Automating the Composition and Scheduling Process for Synoptic Assessment Panels 
9th SDC-SLAIHEE Higher Educationm Conference 2013
The Bachelor of Engineering (Honours) program of the Department of Computer Science and Engineering at the University of Moratuwa has a compulsory software engineering project course in the 5th semester. This course has been designed to foster creativity and software engineering rigor. Since the design of this course (CS3202) straddles several program ILOs at a 5th semester level, along with a strong emphasis on creativity, a synoptic assessment approach was selected in the course evaluation framework. This involved constituting expert evaluation panels. Previously, compilation and scheduling of evaluator panels aligned with the heterogeneous technology profiles of the student projects was done manually. However, it was a highly tedious and time consuming task which was further complicated due to the limited number of evaluators and conflicting time constraints. The objective of current research was to evaluate the efficiency of automating the process of constituting synoptic assessment panels to evaluate the above mentioned student projects (n=101). An action research methodology was followed in the study. In the research 'planning' phase, data on student project technology profiles, competency and availability of the evaluators, and course dependent restrictions was gathered. In the research 'action' phase, an algorithm was devised with the following primary objective: "Each student will be assigned a 'best-fit' panel of evaluators considering the technologies used in the student's project." The research 'observation phase' showed that 120 of the total 141 feasible student project profiles were successfully matched by the algorithm to the areas of technology expertise of the evaluators, resulting in an 85.12\% success rate. Thus it can be concluded that this approach is a very important improvement over the manual assignment of panels. The future work is to implement an online application. and our recommendation is that other educators could use this application for a similar purpose.
Keywords: Education | Machine Learning / Deep Learning |