Professor Matthew Montebello (Department of Artificial Intelligence, Faculty of ICT) is assisting researchers at the University of Illinois to employ Artificial Intelligent techniques to assist training of medics. The team of academics from the Computer Science Department and College of Education developed an analytics tool called Common Ground Scholar which aims to end the traditional division between learning and assessment. Data collected through the online learning portal is collected, processed and graphically visualized through a colorfully charted Aster plot that reveals the progress assessment of students.
In this way millions of data points derived from thousands of meaningful interactions that have contributed constructively to learning during the class are not lost or ignored, but machine processed to offer fruitful insights to tutors and students. Students have continuous access to their own analytics Aster plot to take greater control of their learning and increase the responsibility of their outcomes, while educators can monitor the progress of the entire class in one visual, covering multiple educational aspects.
Medicine students using the platform were able to offer their views and revisions about different cases, provided peer reviews as well as feedback on the reviews they receive. In the process a great deal of useful, unique data is generated and analyzed to shed light on students’ behavior and understanding of how they learn, that consequently enabled the development of techniques that could automate some of the assessment.
The upshot is that students put together their own case studies rather than having information fed to them, and thereby produce better doctors by increasing their exposure to real-life situations in the medical field and peer reviews as it surpasses being evaluated through rote memorization or taking exams.
The team of researchers concluded that the online learning portal and the analytics tool have the ability to offer highly scalable education, providing an opportunity to develop interesting algorithms that compare revisions of students’ work, analyze any changes, critically think, and provide assessment feedback to students and tutors.
