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Descripción
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This dataset consists of the file "data.csv", which contains the research outcomes. The file includes the following information for each student: final evaluation scores, data on watched videos (H5P activity), completed tasks, and results from the final test (including the total grade and individual question grades). This paper addresses the problem of low student engagement, motivation, and deep learning in traditional university teaching methods. It explores how integrating flipped classroom strategies and gamification through digital badges can effectively overcome some of these limitations. With the consolidation of active methodologies in higher education, supported by blended learning environments and innovative tools, the flipped classroom and gamification have gained prominence as strategies to enhance participation and self-regulated learning. Yet, their joint application in teacher training programs remains underexplored, particularly regarding their impact on digital competence development. The study employed a quantitative research approach, analyzing student performance data (grades, tasks completed, video viewing) and survey responses on perceptions of the teaching methods used. The research sample consisted of 63 first-year university students enrolled in an Early Childhood Education bachelor's degree at Universidad Rey Juan Carlos. This paper contributes by providing empirical evidence on the effectiveness of combining flipped classroom methodologies with gamification elements, specifically digital badges, to enhance student motivation, engagement, and academic performance. It highlights how structured integration of technology-supported active learning strategies can successfully address challenges commonly associated with traditional educational methods.
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Notas
| Each row in the dataset represents an individual student. The columns are structured as follows: Column A: Student identification number. Column B: Final subject grade (overall score). Columns C to L: Grades for H5P activities (videos). Column M: Number of videos watched by each student. Columns N to AG: Grades for each task, followed by the name of the related lesson. Lessons 5, 9, and 10 include multiple tasks, differentiated by a subtask number in brackets (e.g., TaskName (1)). All tasks have a maximum grade of 2 points. A grade of 0 indicates a task was not submitted. Grades below 2 indicate partially completed tasks or tasks not performed correctly. Column AH: Total number of tasks submitted by each student (maximum possible: 20). Column AI: Total grade for all tasks (maximum possible: 40 points). Column AJ: Total grade for the final test (out of 10 points). Columns AK to BC: Individual grades for each test question (19 questions total). The maximum points per question varied between 0.13 and 0.5 points. |
