The use of Emotions in Student Assessment in a Virtual Learning Environment

Transcription

1 PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E PROCESSOS INDUSTRIAIS - MESTRADO The use of Emotions in Student Assessment in a Virtual Learning Environment Nadiesca Homrich Scherer Jacques Nelson Corleta Schreiber Rejane Frozza (Lecturer) Santa Cruz do Sul University

2 POST-GRADUATION PROGRAM IN INDUSTRIAL SYSTEMS AND PROCESSES - MASTERS The use of Emotions in Student Assessment in a Virtual Learning Environment Nadiesca Homrich Scherer Prof. Jacques Nelson Corleta Schreiber Prof. Rejane Frozza Prof. (Lecturer) University of Santa Cruz do Sul

3 Summary 1. Introduction 2. Objectives 3. Theoretical Foundation 4. Method of Inference and VLE 5. Methodology 6. Conclusion 7. References 3

4 Introduction In distance learning, teachers and students communicate and interact through a Virtual Learning Environment (VLE). Knowing the student's emotions during the access to VLE may support the improvement of the quality of interaction. 4

5 Introduction In the learning process there is a relationship between cognition and emotion. Longhi et al (2007), say "affection may assist in reasoning. Piaget (1983) says "Surely affectivity or its deprivation may be the cause of acceleration or delay in cognitive development. Picard et al. (2004) talk about the tendency to see the computer as a learning "motivator" by making use of emotions for better interaction. 5

6 Introduction Therefore, it is possible to see the importance of studies in the relationship between cognition and emotion in VLE. 6

7 Objective To capture the emotions of the student, through a specific software during the process of interaction with the VLE, and together with the result of the assessment of student learning in an activity course, to realize the existence of the relationship between emotion and cognition, and use this information to make improvements in learning. 7

8 Theoretical Foundation 8

9 Theoretical Foundation According to Nunes (2012), when computational tools may infer emotions of users in applications such as VLEs, it is possible to say that these tools promote affective computing. In Nunes et al. (2011), say that in the process of learning The teacher take on the role of mediator; Students seek more knowledge. 9

10 Theoretical Foundation According to Duran et al. (2004), cognition is a knowing process that involves some aspects such as attention, perception, memory, judgment, reasoning, imagination, thought and speech. For the student to have improvement in the learning process, he/she must be motivated for cognitive processes to be achieved in all respects. Infer Student s emotion: Joy, Sadness, Anger, Fear, Disgust or Surprise. Action: Adaptation of VLE according to the emotion inferred. 10

11 Method of Inference and the VLE To infer the emotion of the student in the Virtual Learning Environment, the method will use software that was developed at UNISC. (Böhm, 2011) 11

12 Method of Inference and the VLE The VLE was developed by a research group at UNISC. This environment has already expressed emotions through intelligent agents. 12

13 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE. Stages of work: Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning. 13

14 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. To Capture the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE. Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning in order to prove the hypothesis in this paper. 14

15 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE. Integrating inference method with VLE. Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning in order to prove the hypothesis in this paper.. 15

16 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE. Defining moments when the emotion of the student will be inferred. Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning in order to prove the hypothesis in this paper. 16

17 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE.. Adapting the presentation of the content. Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning in order to prove the hypothesis in this paper. 17

18 Methodology Capturing the emotion of the student by the use of software that makes the inference through facial expressions. Integrating inference method with VLE.. Assessing learning. Defining moments when the emotion of the student will be inferred. Adapting the presentation of the content. Assessing learning in order to prove the hypothesis in this paper. 18

19 Conclusion This research aims to capture the emotion of the student through software that analizes facial expressions; To make this software work with VLE, or taking part in it, which sets up an integration between both; To adapt the content of the VLE according to the inferred emotion; And to assess student learning to find out if the process was useful or not in improving student learning. 19

20 Acknowledgments FAPERGS UNISC PPGSPI 20

21 References BOHM, Diogo Luiz. Detecção automática de expressões faciais em imagens de faces humanas f. Monografia (Graduação) - Universidade de Santa Cruz do Sul, DURAN, Kelly Marion; VENANCIO Lauro Ramos; RIBEIRO Lucas dos Santos. Influência das Emoções na Cognição. UNICAMP, KAPOOR, A. e PICARD, R. E. Multimodal Affect Recognition in Learning Environments, In: ACM MM 05, November 6-11, Singapore PIAGET, Jean (1983). A epistemologia Genética/Sabedoria e Ilusões da Filosofia/Problemas de Psicologia Genética. 2. Ed São Paulo: Abril Cultura, (Os pensadores). PICARD, R.W.; Papert, S.; Bender, W.; Blumberg, B.; Breazeal, C.; Cavallo, D.; Machover, T.; Resnick, M.; Roy, D.; Strohecker, C. Affective Learning-A Manifesto. BT Technical Journal, Volume 22, No. 4, pp October, LONGHI, Magalí Teresinha; BERCHT, Magda; BEHAR Patricia Alejandra (2007). Reconhecimento de Estados Afetivos do Aluno em Ambientes Virtuais de Aprendizagem. Porto Alegre, UFRGS. V. 5 Nº 2, Dezembro, NUNES Maria Augusta S. N; REHEN Almerindo; BEZERRA Jonas S.; ROCHA Alex; SANTOS Celso A. S. Uso do Kinect para a extração de caracterísiticas afetivas do usuário. Anais do XXII SBIE XVII WIE. Aracaju, NUNES Maria Augusta Silveira Netto. Computação Afetiva personalização de interfaces, interações e recomendações de produtos, serviços e pessoas em ambientes computacionais. Capítulo de Livro. 2012, v. 1, p

22 Thank you very much! Liane Mählmann Kipper 22