Predictive Model of Postgraduate Student’s Dropout and Delay Using Machine Learning Algorithms

Neural networks and Logical Regression algorithm provide the best ways to classify data, but they are outperformed continuously by the Decision Tree in analyzing student performance. Therefore, many scholars have used the Decision Tree to predict student performance with greater success. This research analyzed postgraduate student degree outcomes using socioeconomic data to develop a prediction model, where Decision Tree recorded the highest accuracy of 92.79%, better than Logical Regression and Neural Network. For brevity, the Decision Tree was used to produce the prediction model. Based on the study findings, postgraduate students who delay or drop out at the university mostly lack sponsors or had decreased income. Besides, male students delay or drop out if they had financial issues more than their female counterparts. Age, money management skills, number of children, and health expenses are the other factors that contribute to higher dropout or delay at the university. Therefore, this study provides a reliable prediction model for degree outcomes, allowing personalized follow-up to improve graduation rates.

Online and in-Person Delivery of Upper Division Lecture Courses in Undergraduate Life Sciences Degree Programs Leads to Equivalent Post-Graduate Degree Outcomes

Although many studies demonstrate that online education is as good as face-to-face education with regard to learning gains, course grades, and other near-term metrics, there is a major gap in exploring the long-term outcomes of online vs. face-to-face education, particularly in STEM programs. In this study, the effect of course delivery method on the long-term academic success of B.S. graduates was tested by comparing two similar life sciences undergraduate programs at the University of Florida. The Microbiology and Cell Science program teaches all upper division lecture courses online while the Biology program teaches nearly all of its upper division courses face-to-face. Graduate degree outcomes of 4978 students who completed their B.S. degree from either program (2011–2018) were determined using StudentTracker from the National Student Clearinghouse. The percentage of graduates with any doctoral degree (M.D., D.O., Ph.D., or other) did not differ. However, a significantly higher percentage of Microbiology and Cell Science graduates completed a Ph.D. or master’s degree compared to Biology graduates. Thus, online delivery of upper division undergraduate courses had no adverse effect on the future academic success of these students.

‘We Belong’: differential sense of belonging and its meaning for different ethnicity groups in higher education

This paper covers two studies that explore student belonging in higher education and how a sense of belonging differs between ethnicity groups. The research took a mixed methodology approach, collecting both quantitative data via a survey and qualitative data via focus groups. Study One explored the differential experiences of belonging via the Belongingness Survey (Yorke, 2016), with a group of 941 students. This was followed by Study Two, which used focus groups to generate a greater understanding of what belonging meant to the students, how belonging developed and to identify barriers to developing a sense of belonging.This work concluded that ethnicity-based differences in students’ sense of belonging are apparent, which mirror the differences that are witnessed at a sector level in degree outcomes. Additionally, belongingness is found to have an unstable nature in that it waxes and wanes, and can be lost or developed at any part of the student lifecycle. Some student-identified initiatives to support the development of belonging are presented. The findings are discussed in the light of the current literature on differential outcomes.

From Local to Institutional Attainment Change: scaling-up local initiatives

Ethnicity-based gaps in degree outcomes are a pervasive sector issue. At the University of Wolverhampton, substantial investments have been made a) to fund research into why the outcomes gap occurs, the better to understand it, and then b) to implement and evaluate initiatives to reduce it. However, upscaling smaller initiatives to university-wide actions can be fraught with issues. This case study will provide a synthesis of the research carried out at Wolverhampton and the ways this was used as an evidence-base to inform institutional change. The study will also consider some of the lessons learnt from our attempts to embed the outcomes into institutional ‘business as usual’.

Widening Participation at the University of Edinburgh: Contextual Admissions, Retention, and Degree Outcomes

In Scotland, as in the rest of the UK, there is growing recognition that prior qualifications may not provide an adequate indication of the ‘potential’ of applicants from educationally-disadvantaged backgrounds to succeed at university. Universities are being encouraged to use contextual data on neighbourhood characteristics and school performance to identify disadvantaged applicants in the admissions process. Contextualised admissions have been pioneered at the University of Edinburgh since 2004, and this article reports findings on the prior qualifications, retention and degree outcomes of a sample of students who entered the University in 2004–2006. The article describes the distribution of contextual data and discusses the limitations of indicators based on geographical area and school characteristics. Differences in average prior qualifications, retention and degree outcomes associated with indicators of widening participation are small. Statistical models suggest that after taking account of prior qualifications WP-indicated students were as likely to complete an HE qualification and achieve an Honours degree as non-WP students, but they had a lower probability of achieving a higher classification of degree. The findings raise questions for the University about possible causes for lower achievement by disadvantaged students.