A Study Will Determine Factors Influencing Passing Of A Clas ✓ Solved

A study will determine factors influencing passing of a cla.

A study will determine factors influencing passing of a class. The research question is: What factors affect the academic performance of a tertiary level student? The dependent variable is the final score in the class. Independent variables include personal factors (Gender, Age, Class discipline, Parent’s highest education level, Hours of self-study per week) and school factors (Class duration in weeks, Number of students in class, Maximum class hours, Gender of instructor, Years of instructor experience, Student-centered vs teacher-centered approach, Course outline, Instructor covered course syllabus). Hypotheses: Null hypothesis: there is no statistical relationship between the independent variables and the final score. Alternative hypothesis: there is a relationship (non-directional). Significance level: α = 0.05; if p

These cleaned instructions frame a study that seeks to disentangle how a combination of personal circumstances and instructional conditions influence academic outcomes at the tertiary level. The objective is not merely to describe the data but to identify which factors consistently relate to final performance and to what extent they explain variation in final scores. The variables proposed span a broad spectrum—from demographic and cognitive factors to instructional design and classroom context—reflecting established theoretical perspectives on student success in higher education. Researchers are encouraged to articulate a clear, testable model that can be estimated with appropriate statistical techniques and reported with transparency about measurement, assumptions, and limitations.

Paper For Above Instructions

Introduction. Understanding what drives academic performance in higher education has long been a central concern for scholars, practitioners, and policy makers. A robust body of theory suggests that student success emerges from the dynamic interaction of individual characteristics, educational experiences, and institutional contexts (Astin, 1993; Tinto, 1993). In this study, the central question is: What factors affect the academic performance of a tertiary level student? Framing the investigation around a standard metric—final course score—allows for an interpretable, outcome-focused analysis while acknowledging the multifaceted influences that shape learning. The theoretical lens combines student involvement (Astin, 1993; Kuh et al., 2005), social-cognitive regulation (Bandura, 1997; Zimmerman, 2000), and instructional alignment (Biggs & Tang, 2011). Together, these perspectives help explain why some students achieve higher scores while others struggle, despite comparable prerequisites and curricula (Pascarella & Terenzini, 2005). The analytical aim is to model the final score as a function of a set of personal and school factors, test the null hypothesis of no relationship (Yule, 2012), and discuss implications for evidence-based teaching and learning design (Kuh et al., 2005; Chickering & Gamson, 1987).

Conceptualization and variables. The dependent variable is the final score earned by a student in a given course, treated as an interval/ratio measure that captures performance across assessments. Independent variables are organized into two broad domains. Personal factors include Gender, Age, Class discipline (e.g., Science, Arts, Business, etc.), Parents’ highest education level, and Hours of self-study per week. These variables align with prior research showing that demographic background, disciplinary context, and time-on-task influence academic outcomes (Astin, 1993; Pascarella & Terenzini, 2005). School factors include Class duration in weeks, Number of students in class, Maximum class hours, Gender of instructor, Years of instructor experience, Student-centered versus teacher-centered approach, Course outline (whether provided), and Instructor’s coverage of the syllabus (e.g., percentage of content taught). These classroom and instructional variables reflect well-established determinants of learning opportunities and instructional quality (Biggs & Tang, 2011; Kuh et al., 2005; Chickering & Gamson, 1987).

Theoretical framing and hypotheses. The null hypothesis posits that none of the independent variables has a statistically significant relationship with the final exam score. The alternative hypothesis posits that at least one independent variable is related to the final score. The research hypothesis is non-directional, consistent with the typical educational research approach that seeks to detect any meaningful association rather than assume a particular direction of effect (Yule, 2012). A conventional alpha level of 0.05 is specified for statistical testing. The analysis should consider potential interaction effects (for example, whether the impact of class size differs by discipline) and control for confounding factors such as prior achievement or baseline ability when data permit (Pascarella & Terenzini, 2005).

Measurement and data analysis plan. Operational definitions should be clearly specified. Final score can be operationalized as a course grade or a standardized composite score across assessments. Independent variables should be measured with reliable instruments or validated proxies: gender as a nominal variable; age as a continuous measure; class discipline as a nominal factor; parental education as an ordinal category; hours of self-study per week as a continuous variable; class duration, number of students, and maximum class hours as continuous variables; instructor gender as nominal; instructor experience as continuous; instructional approach as categorical; course outline as binary; syllabus coverage as ordinal (e.g., >50%, ≤50%, not provided). A multiple regression framework is appropriate for estimating the relative contribution of each predictor while controlling for others (Yule, 2012). Additional analyses could include hierarchical linear modeling if data are nested (students within classes) to account for clustering and to partition variance across levels (Astin, 1993; Kuh et al., 2005).

Interpreting results and theoretical integration. If results indicate that personal factors (e.g., gender, age, or parental education) significantly relate to final scores, these findings would be interpreted in light of socio-cultural and educational equity considerations, drawing on Astin (1993) and Pascarella & Terenzini (2005). If school factors (e.g., class size, instructor experience, or student-centered approaches) show robust associations, this would support the view that instructional quality and classroom environments shape learning opportunities beyond student characteristics (Biggs & Tang, 2011; Chickering & Gamson, 1987). Self-regulation and self-efficacy mechanisms offer additional explanatory power when significant relationships are linked to self-study time and instructional engagement (Bandura, 1997; Zimmerman, 2000). Mindset-related processes may moderate these relationships, with more adaptive mindsets (Dweck, 2006) potentially strengthening persistence and performance under challenging courses. In sum, the integrated model should reflect both individual agency and structural supports as central to academic success (Kuh et al., 2005; Pascarella & Terenzini, 2005).

Implications, limitations, and future directions. Practical implications include targeted interventions to optimize study time, refine instructional approaches, and improve syllabus transparency and coverage, all of which can contribute to improved outcomes across diverse student populations (Kuh et al., 2005; Biggs & Tang, 2011). Limitations should be acknowledged, including the cross-sectional nature of many educational data sets, potential measurement error, and unobserved confounders such as prior academic preparation or motivation. Where feasible, panel designs or longitudinal follow-ups could strengthen causal inferences. Future research could explore the moderating role of institutional factors such as academic advising quality and support services, as well as cross-cultural comparisons to understand contextual variability (Astin, 1993; Pascarella & Terenzini, 2005).

Conclusion. The proposed study advances a comprehensive, theoretically informed framework for understanding how personal and school factors interact to influence academic performance at the tertiary level. By integrating concepts of involvement, self-regulation, pedagogy, and alignment, the research aims to illuminate actionable levers for improving student outcomes and closing achievement gaps, while also respecting the complexity of higher education learning environments (Bandura, 1997; Dweck, 2006; Kuh et al., 2005; Tinto, 1993).

References

• Astin, A. W. (1993). What Matters in College? Four Critical Years Revisited. San Francisco, CA: Jossey‑Bass.
• Bandura, A. (1997). Self-efficacy: The Exercise of Control. New York, NY: Freeman.
• Biggs, J., & Tang, C. (2011). Teaching for Quality Learning at University (4th ed.). Maidenhead, UK: Open University Press.
• Dweck, C. S. (2006). Mindset: The New Psychology of Success. New York, NY: Random House.
• Kuh, G. D., Kinzie, J., Schuh, J. H., & Whitt, E. J. (2005). Student Engagement in Higher Education: A Synthesis. ASHE Higher Education Report.
• Pascarella, E. T., & Terenzini, P. T. (2005). How College Affects Students: A Third Decade of Research. San Francisco, CA: Jossey-Bass.
• Tinto, V. (1993). Leaving College: Rethinking the Causes and Cures of Student Dropout. Chicago, IL: University of Chicago Press.
• Yule, G. U. (2012). An Introduction to the Theory of Statistics. Whitefish, MT: Kessinger Publishing.
• Zimmerman, B. J. (2000). Attaining Self-Regulation: A Social Cognitive Perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation: Academic Applications (pp. 13‑39). San Diego, CA: Academic Press.
• Chickering, A. W., & Gamson, Z. F. (1987). Seven Principles for Good Practice in Undergraduate Education. AAHE Bulletin.