Effective instructional strategies in classroom can raise students’ learning motivation, participation will, and study performance. However, evaluating the effectiveness of instructional strategy is usually done after class. Since tutors usually do not know the learning performance of students on class in real-time, regulating the depth of teaching materials to fit students learning status is also not possible. Therefore, it is worthy to develop assistive learning technologies to help tutors understand students’ mental stress and cognitive load in classroom scenario. With the development of modern technologies, it allows people to study students’ physiological states by means of measuring subjects’ electrocardiography (ECG) and electroencephalography (EEG) signals. In this project, we intend to realize a biofeedback system to measure subjects’ physiological state during classroom learning. The system enables tutors to understand the relations between their instruction strategies and subjects’ learning performances. The proposed project is the subproject III of a three-year integrated project. In this first year, we will construct a CloudClassRoom (CCR) with biofeedback to study the influence of open- /close- ended questions on students’ cognitive loads. In the second year, we will develop wearable ECG/EEG monitoring system to discuss the effects of prior knowledge and anonymity learning environment on student’s cognitive load, memory retrieval and mental stress. In the last year, we will integrate our wearable devices with CCR. Biomarkers will be real-time calculated and provided to subproject I and II to improve the construction of adaptive question-driven science instruction in CloudClassRoom scenario.
|Effective start/end date||1/08/18 → 31/07/19|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):