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 computing education to help make computing more accessible to all learners. 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 firstname.lastname@example.org 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
*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 (see year two project update).
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
Decker, A., Margulieux, L. E., Morrison, B. B. (2019). Using the SOLO Taxonomy to understand subgoal labels effect on problem solving processes in CS1. In Proceedings of the Fifteenth Annual Conference on International Computing Education Research. New York, NY: ACM. doi: 10.1145/3291279.3339405
In the upcoming, final year, we will be developing materials for Python and working with instructors at about 10 different schools to test the instructional materials in their intro programming courses.
Separate from this project, I’ve worked on using the subgoal learning framework to guide self-explanation. 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.
I first started researching subgoal labels 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. I expanded the subgoal learning framework by exploring the use of subgoal labels in expository text in addition to worked examples.
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., 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., 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.
Computing Integration in Preservice Teacher Preparation Programs
K-12 computer science education is rapidly growing in Georgia, and one of the main areas of growth is integrating computing into other subject areas. This approach ensures that all students are exposed to computing, not just those who elect to take standalone CS courses or out-of-school programs. To prepare for the next generation of students, the College of Education and Human Development at Georgia State is committed to preparing our preservice teachers to use computing integration activities in their future classes. I’m working with the teacher preparation faculty to fulfill this goal. The work is still in the early stages, but check back soon or let me know if you’d like to talk about our approach.
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.