Assessment of the Implementation (Orchestration) of Learning Design for a Targeted Group of Students

6. Case Study 2

2. Course: Artificial Intelligence – Theory and Application – Adjustment of Assessment Methods

Context: The course is delivered at the graduate level of a technical faculty and is attended by 120 students. The student pass rate in the previous year was 85%, and no significant changes were made to the learning design before delivering the course in the current academic year. In addition to theoretical knowledge, the course requires students to demonstrate the ability to apply theory to solving practical problems in the technical domain, through teamwork.

Evaluation: Data are analysed from the LMS (login frequency, time spent in the wiki, results of midterms, and the project assignment) as well as from the BDP tool.

Results: The assessment structure was designed using the BDP tool.

The following graph shows the planned workload by types of teaching and learning activities (in the BDP tool) for the learning outcome related to applying artificial intelligence to solving real-world problems. At the end, the results of the midterm assessment are presented based on Moodle statistics.

The problem-based task was completed by students working in teams of four. They were required to solve the task using the wiki in Moodle and then submit their solution to the Workshop activity, where the project work was assessed by instructors (two instructors taught the course) and by peers, according to predefined criteria.

The average project score was 18/27, and the average score students received for the quality of their peer assessment in the Workshop (strict grading) was 1.5/3.