Implementation of SOLO taxonomy and Newman error analysis in first-order differential equation

First-order Ordinary Differential Equation (ODE) has many applications in physics, engineering, biology, economics, and ecology. Therefore, mastering the concepts and methods of solving ODE is essential for students to be able to apply mathematics in solving real-world problems. However, the teaching of first-order ODE has not paid attention to practical applications, so that students have difficulty linking theory with real cases. This study aims to analyze the implementation of the SOLO taxonomy and Newman Error Analysis (NEA) in first-order ODE. The methodology used is a case study. The research subjects consisted of nine students of the mathematics department of FMIPA Universitas Negeri Padang. Data were collected through tests, interviews, and documentation. Then the data were analysed quantitatively and qualitatively. The results showed that there were five errors in solving first-order ODE made by students, namely Reading Errors (RE), Comprehension Errors (CE), Transformation Errors (TE), Process Skill Errors (PE), and Encoding Errors (EE). Some of the causes of these errors include students' low ability to read mathematical symbols, students' inaccuracy, not being able to use algorithms correctly, not mastering the concepts of algebra, differential, and integral, as well as not understanding in determining the systematic solution of the problem and not being accustomed to writing the final answer. This information can be used as a guideline for lecturers in designing strategies and lecture designs for first-order ODE.

Authors

Institutions

Publication Details

Journal
Infinity Journal
Published
2025-07-14
DOI
https://doi.org/10.22460/infinity.v14i3.p695-710
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Implementation of SOLO taxonomy and Newman error analysis in first-order differential equation

Dewi Murni, Fridgo Tasman, Yarman Yarman
Infinity Journal
Model Reduction and Neural Networks
article

Implementation of SOLO taxonomy and Newman error analysis in first-order differential equation

Dewi Murni, Fridgo Tasman, Yarman Yarman
article en

Abstract

First-order Ordinary Differential Equation (ODE) has many applications in physics, engineering, biology, economics, and ecology. Therefore, mastering the concepts and methods of solving ODE is essential for students to be able to apply mathematics in solving real-world problems. However, the teaching of first-order ODE has not paid attention to practical applications, so that students have difficulty linking theory with real cases. This study aims to analyze the implementation of the SOLO taxonomy and Newman Error Analysis (NEA) in first-order ODE. The methodology used is a case study. The research subjects consisted of nine students of the mathematics department of FMIPA Universitas Negeri Padang. Data were collected through tests, interviews, and documentation. Then the data were analysed quantitatively and qualitatively. The results showed that there were five errors in solving first-order ODE made by students, namely Reading Errors (RE), Comprehension Errors (CE), Transformation Errors (TE), Process Skill Errors (PE), and Encoding Errors (EE). Some of the causes of these errors include students' low ability to read mathematical symbols, students' inaccuracy, not being able to use algorithms correctly, not mastering the concepts of algebra, differential, and integral, as well as not understanding in determining the systematic solution of the problem and not being accustomed to writing the final answer. This information can be used as a guideline for lecturers in designing strategies and lecture designs for first-order ODE.

Infinity JournalVol. 14(3)
State University of Padang (ID)
Universitas Negeri Padang
Openalex Percentile: Top 86%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.