Validation of Questionnaire for B-Learning Using 2-Tuple Fuzzy Linguistic Delphi Method

Core Concepts

The author proposes a 2-Tuple Fuzzy Linguistic Delphi method to validate questionnaires, emphasizing the importance of consensus among experts through linguistic perspectives.

Abstract

A study introduces a novel method, the 2-Tuple Fuzzy Linguistic Delphi, to assess questionnaire validity for b-learning. The approach involves linguistic judgments by experts and iterative consensus-building processes. The research aims to enhance questionnaire validation in educational settings using innovative linguistic models.
Classic and Fuzzy Delphi methods are compared for content validity testing. The proposed 2-Tuple Fuzzy Linguistic Delphi extends these methods to address varying expertise levels among judges. An online tool is developed under GPL v3 license to visualize collective valuations and facilitate consensus-reaching in questionnaire modification.
Key aspects include the application of Computing with Words methodology, linguistic hierarchies, and multi-granular semantics in Decision Making problems. The study emphasizes the importance of expert opinions represented as 2-tuple linguistic values for comprehensive questionnaire validation.

Stats

A 2-Tuple Fuzzy Linguistic Delphi method is proposed for questionnaire validation.
Judges' opinions are represented using fuzzy numbers and linguistic terms.
An online tool is developed under GPL v3 license.
Extended Linguistic Hierarchies are used for flexible representation.
Expert weights per dimension are considered in the evaluation process.

Quotes

Key Insights Distilled From

Teranga Go!

by Rosana Monte... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05550.pdf

Deeper Inquiries

How can the proposed method be adapted for different types of questionnaires

The proposed 2-Tuple Fuzzy Linguistic Delphi method can be adapted for different types of questionnaires by adjusting the linguistic term sets and criteria based on the specific characteristics of each questionnaire. For instance, if a questionnaire is focused on measuring customer satisfaction in a retail setting, the linguistic terms and criteria used for evaluation would differ from those used in an educational assessment questionnaire. By customizing the linguistic hierarchies, expertise degrees, and dimensions to align with the unique requirements of each type of questionnaire, the method can effectively validate content validity across various domains.

What implications does this research have on improving educational assessment practices

This research has significant implications for improving educational assessment practices by providing a structured approach to validating questionnaires used in educational settings. By incorporating expert judgments through a fuzzy linguistic model, this method ensures that content validity is rigorously evaluated before implementing questionnaires in educational assessments. This not only enhances the reliability and accuracy of data collected but also promotes evidence-based decision-making in education. Additionally, by utilizing extended linguistic hierarchies and diverse expert panels, this research contributes to enhancing the quality and effectiveness of educational assessments.

How might incorporating diverse expert panels impact the outcomes of questionnaire validation

Incorporating diverse expert panels can have a substantial impact on the outcomes of questionnaire validation. A diverse panel brings together individuals with varied perspectives, experiences, and expertise levels which can lead to more comprehensive evaluations of questionnaires. Different experts may identify strengths or weaknesses in items that others might overlook, resulting in a more thorough validation process. Additionally, diversity within expert panels ensures that multiple viewpoints are considered during consensus-building exercises, leading to well-rounded decisions regarding item validity. Overall, incorporating diverse expert panels enriches the validation process and enhances the overall quality of questionnaire assessments.