A2E 2018 Abstracts


Full Papers
Paper Nr: 2
Title:

A Personalized Reading Coach using Wearable EEG Sensors - A Pilot Study of Brainwave Learning Analytics

Authors:

Xiaodong Qu, Mercedes Hall, Yile Sun, Robert Sekuler and Timothy J. Hickey

Abstract: The advent of wearable consumer-grade brainwave sensors opens the possibility of building educational technology that can provide reliable feedback about the focus and attention of a student who is engaged in a learning activity. In this paper, we demonstrate the practicality of developing a simple web-based application that exploits EEG data to monitor reading effectiveness personalized for individual readers. Our tool uses a variant of k-means classification on the relative power of the five standard bands (alpha, beta, gamma, delta, theta) for each of four electrodes on the Muse wearable brainwave sensor. We demonstrate that after 30 minutes of training, our relatively simple approach is able to successfully distinguish between brain signals produced when the subject engages in reading versus when they are relaxing. The accuracy of classification varied across the 10 subjects from 55% to 85% with a mean of 71%. The standard approach to recognize relaxation is to look for strong alpha and/or theta signals and it is reasonably effective but is most associated with closed eye relaxation and it does not allow for personalization. Our k-means classification approach provides a personalized classifier which distinguishes open eye relaxation from reading and has the potential to detect a wide variety of different cognitive states.

Paper Nr: 3
Title:

Word Clouds as a Learning Analytic Tool for the Cooperative e-Learning Platform NeuroK

Authors:

Fernando Calle-Alonso, Vicente Botón-Fernández, Jesús M. Sánchez-Gómez, Miguel A. Vega-Rodríguez, Carlos Javier Pérez and Daniel de la Mata

Abstract: Word cloud or tag cloud is very popular these days. It is a tool used to display text data summarization in a visual way very easy to understand. However, it has not been extensively used in teaching, especially in e-learning, where it would make a differential advantage. This research presents the definition and implementation of a word cloud tool in a social network-based e-learning platform (NeuroK), which is based on the principles of neurodidactics. The different features developed and the results are shown. Several options to compare word clouds from students and teachers allow the teacher to follow the development of the course, and they provide him more information to facilitate the evaluation process.

Paper Nr: 5
Title:

Studying Programming Students Motivation using Association Rules

Authors:

Paula Correia Tavares, Elsa Ferreira Gomes and Pedro Rangel Henriques

Abstract: For Programming teachers it is of utter most importance to understand the factors that impact on students’ motivation to improve their ability to become good computer programmers. To understand a problem, to develop an algorithm for its solution, and to write the corresponding program is a challenging and arduous task, demanding time and self-confidence. In previous work we studied computer based technics to engage students in the learning activity; visualization, animation, automatic program assessment were some approaches that we combined. To support that work we studied carefully students’ motivation and complemented that study with an inquiry to a group of students of Algorithm and Programming course of the first year of an Engineering degree. In this paper we show how Association Rules can be used to mine the data gathered in the inquiry to discover relationships among factors influencing extrinsic motivation.

Short Papers
Paper Nr: 4
Title:

Graphs and Key Players in an Educational Social Network

Authors:

Fernando Calle-Alonso, Vicente Botón-Fernández, Dimas de la Fuente, Carlos Javier Pérez, Miguel A. Vega-Rodríguez and Daniel de la Mata

Abstract: A new social learning graph tool has been proposed and implemented in NeuroK. This is an interactive visualization of the relationships among students, based on their comments, favorites, mentions and rates in a course or a learning unit. Some illustrative examples are provided showing its possibilities, challenges and future potential of using the social graph tool. Social graph can help teachers and students to have an easy visual image of their relationships, discovering the key players in the network and also the isolated students in risk of dropping out. The teacher can use this information to reach the students “in risk” and try to retain them with some motivating and engaging actions. The social graph and the indices that can be obtained from it show up to be also a very good tool to analyse the development of the course and to help the teacher to evaluate the students.

Paper Nr: 6
Title:

Business Intelligence - Implantation on Federal Institute of Triângulo Mineiro (IFTM) System

Authors:

Ernani Damasceno, Ana Azevedo and Agostinho Pinto

Abstract: Every organization aims to perform the activities in an efficiently way at selling products and services to obtain profits. However, most of the times, there is not an effective project to support the company in the process management. Every information system (IS) must be efficient, supported by substantial and fast computer system and trained users to manipulate them without troubles. Based on this assumption, this paper aims to analyze possible vulnerabilities in the Federal Institute of Triângulo Mineiro (IFTM) system in order to implant a Business Intelligence (BI) system to help at decision making. It was noticed that the IFTM- Paracatu campus system does not have proper Analytics tools to help managers in the decision making. Thus, after a detailed survey of the necessity of the system, it was verified that the institute secretary module, named Academic Registration Control (ARC), has important failures, for example, delaying, inconsistent information and repetitive processes. As noted above, it was created a BI Data Mart on ARC module, in order to solve basic failures, such as: weak reports, inconsistent student records and lack of graphical analysis.