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Analysing performance of first year engineering students

Zdrahal, Zdenek; Hlosta, Martin and Kuzilek, Jakub (2016). Analysing performance of first year engineering students. In: Learning Analytics and Knowledge: Data literacy for Learning Analytics Workshop, 26 Apr 2016, Edinburgh.

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Abstract

Many students in the engineering disciplines do not complete their higher education degree and drop out. This problem is serious, especially for first-year university students. In this paper, we analyse how students earn the credits required for their successful completion of the first study year. Using the example of a European technical university with traditional classroom-based education, we identify three groups of students: those who pass, those who earn only enough credits for staying in the program, and those who fail. Important patterns can be found at the end of the first semester. We present a simple algorithm that identifies students who may benefit from early additional support, which would increase their chances of progression to the second year and improve the retention improvement for the university. The results are evaluated in four consecutive academic years. The data from years 2013/14 and 2014/15 have been used to develop and verify the prediction model. In study years 2015/16 and 2016/17 the model has been applied to predict at-risk students, where the university tutors intervened and provided additional support and a significant improvement was achieved.

Item Type: Conference or Workshop Item
Keywords: Student drop-out; learning analytics; intervention; progression; engineering education; STEM subjects; prediction of study results; ECTS credits; early exam period; first-year bachelor’s program; traditional classroom-based university
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Education and Educational Technology (CREET)
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Item ID: 58597
Depositing User: Zdenek Zdrahal
Date Deposited: 15 Jan 2019 16:37
Last Modified: 03 May 2019 16:17
URI: http://oro.open.ac.uk/id/eprint/58597
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