Technology-enhanced assessment visualization for smart learning environments

Hiran Ferreira,Fabiano Dorça,Rafael Araújo,Renan Cattelan,Guilherme P De Oliveira

doi:10.1186/s40561-019-0096-z

Abstract

In Smart Learning Environments, students need to be aware of their academic performance so they can self-regulate their learning process. Likewise, the teaching process can also be improved if instructors are able to supervise the progress of students, both individually and globally, and anticipate proper pedagogical strategies. Thus, effective Student Models, capable of identifying and predicting the level of knowledge of students, are a key requirement in modern educational systems. In this article, we revisit OSM-V, an Open Student Model with Information Visualization capabilities that allow students and instructors to assess performance-related information in educational systems. We detail its architecture and how it was integrated into Classroom eXperience, a Smart Learning Environment with multimedia capture capabilities. We also present extended results from experiments that evaluate both the perception of utility and behavioral changes in students who used OSM-V, showing that it can positively impact students’ learning and positively influence their study habits.

Highlights

Intelligent interfaces can enable learning to be clearer and easier, fostering interaction according to the cognitive abilities of those directly involved in the process (Lindstaedt et al, 2009)
According to Brusilovsky (2001), the teaching process would have a better efficacy if it were possible to identify the real state of knowledge of each student individually, allowing instructors to address the individual limitations of each student
Student Models (SM) have been used to map the cognitive characteristics of students (Self 1990). This method has proved to be effective in many situations (Mitrovic and Thomson 2009; Li et al 2011), allowing automated systems to guide new actions to intervene in the teaching process of each student

Summary

Introduction

Intelligent interfaces can enable learning to be clearer and easier, fostering interaction according to the cognitive abilities of those directly involved in the process (Lindstaedt et al, 2009). The questions were classified into three groups: the first one related to the perception of utility of the OSM-related functionalities; the second one to assess whether or not there was a change in the way the student studied; and a third set of questions to verify the satisfaction with the use of the visualization tool and which graphics allowed a better visualization for different situations. This guided the choice of the test to verify the difference between the means: the non-parametric Kruskal-Wallis test, which showed that student behavior interferes with their performance (H(2) = 7.063; p < 0.05) In this case, it was possible to identify that there is a statistically significant difference between the grades of the students that are grouped in the different clusters. The students of Profile C fall into a region of more uncertainty, in which it is not possible to make statements with strong statistical foundations

Conclusions

Findings

Limitations