Automatic Generation of Moodle Cloze Questions for the Assessment of Knowledge About Lexical Analysis Algorithms

Moodle quizzes are a convenient way of online assessment, benefiting both teachers and students. Unfortunately, their preparation is slow, cumbersome, and error-prone. In addition to the effort of designing the questions, it is necessary to enter them in Moodle. Furthermore, for questions that requi...

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Bibliographic Details
Main Authors: Roberto Izquierdo-Amo, José Antonio Barbero-Aparicio, José Luis Garrido-Labrador, Alicia Olivares-Gil, César Ignacio García-Osorio
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Education Sciences
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Online Access:https://www.mdpi.com/2227-7102/15/1/75
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Summary:Moodle quizzes are a convenient way of online assessment, benefiting both teachers and students. Unfortunately, their preparation is slow, cumbersome, and error-prone. In addition to the effort of designing the questions, it is necessary to enter them in Moodle. Furthermore, for questions that require images, these must first be created and then uploaded to Moodle. If this process has to be repeated with many questions, the required work becomes prohibitive. This paper presents a tool that solves all these problems, allowing the automatic and massive generation of questions for the evaluation of the knowledge about lexical analysis algorithms. The evaluation of these algorithms is relevant in the subjects of both automata and formal languages and in those of compiler design. The tool allows the creation of exercises of configurable complexity, after which the text, tables, and associated images are generated for quick upload to Moodle. The main impact of this tool is the substantial reduction of question preparation time, reducing hundreds or thousands of interactions with Moodle forms to just a few simple steps. In addition, the ease of question generation makes it possible to prepare self-assessment questionnaires for the students, something that they greatly appreciate as a study aid.
ISSN:2227-7102