Guidelines for big data projects in artificial intelligence mathematics education

Resumo

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics.Integrating big data statistical projects with digital technologies in high school mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills.This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria.The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning.The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks.In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing.This can potentially result in misconceptions among students regarding the nature of big data.Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks.This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

Dados da obra

Tipo

Artigo

Periódico

The Mathematical Education, v. 62, n. 2, p. 289-302

Ano

2023 (2023-05-31)

Idioma

en

Palavras-chave

big data in education; project-based learning; high school mathematics; data preprocessing; decision-making skills; cooperative learning; educational technology in mathematics; curriculum alignment

Temas

Currículo, Resolução de Problemas, Tecnologias Digitais

DOI

10.63311/mathedu.2023.62.2.289

ISSN

1225-1380, 2287-9633

Acesso

Acesso aberto

Fontes

OpenAlex

Na Biblioteca desde

01/10/2026 · verificada em 03/10/2026