Pregled bibliografske jedinice broj: 1007784
Predicting student academic performance in Machine elements course
Predicting student academic performance in Machine elements course // Advances in Mechanism and Machine Science / Tadeusz, Uhl (ur.).
Cham: Springer, 2019. str. 825-834 doi:10.1007/978-3-030-20131-9_82
CROSBI ID: 1007784 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Predicting student academic performance in Machine elements course
Autori
Miler, Daniel ; Perišić, Marija Majda ; Mašović, Robert ; Žeželj, Dragan
Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni
Knjiga
Advances in Mechanism and Machine Science
Urednik/ci
Tadeusz, Uhl
Izdavač
Springer
Grad
Cham
Godina
2019
Raspon stranica
825-834
ISBN
978-3-030-20130-2
ISSN
2211-0984
Ključne riječi
Machine elements ; Student performance prediction ; Academic performance
Sažetak
The students frequently regard the fundamental mechanical engineering courses as demanding, and they often have high drop-out rates. Due to the width of prerequisite knowledge which needs to be integrated, advanced, and applied, Machine elements is one of such courses. In this article, the authors have used mathematical methods to determine the predictors of student performance in Machine elements course held at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb. The secondary education data and grades of preceding courses were collected for 729 students enrolled in Machine elements course. The obtained data were described using basic statistical methods and further used to develop models for predicting the students’ performance on the Machine elements course. Building on the results, the authors have answered three research questions: The preceding courses are better predictors when compared to secondary education (1). The Strength of Materials and Mathematics II were the best predictors ; generally, the course’s complexity, rather than its scope, was an indicator of its importance for the prediction of student’s future success (2). Lastly, it was possible to group the students based on predicted future academic performance which, consequently, enables early segmentation and detection of students at risk (3).
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb