Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/4405
Title: Identifying variables affecting students' academic performance among engineering students
Authors: Wali, Fahad
Keywords: Structural Equation Modelling;Path analysis;Confirmatory Factor Analysis;Evaluating academic performance;Demographic variables and student success
Issue Date: 2018
Abstract: An essential consideration for campus administrators and faculty members is that students complete their degree with good academic grades. Being able to predict factors affecting students performance is necessary to help ensure the supply of quality students. The purpose of this study is to determine the factors affecting transfer students' academic performance (AP) who are taking Baccalaureate degree in the university. The sample used in this study includes 996 students (934 males and 62 females). The data was filtered by removing students whose cohort year is greater than the first term registered, students who deceased while studying, and students with a degree other than Baccalaureate degree. The data were analysed using descriptive statistics and structural equation modelling (SEM) approaches (like Path analysis and Con frmatory Factor Analysis (CFA)). Results revealed that (i) male students older than 25 to be a strong predictor of students' academic performance, (ii) females and the students younger than 21 significantly complete their studies on-time, (iii) students who are on a Permanent resident immigration status, have French as their native language or are from India, Pakistan or other countries perform better, (iv) students from Institute N (anonymised institute) significantly complete their studies on-time, (v) students' past grades from Institute L and J shows significant positive effect on their current grades at the university. Furthermore, students with fewer bridging courses or are from group 3 perform better at the university. These findings will help institutional planning for future students.
URI: http://knowledgecommons.lakeheadu.ca/handle/2453/4405
metadata.etd.degree.discipline: Computer Science
metadata.etd.degree.name: Master of Science
metadata.etd.degree.level: Master
metadata.dc.contributor.advisor: Mago, Vijay
metadata.dc.contributor.committeemember: Wei, Ruizhong
Jackson, Piper
Appears in Collections:Electronic Theses and Dissertations from 2009

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