DMA Application to Identify Students’ Decision-Making Ability Profiles

This study aims to develop and validate a Decision Making Assesment (DMA) application to identify and enhance students' decision-making skills in mathematics learning, particularly among pre-service mathematics teachers at Universitas PGRI Madiun. The research employed a Research and Development (R&D) approach using the ADDIE model, comprising Analysis, Design, Development, Implementation, and Evaluation stages. The application was designed to assess higher-order thinking processes such as identifying problems, generating ideas, clarifying solutions, and evaluating their feasibility. During the development phase, the application underwent expert validation involving three experts in mathematics education, instructional media, and assessment. The average validation score was 3.57, indicating the application is valid. In the implementation phase, 94% of student respondents expressed positive perceptions, reflecting the application's high practicality. Effectiveness was confirmed by 92% of lecturers who reported that the application supported students’ decision-making development. These findings highlight the application's potential to foster active, reflective, and systematic reasoning processes through mobile learning. Furthermore, it aligns with 21st-century educational demands by integrating digital technology into authentic mathematics learning contexts. The study contributes to the innovation of digital-based assessment tools that support cognitive engagement and pedagogical relevance in higher education mathematics instruction.

Authors

Institutions

Publication Details

Journal
International Journal of Technology and Education Research
Published
2024-09-27
DOI
https://doi.org/10.63922/ijeter.v2i03.1371
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DMA Application to Identify Students’ Decision-Making Ability Profiles

Marheny Lukitasari, Nurcholif Diah Sri Lestari, Wasilatul Murtafiah
International Journal of Technology and Education Research
Online Learning and Analytics
article

DMA Application to Identify Students’ Decision-Making Ability Profiles

Marheny Lukitasari, Nurcholif Diah Sri Lestari, Wasilatul Murtafiah
article en

Abstract

This study aims to develop and validate a Decision Making Assesment (DMA) application to identify and enhance students' decision-making skills in mathematics learning, particularly among pre-service mathematics teachers at Universitas PGRI Madiun. The research employed a Research and Development (R&D) approach using the ADDIE model, comprising Analysis, Design, Development, Implementation, and Evaluation stages. The application was designed to assess higher-order thinking processes such as identifying problems, generating ideas, clarifying solutions, and evaluating their feasibility. During the development phase, the application underwent expert validation involving three experts in mathematics education, instructional media, and assessment. The average validation score was 3.57, indicating the application is valid. In the implementation phase, 94% of student respondents expressed positive perceptions, reflecting the application's high practicality. Effectiveness was confirmed by 92% of lecturers who reported that the application supported students’ decision-making development. These findings highlight the application's potential to foster active, reflective, and systematic reasoning processes through mobile learning. Furthermore, it aligns with 21st-century educational demands by integrating digital technology into authentic mathematics learning contexts. The study contributes to the innovation of digital-based assessment tools that support cognitive engagement and pedagogical relevance in higher education mathematics instruction.

International Journal of Technology and Education ResearchVol. 2(03)
Universitas Jember (ID), Universitas PGRI Madiun (ID)
Openalex Percentile: Top 68%
Online Learning and Analytics
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.