Effectiveness of Multi-Abstraction Computing Tools on Promoting Exploratory Self-learning in Engineering: a Case Study using a Custom Real-Time Operating System for Remote Learning

This manuscript reports the results of an experiment to assess the effectiveness of multi-abstraction computing tools on promoting exploratory self-learning in engineering, using a custom Real-Time Operating System (RTOS) during remote learning. Our goals were to determine whether students respond to guided exploratory learning opportunities during remote learning, and whether these contributed to immediate and further student success.Students in a 3rd year course were given the custom multi-abstraction RTOS. The higher abstraction layer was taught directly, whilst students were directed towards exploratory self-learning among the lower abstraction layer (which includes details beyond prior knowledge or the content of the course). In a survey, students were asked about their perceptions of using higher and lower abstraction layers and how these impacted their learning. Responses were correlated with course grades.Students that engaged with the lower abstraction layer demonstrated higher success (3 percentage points above class average), engagement (22.5% reporting they were more engaged), accuracy of self-assessment (1.3 percentage points above class average), and learning beyond course’s expectations (14% reporting they learned beyond the course’s learning outcomes).