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 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.
Computing Integration in Teacher Preparation Programs (NSF CAREER #1941642; NSF EAGER #2016010)
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. I am working with teacher preparation colleagues at Georgia State to design integration activities within disciplines and grade bands and to prepare our preservice teachers to use the activities in their future classes. Some examples of activities we’ve developed are for learning about electromagnetic waves in middle school science, dialogue in secondary school English Language Arts, and patterns in 3-5th grade math.
Enderle, P. J., Margulieux, L. E., & King, N. S. (2021). What’s in a wave? Using modeling and computational thinking to enhance students’ understanding of waves. The Science Teacher, 88(March/April), 54-58.
Margulieux. L. E., & Yadav, A. (2021). Middle science computing integration with preservice teachers. Journal of Computers in Mathematics and Science Teaching, 40(1), 29-49.
The two goals of this project are
- to better understand which computing concepts are most valuable for computing integration activities, to best utilize computing as a tool for learning in other disciplines
- to better understand how to prepare teachers to learn and use computing integration activities in their classrooms, given the already extensive demands on their time
Computing Integration Micro-credentials (NSF EAGER #2016010; IES TQP #U336S190026)
To promote computing integration and recognition of teachers’ skills, we worked with International Society of Technology in Education (ISTE) and Georgia Dept. of Education to create a series of 1-credit hour courses focused on integrated computing. For each course, teachers can earn a micro-credential badge from ISTE certifying this knowledge and skill based on their computational thinking competencies. Funding from NSF allows us to provide access to the courses free of charge. The courses are
- Computing in Everyday Life
- Computational Thinking
- Methods for Integrated Computing
The badges for these three courses can also be exchanged for course credit in Georgia State’s online Computer Science Endorsement program, also free of cost, for teachers who are interested in becoming certified in CS.
Subgoal Labels for Programming Education (NSF IUSE Level 1 #1712231; Level 2 #2111578)
Students solve novel problems better when they understand the subgoals, i.e., conceptual 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 extended 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. We used a cognitive task analysis procedure to identify the subgoals for these topics, develop Java-based instructional materials, and test 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
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
We found that subgoal labeled materials improve student performance early in the learning process. They are particularly beneficial to students who are at risk of withdrawing or failing the course.
Margulieux, L. E., Morrison, B. B., & Decker, A. (2020). Reducing dropout and failure rates in introductory programming with subgoal labeled worked examples. International Journal of STEM Education, 7(19). doi: 0.1186/s40594-020-00222-7
Starting Fall 2021, we have Level 2 funding from NSF’s IUSE program to expand our instructional design within Java and to Python and to test these materials with universities across the US.
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.
Theory Building and Interdisciplinary Synthesis
I am an interdisciplinary researcher who equally values my background in psychology, alignment with learning sciences, and application in computing education and instructional design and technology. In addition, I like to work with new people and explore new topics and pockets of literature to better understand debated areas in education practice. As a result, I regularly write theory building and syntheses papers that connect research from various fields about the same topic.
- Blended/flipped learning – 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
- Standardized measurement in computing education – Margulieux, L. E., Ketenci, T. A., Decker, A. (2019). Review of measurements used in computing education research and suggestions for increasing standardization. Computer Science Education, 29(1), 49-78. doi: 10.1080/08993408.2018.1562145
- Spatial skills and training – Margulieux, L. E. (2019). Spatial Encoding Strategy theory: The relationship between spatial skill and STEM achievement. In Proceedings of the Fifteenth Annual Conference on International Computing Education Research (pp. 81-90). New York, NY: ACM. doi: 10.1145/3291279.3339414
- Metacognition and self-regulation – Prather, J., Becker, B., Craig, M., Denny, P., Loksa, D., & Margulieux, L. E. (2020). What do we think we think we are doing?: Metacognition and self-regulation in programming. In Proceedings of the Sixteenth Annual Conference on International Computing Education Research (pp. 2-13). New York, NY: ACM. doi: 10.1145/3372782.3406263.
- Conceptual learning – Margulieux, L. E., Denny, P., Cunningham, K., Deutsch, M., & Shapiro, B. (2021). When wrong is right: The instructional power of multiple conceptions. In Proceedings of the Seventeenth Annual Conference on International Computing Education Research (pp. 184-197). New York, NY: ACM. doi: 10.1145/3446871.3469750.