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 an email at email@example.com.
I recently 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 white papers about 1) creating a flipped classroom and 2) methods and analyses in educational research, click here.
Self-Explaining Worked Examples by Creating Subgoal Labels
Students solve novel problems better when they understand the subgoals, i.e., functional pieces, of the problem solving procedure. The common method of teaching subgoals is to provide subgoal labels, which is a passive method of learning. This project explores 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. (2016). Using subgoal learning and self-explanation to improve programming education. In A. Papafragou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2009-2014). Austin, TX: Cognitive Science Society. Download a copy (Conference proceeding length)
*Margulieux, L. E., & Catrambone, R. (in press). Finding the best types of guidance for constructing self-explanations of subgoals in programming. Approved Manuscript Version. The Version of Record of this manuscript has been published and is available in The Journal of the Learning Sciences, published online 06/26/18, doi: 10.1080/10508406.2018.1491852.
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 ACM International Computing Education Research conference (ICER’17), Tacoma, WA.
*This work is based on my doctoral dissertation for which I earned the Emerald/HETL Education Outstanding Doctoral Research Award.
I’m working with Briana Morrison and Adrienne Decker to extend this work for introductory college-level CS courses with text-based programming languages. This work is being funded by the NSF IUSE program (#1712231).
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: Association for Computing Machinery. doi: 10.1145/2960310.2960330
**Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. 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
Mixed Instructional eXperiences (MIX) Taxonomy for Classifying Hybrid, Blended, Flipped, and Inverted Courses
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. doi: 10.1016/j.edurev.2016.07.001
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
***Margulieux, L. E., Guzdial, M., & Catrambone, R. (2012). Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications. Proceedings of the Ninth Annual International Conference on International Computing Education Research (pp. 71-78). New York, NY: Association for Computing Machinery.
***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. doi: 10.1007/s11251-018-9451-7
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