2022, Pustulka, Elzbieta, de Espona, Lucía, Kennel, Andrea

SQL (Structured Query Language) allows a business user to communicate with a relational database. A learner who wants to master SQL needs practice, patience and motivation, which we support in a game called SQL Scrolls. Student surveys we carried out show that this approach encourages our students to practice and students are enthusiastic and want to see more games in other subjects. We are now extending the game to cover all of SQL DML and offer 500 questions.

2022, Pustulka, Elzbieta, Krause, Kai, de Espona, Lucía, Kennel, Andrea, Stikkolorum, Dave, Rahimi, Ebrahim

The teaching of databases and SQL is an active research area. We contribute by presenting a reusable and extensible SQL teaching experiment which uses a game and fits the paradigm of digital game based learning (DGBL). Although DGBL is hampered partly by the difficulty of obtaining statistically significant empirical results, the research shows that it may be an effective learning method and that it is in demand. We investigate the acceptance and effectiveness of an SQL learning game and focus on two areas: student reaction to games as a vehicle for teaching, and educational effectiveness. We designed a game prototype and administered a pre-test, post-test and an acceptance survey, with seven part-time and sixteen full-time students. A statistical analysis of effect sizes revealed a moderate intervention effect for the game group (d= -0.562) and a small one for the traditional group (d= -0.234). The acceptance survey means were between 4.43 and 4.70 out of 5, which shows that the game is highly acceptable. Our experiment demonstrated positive student attitudes towards DGBL in SQL teaching and showed the game to be as effective as exercises done using a workbench. We further observed interesting differences in teaching using a game and a "natural" workbench environment and had excellent course feedback. We have released the game as open source in the hope that other researchers will replicate or contradict our findings or simply use it in teaching. We close with an outline of ongoing research.

2021, Pustulka, Elzbieta, Hanne, Thomas, de Espona, Lucía

FLIE (Form Labelling for Information Extraction) allows us to extract information from Swiss insurance policies. Insurance policies are forms which are weakly aligned and do not lend themselves to automated data extraction without preprocessing. Our preprocessing annotates data with geometry and combined with manual training data generation gives the extraction accuracy of over 80% for a subset of attributes which have been seen 8 times or more. In this paper we extend FLIE with rules. The aim is to compare machine learning used in FLIE to the standard industry approach of using rules to extract data. We hand crafted rules (regular expressions in Python) for the KTG insurance (27 rules), UVG insurance (29 rules), and UVG-Z (23 rules), for each insurance type covering around 20 attributes. We also generated rules for building insurance policies which we were new to (16 rules encoded in SpaCy). In all cases we saw that using rules alone gives us a similar accuracy in data extraction to machine learning (around 80%). In the case of building insurance the accuracy is higher, above 96%, with precision and recall around 89-92%. To support annotation and experimental evaluation, we created an annotation GUI and a GUI which automates the ML experiment. Planned work includes a comparison of rule based and ML approaches and extension to further policy types.