Research Interests

This is what I like.

I am currently on the job market. If you are interested in working with me on any of these directions, please reach out!

Motivation and past work

Over the course of my Master's degree, I worked on topics related to the intersection between humans and databases. After attaining my Master's degree, I pursued a PhD in which I wanted to dive deeper into this topic. As I have a passion for education, and students' interaction with databases can be a struggle, I decided to specialize in education on the Structured Query Language (SQL). As SQL is the number one requested programming language in current job openings, to prepare them for the job market it is essential that our students learn this language well. Although SQL was invented in the 1970s and quickly gained traction, research as to how we can best teach this language has been under-explored.

Prior education research has primarily focused on which errors SQL learners make while they write their queries [1, 3, 16]. My work during the PhD focused on extending our current understanding of SQL education, for example by identifying what the underlying problems were for these errors. This can then help us restructure and improve our teaching methods to reduce errors and better support students. To this end, I interviewed students using a think-aloud protocol [7], developed questionnaires for identifying such underlying causes [10], and used a Delphi-protocol for analyzing experts' thoughts on the causes [9]. We are already looking into applying some of our findings in the curriculum at Eindhoven University of Technology.

Other prior work has aimed to support SQL learners through the development of visual tools [4, 5, 6, 12, 13]. While most of these tools aim to visualize results, our aim was to explicitly represent the query itself. With such a representation, students don't have to translate backwards from result tables and visual representations to edits in the query. I developed such a tool, by representing the query structure as a graph [8]. This also supports students' mental representations of the relational database, in which relations are normally implicit. This work is being continued by master students I supervise, and is also available to students in one of our database courses.

In my most recent project, I have worked on analyzing what students believe would make their database education more engaging. In collaboration with the University of Jyväskylä, we analyzed data from students' database design assignments to identify themes and arguments on engagement. We found that students like databases that allow for various types of queries and varying levels of difficulty, databases that can present a challenge, and exercises that are related to the real world [11, 15]. With our guidelines, teachers might be able to reach higher student engagement.

Together, these research directions give a good insight into the problems students run into while writing SQL queries. We can identify errors, support through visual tools that reduce their mental load, and finally increase student engagement, which hopefully leads to student retention and improved student performance.

Research plans

I next describe some research directions that I would be interested to explore in the future.

First, I would like to extend the work I did on visual representations for query languages. The tool I developed is meant to help students write queries by lowering their cognitive load. I believe there is potential to take the support provided by this system even further. Features to be researched and implemented include:

  • To facilitate students' different learning styles, we could embed explanations and hints about the working of SQL syntax.
  • To facilitate cultural and personal differences, we can make learning SQL more engaging by allowing students to select the domain that they would like to practice on.
  • To help students work through errors, we can redesign error messages which are currently often unclear and incomplete.

Additionally, the system has proven a topic of interest for student projects, so this project could be one way in which I could provide undergrad and grad student opportunities to gain research experience.

From the research I discussed above, we can conclude that much is still unknown about teaching our students SQL. From a quick search through the Computer Science Education Research venues, we can see that this holds to an even stronger extent for teaching students other database content. There is much work to be done for understanding this, and one research direction I would be interested in exploring is how to best teach database concepts to non-CS majors.

  • An analysis of students' deliverables: What are topics that students struggle with, and does this depend on what they studied before?
  • An app- or messaging-based study to explore students' anxiety levels surrounding their submission of deliverables, to find out if database anxiety exists (from math anxiety, see [2])

I am also interested in exploring knowledge transfer between different query languages. In our introductory database course in Eindhoven, we teach four languages that cover different perspectives on querying: SQL, Datalog, Tuple Relational Calculus and Relational Algebra. In our teaching of these languages, we map and relate them to each other. However, not much is known about the knowledge transfer between these languages. Exploring which concepts transfer well between languages, and which do not, can help us teach more effectively. Some possible studies could be:

  • A think-aloud study to investigate: How do students approach the translation from one to the other query language?
  • A questionnaire study to investigate: Are there relations between students' performance on query languages?
  • A teacher interview study to find: Which metaphors do you use to teach various concepts in query languages?
  • A study of educational textbooks to analyze the parallels in teaching the query languages.

The fourth set of studies that I would be interested in carrying out is to explore problem-solving behavior in reading and interpretation. Many programming languages are taught by first teaching the student how to read code before they start writing it. Research shows that this is likely to be a good idea for general programming languages; learning the know-how before learning how to use a language [14, 17, 18]. However, I wonder whether this also holds for SQL. As it is a declarative language, with non-deterministic query execution plans, there are various uncertainties in the interpretation of queries.

  • An eye-tracking study to identify: What is a learner's reading order/pattern in queries?
  • A think-aloud study to identify: What are salient points in learners' query interpretation processes?
  • An online tool study to identify: Which of a set of query variations are easier to interpret for learners?

The final research direction I will elaborate on here is about query decomposition and patterns. In my studies about underlying causes for problems, and in my experience as a teacher, I have found that students often study a set of patterns that should help them formulate queries. However, they regularly choose to apply a template or pattern that is not applicable to the question that they are trying to answer. To learn more about this, some things I would like to examine include:

  • A think-aloud study to investigate: Which patterns are learners familiar with and how do they identify when to apply them?
  • A teacher study to investigate: How do we as teachers solve problems, and is this congruent with how we teach the material?
  • A think-aloud study to investigate: (How) do learners split up query formulation problems?
  • A study about the interpretation of query plans and students' perceived influence on query execution.

All of the research directions above can increase our knowledge on how to teach Data Systems, and through this will lead to innovations in education. These innovations can then reduce students' struggle to learn query languages, which results in better performance. Applying new methods in our teaching should also lead to higher retention of students.


  1. Alireza Ahadi, Julia Prior, Vahid Behbood, and Raymond Lister. 2016. Students semantic mistakes in writing seven different types of SQL queries. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE. 272–277.
  2. Mark H Ashcraft. 2002. Math anxiety: Personal, educational, and cognitive consequences. Current directions in psychological science 11, 5 (2002), 181–185.
  3. Stefan Brass and Christian Goldberg. 2006. Semantic errors in SQL queries: A quite complete list. Journal of Systems and Software 79, 5 (2006), 630–644.
  4. Maurizio Cembalo, Alfredo De Santis, and Ferraro Petrillo Umberto. 2011. SAVI: A new system for advanced SQL visualization. SIGITE’11 ‑ Proceedings of the 2011 ACM Special Interest Group for Information Technology Education Conference (2011), 165–170.
  5. Changjiu Jin, Sourav S Bhowmick, Byron Choi, and Shuigeng Zhou. 2012. Prague: towards blending practical visual subgraph query formulation and query processing. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on. IEEE, 222–233.
  6. Aristotelis Leventidis, Jiahui Zhang, Cody Dunne, Wolfgang Gatterbauer, HV Jagadish, and Mirek Riedewald. 2020. QueryVis: Logic‑based diagrams help users understand complicated SQL queries faster. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2303–2318.
  7. Daphne Miedema, Efthimia Aivaloglou, and George Fletcher. 2021. Identifying SQL Misconceptions of Novices: Findings from a Think‑Aloud Study. In Proceedings of the 17th ACM Conference on International Computing Education Research. 355–367.
  8. Daphne Miedema and George Fletcher. 2021. SQLVis: Visual query representations for supporting SQL learners. In 2021 IEEE Symposium on Visual Languages and Human‑Centric Computing (VL/HCC). IEEE, 1–9.
  9. Daphne Miedema, George Fletcher, and Efthimia Aivaloglou. 2022. Expert Perspectives on Student Errors in SQL. ACM Transactions on Computing Education (TOCE) (2022).
  10. Daphne Miedema, Michael Liut, George Fletcher, and Efthimia Aivaloglou. 2023a. MSMI1: Towards a Validated SQL Misconceptions Instrument. In Proceedings of the 2023 ACM Conference on International Computing Education Research‑Volume 2. 16–17.
  11. Daphne Miedema, Toni Taipalus, and Efthimia Aivaloglou. 2023b. Students’ Perceptions on Engaging Database Domains and Structures. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 122–128.
  12. Hiroyuki Nagataki, Yoshiaki Nakano, Midori Nobe, Tatsuya Tohyama, and Susumu Kanemune. 2013. A visual learning tool for database operation. ACM International Conference Proceeding Series (2013), 39–40.
  13. George Obaido, Abejide Ade‑Ibijola, and Hima Vadapalli. 2019. Generating SQL queries from visual specifications. Communications in Computer and Information Science 963 (2019), 315–330.
  14. Simon, Mike Lopez, Ken Sutton, and Tony Clear. 2009. Surely We Must Learn to Read before We Learn to Write!. In Eleventh Australasian Computing Education Conference, Vol. 95.
  15. Toni Taipalus, Daphne Miedema, and Efthimia Aivaloglou. 2023. Engaging Databases for Data Systems Education. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2023). ACM, Turku, Finland.
  16. Toni Taipalus, Mikko Siponen, and Tero Vartiainen. 2018. Errors and Complications in SQL Query Formulation. ACM Transactions on Computing Education 18, 3 (2018).
  17. Tammy VanDeGrift. 2005. Reading Before Writing: Can Students Read and Understand Code and Documentation?. In Proceedings of the 36th SIGCSE technical symposium on Computer science education.
  18. Benjamin Xie, Dastyni Loksa, Greg L. Nelson, Matthew J. Davidson, Dongsheng Dong, Harrison Kwik, Alex Hui Tan, Leanne Hwa, Min Li, and Amy J. Ko. 2019. A theory of instruction for introductory programming skills. Computer Science Education 29, 2–3 (Jul 2019), 205–253.