On data-driven decision-making for quality education

Abstract

Effective quality education is one of the most efficient tools to empower individuals and help them out of poverty and exclusion. The paper is aimed to present and analyse data-driven decision making methodology to evaluate suitability, acceptance and use of innovative technologies to enhance the quality of education. Analysed example of those innovative technologies is Augmented Reality (AR). AR is known as one of the most recent technological advances that can be used as an educational tool to enhance the quality of education. In the paper, user-centered methodology (i.e. model and method) to evaluate suitability, acceptance and use of AR applications in education is presented. Many recent studies have suggested that differences between individuals influence acceptance and use of Information Technology (IT). AR suitability, acceptance and use evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify AR applications' suitability to particular students' needs i.e. learning styles. In the paper, there is also an example of implementing the methodology using different weights of evaluation criteria. This methodology is a typical case of data-driven decision-making for quality education. It is applicable in real life situations where teachers have to help students to apply IT that is the most suitable for their needs and thus to improve learning motivation, and consequently, its quality and effectiveness.