teaching assistants' belief in their ability to teach in the STEM area. Alpay and Walsh's skills-perception inventory assesses the participants' perceptions of transferable leadership skills, such as time management, self-awareness, communication, and teamwork. We modified the inventory to see if GTAs perceived that teaching would provide them with opportunities to enhance transferable leadership skills. In this presentation, we will describe our collaborative design process, strategic partnerships with various engineering departments, and enhancements of the integrative approach. Additionally, we will discuss students’ perception in enhancing their teaching and leadership skills and viewing teaching opportunities to foster transferable leadership skills though our program.

  PublisherDOI

• Improv

  2018-06-28 · 3 citations

  article

  Often, people such as educators, artists, and researchers wish to quickly generate robot motion. However, current toolchains for programming robots can be difficult to learn, especially for people without technical training. This paper presents the Improv system, a programming language for high-level description of robot motion with immediate visualization of the resulting motion on a physical or simulated robot. Improv includes a "live coding" wrapper for ROS ("Robot Operating System", an open-source robot software framework which is widely used in academia and industry, and integrated with many commercially available robots). Commands in Improv are compiled to ROS messages. The language is inspired by choreographic techniques, and allows the user to compose and transform movements in space and time. In this paper, we present our work on Improv so far, as well as the design decisions made throughout its creation.

  PublisherDOI

• Logic and Theory of Algorithms

  2008-01-01 · 1 citations

  book1st authorCorresponding

  This book constitutes the refereed proceedings of the 4th International Conference on Computability in Europe, CiE 2008, held in Athens, Greece, in June 2008. The 36 revised full papers presented together with 25 invited tutorials and lectures were carefully reviewed and selected from 108 submissions. Among them are papers of 6 special sessions entitled algorithms in the history of mathematics, formalising mathematics and extracting algorithms from proofs, higher-type recursion and applications, algorithmic game theory, quantum algorithms and complexity, and biology and computation.

  Publisher

• A Role-Based Coordination Model and its Realization

  2008-11-14 · 2 citations

  articleSenior author

  This paper presents a framework to support Open Distributed and Embedded (ODE) application development based on the Actor-Role-Coordinator (ARC) model. The ARC model is a role-based coordination model developed to address three main concerns inherent in an ODE system: dynamicity, scalability, and stringent QoS requirements. It treats an ODE system as a composition of concurrent computation and coerced coordination. In particular, the ARC model uses concurrent objects that communicate with each other through asynchronous messages, i.e., actors, to model the concurrent computation of an ODE system, while the system’s QoS requirements are mapped to coordination constraints. Coordination entities, i.e., roles and coordinators, impose coordination constraints on concurrent actors transparently through message interceptions and manipulations. In the ARC model, roles provide actor behavior abstractions for coordinators and coordinators are responsible for coordinating roles. In addition, a role also has local coordination responsibilities among actors belonging to that role. This coordination is called intra-role coordination which complements the inter-role coordination performed by the coordinators. In other words, under the ARC model, an ODE application is modeled by three orthogonal layers: computation, intrarole coordination and inter-role coordination. This separation not only improves software modularity and reusability, but also allows different levels of compositions. Our experiments show that the model scales well as the number of entities involved in the system increases, and that the performance overhead introduced by the external coordination layers is limited. Povzetek: Opisano je ogrodje za model aktor-vloga-koordinator (ARC).

  Publisher

Frequent coauthors

• Hyun Il Choi

  Rensselaer Polytechnic Institute

  17 shared

• Lucas Anderson

  University of Illinois Urbana-Champaign

  17 shared

• Matthew D. Goodman

  University of Illinois Urbana-Champaign

  9 shared

• Yu‐Ting Chen

  University of Illinois Urbana-Champaign

  9 shared

• Blake Johnson

  University of Illinois Urbana-Champaign

  8 shared

• Charlotte Hathaway

  University of Illinois Urbana-Champaign

  4 shared

• Yuting Chen

  Western Washington University

  4 shared

• Selim Havan

  Rensselaer Polytechnic Institute

  4 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• PhD, Computer Science

  University of Illinois at Urbana-Champaign

  2003

• BS

  University of Illinois at Urbana-Champaign

  1992

Awards & honors

• ICPC Coach's Award (2024)