The goal of my research is to improve problem solving, particularly for students who do not have access to an instructor to help them overcome problem solving impasses, like in online learning. I focus on problem solving in computer science education. My work also extends to recognizing the limitations of online learning and explores the best combinations of face-to-face and online learning. More details about my projects can be found below.
All of my papers that I am allowed to share openly can be found here. If you’d like a copy of something not in that list, please send me a message at email@example.com or through my ResearchGate page.
I published a book chapter on feedback via educational technology in the book “Educational Technologies: Challenges, Applications, and Learning Outcomes.” Download a copy
To access my primer about methods and analyses in educational research, click here.
NSF IUSE (#1712231) Developing and Assessing Subgoal Labels for Imperative Programming
Students solve novel problems better when they understand the subgoals, i.e., functional pieces, of the problem solving procedure. Building on my previous work with block-based languages, Briana Morrison, Adrienne Decker, and I applied this work to text-based languages.
*Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (pp. 21-29). New York, NY: Association for Computing Machinery. doi: 10.1145/2787622.2787733
Morrison, B. B., Decker, A., & Margulieux, L. E. (2016). Learning loops: A replication study illuminates impact of HS courses. In Proceedings of the Twelfth Annual International Conference on International Computing Education Research (pp. 221-230). New York, NY: ACM. doi: 10.1145/2960310.2960330
*Awarded Chairs’ Best Paper Award at ICER 2015
We are now extending this work for core procedures taught in introductory college-level CS courses: using expression (assignment) statements, selection statements, loops, object instantiation and method calls, writing classes, and arrays. During the first two years, we used a cognitive task analysis procedure to identify the subgoals for these topics, develop Java-based instructional materials, and pilot testing them.
Margulieux, L. E., Morrison, B. B., & Decker, A. (2019). Design and pilot testing of subgoal labeled worked examples for five core concepts in CS1. In ITiCSE ’19: Innovation and Technology in Computer Science Education Proceedings. New York, NY: ACM. doi: 10.1145/3304221.3319756
In the upcoming, final year, we will be developing materials for Python and working with instructors at about 15 different schools to test the instructional materials in their intro programming courses
Self-Explaining Worked Examples by Creating Subgoal Labels
This project explored active and constructive methods of learning subgoals to improve subgoal learning and, thus, problem solving performance. I found that when learners self-explained the purpose of subgoals to themselves, i.e., constructive learning, they performed better than learners who passively or actively learned subgoals, sometimes. This effect occurred only when students received hints about the subgoals of the procedure OR received feedback on the explanations that they made. Learners who received both hints and feedback did not perform as well as those who received only hints or feedback.
**Margulieux, L. E., & Catrambone, R. (2019). Finding the best types of guidance for constructing self-explanations of subgoals in programming. Approved Manuscript Version. The Journal of the Learning Sciences, 28(1), 108-151. doi: 10.1080/10508406.2018.1491852 Article summary
When I used the labels that learners created to scaffold their initial problem solving attempts (i.e., while solving practice problems), they performed better than those who received no scaffolding or received expert-created labels as scaffolds.
**Margulieux, L. E., & Catrambone, R. (2017). Using learners’ self-explanations to guide initial problem solving. In Proceeding of the Thirteenth Annual Conference on International Computing Education Research (pp. 21-29). New York, NY: ACM. doi: 10.1145/3105726.3106168
**This work is based on my doctoral dissertation for which I earned the Emerald/HETL Education Outstanding Doctoral Research Award.
Subgoal Labeled Worked Examples in Computer Programming Education
With funding from Georgia Tech’s GVU Center and IPaT, Richard Catrambone, Mark Guzdial, and I developed instructional materials to teach undergraduates to make apps for Android devices using Android App Inventor. After we found the subgoal label manipulation to be successful, we developed a 4-week program to teach the same to K-12 teachers who were interested in becoming certified to teach computer science.
Margulieux, L. E., Catrambone, R., & Guzdial, M. (2016). Employing subgoals in computer programming education. Computer Science Education, 26(1), 44-67. doi: 10.1080/08993408.2016.1144429 Article Summary
***Margulieux, L. E., Guzdial, M., & Catrambone, R. (2012). Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications. In Proceedings of the Ninth Annual International Conference on International Computing Education Research (pp. 71-78). New York, NY: ACM. doi: 10.1145/2361276.2361291
***Work referenced in MIT App Inventor Resources.
Subgoal Labels in Expository Text and Worked Examples in STEM Education
Subgoal labels have been effectively used in worked examples to improve problem solving in science, technology, and mathematics, but learners often receive more instruction that only worked examples. I explored the effect of subgoal labels in expository text (i.e., conceptual descriptions of the problem solving procedure) on problem solving performance in computer programming. I found that subgoal labels in procedural text have a different beneficial effect than subgoal labels in worked examples.
Margulieux, L. E., Catrambone, R., & Schaeffer, L. M. (2018). Varying effects of subgoal labeled expository text in programming, chemistry, and statistics. Instructional Science, 46(5), 707-722. doi: 10.1007/s11251-018-9451-7 Article summary
Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in procedural instructions and worked examples. Learning and Instruction, 42, 58-71. doi: 10.1016/j.learninstruc.2015.12.002
Blended/Flipped Learning and Mixed Instructional eXperiences (MIX) Taxonomy
To address inconsistencies in how people define flipped, hybrid, blended, and inverted classes, I developed the MIX taxonomy to define these terms and reclassify studies in the literature. After reclassifying the studies, I was able to uncover trends that were previously buried by inconsistent definitions. These trends validated the dimensions of the taxonomy and identified the benefits of flipped, hybrid, and blended courses.
A recording of my presentation at the C21U Seminar Series can be found here.
Margulieux, L. E., McCracken, W. M., & Catrambone, R. (2016). Mixing face-to-face and online learning: Instructional methods that affect learning. Educational Research Review, 19, 104-118. doi: 10.1016/j.edurev.2016.07.001
I also wrote white papers at C21U including an interdisciplinary guide to flipping a class and a position paper on the strengths of technology, instructors, and peers (summary).
The MIX taxonomy was used as a cohesive structure for an edited volume about the design of and research on blended and flipped courses at Georgia Tech. The chapters include courses from many disciplines (5 of 6 colleges at Georgia Tech), many sizes (20 students to 1000 students), many research methods (qualitative and quantitative), and many redesign goals (improve consistency across sections of a course, improve learning outcomes, increase number of students without decreasing one-on-one interactions, etc.).
Madden, A., Margulieux, L. E., Goel, A. K., & Kadel, R. S. (Eds.). (2019). Blended Learning in Practice: A Guide for Practitioners and Researchers. Cambridge, MA: MIT Press.