Exploring Technology Integration in Education using Fuzzy Representation and Feature Selection

Digital technology integration in schools and what this means for teaching and learning plays an significant role in shaping the education environment. There has been a growing body of literature addressing students' perceptions towards technology integration. A large amount of student and teacher self-reported questionnaire or survey data therefore has been collected for different modelling purposes. Yet, considerable questions are still remaining due to this huge-volume, diversified and uncertain survey data. This paper demonstrates the use of fuzzy representation and feature selection to discover unique patterns via survey data. More precisely, fuzzy representation is used to quantify survey response and reform response using linguistic expression. Furthermore, a novel feature selection algorithm is applied to identify important features. This proposed algorithm, based on the sparse representation model, selects features that minimize the residual output error iteratively, thus the resulting features have a direct correspondence to the given problem. The efficiency of the proposed work is evaluated using a state-level student survey. The employed dataset (N = 8528) is used to discover unique patterns among computer efficacy, engagement and school engagement. Experimental results show that the proposed algorithm outperforms traditional approaches.