Automation of student knowledge assessment in the ECOLE elearning system
The paper concerns assessment of students'' knowledge based on their learning results in ECOLE system. ECOLE system is an online eLearning system, whose functionality is based on ontologies and semantic technologies. Stu-dents'' activity in the system is collected and analysed automatically in the system to give students recommendations on sub-ject terms, concepts or domains they know excellent or poor, to advise them which educational program to choose and which scientific activities in the university are close to the courses they passed best. The system''s educational content comes from various sources: linked open data, tests published on the Web, video lectures and tests manually uploaded by the university staff. ECOLE system allows interlinking subject terms from different courses and domains and calculates several educational rates: subject term knowledge rate, total subject term knowledge rate, domain knowledge rate and subject term significance rate. All of these rates are used to give recommendations for a student about the activities he has to undertake to pass a course successfully. A term knowledge rate is calculated using predefined sets of metrics and algorithms. The paper describes the concept of subject term importance in the learning process. Term importance and a total subject term knowledge rate allow calculating the student’s knowledge rates for different subject fields and learning courses.