I am committed to evidence-based instructional practices (instructional practices demonstrated to be effective by research): using them in my teaching, creating new ones via my research, and helping others adopt them. I discuss this more in My Teaching Philosophy.
My interests are primarily focused on first- and second-year computer science and engineering courses. These courses include digital logic design, computer organization, introductory programming, analog signal processing, digital signal processing.
I am also interested in teaching graduate-level courses in engineering and computer science education such as cognition and and the science of learning, educational research methodologies, assessment.
|Learning and Computer Science (Graduate Level)||Spring 2019|
|Learning and Computer Science (Undergraduate Level)||Spring 2018|
|Online Learning Systems||Fall 2017|
|Computer Architecture (CS 233)||Fall 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, Spring 2021, Fall 2021|
|Survey of Engineering Education Research||Spring 2015|
|Introduction to Computer Engineering||Summer 2008, Fall 2012, Spring 2013, Fall 2013, Spring 2014|
|Digital Signal Processing||Summer 2010|
- List of Teachers Ranked as Excellent, CS 233, Fall 2021
- List of Teachers Ranked as Excellent, CS 233, Spring 2021
- IEEE Education Society Mac Van Valkenburg Early Career Teaching Award, Fall 2020
- List of Teachers Ranked as Excellent, CS 233, Spring 2020
- List of Teachers Ranked as Excellent, CS 233,Fall 2019
- List of Teachers Ranked as Excellent, CS 598GH, Fall 2019
- List of Teachers Ranked as Excellent, CS 233, Spring 2019
- List of Teachers Ranked as Excellent CS 498GH, Spring 2018
- List of Teachers Ranked as Excellent, ECE 290, Spring 2007
- List of Teachers Ranked as Excellent, ECE 385, Spring 2006
- List of Teachers Ranked as Excellent, ECE 110, Fall 2005
- Olesen Award for Excellence in Undergraduate Teaching for the Department of Electrical and Computer Engineering, 2007
Evidence-Based Instructional Practices
I incorporate a variety of evidence-based instructional practices into my teaching. Resources I use to implement a variety of these practices can be found in my Teaching Portfolio. Selected exemplars can be found below.
Flipped classroom (pdf): I create video lectures for students to watch before class. Students complete a small homework exercise before coming to class. We then work through a series of short problems in class to help students better understand the concepts in class. Additional slides can be found in my Teaching Portfolio.
Video lectures designed according to principles from Cognitive Load Theory (video): To maximally take advantage of how the brain processes information from videos, the audio and visual channels of a video should never have duplicate information (e.g., never read the words on screen) but instead should complement each other, forcing the video watcher to connect the words being spoken with the image being displayed. This tactic forces the watcher to actively engage when watching, making neural connections and accelerating learning. Additional videos can be found in my Teaching Portfolio.
Peer Instruction (pdf): In lecture-based classes, I use many short, multiple-choice questions to help students assess their own learning and engage students in rapid feedback. Students are encouraged to talk to their neighbors to help them further refine their understanding of course content.
Assertion-Evidence slide design (pdf): To help students easily identify the main point of each slide in a lecture, slide headings are full-sentence assertions rather than simply topical headings. The rest of the slide is designed as evidence to support the main assertion.
Context Rich Collaborative Problem Solving (pdf): In discussion-based classes, students work in teams of 3-4 solving complex, real-world problems that engage students in deeper thinking about core concepts and problem solving methods. Additional worksheets can be found in my Teaching Portfolio.
CATME team assignments: We use the CATME (http://catme.org) tool to assign students to teams for collaborative problem solving. The CATME tool can help create teams that help all students feel like they can contribute and improve learning.
Frequent testing and second-chance testing: With the help of the PrairieLearn platform (https://prairielearn.engr.illinois.edu), students take frequent, small exams rather than 1 or 2 large midterm exams. These frequent exams help students stay current with course material and deepen students’ learning of core course material. To encourage mastery-oriented learning, students can opt to re-take any exam to improve their performance. Anyone is welcome to log into PrairieLearn and peruse our homework assignments and practice exams.