Abstract
his work presents an innovative approach to engage student participation through active learning, using the popular online word-guessing game, named Wordle. Wordle is a word-guessing game where players have six attempts to guess a five-letter word. With each guess, the letters turn the background color to indicate correctness: green for correct letters in the right position, yellow for correct letters in the wrong position, and gray for incorrect letters. Players aim to deduce the mystery word using logic and word association within the given attempts, making it both challenging and rewarding. The method proposed here involves students through the challenge of solving a set of personalized Wordle puzzles, at the end of the class, which include key concepts studied on the previous lecture. In this case, the words might have a different length. Students compete in a Wordle league, that is active during the whole semester accumulating points for each word guessed, fostering competition and motivation. The standings of the league are publicly available for all students. In addition, students are asked, at the end of the course, to create a concept map with all the keywords found, following the Unified Modeling Language (UML) standard, which is a fundamental topic of the subject. This activity aids in consolidating acquired knowledge and developing synthesis and information organization skills. The proposal was validated in the academic year 2022/2023, in the Engineering of Requirements (ER) course, which is a subject within the Software Engineering degree at Universidad Rey Juan Carlos (Madrid, Spain). The results showed a positive correlation between Wordle participation and academic performance. Furthermore, they suggested that students who actively engaged in the activity demonstrated a greater commitment to the subject and a better understanding of the key concepts. The benefits of this active learning proposal are manifold. It encourages class attendance, improves attention in class, and increases students’ motivation. It also aids in consolidating acquired knowledge and developing synthesis and information organization skills. Further research is needed to understand the impact of this strategy, but preliminary results are encouraging and suggest a promising path towards innovation in digital education.
Sergio Cavero
Phd in Artificial Intelligence
Sergio Cavero was born Madrid (Spain) on September 24, 1997. He graduated in Software Engineering from Universidad Politécnica de Madrid in 2019. During his undergraduate studies he made a stay at the University of Bradford (UK). In addition, he was awarded twice with the ‘Beca de Excelencia of the Comunidad de Madrid, and also awarded for the Best Final Degree Project. Later, he completed a Master’s Degree in Artificial Intelligence at the same university (UPM) obtaining awards for Best Academic Record (‘Premio José Cuena’) and Best Master’s Thesis. He academic results lend him be beneficiary of one of the ‘Ayudas Para la Formación de Profesorado Universitario (FPU)’, funded by the Spanish Government. He is currently carrying out his doctoral thesis at the Universidad Rey Juan Carlos, supervised by Professors Abraham Duarte and Eduardo G. Pardo. His main research interests focus on the interface among Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristics procedures for optimization problems modeled by graphs.