Estudio de la predisposición desfavorable hacia el aprendizaje de la matemática en la educación media de Costa Rica - Meza, Luis Gerardo (2023)
The following article aims to study the level of unfavorable predisposition towards learning mathematics in students of official Costa Rican daytime secondary education, enrolled in the year 2022. It is understood as the “unfavorable perception towards facing or approaching tasks in the mathematical field. It is manifested as dislike, displeasure, lack of perseverance or disregard towards mathematical tasks.” (Cerda, Pérez, Aguilar, & Aragón, 2018). This is quantitative research of descriptive type, which studies the variable by sex, educational level, or high school location area, applying the “Scale of Predisposition towards Mathematics (EPMAT, for its acronym in Spanish)”, created and validated by Cerda, Ortega, DelRey, and Pérez (2016). The sample consisted of 2497 students in the year 2022 (53% female) and was selected by simple cluster sampling. The statistical techniques used in the data analysis were Student’s t-test and ANOVA (complemented with Scheffé’s test). As a result, it can be concluded that there are differences between men and women in the level of unfavorable predisposition towards learning mathematics, showing the first lowest levels with low effect size. Also, differences were detected between students in the seventh and tenth year, the latter showing higher levels. In addition, differences were detected in the unfavorable predisposition towards mathematics due to the location type of the high school, with higher levels for those in rural areas and with a low effect size. The research reveals that about 70% of the students showed low levels of unfavorable predisposition towards learning mathematics, which is considered a positive finding. Nevertheless, this suggests that it is convenient to develop intervention programs to improve the level of this variable for the remaining 30%, with no need to differentiate by sex, level, or type of high school because the differences detected have a low “effect size”.