Predicting student academic performance in Machine elements course

  • Daniel Miler
  • Marija Majda PerišićEmail author
  • Robert Mašović
  • Dragan Žeželj
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)


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).


Machine elements Student performance prediction Academic performance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Techniche Universität München (2017) Studienplan B. Sc. Maschinenwesen (2017/18).Google Scholar
  2. 2.
    Techniche Universität Wien (2011) Curriculum für das Bachelorstudium Maschinenbau.Google Scholar
  3. 3.
    Veenstra CP, Herrin GD (2008) Is Modeling of Freshman Engineering Success Different from Modeling of Non-Engineering Success? J Eng Educ 467–479Google Scholar
  4. 4.
    Lin JJJ, Reid KJ (2009) Student Retention Modelling : An Evaluation of Different Methods and their Impact on Prediction Results. Eng Educ 1–6Google Scholar
  5. 5.
    Howell LL, Sorensen CD, Jones MR (2014) Are undergraduate GPA and general GRE percentiles valid predictors of student performance in an enginerring graduate program? Int J Eng Educ 30:1145–1165Google Scholar
  6. 6.
    French BF, Immekus JC, Oakes WC (2005) An Examination of Indicators of Engineering Student. J Eng Educ 94:419–425Google Scholar
  7. 7.
    Laugerman M, Rover DT, Shelley MC, Mickelson SK (2015) Determining Graduation Rates in Engineering for Community College.Google Scholar
  8. 8.
    Asif R, Merceron A, Ali SA, Haider NG (2017) Analyzing undergraduate students’ performance using educational data mining. Comput Educ 113:177–194Google Scholar
  9. 9.
    Huang S, Fang N (2013) Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Comput Educ 61:133–145Google Scholar
  10. 10.
    Miguéis VL, Freitas A, Garcia PJV, Silva A (2018) Early segmentation of students according to their academic performance: A predictive modelling approach. Decis Support Syst 115:36–51Google Scholar
  11. 11.
    Lent RW, Miller MJ, Smith PE, Watford BA, Lim RH, Hui K (2016) Social cognitive predictors of academic persistence and performance in engineering: Applicability across gender and race/ethnicity. J Vocat Behav 94:79–88Google Scholar
  12. 12.
    Muñoz-Bullón F, Sanchez-Bueno MJ, Vos-Saz A (2017) The influence of sports participation on academic performance among students in higher education. Sport Manag Rev 20:365–378Google Scholar
  13. 13.
    Rončević A (2008) Teacher’s convictions to the multimedia and learning outcomes of pupils. In: Cindric M, Domovic V, Matijevic M (eds) Pedagog. Knowl. Soc, pp 315–324Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel Miler
    • 1
  • Marija Majda Perišić
    • 1
    Email author
  • Robert Mašović
    • 1
  • Dragan Žeželj
    • 1
  1. 1.Faculty of Mechanical Engineering and Naval ArchitectureUniversity of ZagrebZagrebCroatia

Personalised recommendations