Identification of Computational Thinking skills in the face of Emotional States under the Multimodal Learning Analytics approach
Computational Thinking, Emotional States, Multimodal Learning Analytics approach
The need to work on computational thinking (CP) in educational contexts comes as a proposal in the teaching and learning of basic education, but there are some limitations for its implementation in the classroom, among them it could be cite: lack of didactic material, technological devices, methods and instruments for the assessment of CP skills. However, computing curricula from basic education to high school have been modified and improved in Brazil and in the world, there are promising steps in teaching units, however, the gaps in relation to the identification of CP skills are the focus of much research. ; our approach to Multimodal Learning Analytics (MMLA), in order to be used as a tool to enhance pedagogical practices, which aims to support the process of capturing associated data through the teaching contexts: unplugged, with the execution of activities completely offline, that is, without using a digital tool; plugged in,
through the use of an online visual programming tool, the Scratch platform; and; plugged in with robotics, with the use of a visual programming tool interconnected to the robotic artifact FRANZMakey, from the perspective of levels of cognition: use, modify and create. The project aims to serve an educational institution, involving students from the 1st year of high school; 94 activity meetings are planned, 47 of which in each group of students; in the first and last, the pre and post-test will be administered; in the forty-seven remaining meetings, three programming contents will be addressed: sequence, repetition and conditional. In view of this, it sought to answer the following question: how can MMLA contribute to capturing evaluative aspects during the development of CP skills? During each meeting, data are captured and analyzed in order to answer the question defined in this investigation. We will use EZMMLA tookit to capture videos, coding logs and footage of the face, in addition to evaluative tests by encounters; in the analysis, Time Series techniques will be applied, in order to identify possible statistically significant differences between the different contexts and approaches to CP promotion; hope2 to demonstrate how an MMLA-based approach can contribute to the challenge of evaluating the PC, and advance research in the area.