Investigating Trends in Academic Achievement: A Longitudinal Analysis of Grade Fluctuations among Students

Grades are essential in replicating global learning outcomes and indicating student success in higher education retention or declaration. The main objective of the study was to investigate trends in academic achievement among the students and analyze grade fluctuations among students at higher education levels. The study employed multilevel descriptive research to achieve its objectives. This study employed a balanced qualitative approach, integrating a multidisciplinary approach and qualitative data collection techniques to align with the research objectives. A longitudinal analysis was conducted to identify patterns among postgraduate students. The Administrative Directorate of Punjab recorded the data of students who successfully passed the CGPA and corresponding marks of the last five (2012-2016) BS 4-year courses in 37 departments of 3 state universities in Punjab. The collected data were analysed by applying percentage-wise analysis of the results of the students. The study reveals a trend in students' final grades favoring A and B grades over C grades. Over five sessions, final CGPAs increased among university students in three public universities. A longitudinal analysis showed improvements in A and B grades from 2012 to 2016 while decreasing C grades. The study suggests that upholding higher educational standards can restore grade reliability and demonstrate discriminating decision-making abilities. It recommends conducting the study at various colleges in Pakistan and sharing results to enhance analytical skills.

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Publication Details

Journal
˜The œcritical review of social sciences studies
Published
2024-11-18
DOI
https://doi.org/10.59075/xwyj4v44
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
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article

Investigating Trends in Academic Achievement: A Longitudinal Analysis of Grade Fluctuations among Students

Sumaira Khan, Inza Shoqat, Aamir Abbas, Erum Aslam Khan
˜The œcritical review of social sciences studies
Online Learning and Analytics
article

Investigating Trends in Academic Achievement: A Longitudinal Analysis of Grade Fluctuations among Students

Sumaira Khan, Inza Shoqat, Aamir Abbas, Erum Aslam Khan
article en

Abstract

Grades are essential in replicating global learning outcomes and indicating student success in higher education retention or declaration. The main objective of the study was to investigate trends in academic achievement among the students and analyze grade fluctuations among students at higher education levels. The study employed multilevel descriptive research to achieve its objectives. This study employed a balanced qualitative approach, integrating a multidisciplinary approach and qualitative data collection techniques to align with the research objectives. A longitudinal analysis was conducted to identify patterns among postgraduate students. The Administrative Directorate of Punjab recorded the data of students who successfully passed the CGPA and corresponding marks of the last five (2012-2016) BS 4-year courses in 37 departments of 3 state universities in Punjab. The collected data were analysed by applying percentage-wise analysis of the results of the students. The study reveals a trend in students' final grades favoring A and B grades over C grades. Over five sessions, final CGPAs increased among university students in three public universities. A longitudinal analysis showed improvements in A and B grades from 2012 to 2016 while decreasing C grades. The study suggests that upholding higher educational standards can restore grade reliability and demonstrate discriminating decision-making abilities. It recommends conducting the study at various colleges in Pakistan and sharing results to enhance analytical skills.

˜The œcritical review of social sciences studiesVol. 2(2)
Bahauddin Zakariya University (PK), The Women University Multan (PK), Institute of Southern Punjab (PK)
Reduced inequalities
Openalex Percentile: Top 78%
Online Learning and Analytics
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