This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains specifically to the grading of computer skills with a focus on personal productivity software. Feedback has been identified as a key component of successful learning among students. This research builds upon the previous knowledge from the cognitive, behavioral, and resourcebased views of learning as well as upon the establishment of grading rubrics. An automated grading system was developed that allows instructors to quickly grade multiple complex computer literacy assignments. Key to the success of the system is the ability of the system to “learn” the correct and incorrect responses and store them for future use. To understand the impact of the system on feedback, three hypotheses were created and experiments were developed to test them. The system was shown to positively affect the quantity of feedback and reduce the time required for grading assignments. No effect on the quality of the feedback comments was shown and may be a subject of further study.
Matthews, Kevin; Janicki, Thomas; He, Ling; and Patterson, Laurie
"Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time,"
Journal of Information Systems Education: Vol. 23
Available at: https://aisel.aisnet.org/jise/vol23/iss1/7
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