Virtual Learning Ecosystems for a Science Course: Caso de Diseño Instruccional para Minimizar la Carga Cognitiva en Estudiantes de Educación Básica Regular en una Escuela Pública en Perú

Abstract

The objective of the study is to implement an instructional design model to minimize the cognitive load in virtual learning ecosystems for a science course with Basic Education students in a public school in Peru. The post-pandemic context led to the design of a hybrid learning model that later transitioned to greater face-to-face development of the Science and Technology curricular area with 79 secondary level students in a public school in Peru. Theoretical complementarity and methodological complementarity were assumed, the quantitative method in one phase and another phase with application of the qualitative method, in order to have a more precise and complete image of the problem addressed. Regarding the instructional design, this was based on minimizing the cognitive load and working memory, which took into account the didactic moments, techniques and digital resources for student learning. The instructional design in virtual learning ecosystems promoted the three curriculum competencies for the science and technology area: Called “Inquire through scientific methods.” “It explains the natural and artificial world based on knowledge about living beings and builds technological solutions.” It was concluded that the instructional design reduced the cognitive load and favored the quality of learning; the level of achievement in the three competitions was raised, scientific inquiry was strengthened, technological solutions were designed based on the needs of the users, by the students in the curricular area of Science and Technology

Presenters

Carmen Arbulu Pérez Vargas
Docente Investigador, Escuela de Posgrado, Universidad César Vallejo, Lambayeque, Peru

Details

Presentation Type

Paper Presentation in a Themed Session

Theme

Science, Mathematics and Technology Learning

KEYWORDS

COGNITIVE, LOAD, VIRTUAL, LEANING, ECOSYSTEMS, SCIENCE