Difficulty Analysis of SQL Scrolls Tasks

Difficulty Analysis of the SQL Learning game: The SQL Scrolls’ game log has been analysed to reveal the task difficulty levels of the SQL Learning game, opening the path for optimum curriculum preparation and effective student assistance.

Christine Dyan Aseral, 2023

Bachelor Thesis, Institut für Wirtschaftsinformatik, HSW FHNW
Betreuende Dozierende: Thomas Hanne
Keywords: difficulty analysis, difficulty levels, SQL, game-based learning, learning game, machine learning, sklearn, database technology
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The SQL Scrolls is designed especially for students visiting the Database Technology module to learn and improve their knowledge about the SQL queries. The present difficulty levels have been manually defined and have not been updated, resulting in erroneous difficulty information. Difficulty Levels are crucial as they indicate the complexity and skill required to complete the tasks, which provides an engaging experience. Analysing difficulty levels in education can reveal perspectives on how students perceive challenges and can inform adjustments to better accommodate different skill levels.
The difficulty analysis of the SQL Scrolls Tasks consists of five phases. The first two steps are a difficulty analysis based on the number of attempts, and a difficulty analysis based on the queries, which results in measuring the cosine similarity between the submitted queries and the real answer with the use of a machine learning library (sklearn). The “simple approach” as the third phase, which is a fusion of the two prior analyses, the difficulty analysis based on the correlations, and lastly, the overall analysis with the aforementioned analyses, including the effect of the game type.
The conclusion of the research reveals that tasks with a low average number of attempts and a high average of cosine similarity are interpreted and evaluated at easy levels. The tasks that results the opposite outcome from this circumstance are interpreted as difficult. Furthermore, based on the information and results shown throughout this thesis, it was observed that the Drag and Drop tasks are perceived as easier for students since they require less cognitive effort in comparison to tasks that require the actual answer to be fully typed in order to successfully complete the task. Obtaining this information will be beneficial to the client, Prof. Dr. Pustulka, as it will provide insight into which concepts are challenging for the students based on the results. These concepts can be thoroughly presented in class again and the curriculum can be adjusted to place greater emphasis on the challenging concepts, resulting in additional problems in the SQL Scrolls game for students to practice on.
Studiengang: Business Information Technology (Bachelor)
Fachbereich der Arbeit:
Vertraulichkeit: öffentlich
Art der Arbeit
Bachelor Thesis
Institut für Wirtschaftsinformatik, HSW FHNW, Olten
Autorinnen und Autoren
Christine Dyan Aseral
Betreuende Dozierende
Thomas Hanne
Sprache der Arbeit
Business Information Technology (Bachelor)
Standort Studiengang
difficulty analysis, difficulty levels, SQL, game-based learning, learning game, machine learning, sklearn, database technology