La Comprensión Lectora Y El Rendimiento Académico en Estudiantes De Ingeniería

Chinchilla, Claudia Marcela Durán; Gómez, Alveiro Alonso Rosado

doi:10.24054/16927257.v33.n33.2019.3317

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Diversos estudios practicados en estudiantes de reciente ingreso a la universidad dan cuenta de la relación intrínseca entre la comprensión lectora y el rendimiento académico. Así lo indican Vílchez (2018) y Rosado (2018), que muestran la fuerte relación entre una mejor comprensión lectora y un mayor rendimiento académico. Es importante resaltar que no todos desarrollan las mismas habilidades de análisis, síntesis y vocabulario; unos manejan con mayor o menor destreza los recursos lingüísticos que otros.…”

Section: Diferencias Relacionadasunclassified

Aplicación de los mapas conceptuales para el desarrollo de la comprensión lectora y del aprendizaje significativo en universitarios

Ruiz,

Vílchez,

Marín

et al. 2023

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Aplicación de los mapas conceptuales para el desarrollo de la comprensión lectora y del aprendizaje significativo en universitarios 1ª Edição Foz do Iguaçu 2023 © 2023, CLAEC Todos los derechos reservados y protegidos por la Ley 5988 de 14/12/73. Ninguna parte de este libro se podrá reproducir o transmitir para fines comerciales sin la autorización previa por escrito de la editora, sin consideración de los medios empleados: electrónicos, mecánicos, fotográficos, grabación o cualquier otro. Se aplica subsidiariamente la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0).

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Section: Diferencias Relacionadasunclassified

Aplicación de los mapas conceptuales para el desarrollo de la comprensión lectora y del aprendizaje significativo en universitarios

Ruiz,

Vílchez,

Marín

et al. 2023

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“…The level of reading comprehension of students entering universities in Colombia is worryingly low. PISA claims that around 70% of teenagers over 15 in Colombia lack basic literacy (OECD, 2019) and students in general have shown a serious deficiency in text production and interpretation of what is read, seriously affecting their academic performance (Chinchilla, & Gómez, 2019). In fact, this is an international problem.…”

Section: Context Of the Studymentioning

confidence: 99%

A Socio-Cultural Approach to Evaluating and Designing Reading Comprehension Apps for Language Learning

Robles

Burden

Villalba

2021

International Journal of Mobile and Blended Learning

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Mobile devices are increasingly promoted as tools to facilitate ubiquitous and individualized learning, allowing learners to work at their own pace in authentic and meaningful settings. However, in the case of second language learning, there is a paucity of apps and tools related to improving students' reading comprehension in both Spanish and English. Additionally, there are few studies that address the evaluation of applications for reading comprehension and innovation in this field and this is required in order to respond to the needs of transformation in language learning teaching. The authors present an original evaluation of 25 English language learning mobile apps using the iPAC app rubric, which identifies the pedagogical features of mobile learning: personalisation, authenticity, and collaboration. The results indicate that many of the existing apps fail to fully exploit the affordances of mobile learning and collaboration in particular. The findings suggest recommendations for app developers to design comprehension apps that address these shortcomings.

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Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

Bravo

Nieves-Pimiento²,

Gonzalez-Guerrero³

2022

Ing.

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Context: In the education sector, variables have been identified which considerably affect students’ academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance. Method: Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students’ academic life are selected through wrapping, embedded, filter, and assembler methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language. Results: The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters. Conclusions: Regarding the variables, it cannot be said that a student’s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students’ academic life allow institutions and teachers to take actions that contribute to the teaching-learning process.

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La Comprensión Lectora Y El Rendimiento Académico en Estudiantes De Ingeniería

Cited by 4 publications

References 4 publications

Aplicación de los mapas conceptuales para el desarrollo de la comprensión lectora y del aprendizaje significativo en universitarios

Aplicación de los mapas conceptuales para el desarrollo de la comprensión lectora y del aprendizaje significativo en universitarios

A Socio-Cultural Approach to Evaluating and Designing Reading Comprehension Apps for Language Learning

Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

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