The effect of Academic Preference-Placement Mismatches on Motivation and Academic Performance of University Students: A Prospective Cohort Study

The effect of Academic Preference-Placement Mismatches on Motivation and Academic Performance of University Students: A Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of Academic Preference-Placement Mismatches on Motivation and Academic Performance of University Students: A Prospective Cohort Study Ermiyas Mulu Kebede, Robel Tezera Zegeye, Shewatatek Gedamu Wonde, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6751390/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Alignment between students’ academic program preferences and institutional placement decisions is critical for fostering motivation and academic success. However, state-controlled higher education systems often assign students to programs mismatched with their preferences, potentially undermining performance. Objective This study examined the effect of academic program preference-placement mismatches on motivation and academic performance of undergraduate health science students. Method A prospective cohort study (September–December 2019) included 136 students: 61 placed in preferred programs and 75 in non-preferred programs. Data from self-administered questionnaires and registrar records were analyzed. Motivation was measured using the UWES–9S. Structural equation modeling (SEM) assessed UWES–9S validity and relationships between placement, motivation, and performance. Linear regression identified predictors of academic performance. Results Students placed in their preferred academic programs demonstrated significantly higher motivation levels (M = 34.9, SD = 5.69) compared to those in mismatched placements (M = 30.4, SD = 5.88). Academic program preference-placement mismatches had a total negative effect (-0.19) on cumulative GPA, indicating a 0.19 GPA decrease among students placed in non-preferred programs. This effect was partially mediated by motivation. Linear regression revealed that first-year GPA, motivation, enrollment in nursing/midwifery programs, and extremely low pocket money significantly predicted academic performance. Conclusion This study highlights that academic preference-placement mismatches reduce motivation among health science students, directly and indirectly impairing academic performance (mediated by motivation). Institutions should prioritize tailored program alignment, career guidance, and support systems to mitigate these effects. Academic program preference Academic performance University students Learning motivation Figures Figure 1 INTRODUCTION Choice, defined as the ability to freely decide between alternatives, is a multifaceted concept central to human cognition and autonomy. Philosophically debated across disciplines, it intersects with autonomy—self-governance rooted in cultural and ethical contexts (Keller, 2016). Daily life demands prioritization among competing solutions due to resource constraints, with external environments shaping decisions. For students, critical choices include academic program selection, a pivotal decision influencing career trajectories and personal fulfillment (Pendergrass, 1997). Mismatches between student preferences and institutional placements, however, risk demotivation and poor performance (Evans & Boucher, 2015). Self-Determination Theory (SDT) posits that autonomy, competence, and relatedness drive intrinsic motivation and well-being (Ryan & Deci, 2000). External factors undermining these needs hinder motivation, while choice fosters integration of extrinsic goals (Kuhl & Fuhrmann, 1998). Glasser’s Choice Theory similarly links autonomy to fulfilling basic psychological needs (Glasser, 1998). Conversely, excessive choice may lead to ego-depletion, reducing decision-making capacity (Baumeister et al., 1998). Empirical studies highlight autonomy’s role in deep learning and academic achievement (Patall et al., 2008), though cultural contexts modulate its effects (Markus & Kitayama, 1991). Career development theories, such as Holland’s vocational typology and Super’s self-concept theory, emphasize alignment between personal traits and academic paths (Holland, 1996; Super, 1990). Empirical evidence identifies key influencers of academic program choice, including parental guidance, financial prospects, and employability (Kumar, 2016; Zotorvie, 2016). Motivation, the “engine of learning” (Paris & Turner, 1994), encompasses self-efficacy, goal orientation, and intrinsic drive. SDT underscores its role in fostering engagement and persistence (Ryan & Deci, 2000b). Meta-analyses confirm that autonomy-supportive environments enhance outcomes such as GPA and retention (Lazowski & Hulleman, 2016), though cultural and socioeconomic factors (e.g., family income, rural-urban divides) modulate these effects (Verma & Bakshi, 2017; Ferrao & Almeida, 2018). In Ethiopia’s state-regulated higher education system, students often lack autonomy in program selection, with institutional placements frequently misaligned with learners’ academic preferences (Aboye, 2019; Semela, 2010; Teka Mulu, 2019). These preference-placement mismatches, compounded by limited emotional or institutional support, have been linked to diminished academic motivation and performance (Igere, 2017; Getachew & Tekle, 2020). Without adequate counseling services, students in non-preferred programs are likely to face prolonged dissatisfaction, exacerbating declines in engagement and achievement. While global research underscores the adverse effects of unmet academic preferences on educational outcomes, empirical evidence in Ethiopia remains scarce. Furthermore, theoretical debates and inconsistent findings internationally highlight the need for context-specific investigations into the mechanisms driving these disparities. This study addresses critical gaps in the literature by examining the mechanisms through which institutional placement decisions interact with student motivations to shape academic performance. We investigate three interrelated inquiries: ( 1 ) The direct effect of program placement mismatches on academic performance, ( 2 ) Comparative disparities in motivation levels between students assigned to preferred versus non-preferred programs, and ( 3 ) The mediating role of motivation in linking student autonomy to academic achievement. The findings hold significant implications for policymakers and educators, offering evidence-based strategies to realign placement processes with student aspirations, enhance counseling frameworks, and foster equitable outcomes in health science education. METHODS Study Design and Setting A prospective cohort study design was employed to examine whether student preference and institutional placement into academic programs predicted subsequent academic performance among undergraduate health science students at Ambo University’s College of Medicine and Health Sciences (September–December 2019). The cohort design was appropriate as students were grouped based on institutional assignment (exposure) and followed longitudinally to compare semester-grade outcomes between those placed in preferred versus non-preferred programs. Participants The study included all 159 students admitted in 2018 who were assigned to one of six programs in September 2019. Institutional placement criteria prioritized first-year GPA (71.4%), entrance exam scores (28.6%), and gender-based quotas. Of these, 89 students were assigned to programs misaligned with their preferences (exposed group), while 70 received their preferred placement (unexposed group). After mid-semester questionnaire administration, 151 students (84 exposed, 67 unexposed) participated. Final academic records were analyzed for 136 students (75 exposed, 61 unexposed) after excluding 17 lost to follow-up and 6 with incomplete grades. Variables The primary outcome was semester GPA measured four months post-placement. Exposure status reflected institutional assignment to a non-preferred program. Covariates included learning motivation (validated scales), prior academic performance (first-year GPA, entrance scores), sociodemographic characteristics, and socioeconomic factors. Data Sources and Measurement This study utilized primary and secondary quantitative data. Secondary data concerning prior academic achievement (exposure), academic program preferences, and institutional enrollment were extracted from the college registrar system at semester commencement (2021 G.C.). The primary outcome (second-year first-semester GPA) was obtained from the same system post-semester. Mid-semester, primary data on the mediator (academic motivation) were collected via a structured, self-administered questionnaire. Motivation was assessed using two validated instruments: "The University Student Motivation and Satisfaction Questionnaire version 2" (TUSMSQ2) and "The Utrecht Work Engagement Scale for Students" (UWES–9S) (Neill, 2004; Carmona-Halty et al., 2019). Both instruments demonstrated high internal consistency, with Cronbach's alpha coefficients of 0.89 for the TUSMSQ2 and 0.90 for the UWES-9S. Statistical Analysis Data completeness and accuracy were verified. Questionnaire data (Excel) were merged with registrar records using unique student identifiers. Analyses employed StataSE 16. Baseline characteristics between cohorts (preferred-program vs. non-preferred-program enrollment) were compared using independent *t*-tests and χ² tests. Normality was assessed via Shapiro-Wilk tests and histograms. Inter-cohort differences in baseline characteristics, motivation (mediator), and GPA (outcome) were evaluated similarly. Path analysis examined relationships between program choice, motivation, and academic performance. Multiple linear regression identified GPA predictors while controlling confounders. Regression diagnostics included: multicollinearity (VIF, added-variable plots), linearity (augmented component-plus-residual plots), and homoscedasticity (residual vs. predicted plots). Statistical significance was defined as *p*<0.05 (two-tailed). Ethical Considerations Ethical conduct for this research adhered strictly to the World Medical Association's Declaration of Helsinki (Fortaleza, Brazil, October 2013). Prior to commencement, the study protocol received formal ethical approval from Jimma University's Institutional Health Research Ethics Review Committee (IHRERC). Informed consent, documented in writing, was obtained voluntarily from every participant after a comprehensive explanation of the study. We maintained stringent confidentiality safeguards for all participant data and identifiers. Furthermore, during the data collection phase, specific COVID-19 precautions were implemented to protect participants and researchers, including mandatory personal protective equipment and strategies to minimize direct physical contact. RESULTS Participant Characteristics Of the 159 enrolled students, 10.7% (n = 17) were lost to follow-up and 3.8% (n = 6) did not respond by the study’s conclusion. Among the 136 participants analyzed, 51.8% reported medicine as their preferred academic program, while nursing was the least preferred (5.9%). Overall, 73.5% of students were placed in one of their top three program preferences. However, 55.1% (n = 136) were enrolled in programs misaligned with their primary preference, with the highest mismatch observed in nursing (68.0%) and the lowest in public health (36.8%). Differences in unmet preferences across programs were not statistically significant (χ²( 4 ) = 4.23, p = 0.378). Sociodemographic Comparisons As shown in Table 1 , both groups (met vs. unmet preferences) were predominantly male, with a combined mean age of 22.2 years (± SD). Approximately 72.1% of participants reported rural family residences. Parental educational attainment varied: 47.0% of fathers and 61.0% of mothers lacked formal education, while the remainder had nonformal to university-level qualifications. Regarding financial resources, 30.1% of students described their regular pocket money as “extremely low” or “low.” Chi-square and independent t-tests revealed no statistically significant differences in sociodemographic characteristics between the two groups (all p > 0.05). Table 1 Sociodemographic characteristics of students by academic program preference Variable Overall Met Unmet Gender Female 51(37.5) 21(41.2) 30(58.8) Male 85(62.5) 40(47.1) 45(52.9) Age Mean (SD) 22.21(1.8) 22.45(2.1) 22.01(1.5) Family residence Rural 98(72.1) 41(41.8) 57(58.2) Urban 38(27.9) 20(52.6) 18(43.4) Father’s education Cannot read and write 19(14.8) 11(57.9) 8(42.1) Can read& write or primary education 71(55.5) 27(38.0) 44(62.0) High school and above 38(29.7) 19(50.0) 19(50) Mother’s education Cannot read and write 49(37.1) 22(44.9) 27(55.1) Can read& write or primary education 50(37.9) 19(38.0) 31(52.0) High school and above 33(25.0) 17(51.5) 16(48.5) Pocket money Extremely low 18 (13.2) 11(58.3) 7(41.7) Low 23 (16.9) 13(56.5) 10(43.5) Adequate or more than adequate 95(69.9) 37(39.0) 58(61.0) Academic Program Placement, Motivation, and Academic Performance Academic performance and motivation metrics differed between students placed in preferred (met) versus non-preferred (unmet) programs. Entrance exam scores were comparable between groups (met: 416.9 ± 36.5 vs. unmet: 412.0 ± 65.2; t = 0.470, p > 0.05). However, students assigned to preferred programs demonstrated significantly higher first-year GPAs (3.60 ± 0.23 vs. 3.50 ± 0.29; t = 2.236, p < 0.05) and second-year GPAs (3.58 ± 0.34 vs. 3.39 ± 0.38; t = 2.969, p < 0.01). Students placed in their preferred academic programs demonstrated significantly higher motivation levels (M = 34.9, SD = 5.69) compared to those in mismatched placements (M = 30.4, SD = 5.88), as measured by the UWES-9S scale (*t* = 4.527, *p* 0.05) (Table 2 ). Table 2 The two groups academic performance and motivation by academic program placement Variable Group Obs Min Max Mean St.Dev. [95% CI] T Entrance score Met 51 416.9 36.5 [406.6, 427.2] Unmet 57 412.0 65.2 [394.7, 429.3] 0.470 Combined 108 343 525 414.3 53.5 [404.1, 424.5] 1st Year CGPA Met 61 3.60 0.23 [3.55,3.66] * 2.236 Unmet 75 3.50 0.29 [3.44,3.57] Combined 136 2.36 3.87 3.55 0.27 [3.50, 3.59] 2nd Year GPA Met 61 3.58 0.34 [3.49, 3.66] ** 2.969 Unmet 75 3.39 0.38 [3.30, 3.48] Combined 136 2.15 4.00 3.47 0.38 [3.41, 3.54] UWES-9S score Met 61 34.9 5.69 [33.5, 36.4] ** 4.527 Unmet 75 30.4 5.88 [29.1, 31.8] Combined 136 13 46.5 32.5 6.19 [31.4, 33.5] TUSMSQ2 score Met 61 119.7 18.7 [114.9, 124.5] Unmet 75 116.0 25.7 [110.1, 121.9] 0.932 Combined 136 32 171 117.7 22.8 [113.8, 121.5] *p < 0.05; **p < 0.01; ; obs, observations; St.Dev, standard deviation; 95% CI, 95% confidence interval; GPA, grade point average, The Utrecht Work Motivation Scale for Students validated in the study Mediating Role of Learning Motivation Path analysis demonstrated that unmet academic program preferences adversely influenced academic performance through both direct and indirect pathways. Direct placement into non-preferred programs reduced semester GPA scores by 0.11 ( p < 0.05) compared to students placed in preferred programs. Indirect effects emerged via diminished motivation, which partially mediated the relationship between unmet preferences and academic performance. The total effect of unmet preferences—combining direct and mediation pathways—was a GPA reduction of 0.19 (95% CI: [− 0.27, − 0.11]). This indicates that institutional placement misaligned with student preferences, coupled with motivation variability, resulted in a net 0.19 decline in semester GPA relative to preferred placements (Fig. 1 ). Predictors of Academic Performance A multivariable linear regression model identified factors associated with academic performance, measured as semester GPA. First-year GPA, unmet academic program preferences, and UWES-9S motivation scores emerged as significant predictors in preliminary analyses. The final model, adjusted for covariates, explained 54% of the variance in academic performance ( R² = 0.54, adjusted R² = 0.49; F (12,123) = 11.93, p < 0.001) (Table 3 ). First-year GPA was the strongest predictor: each one-point increase corresponded to a 1.02-point rise in subsequent GPA (β = 0.73, t = 9.33, p < 0.01). Higher UWES-9S motivation scores also positively predicted performance, with each additional point associated with a 0.014 GPA increase (β = 0.26, t = 3.07, p 0.05) but became significant when motivation was excluded (β = -0.12, t = -2.11, p < 0.05), suggesting partial mediation by motivation. Perceived inadequacy of pocket money negatively impacted performance: students reporting "extremely low" funds exhibited a 0.19-point GPA reduction compared to peers with adequate resources (β = -0.17, t = -2.40, p < 0.05). Academic program enrollment also influenced outcomes: Nursing and Midwifery students achieved significantly higher GPAs than Medical Laboratory Technology students (β = 0.40 and β = 0.36, respectively; p < 0.01). Entrance exam scores, age, gender, and other covariates showed no significant associations. Table 3 OLS estimates of students’ academic performance one semester after placement Academic Performance Unstandardized β Standardized β t [95% CI.] Academic program preference Unmet -0.076 -0.097 -1.46 [-0.178, 0.027] UWES-9S score 0.014 0.255 3.07 [0.005, 0.023] ** Previous GPA 1.02 0.726 9.33 [0.807, 1.242] ** Entrance Score 0.000 0.026 0.35 [-0.000, 0.000] Age -0.008 -0.034 -0.53 [-0.036, 0.021] Pocket money Extremely low -0.186 -0.167 -2.40 [-0.340, -0.032] * Low -0.018 -0.023 -0.27 [-0.145, 0.111] Academic Program Midwifery 0.360 0.382 4.22 [0.190, 0.527] ** Nursing 0.403 0.402 4.83 [0.238, 0.569] ** Pharmacy 0.007 0.000 0.10 [-0.139, 0.152] Public Health -0.046 -0.049 -0.56 [-0.210, 0.118] Gender Male -0.055 -0.072 -0.86 [-0.181, 0.072] Model Statistics Residual standard error: 0.07 on 123 degrees of freedom Multiple R-Squared: 0.5379 , Adjusted R-squared: 0.4928 F-statistic: 11.93 on 12 and 123 DF, p-value: < 0.0000 Root MSE = .26747 *Significant at 5%, **significant at 1%, GPA, grade point average; 95% Cl, 95% confidence interval , UWES-9S score: The Utrecht Work Motivation Scale for Students validated in the study DISCUSSION This study examined the relationship between institutional academic program placement decisions and academic performance among undergraduate health science students in Ethiopia. Annually, numerous students within the Ethiopian public higher education system are assigned to academic programs incongruent with their preferences. While theoretical frameworks suggest unmet program choice may negatively impact performance, empirical evidence within this context remains limited. Consequently, this research provides empirical insights into the associations between institutional placement, student motivation, and academic performance at a public Ethiopian higher education institution. Analysis revealed a concentration of student preferences towards specific academic programs, necessitating institutional placement into less desired alternatives. This preference clustering may stem from various factors, including external influences on career decisions (Kumar, 2016), varying levels of career development maturity (Yibeltal, 2020), insufficient information dissemination, professional silos, misinformation, and perceptions of employability (Tadesse et al., 2020; Semela, 2010). Established theoretical perspectives posit that student choice significantly influences motivation and subsequent academic performance (Chapman, 1994; Holland, 1996; Leung, 2008). Empirical evidence widely supports the positive effect of choice on motivation. Consistent with this, our findings indicate that academic program placement is associated with both student motivation and academic performance. Path analysis demonstrated that unmet academic program preference was associated with significantly lower UWES–9S scores and lower GPAs. This aligns with studies across diverse settings showing that student preference regarding content, learning methods, and autonomy-supportive teaching enhances motivation and performance (Birdsell et al., 2009; Haider et al., 2015; Kusurkar et al., 2011, 2013; Wang et al., 2015). Notably, unmet choice may be more demotivating than the absence of choice altogether. Anecdotal observations during the investigators' undergraduate studies noted student frustration, stress, diminished learning interest, anxiety, and even attrition following placement into undesired programs. Research suggests the demotivating effect of unmet choice on intrinsic motivation is particularly pronounced when external rewards are absent (Baldwin & Magjuka, 1991; Patall et al., 2008). Furthermore, studies in Ethiopian universities link concerns about future career prospects to depressive symptoms (Worku et al., 2020), which are associated with learning difficulties (Tesera & Wohabie, 2021). When both motivation and program preference were included in the multiple linear regression (MLR) model, the direct association between unmet preference and GPA became statistically non-significant. However, unmet preference remained negatively associated with performance until UWES–9S scores were constrained or removed, suggesting motivation mediates this relationship. This finding is theoretically supported; proponents of Self-Determination Theory (SDT) argue that external factors undermining choice thwart psychological needs for autonomy, competence, and relatedness, thereby hindering motivation and performance (Deci, Koestner, & Ryan, 1999). While our results and others confirm the positive impact of choice on motivation and performance (Birdsell et al., 2009; Haider et al., 2015; Kusurkar et al., 2011, 2013; Wang et al., 2015), the non-significant direct effect after controlling for motivation suggests additional pathways. Schneider et al. propose non-motivational pathways, such as choice reducing cognitive load and enhancing learning retention, thereby improving performance directly (Schneider et al., 2018; Schraw et al., 1998). Placement decisions by the registrar primarily rely on first-year cumulative GPA and program quotas, explaining the significant baseline CGPA difference between cohorts. Importantly, after controlling for program preference and other variables, prior academic achievement remained the strongest predictor of second-year GPA. This aligns with research demonstrating the significant relationship between prior academic achievement and university performance (Anderton et al., 2016; Ferrao & Almeida, 2018; McKenzie & Schweitzer, 2001; Yigermal, 2017). Schneider and Preckel's (2017) meta-analysis further associates high achievement with traits like self-efficacy, conscientiousness, and effective learning strategies. Conversely, entrance exam scores did not predict performance in our study, consistent with some previous findings (Akessa & Dhufera, 2015; Ferrao & Almeida, 2018; Häkkinen, 2004). Research indicates the field of study variably impacts academic achievement (Akessa & Dhufera, 2015; Ferrao & Almeida, 2018). Our results identified academic program as the second strongest predictor, with Nursing and Midwifery students achieving higher GPAs. Potential explanations include variations in course structure, workload, teaching methodologies, assessment practices, and departmental resources. Economic factors also play a role; students' perception of inadequate pocket money predicted poorer academic performance. This finding resonates with studies linking parental income to university achievement in Ethiopia (Akessa & Dhufera, 2015) and Portugal (Ferrao & Almeida, 2018), suggesting economic insecurity impacts access to resources and potentially motivation. This study addresses a significant gap in the Ethiopian higher education literature. The epidemiological design, sample size, and statistical power were appropriate for the research objectives. However, the findings should be interpreted by taking the following limitations warrant consideration. First, potential misclassification exists in grouping all students not enrolled in their first-choice program together, regardless of their preference ranking. Second, reliance on self-reported measures for motivation introduces the possibility of social desirability bias. CONCLUSION This study highlights the effects of academic program preference-placement mismatches on health science students' motivation and academic performance. Key findings demonstrate that unmet academic preferences directly reduced semester GPAs and indirectly affected performance through diminished motivation, as measured by validated scales (UWES-9S). First-year GPA emerged as the strongest predictor of subsequent academic achievement, while entrance exam scores showed no significant association. Economic insecurity—reflected in perceived inadequacy of pocket money—and enrollment in specific programs (e.g., Nursing, Midwifery) further influenced outcomes, underscoring the interplay of psychological, socioeconomic, and curricular factors. These findings advocate for institutional reforms, including deferred program placement post-freshman year to leverage academic performance data, enhanced career counseling to address demotivation, and financial support systems for economically vulnerable students. Future research should explore the psychological toll of unmet preferences and validate assessment tools across diverse educational contexts. By prioritizing student autonomy and equitable resource allocation, institutions can mitigate adverse outcomes and foster academic success in resource-constrained settings. Abbreviations CI: Confidence Interval GPA: Grade Point Average IHRERC: Institutional Health Research Ethics Review Committee OLS: Ordinary Least Squares SD: Standard Deviation SDT: Self-Determination Theory SEM: Structural equation modeling TUSMSQ2: The Turkish University Students Motivation Scale Questionnaire with 27 items UWES–9S: The Utrecht Work Motivation Scale for Students with 9 items Declarations Ethics approval and consent to participate Ethical conduct for this research adhered strictly to the World Medical Association's Declaration of Helsinki (Fortaleza, Brazil, October 2013). Prior to commencement, the study protocol received formal ethical approval from Jimma University's Institutional Health Research Ethics Review Committee (IHRERC). Informed consent, documented in writing, was obtained voluntarily from every participant after a comprehensive explanation of the study. We maintained stringent confidentiality safeguards for all participant data and identifiers. Furthermore, during the data collection phase, specific COVID-19 precautions were implemented to protect participants and researchers, including mandatory personal protective equipment and strategies to minimize direct physical contact. Consent for publication Not applicable, all of the material is owned by the authors and/or no permissions are required. Availability of data and materials The data that support the findings of this study are available from Meraki Consulting, but restrictions apply to the availability of these data and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Meraki Consulting. Competing Interests I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding Data collection for this study was funded by Meraki Consulting PLC. The funder had no role in the study design, analysis, interpretation of results, or manuscript preparation. Authors' contributions E.K. served as the principal investigator of the study. He conceptualized, designed, and executed the research. T.A. and T.Z. contributed in the design of the study. E.K., R.T., S.W.T.A. ,and T.Z. wrote the main manuscript text . All authors critically reviewed and approved the final version of the manuscript. Acknowledgements We extend our sincere gratitude to Meraki Consulting for their generous funding of this study. We are deeply thankful to the respondents who generously contributed their time and shared their insights, without whom this research would not have been possible. We also acknowledge the invaluable support of the registrar staff at Ambo University College of Health Sciences, whose assistance in facilitating data access and institutional coordination was instrumental to this work. References Aboye A. 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Centre for Applied Psychology, University of Canberra; 2004. Paris SG, Turner JC. Situated motivation. In: Pintrich PR, Brown DR, Weinstein CE, editors. Student motivation, cognition, and learning: Essays in honor of Wilbert J. McKeachie. Erlbaum; 1994. pp. 213–37. Patall EA, Cooper H, Robinson JC. The effects of choice on intrinsic motivation and related outcomes: A meta-analysis of research findings. Psychol Bull. 2008;134(2):270–300. https://doi.org/10.1037/0033-2909.134.2.270 . Pendergrass LA. Career decision-making and academic performance in college students. J Coll Student Dev. 1997;38(4):401–8. Ryan RM, Deci EL. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist , *55*(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Schneider M, Preckel F. Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychol Bull. 2017;143(6):565–600. https://doi.org/10.1037/bul0000098 . Schneider M, Preckel F, Schmidt I. The role of choice in cognitive load and academic performance. Educational Psychol Rev. 2018;30(4):931–56. https://doi.org/10.1007/s10648-018-9434-x . Schraw G, Flowerday T, Lehman S. Increasing situational interest in the classroom. Educational Psychol Rev. 1998;10(4):403–27. https://doi.org/10.1023/A:1022877620356 . Semela T. Who is joining physics and why? Factors influencing the choice of physics among Ethiopian university students. Int J Environ Sci Educ. 2010;5(3):319–40. Super DE. A life-span, life-space approach to career development. In: Brown D, Brooks L, editors. Career choice and development. 2nd ed. Jossey-Bass; 1990. pp. 197–261. Tadesse T, Manathunga CE, Gillies RM. Making sense of quality in the context of higher education: A case study of Ethiopia. Qual High Educ. 2020;26(1):37–52. https://doi.org/10.1080/13538322.2020.1732789 . Teka Mulu A. Academic advising in Ethiopian higher education: A qualitative study. Afr J High Educ Stud. 2019;14(3):112–29. Tesera M, Wohabie B. Depression and its effect on academic performance among university students in Ethiopia: A systematic review and meta-analysis. Ethiop J Health Sci. 2021;31(5):1077–88. https://doi.org/10.4314/ejhs.v31i5.19 . Vallerand RJ, Pelletier LG, Blais MR, Briere NM, Senecal C, Vallieres EF. The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educ Psychol Meas. 1992;52(4):1003–17. https://doi.org/10.1177/0013164492052004025 . Verma S, Bakshi A. Socioeconomic status and academic achievement: A meta-analysis. J Educ Psychol. 2017;109(8):1121–46. https://doi.org/10.1037/edu0000203 . Wang C, Kattan MW, Schneider B. Autonomy-supportive teaching and student engagement: A meta-analysis. Educational Psychol. 2015;35(6):722–44. https://doi.org/10.1080/01443410.2014.895801 . Worku H, Abebe L, Tesfaye M. Financial insecurity and mental health among Ethiopian university students. J Adolesc Health. 2020;67(4):512–8. https://doi.org/10.1016/j.jadohealth.2020.03.021 . Yibeltal A. Career maturity and its correlates among preparatory school students in Addis Ababa, Ethiopia. Afr J Career Dev. 2020;2(1):a23. https://doi.org/10.4102/ajcd.v2i1.23 . Yigermal HE. (2017). Predictors of academic achievement of undergraduate students at Addis Ababa University [Unpublished master's thesis]. Addis Ababa University. Zotorvie JST. Career decision-making in Sub-Saharan Africa: A review of empirical studies. Afr J Career Dev. 2016;2(1):1–12. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6751390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486438988,"identity":"11060f56-869c-4e83-9dc5-72020a25a267","order_by":0,"name":"Ermiyas Mulu Kebede","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYJCCAxCKuYHhAwNDAoTDRpQWxgbGGShaCGgDa2HmIUYLv/TpxMM8NXUMujMS26Rt/tzJ0512+AHDh7LDDPzyDVi1SPblbjjMc+wwg9kNoJbctmfFZrfTDBhnnDvMINmG3RaDM7xALWwHoFoaDiduu53DwMzbdpjB4Bh2LfZgLf/qIFos/kC1/AVqscehxYAHqIW3jRmihYENqoURZAsO70sAbTk4t+8wj9mZh82WvW3PgFrSDA72nEvnkTiWgD3Eeng3f3jzrU7O7HjywRs//twBakl++OBHmbUcf/MB7NYAARMwOnig7ANwkgeHajBg/IFg4zZ4FIyCUTAKRi4AAJV8Zl4HTYzOAAAAAElFTkSuQmCC","orcid":"","institution":"Saint Paul's Hospital Millennium Medical College","correspondingAuthor":true,"prefix":"","firstName":"Ermiyas","middleName":"Mulu","lastName":"Kebede","suffix":""},{"id":486438989,"identity":"792302ef-cd62-4398-8336-66aabb97c3bb","order_by":1,"name":"Robel Tezera Zegeye","email":"","orcid":"","institution":"Addis Ababa University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Robel","middleName":"Tezera","lastName":"Zegeye","suffix":""},{"id":486438990,"identity":"5de5d8a2-f01e-4d7c-9f46-bbedfa4dc46f","order_by":2,"name":"Shewatatek Gedamu Wonde","email":"","orcid":"","institution":"Jimma University Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Shewatatek","middleName":"Gedamu","lastName":"Wonde","suffix":""},{"id":486438991,"identity":"eef96ecb-d6fe-40b9-8c7b-f3f623566b8b","order_by":3,"name":"Tesfamichael Alaro Agago","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Tesfamichael","middleName":"Alaro","lastName":"Agago","suffix":""},{"id":486438992,"identity":"4e6114d1-47a0-4863-89e7-68d41198348b","order_by":4,"name":"Tadiwos Hailu Zewdie","email":"","orcid":"","institution":"Arba Minch University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tadiwos","middleName":"Hailu","lastName":"Zewdie","suffix":""}],"badges":[],"createdAt":"2025-05-26 13:39:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6751390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6751390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86957846,"identity":"079c8626-f5a7-441b-bf1a-8d2015e29fbb","added_by":"auto","created_at":"2025-07-17 15:29:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86663,"visible":true,"origin":"","legend":"\u003cp\u003epath diagram of academic program placement, motivation to learn, and academic performance\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6751390/v1/b6ecb4b634aabaf4774ef3f7.png"},{"id":86959333,"identity":"446ffc94-6c77-4a03-9955-53651970e4d8","added_by":"auto","created_at":"2025-07-17 15:45:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1008934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6751390/v1/5443f62a-9dce-4893-b530-6921476d3447.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of Academic Preference-Placement Mismatches on Motivation and Academic Performance of University Students: A Prospective Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChoice, defined as the ability to freely decide between alternatives, is a multifaceted concept central to human cognition and autonomy. Philosophically debated across disciplines, it intersects with autonomy\u0026mdash;self-governance rooted in cultural and ethical contexts (Keller, 2016). Daily life demands prioritization among competing solutions due to resource constraints, with external environments shaping decisions. For students, critical choices include academic program selection, a pivotal decision influencing career trajectories and personal fulfillment (Pendergrass, 1997). Mismatches between student preferences and institutional placements, however, risk demotivation and poor performance (Evans \u0026amp; Boucher, 2015).\u003c/p\u003e\u003cp\u003eSelf-Determination Theory (SDT) posits that autonomy, competence, and relatedness drive intrinsic motivation and well-being (Ryan \u0026amp; Deci, 2000). External factors undermining these needs hinder motivation, while choice fosters integration of extrinsic goals (Kuhl \u0026amp; Fuhrmann, 1998). Glasser\u0026rsquo;s Choice Theory similarly links autonomy to fulfilling basic psychological needs (Glasser, 1998). Conversely, excessive choice may lead to ego-depletion, reducing decision-making capacity (Baumeister et al., 1998). Empirical studies highlight autonomy\u0026rsquo;s role in deep learning and academic achievement (Patall et al., 2008), though cultural contexts modulate its effects (Markus \u0026amp; Kitayama, 1991).\u003c/p\u003e\u003cp\u003eCareer development theories, such as Holland\u0026rsquo;s vocational typology and Super\u0026rsquo;s self-concept theory, emphasize alignment between personal traits and academic paths (Holland, 1996; Super, 1990). Empirical evidence identifies key influencers of academic program choice, including parental guidance, financial prospects, and employability (Kumar, 2016; Zotorvie, 2016). Motivation, the \u0026ldquo;engine of learning\u0026rdquo; (Paris \u0026amp; Turner, 1994), encompasses self-efficacy, goal orientation, and intrinsic drive. SDT underscores its role in fostering engagement and persistence (Ryan \u0026amp; Deci, 2000b). Meta-analyses confirm that autonomy-supportive environments enhance outcomes such as GPA and retention (Lazowski \u0026amp; Hulleman, 2016), though cultural and socioeconomic factors (e.g., family income, rural-urban divides) modulate these effects (Verma \u0026amp; Bakshi, 2017; Ferrao \u0026amp; Almeida, 2018).\u003c/p\u003e\u003cp\u003eIn Ethiopia\u0026rsquo;s state-regulated higher education system, students often lack autonomy in program selection, with institutional placements frequently misaligned with learners\u0026rsquo; academic preferences (Aboye, 2019; Semela, 2010; Teka Mulu, 2019). These preference-placement mismatches, compounded by limited emotional or institutional support, have been linked to diminished academic motivation and performance (Igere, 2017; Getachew \u0026amp; Tekle, 2020). Without adequate counseling services, students in non-preferred programs are likely to face prolonged dissatisfaction, exacerbating declines in engagement and achievement. While global research underscores the adverse effects of unmet academic preferences on educational outcomes, empirical evidence in Ethiopia remains scarce. Furthermore, theoretical debates and inconsistent findings internationally highlight the need for context-specific investigations into the mechanisms driving these disparities.\u003c/p\u003e\u003cp\u003eThis study addresses critical gaps in the literature by examining the mechanisms through which institutional placement decisions interact with student motivations to shape academic performance. We investigate three interrelated inquiries: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The direct effect of program placement mismatches on academic performance, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Comparative disparities in motivation levels between students assigned to preferred versus non-preferred programs, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The mediating role of motivation in linking student autonomy to academic achievement. The findings hold significant implications for policymakers and educators, offering evidence-based strategies to realign placement processes with student aspirations, enhance counseling frameworks, and foster equitable outcomes in health science education.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eA prospective cohort study design was employed to examine whether student preference and institutional placement into academic programs predicted subsequent academic performance among undergraduate health science students at Ambo University\u0026rsquo;s College of Medicine and Health Sciences (September\u0026ndash;December 2019). The cohort design was appropriate as students were grouped based on institutional assignment (exposure) and followed longitudinally to compare semester-grade outcomes between those placed in preferred versus non-preferred programs.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study included all 159 students admitted in 2018 who were assigned to one of six programs in September 2019. Institutional placement criteria prioritized first-year GPA (71.4%), entrance exam scores (28.6%), and gender-based quotas. Of these, 89 students were assigned to programs misaligned with their preferences (exposed group), while 70 received their preferred placement (unexposed group). After mid-semester questionnaire administration, 151 students (84 exposed, 67 unexposed) participated. Final academic records were analyzed for 136 students (75 exposed, 61 unexposed) after excluding 17 lost to follow-up and 6 with incomplete grades.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was semester GPA measured four months post-placement. Exposure status reflected institutional assignment to a non-preferred program. Covariates included learning motivation (validated scales), prior academic performance (first-year GPA, entrance scores), sociodemographic characteristics, and socioeconomic factors.\u003c/p\u003e\n\u003ch3\u003eData Sources and Measurement\u003c/h3\u003e\n\u003cp\u003eThis study utilized primary and secondary quantitative data. Secondary data concerning prior academic achievement (exposure), academic program preferences, and institutional enrollment were extracted from the college registrar system at semester commencement (2021 G.C.). The primary outcome (second-year first-semester GPA) was obtained from the same system post-semester. Mid-semester, primary data on the mediator (academic motivation) were collected via a structured, self-administered questionnaire. Motivation was assessed using two validated instruments: \"The University Student Motivation and Satisfaction Questionnaire version 2\" (TUSMSQ2) and \"The Utrecht Work Engagement Scale for Students\" (UWES\u0026ndash;9S) (Neill, 2004; Carmona-Halty et al., 2019). Both instruments demonstrated high internal consistency, with Cronbach's alpha coefficients of 0.89 for the TUSMSQ2 and 0.90 for the UWES-9S.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData completeness and accuracy were verified. Questionnaire data (Excel) were merged with registrar records using unique student identifiers. Analyses employed StataSE 16. Baseline characteristics between cohorts (preferred-program vs. non-preferred-program enrollment) were compared using independent *t*-tests and χ\u0026sup2; tests. Normality was assessed via Shapiro-Wilk tests and histograms. Inter-cohort differences in baseline characteristics, motivation (mediator), and GPA (outcome) were evaluated similarly. Path analysis examined relationships between program choice, motivation, and academic performance. Multiple linear regression identified GPA predictors while controlling confounders. Regression diagnostics included: multicollinearity (VIF, added-variable plots), linearity (augmented component-plus-residual plots), and homoscedasticity (residual vs. predicted plots). Statistical significance was defined as *p*\u0026lt;0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e Ethical conduct for this research adhered strictly to the World Medical Association's Declaration of Helsinki (Fortaleza, Brazil, October 2013). Prior to commencement, the study protocol received formal ethical approval from Jimma University's Institutional Health Research Ethics Review Committee (IHRERC). Informed consent, documented in writing, was obtained voluntarily from every participant after a comprehensive explanation of the study. We maintained stringent confidentiality safeguards for all participant data and identifiers. Furthermore, during the data collection phase, specific COVID-19 precautions were implemented to protect participants and researchers, including mandatory personal protective equipment and strategies to minimize direct physical contact.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eOf the 159 enrolled students, 10.7% (n\u0026thinsp;=\u0026thinsp;17) were lost to follow-up and 3.8% (n\u0026thinsp;=\u0026thinsp;6) did not respond by the study\u0026rsquo;s conclusion. Among the 136 participants analyzed, 51.8% reported medicine as their preferred academic program, while nursing was the least preferred (5.9%). Overall, 73.5% of students were placed in one of their top three program preferences. However, 55.1% (n\u0026thinsp;=\u0026thinsp;136) were enrolled in programs misaligned with their primary preference, with the highest mismatch observed in nursing (68.0%) and the lowest in public health (36.8%). Differences in unmet preferences across programs were not statistically significant (χ\u0026sup2;(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;4.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.378).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic Comparisons\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, both groups (met vs. unmet preferences) were predominantly male, with a combined mean age of 22.2 years (\u0026plusmn;\u0026thinsp;SD). Approximately 72.1% of participants reported rural family residences. Parental educational attainment varied: 47.0% of fathers and 61.0% of mothers lacked formal education, while the remainder had nonformal to university-level qualifications. Regarding financial resources, 30.1% of students described their regular pocket money as \u0026ldquo;extremely low\u0026rdquo; or \u0026ldquo;low.\u0026rdquo; Chi-square and independent t-tests revealed no statistically significant differences in sociodemographic characteristics between the two groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of students by academic program preference\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21(41.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30(58.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40(47.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45(52.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.21(1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.45(2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.01(1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98(72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41(41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57(58.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUrban\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38(27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20(52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18(43.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFather\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCannot read and write\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11(57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8(42.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCan read\u0026amp; write or primary education\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71(55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27(38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44(62.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh school and above\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38(29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19(50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMother\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCannot read and write\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49(37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22(44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27(55.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCan read\u0026amp; write or primary education\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50(37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19(38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31(52.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh school and above\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(48.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePocket money\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eExtremely low\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7(41.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13(56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10(43.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdequate or more than adequate\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95(69.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37(39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58(61.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAcademic Program Placement, Motivation, and Academic Performance\u003c/h2\u003e\u003cp\u003eAcademic performance and motivation metrics differed between students placed in preferred (met) versus non-preferred (unmet) programs. Entrance exam scores were comparable between groups (met: 416.9\u0026thinsp;\u0026plusmn;\u0026thinsp;36.5 vs. unmet: 412.0\u0026thinsp;\u0026plusmn;\u0026thinsp;65.2; t\u0026thinsp;=\u0026thinsp;0.470, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, students assigned to preferred programs demonstrated significantly higher first-year GPAs (3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 vs. 3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29; t\u0026thinsp;=\u0026thinsp;2.236, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and second-year GPAs (3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 vs. 3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38; t\u0026thinsp;=\u0026thinsp;2.969, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eStudents placed in their preferred academic programs demonstrated significantly higher motivation levels (M\u0026thinsp;=\u0026thinsp;34.9, SD\u0026thinsp;=\u0026thinsp;5.69) compared to those in mismatched placements (M\u0026thinsp;=\u0026thinsp;30.4, SD\u0026thinsp;=\u0026thinsp;5.88), as measured by the UWES-9S scale (*t* = 4.527, *p* \u0026lt; .01). In contrast, the TUSMSQ2 motivation scores showed no significant group differences (met: 119.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7 vs. unmet: 116.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.7; t\u0026thinsp;=\u0026thinsp;0.932, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe two groups academic performance and motivation by academic program placement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSt.Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntrance score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e416.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[406.6, 427.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e412.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e65.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[394.7, 429.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e414.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[404.1, 424.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st Year CGPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.55,3.66] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.44,3.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.50, 3.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2nd Year GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.49, 3.66] **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.30, 3.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[3.41, 3.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUWES-9S score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[33.5, 36.4] **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[29.1, 31.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[31.4, 33.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTUSMSQ2 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e119.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[114.9, 124.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e116.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[110.1, 121.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e117.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e[113.8, 121.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ; obs, observations;\u003c/em\u003e St.Dev, standard deviation; \u003cem\u003e95% CI, 95% confidence interval; GPA, grade point average, The Utrecht Work Motivation Scale for Students validated in the study\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMediating Role of Learning Motivation\u003c/h2\u003e\u003cp\u003ePath analysis demonstrated that unmet academic program preferences adversely influenced academic performance through both direct and indirect pathways. Direct placement into non-preferred programs reduced semester GPA scores by 0.11 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to students placed in preferred programs. Indirect effects emerged via diminished motivation, which partially mediated the relationship between unmet preferences and academic performance. The total effect of unmet preferences\u0026mdash;combining direct and mediation pathways\u0026mdash;was a GPA reduction of 0.19 (95% CI: [\u0026minus;\u0026thinsp;0.27, \u0026minus;\u0026thinsp;0.11]). This indicates that institutional placement misaligned with student preferences, coupled with motivation variability, resulted in a net 0.19 decline in semester GPA relative to preferred placements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of Academic Performance\u003c/h2\u003e\u003cp\u003eA multivariable linear regression model identified factors associated with academic performance, measured as semester GPA. First-year GPA, unmet academic program preferences, and UWES-9S motivation scores emerged as significant predictors in preliminary analyses. The final model, adjusted for covariates, explained 54% of the variance in academic performance (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.54, adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.49; \u003cem\u003eF\u003c/em\u003e (12,123)\u0026thinsp;=\u0026thinsp;11.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirst-year GPA was the strongest predictor: each one-point increase corresponded to a 1.02-point rise in subsequent GPA (β\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Higher UWES-9S motivation scores also positively predicted performance, with each additional point associated with a 0.014 GPA increase (β\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, unmet academic preferences showed no direct association with GPA in the adjusted model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) but became significant when motivation was excluded (β = -0.12, \u003cem\u003et\u003c/em\u003e = -2.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting partial mediation by motivation.\u003c/p\u003e\u003cp\u003ePerceived inadequacy of pocket money negatively impacted performance: students reporting \"extremely low\" funds exhibited a 0.19-point GPA reduction compared to peers with adequate resources (β = -0.17, \u003cem\u003et\u003c/em\u003e = -2.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Academic program enrollment also influenced outcomes: Nursing and Midwifery students achieved significantly higher GPAs than Medical Laboratory Technology students (β\u0026thinsp;=\u0026thinsp;0.40 and β\u0026thinsp;=\u0026thinsp;0.36, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Entrance exam scores, age, gender, and other covariates showed no significant associations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOLS estimates of students\u0026rsquo; academic performance one semester after placement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic Performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnstandardized β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandardized β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[95% CI.]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic program preference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.178, 0.027]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUWES-9S score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.005, 0.023] **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.807, 1.242] **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntrance Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.000, 0.000]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.036, 0.021]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePocket money\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremely low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.340, -0.032] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.145, 0.111]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic Program\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMidwifery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.190, 0.527] **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.238, 0.569] **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharmacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.139, 0.152]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.210, 0.118]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.181, 0.072]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eResidual standard error: 0.07 on 123 degrees of freedom\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMultiple R-Squared: 0.5379\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared: 0.4928\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eF-statistic: 11.93 on 12 and 123 DF, p-value: \u0026lt; 0.0000\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRoot MSE\u0026thinsp;=\u0026thinsp;.26747\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Significant at 5%, **significant at 1%, GPA, grade point average; 95% Cl, 95% confidence interval\u003c/em\u003e, \u003cem\u003eUWES-9S score: The Utrecht Work Motivation Scale for Students validated in the study\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the relationship between institutional academic program placement decisions and academic performance among undergraduate health science students in Ethiopia. Annually, numerous students within the Ethiopian public higher education system are assigned to academic programs incongruent with their preferences. While theoretical frameworks suggest unmet program choice may negatively impact performance, empirical evidence within this context remains limited. Consequently, this research provides empirical insights into the associations between institutional placement, student motivation, and academic performance at a public Ethiopian higher education institution.\u003c/p\u003e\u003cp\u003eAnalysis revealed a concentration of student preferences towards specific academic programs, necessitating institutional placement into less desired alternatives. This preference clustering may stem from various factors, including external influences on career decisions (Kumar, 2016), varying levels of career development maturity (Yibeltal, 2020), insufficient information dissemination, professional silos, misinformation, and perceptions of employability (Tadesse et al., 2020; Semela, 2010).\u003c/p\u003e\u003cp\u003eEstablished theoretical perspectives posit that student choice significantly influences motivation and subsequent academic performance (Chapman, 1994; Holland, 1996; Leung, 2008). Empirical evidence widely supports the positive effect of choice on motivation. Consistent with this, our findings indicate that academic program placement is associated with both student motivation and academic performance. Path analysis demonstrated that unmet academic program preference was associated with significantly lower UWES\u0026ndash;9S scores and lower GPAs. This aligns with studies across diverse settings showing that student preference regarding content, learning methods, and autonomy-supportive teaching enhances motivation and performance (Birdsell et al., 2009; Haider et al., 2015; Kusurkar et al., 2011, 2013; Wang et al., 2015).\u003c/p\u003e\u003cp\u003eNotably, unmet choice may be more demotivating than the absence of choice altogether. Anecdotal observations during the investigators' undergraduate studies noted student frustration, stress, diminished learning interest, anxiety, and even attrition following placement into undesired programs. Research suggests the demotivating effect of unmet choice on intrinsic motivation is particularly pronounced when external rewards are absent (Baldwin \u0026amp; Magjuka, 1991; Patall et al., 2008). Furthermore, studies in Ethiopian universities link concerns about future career prospects to depressive symptoms (Worku et al., 2020), which are associated with learning difficulties (Tesera \u0026amp; Wohabie, 2021).\u003c/p\u003e\u003cp\u003eWhen both motivation and program preference were included in the multiple linear regression (MLR) model, the direct association between unmet preference and GPA became statistically non-significant. However, unmet preference remained negatively associated with performance until UWES\u0026ndash;9S scores were constrained or removed, suggesting motivation mediates this relationship. This finding is theoretically supported; proponents of Self-Determination Theory (SDT) argue that external factors undermining choice thwart psychological needs for autonomy, competence, and relatedness, thereby hindering motivation and performance (Deci, Koestner, \u0026amp; Ryan, 1999). While our results and others confirm the positive impact of choice on motivation and performance (Birdsell et al., 2009; Haider et al., 2015; Kusurkar et al., 2011, 2013; Wang et al., 2015), the non-significant direct effect after controlling for motivation suggests additional pathways. Schneider et al. propose non-motivational pathways, such as choice reducing cognitive load and enhancing learning retention, thereby improving performance directly (Schneider et al., 2018; Schraw et al., 1998).\u003c/p\u003e\u003cp\u003ePlacement decisions by the registrar primarily rely on first-year cumulative GPA and program quotas, explaining the significant baseline CGPA difference between cohorts. Importantly, after controlling for program preference and other variables, prior academic achievement remained the strongest predictor of second-year GPA. This aligns with research demonstrating the significant relationship between prior academic achievement and university performance (Anderton et al., 2016; Ferrao \u0026amp; Almeida, 2018; McKenzie \u0026amp; Schweitzer, 2001; Yigermal, 2017). Schneider and Preckel's (2017) meta-analysis further associates high achievement with traits like self-efficacy, conscientiousness, and effective learning strategies. Conversely, entrance exam scores did not predict performance in our study, consistent with some previous findings (Akessa \u0026amp; Dhufera, 2015; Ferrao \u0026amp; Almeida, 2018; H\u0026auml;kkinen, 2004).\u003c/p\u003e\u003cp\u003eResearch indicates the field of study variably impacts academic achievement (Akessa \u0026amp; Dhufera, 2015; Ferrao \u0026amp; Almeida, 2018). Our results identified academic program as the second strongest predictor, with Nursing and Midwifery students achieving higher GPAs. Potential explanations include variations in course structure, workload, teaching methodologies, assessment practices, and departmental resources. Economic factors also play a role; students' perception of inadequate pocket money predicted poorer academic performance. This finding resonates with studies linking parental income to university achievement in Ethiopia (Akessa \u0026amp; Dhufera, 2015) and Portugal (Ferrao \u0026amp; Almeida, 2018), suggesting economic insecurity impacts access to resources and potentially motivation.\u003c/p\u003e\u003cp\u003eThis study addresses a significant gap in the Ethiopian higher education literature. The epidemiological design, sample size, and statistical power were appropriate for the research objectives. However, the findings should be interpreted by taking the following limitations warrant consideration. First, potential misclassification exists in grouping all students not enrolled in their first-choice program together, regardless of their preference ranking. Second, reliance on self-reported measures for motivation introduces the possibility of social desirability bias.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study highlights the effects of academic program preference-placement mismatches on health science students' motivation and academic performance. Key findings demonstrate that unmet academic preferences directly reduced semester GPAs and indirectly affected performance through diminished motivation, as measured by validated scales (UWES-9S). First-year GPA emerged as the strongest predictor of subsequent academic achievement, while entrance exam scores showed no significant association. Economic insecurity\u0026mdash;reflected in perceived inadequacy of pocket money\u0026mdash;and enrollment in specific programs (e.g., Nursing, Midwifery) further influenced outcomes, underscoring the interplay of psychological, socioeconomic, and curricular factors.\u003c/p\u003e\u003cp\u003eThese findings advocate for institutional reforms, including deferred program placement post-freshman year to leverage academic performance data, enhanced career counseling to address demotivation, and financial support systems for economically vulnerable students. Future research should explore the psychological toll of unmet preferences and validate assessment tools across diverse educational contexts. By prioritizing student autonomy and equitable resource allocation, institutions can mitigate adverse outcomes and foster academic success in resource-constrained settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eCI: Confidence Interval\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGPA: Grade Point Average\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIHRERC: Institutional Health Research Ethics Review Committee\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOLS: Ordinary Least Squares\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSD: Standard Deviation\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSDT: Self-Determination Theory\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSEM: Structural equation modeling\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTUSMSQ2: The Turkish University Students Motivation Scale Questionnaire with 27 items\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUWES\u0026ndash;9S: The Utrecht Work Motivation Scale for Students with 9 items\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical conduct for this research adhered strictly to the World Medical Association\u0026apos;s Declaration of Helsinki (Fortaleza, Brazil, October 2013). Prior to commencement, the study protocol received formal ethical approval from Jimma University\u0026apos;s Institutional Health Research Ethics Review Committee (IHRERC). Informed consent, documented in writing, was obtained voluntarily from every participant after a comprehensive explanation of the study. We maintained stringent confidentiality safeguards for all participant data and identifiers. Furthermore, during the data collection phase, specific COVID-19 precautions were implemented to protect participants and researchers, including mandatory personal protective equipment and strategies to minimize direct physical contact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, all of the material is owned by the authors and/or no permissions are required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Meraki Consulting, but restrictions apply to the availability of these data and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Meraki Consulting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection for this study was funded by Meraki Consulting PLC. The funder had no role in the study design, analysis, interpretation of results, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.K. served as the principal investigator of the study. He conceptualized, designed, and executed the research. T.A. and T.Z. contributed in the design of the study. E.K., R.T., S.W.T.A. ,and T.Z. wrote the main manuscript text . All authors critically reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to Meraki Consulting for their generous funding of this study. We are deeply thankful to the respondents who generously contributed their time and shared their insights, without whom this research would not have been possible. We also acknowledge the invaluable support of the registrar staff at Ambo University College of Health Sciences, whose assistance in facilitating data access and institutional coordination was instrumental to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAboye A. State-controlled higher education and student autonomy in Ethiopia. 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Afr J Career Dev. 2016;2(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Academic program preference, Academic performance, University students, Learning motivation","lastPublishedDoi":"10.21203/rs.3.rs-6751390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6751390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAlignment between students\u0026rsquo; academic program preferences and institutional placement decisions is critical for fostering motivation and academic success. However, state-controlled higher education systems often assign students to programs mismatched with their preferences, potentially undermining performance.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study examined the effect of academic program preference-placement mismatches on motivation and academic performance of undergraduate health science students.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eA prospective cohort study (September\u0026ndash;December 2019) included 136 students: 61 placed in preferred programs and 75 in non-preferred programs. Data from self-administered questionnaires and registrar records were analyzed. Motivation was measured using the UWES\u0026ndash;9S. Structural equation modeling (SEM) assessed UWES\u0026ndash;9S validity and relationships between placement, motivation, and performance. Linear regression identified predictors of academic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eStudents placed in their preferred academic programs demonstrated significantly higher motivation levels (M\u0026thinsp;=\u0026thinsp;34.9, SD\u0026thinsp;=\u0026thinsp;5.69) compared to those in mismatched placements (M\u0026thinsp;=\u0026thinsp;30.4, SD\u0026thinsp;=\u0026thinsp;5.88). Academic program preference-placement mismatches had a total negative effect (-0.19) on cumulative GPA, indicating a 0.19 GPA decrease among students placed in non-preferred programs. This effect was partially mediated by motivation. Linear regression revealed that first-year GPA, motivation, enrollment in nursing/midwifery programs, and extremely low pocket money significantly predicted academic performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights that academic preference-placement mismatches reduce motivation among health science students, directly and indirectly impairing academic performance (mediated by motivation). Institutions should prioritize tailored program alignment, career guidance, and support systems to mitigate these effects.\u003c/p\u003e","manuscriptTitle":"The effect of Academic Preference-Placement Mismatches on Motivation and Academic Performance of University Students: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 15:29:42","doi":"10.21203/rs.3.rs-6751390/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"255974027136977881530586613381379768541","date":"2025-07-15T13:06:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T10:43:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T08:05:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-20T05:01:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T09:14:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-06-19T09:11:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"832546e4-3e6e-4774-bbed-8b0add3dd8d7","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-17T15:29:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 15:29:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6751390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6751390","identity":"rs-6751390","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}