No abstract

This chapter presents a model of a novel adaptive online knowledge assessment system and tests the efficiency of its implementation. System enables continual and cumulative knowledge assessment, comprised of sequence of at least two interconnected assessments, carried-out throughout a reasonably long period of time. Important characteristics of the system are: (a) introduction of new course topics in every subsequent assessment, (b) re-assessment of earlier course topics in every subsequent assessment iteration, (c) in an adaptive manner, based on student’s achievements during previous assessments. Personalized post-assessment feedback guides each student in preparations for upcoming assessments. The efficiency has been tested on a sample of 78 students. Results indicate that the proposed adaptive system is efficient on an individual learning goal level.

No abstract

This study aims to discover groups of students enrolled in the emergency remote teaching online course based on the various course-related data collected throughout the first year of COVID-19 pandemic. Research was conducted among 222 students enrolled in the course “Business Informatics” at the Faculty of Organization and Informatics of the University of Zagreb in the academic year 2020/2021. Overlays were used to model students’ success on the various quizzes and exams within the course. The k-means clustering was employed to classify students into groups, based on combination of students’ overlay values, frequency of accessing course lessons and the final grades. Three distinct clusters (i.e., students’ groups) were discovered and explained in the given context. The identified groups of students can be used for future adaptations of the online course design in order to improve the retention and their final grades.