Toward Questionnaire Complexity Reduction by Decreasing the Questions - Miguel A. Molina-Cabello; José Serrano-Angulo; Jesús Benito-Picazo; Karl Thurnhofer-Hemsi (Applied Sciences)
Data analysis can unearth important insights like patterns, trends, and deductions. In education, it can be utilized to tailor teaching methods to suit student traits or devise new activities to foster different skills or reinforce existing ones, for example. Understanding the particular context and past experiences can aid in this endeavor. Surveys and questionnaires yield a wealth of data. Yet, the sheer volume of questions can lead to challenges in data management for teachers and a decline in student interest due to the time-consuming nature of fulfilling these tasks. This work presents a methodology designed to decrease the number of questions in questionnaires. This method can be applied to general questionnaires that consist of closed-ended questions with a set number of response choices, where each question can have a varying number of options compared to the other questions in the form. This methodology has been adapted into a newly developed software tool for examining learning styles based on a specific learning styles questionnaire: the Honey–Alonso Learning Styles Questionnaire (CHAEA). This software is available to the public and integrated with Moodle, arguably the most extensively used learning management system globally. To evaluate the effectiveness of the proposed method, it has been used across various subjects in Computer Sciences Engineering degrees over different academic years. The outcomes from this case study validate the appropriateness of the technique. Consequently, these findings could establish patterns that could assist in devising more suitable learning methodologies for students.