It is important to monitor the students' attention level during lesson to encourage their engagement in participating the learning activities. The main purpose of this study it to carry out action research for real-time learning analytics aiming to improve the teaching and learning for on-site lessons. The methodology includes the learning activities, collecting only the attention level data, analysis and feedback to both students and instructor to improve the learning activities and learning behaviour. The learning activities here include presenting the content through lectures, concept questions, problem solving as well as hands-on. To achieve this, video taking, and wearable sensors have been setup in the classroom for this study. The monitoring process must be automated and provide instant feedback to the instructor for intervention. There is only one instructor to take are 50 students in a typical lesson, thus automation is essential. The video analytics detect automatically if there is good attention to the lecture, good interaction between the students and instructor, while the wearable sensor senses tracks if the students are active during lesson. The feedback through the dashboard helps the instructor to adjust the lesson delivery such as when to have quiz, or when to take a break, as well as intervention to support the students. The feedback is carried out automatically through learning analytics. It is worth mentioning that it is a group analytics rather than individual. This action research helps to improve the attention level from average 50% to 70% in a series of lessons.
Tee Hui Teo, Singapore University of Technology & Design (SUTD), Singapore
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