248
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Examining students’ mathematical modeling competences in video-based modeling tasks

ORCID Icon & ORCID Icon
Pages 336-348 | Received 26 May 2023, Accepted 01 Oct 2023, Published online: 19 Oct 2023
 

Abstract

Empirical studies demonstrate that students have some difficulties with the mathematical modeling process. Studies contain various applications for developing students’ modeling competencies. This study investigated video-based modeling tasks’ impact on modeling competencies with a descriptive design. In this context, it aims to compare eighth-grade secondary school students’ modeling competencies in the environment where modeling tasks are presented on a video and worksheet. Differences and similarities between the two groups were observed in this study. Specifically, while both groups acted similarly in the mathematical working and interpretation stages, differences between the two groups were found in favor of the group whose video-based activities were applied to creating real and mathematical models. Especially video-based modeling situations can be considered an effective tool for creating real and mathematical models. The study results confirm that video-based modeling tasks contribute to incorporating students’ real-life experiences into the modeling process.

Correction Statement

This research is derived from the first author’s dissertation conducted under the supervision of the second author.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 133.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.