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"Student Success"
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Systematic review: Predictors of students’ success in baccalaureate nursing programs
Nursing schools strive to select a diverse student population who are likely to succeed by ensuring timely student progression through the program and effective use of educational sources. The purpose of this systematic literature review is to explore the preadmission variables and selection criteria that predict student success in 4-year baccalaureate nursing programs in the U.S. Sixteen articles met the eligibility criteria, and six measures were used to define student success: (a) early academic success, particularly during the first and second year; (b) attrition; (c) timely completion of the program; (d) graduation; (e) performance in nursing courses; and (f) academic performance in other science courses. Typically, the core set of cognitive predictors used in the admission process in nursing schools were pre-nursing GPA, pre-nursing collegiate science GPA, and scores on standardized aptitude exams. This review suggests that it is challenging to isolate one single variable as the best predictor of student success; however, using a combination of variables can offer a reliable prediction method. More researchers should consider using a theoretical basis to guide their inquiry on this topic. Additionally, researchers should examine admission variables that are most relevant across programs.
•Nursing schools continue using quantitative models to select nursing applicants.•The theoretical basis is imperative to inform prediction of student success.•Student demographic variables can guide establishing appropriate academic support.•Using student GPA as a selection criterion requires careful consideration.
Journal Article
A new measure for the assessment of the university engagement: The SInAPSi academic engagement scale (SAES)
Despite the growing interest on the notion of academic engagement (AE) and its relevance for students’ success, a few valid and reliable instruments on AE have been developed. Moreover, most of the available measures consider AE as a student’s trait rather than a relational and situated dynamic process. This study presents the development and validation of a new instrument, the SInAPSi Academic Engagement Scale (SAES), which was developed within a project coordinated by the SInAPSi center of the Authors’ University and it aims to measure AE. The main sample was constituted by 680 students and a convenience sample of 312 biology and biotechnologies students was also involved to perform the confirmatory factor analysis of the initial factor structure of the SAES. Construct validity was assessed using the University Student Engagement Inventory (USEI), while criterion-related validity was established with the Academic Motivation Scale (AMS), the students’ confidence in one’s own preparation for academic studies and their academic performance. Results show that the SAES presents a robust factor structure, a good convergent and discriminant validity, and satisfactory psychometric properties. Furthermore, the SAES shows a positive correlation with the USEI and the AMS, the students’ confidence in their preparation for academic studies and their academic performance. The results indicate that the SAES can produce valid and reliable data on AE and it may have strong implications for assessing AE and implementing intervention programs for university students.
Journal Article
A Third Space Approach to Integrated Academic Student Success Advising (ASSA)
The Academic Student Success Advising (ASSA) project enacted an integrated academic and pastoral approach to advising using McIntosh's (2023) fundamental principles of advising. This research conducted at two Australian universities
explores how shared principles of advising can provide an underpinning structure to pan-university advising approaches as a mechanism of student development. Forty staff were interviewed, exploring understandings and experiences of
advising. Data were analysed through the four key advising themes: inclusive, personalised and integrated, developmental, and student-centred. The findings suggest that staff perceive value in integrated advising approaches that connect
students' academic and pastoral experiences through an 'advising as teaching lens' and that link areas of the university to enhance student success. Recommendations highlight the value of investing in staff understandings of advising to
enhance student development, the intentional embedding of co-curricular skills, and the continued need to develop systems to track advising outcomes. [Author abstract]
Journal Article
Listening to and learning from the experiences of Aboriginal and Torres Strait Islander students to facilitate success
Drawing on interviews with current and past Indigenous undergraduate students at the University of Queensland (UQ), this paper reports on findings from a project that explored the experiences of Indigenous Australian students and identified inhibitors and success factors for students. It also discusses one of the outcomes of the project and planned future developments that aim to provide better support for Indigenous Australian students at UQ. By knowing and acting upon the kinds of mechanisms that can assist Indigenous students, their experiences of tertiary study can be enhanced, leading to more students enrolling in and completing university study. [Author abstract]
Journal Article
Assessment Design And Practices Toward Holistic Learning Of Higher Education Students: Empirical Evidence Via Path Analysis Modelling Approach
COVID-19 has revolutionised assessment design and practices in higher education; however, there has not been a shift in the overall objective of enhancing that the association between assessments and learning promotes the holistic development of students. In this study, we provide an empirical evaluation of the perceived effects of assessment practices (invigilated examination and alternative assessments) on students’ mental wellbeing, learning processes and academic misconduct. A cross-sectional study design was employed for this study in which a self-reported survey instrument was administered to 380 social science and nursing undergraduate and postgraduate students in a public university in Australia. We explored the correlations within defined networks by path analysis via partial least square structural equation modelling (PLS-SEM) framework of SmartPLS 3. Model assessment indexes indicated acceptable convergent, divergent and construct validity scores for the instrument used. Compared to invigilated exams, students perceived alternative assessments to have significant positive direct effects on stress levels, research skills, learning process and time management (). In relation to academic misconduct, students generally perceived invigilated exams to restrain such practices; however, the perceived effect was not statistically significant when compared with alternative assessments (. Although, the popularity of alternative assessment practices may have been driven by COVID-19, the pilot findings from this study suggest these assessment designs and practices have greater potential to promote overall student success and productivity and must be encouraged and utilised in the post-COVID-19 era.
Journal Article
Assessment Design And Practices Toward Holistic Learning Of Higher Education Students: Empirical Evidence Via Path Analysis Modelling Approach
COVID-19 has revolutionised assessment design and practices in higher education; however, there has not been a shift in the overall objective of enhancing that the association between assessments and learning promotes the holistic development of students. In this study, we provide an empirical evaluation of the perceived effects of assessment practices (invigilated examination and alternative assessments) on students’ mental wellbeing, learning processes and academic misconduct. A cross-sectional study design was employed for this study in which a self-reported survey instrument was administered to 380 social science and nursing undergraduate and postgraduate students in a public university in Australia. We explored the correlations within defined networks by path analysis via partial least square structural equation modelling (PLS-SEM) framework of SmartPLS 3. Model assessment indexes indicated acceptable convergent, divergent and construct validity scores for the instrument used. Compared to invigilated exams, students perceived alternative assessments to have significant positive direct effects on stress levels, research skills, learning process and time management (). In relation to academic misconduct, students generally perceived invigilated exams to restrain such practices; however, the perceived effect was not statistically significant when compared with alternative assessments (. Although, the popularity of alternative assessment practices may have been driven by COVID-19, the pilot findings from this study suggest these assessment designs and practices have greater potential to promote overall student success and productivity and must be encouraged and utilised in the post-COVID-19 era.
Journal Article
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review
The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.
Journal Article
Predicting academic success in higher education: literature review and best practices
Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.
Journal Article