Teaching computer programming: impact of Brown and Wilson's didactical principles

This research studies the effects of the application of didactics to the teaching of computer programming, focusing on programming skills in the Python computer language. The problem arises from the failure and dropout rates of students in computer programming in computer science careers in INACAP a...

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Vydané v:Frontiers in computer science (Lausanne) Ročník 5
Hlavný autor: Belmar, Hector
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Frontiers Media S.A 22.05.2023
Predmet:
algorithm
computer programming
didactical principles
didactics
evaluation instrument
ISSN:2624-9898, 2624-9898
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Shrnutí:This research studies the effects of the application of didactics to the teaching of computer programming, focusing on programming skills in the Python computer language. The problem arises from the failure and dropout rates of students in computer programming in computer science careers in INACAP and the consequent interest in promoting better learning. The general objective is to study the effects of an innovative methodology, based on Brown and Wilson's didactic principles, on the teaching process of Python programming in computer science students at INACAP. The theoretical framework is based on the didactics of teaching computer programming and the concepts of computational thinking skills of various theoretical references, and in particular on the didactic principles of Brown and Wilson. This research is carried out with a quantitative methodology of explanatory scope and with a quasi-experimental design, with a purposive sample, for the experimental stage the sample will consist of 100 first year undergraduate students of Computer Science, of which 50 will be the experimental group and 50 will be the control group. The hypothesis proposed is that “The students in the experimental group obtain a higher performance when applying Brown and Wilson's didactic principles than the students in the control group who are taught in a traditional way.” The data collection technique used will be a 45-question multiple-choice test. The data analysis will be performed by applying statistical criteria, comparison of means and variances, among others.
ISSN:2624-9898
2624-9898
DOI:10.3389/fcomp.2023.1085507