1 University of Passo Fundo (BRAZIL)
2 Federal University of Rio Grande do Sul (BRAZIL)
About this paper:
Appears in: ICERI2013 Proceedings
Publication year: 2013
Pages: 2463-2469
ISBN: 978-84-616-3847-5
ISSN: 2340-1095
Conference name: 6th International Conference of Education, Research and Innovation
Dates: 18-20 November, 2013
Location: Seville, Spain
Currently, resources of communication, collaboration and interaction between students and teachers are important allies for the qualification of the teaching and learning process. Collaborative learning environments, computational tools of interaction and visualization, as well as educational content available anytime and anywhere are resources that can enhance the quality of education and evolution of students. Among these technologies, e-learning (Electronic Learning) and m-learning (Mobile Learning) are increasingly being used in learning environments. They allow the not present teaching supported by information and communication technologies.

Some authors claim that the new technological resources potentialize the traditional teaching strategies applied by most teachers, especially if these resources are available during the initial formation. In this sense, the learning that the study environment in the classroom provides is standardized and does not reach individual needs. One of the ways to improve the performance of students in the disciplines is to enable the teacher to identify students' difficulties and recommend activities for them. This recommendation shall be based on the profile of each student and reflect the need required by the student to expand their learning.

Therefore, identifying patterns of behavior of these students is crucial and one of the ways to make this possible is through a computational tool. The Taes tool aims to analyze the behavior of the student in the aspect of the learning process. The tool consists of an environment where the teacher prepares questions to be presented to students. The questions can be multiple choice, true or false, relate columns, as well as association. Students answer questions via laptops, tablets, smartphones, desktops or any other device that is connected to internet.

When creating the questions the teacher can assign weights to each response. The weights can be 1, 2, 3, 4 and 5, where 5 represents the correct answer and 4, 3, 2 and 1 represent the wrong answers. Wrong answers are arranged this way so that the teacher can analyze the behavior of the student when answering questions. If the student’s response is 4, that means he almost answered right. If they checked the answer weighting 1, it means that the student made a very big mistake. Since the tool enables to graphically analyze the student’s responses and generate statistics on the evolution or involution of the students, the teacher can identify how the student is responding to the issues and what is their level of trial and error. This is important for the teacher to have time to offer a differentiated work with students who have problems in relation to learning.

This paper aims to present an approach to identify the behavior of students using the Taes tool as support. The proposed approach consists of analyzing the answers that students provide from the Taes tool environment and identify patterns of behavior profiles of these students. With the identification of these profiles the teacher can receive recommendation of how to improve student learning. This can be done through the creation of study groups among the students themselves, the definition of tutors, as well as the creation of extracurricular activities.