Assessing Student Performance in Tutored vs Non-Tutored Modules: A Case Study in a South African University

Research Paper Recommended citation: Bakama, E. M., & Simpson, Z. (2025). Assessing Student Performance in Tutored vs Non-Tutored Modules: A Case Study in a South African University. In Kangaslampi, R., Langie, G., Järvinen, H.-M., & Nagy, B. (Eds.), SEFI 53rd Annual Conference. European Society for Engineering Education (SEFI), Tampere, Finland. DOI: 10.5281/zenodo.17631750. This Conference Paper is brought to you for open access by the 53rd Annual Conference of the European Society for Engineering Education (SEFI) at Tampere University in Tampere, Finland. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. ASSESSING STUDENT PERFORMANCE IN TUTORED VS NONTUTORED MODULES: A CASE STUDY IN A SOUTH AFRICAN UNIVERSITY EM Bakamaa, 1 , Z Simpsonb, 2 a University of Johannesburg, Johannesburg, South Africa, ORCID 0000-0003-44027566 b University of Johannesburg, Johannesburg, South Africa, ORCID 0000-0002-12633812 Conference Key Areas: Improving higher engineering education through researching engineering education; Building the capacity and strengthening the educational competences of engineering educators. Keywords: Tutoring, Engineering Education, Student Performance Analysis, Academic Support Strategies ABSTRACT This study examines the impact of tutoring on student performance by comparing pass rates in tutored and non-tutored modules within the Faculty of Engineering and the Built Environment at University X from 2021 to 2023. Using a quantitative research approach, data on pass rates, enrolment numbers, and tutorial sessions were collected and analysed through descriptive statistics, with findings presented using tables and graphs. Results consistently indicate that modules with tutors outperform those without, with pass rate gaps ranging from 18 to 23 percentage points over the study period. Notably, departments such as Mechanical Engineering Science (MES), Urban and Regional Planning (URP), and Metallurgy (METAL) exhibited the most significant improvements in tutored modules. While pass rates in non-tutored modules showed slight improvement over time, they remained consistently lower than those with tutor support, underscoring the effectiveness of 1 EM Bakama [email protected] 2 Z Simpson [email protected] tutoring interventions. Despite these findings, the study acknowledges that multiple factors, including module complexity, lecturer consultation, and student engagement influence pass rates. Although the data suggest a strong correlation between tutoring and improved student performance, further research is recommended to explore individual module performance, student attendance, and teaching methodologies to understand the interplay of additional variables better. These findings emphasise the need for continued investment in tutoring programs, particularly for at-risk students, while also considering complementary academic support strategies to further enhance student success in engineering education. 1 INTRODUCTION Tutoring in education refers to the practice of providing individualized academic support to students, typically facilitated by a trained tutor. This process aims to enhance students' comprehension and performance in specific subject areas where additional assistance is required. While various tutoring models exist, one-on-one and group tutoring sessions are among the most implemented approaches (National Student Support Accelerator, 2023). These sessions can be conducted either in person or online, with in-person tutoring often being more effective due to higher student engagement and attendance rates. According to Truckee Meadows Community College (n.d.), the effectiveness of tutoring is influenced by several factors, including consistency in tutor-student interactions, adequate tutor training, and the frequency of sessions. In-person tutoring offers advantages such as stronger tutor-student relationships and a better understanding of the institutional environment. Conversely, online tutoring expands accessibility by allowing tutors to participate from diverse geographic locations, potentially increasing the pool of available tutors (National Student Support Accelerator, 2023). Although theoretical perspectives suggest that tutoring enhances student performance (McFarlane, 2016), its actual effectiveness in improving academic performance remains an open question. Since 2017, the Faculty of Engineering and the Built Environment at University X has centrally managed the allocation and appointment of tutors through the Dean’s Office. This system aims to distribute tutor funding based on identified academic needs across various modules. Initially, tutor appointments were primarily allocated to modules with historically low pass rates (below 85%). However, over the past five years, the faculty has expanded tutor support across multiple modules to enhance student learning through structured consultations and teaching assistance. Despite these efforts, financial constraints pose a significant challenge, limiting the faculty’s ability to provide tutors for all requested modules. As a result, the allocation of tutoring resources must be justified by clear, evidence-based assessments of its impact on student performance. While tutoring is generally perceived as beneficial, there remains a need for empirical investigation into its actual effectiveness in improving pass rates. This study seeks to address this gap by comparing student success rates in tutored and non-tutored modules, providing critical insights to inform future resource allocation decisions within the faculty. To achieve this aim, the following objectives have been identified: (1) to compare student pass rates in tutored and non-tutored modules within the Faculty of Engineering and the Built Environment at University X, and (2) to analyse the extent to which tutoring influences student academic performance and engagement in engineering education. The study adopts a case study approach, focusing on the Faculty of Engineering and the Built Environment at University X, South Africa’s largest engineering faculty by enrolment. While the findings may offer insights applicable to similar institutions, the study primarily evaluates student pass rates as a measure of tutoring effectiveness. However, other factors, such as student attendance, teaching methodologies, and engagement, may also influence performance. Despite these limitations, pass rates remain a key indicator of student success, and this study contributes to the ongoing discourse on the role of tutoring in engineering education. 2 BRIEF LITERATURE REVIEW South Africa’s participation rate in tertiary education remains relatively low compared to other middle-income nations. Khuluvhe and Ganyaupfu (2023) analysed South Africa’s Gross Enrolment Ratio (GER) in tertiary education and found that in 2019 and 2020, only 23.9% and 27.2% of the population, respectively, had access to higher education. This is significantly lower than the participation rates observed in comparable countries such as China, Colombia, Algeria, and Malaysia (see Fig. 1). Beyond limited access to higher education, the South African higher education sector is also characterised by poor student pass rates, particularly among first-year students (Badat, 2010; Coughlan, 2006). Research suggests that tutoring can be an effective intervention for improving student performance. McKay (2016) conducted a study analysing three student groups based on tutorial attendance: Group One (students who attended ten or more tutorials), Group Two (students with irregular tutorial attendance), and Group Three (students who did not attend tutorials at all). The results indicated a significant performance difference across the groups. Students in Group One experienced an average increase of nearly 20% in their test scores from Test 1 to Test 2. Conversely, students in Group Two saw their mean scores decrease by 8%, while students in Group Three experienced a 13% decline. Fig. 1. South Africa’s GER in tertiary education relative to selected countries, 2019–2020 (Khuluvhe & Ganyaupfu, 2023). Beyond academic performance, tutoring also plays a crucial role in enhancing student engagement, which is positively correlated with academic success (Faroa, 2017). Engagement includes aspects such as collaborative learning, academic participation, and communication with lecturers and peers (Hu & Kuh, 2001; Coates, 2007). Research indicates that universities that prioritize student engagement see improvements in student retention, academic achievement, and overall psychological well-being (Strydom & Mentz, 2010). Tutors serve as a critical academic support mechanism, bridging knowledge gaps and providing mentorship, particularly for students struggling with coursework. However, Clarence (2016) highlights disparities in tutor training and support across universities, which may influence the overall effectiveness of tutoring programs. Furthermore, student retention is a pressing issue in South African higher education, with dropout rates remaining alarmingly high. Studies indicate that 50–60% of university students do not complete their degrees, with only 22% of first-year students successfully progressing to completion (Council on Higher Education, 2010; Staff Reporter, 2023). Tutoring has been identified as a key intervention to mitigate dropout rates, particularly for historically disadvantaged students, by helping them transition into the university learning environment and providing academic support where needed. These findings highlight the potential of tutoring as a crucial strategy for improving both student performance and retention. However, further research is needed to determine its specific impact within different educational contexts, such as engineering education, to inform evidence-based academic support policies. 3 METHODOLOGY This study employs a quantitative approach, using University X as a case study to assess student performance in tutored vs. non-tutored modules. This method enables statistical analysis, trend identification, and comparison of pass rates, ensuring reliable and generalizable findings. By applying empirical validation, the study provides evidence-based insights into the impact of tutoring on student success in engineering education. The study used a longitudinal data collection approach from 2021 to 2023 to analyse student performance trends in tutored vs. non-tutored modules. Data included pass rates, enrolment numbers, and tutorial sessions, collected at the end of each academic year for accuracy and completeness. Since only the first semester of 2024 was available, that year’s data were excluded to ensure consistency and reliability in the analysis. The target population for this study consists of students at University X, with a specific focus on undergraduate students enrolled in the Faculty of Engineering and the Built Environment. All modules across the Faculty were included in the analysis. Descriptive statistical analysis was employed to interpret and present the collected data effectively. The data were analysed using tables and bar graphs to visually illustrate trends and comparisons in student performance across tutored and nontutored modules. Measures of central tendency, particularly the statistical mean, were primarily used to summarise key insights and highlight variations in pass rates, enrolment numbers, and tutorial attendance. The focus on basic descriptive statistical methods allowed for a clear and accessible interpretation of the results, ensuring that meaningful patterns could be identified and understood. 4 RESULTS AND DISCUSSION 4.1 Tutor allocations At University X, tutors are allocated to "at-risk" modules, defined as those with a pass rate below 75%. Additionally, to ensure adequate academic support, the university follows a policy of appointing one tutor for every 50 students. Tutorials are conducted in-person and in a group format, allowing students to receive structured assistance from trained tutors. Table 1 presents the total number of tutors appointed per department from 2021 to 2023. The data indicate that the Department of Electrical and Electronic Engineering Science (EEES) had the highest number of tutors in 2021 (77 tutors), while the Department of Mechanical and Industrial Engineering Technology recorded the highest tutor allocation in both 2022 and 2023, with 82 and 83 tutors, respectively. The high tutor numbers in these departments can be attributed to a large number of modules and a higher concentration of at-risk modules, as well as a large number of student enrolments, necessitating increased academic support. Overall, 2021 recorded the highest total number of tutors (663) across the faculty. This surge in tutor appointments was driven by lower pass rates, which prompted the university to enhance academic support through additional tutoring interventions. However, in subsequent years, as pass rates improved, the number of tutors appointed was gradually adjusted. Table 1. Tutor allocations per department (2021-2023) Departments Modules Number of undergraduate students Number of tutors With Tutors Without Tutors 2021 2022 2023 2021 2022 2023 CES 20 7 2336 1976 2119 49 51 44 CET 33 19 4256 4443 4298 60 58 68 MES 26 10 3101 3145 3192 53 46 44 CHEMENG 15 4 1601 1780 2058 39 25 26 CM&QS 16 8 2023 1857 1918 36 21 38 EEES 28 22 3670 2975 2803 77 71 58 EEET 20 41 4340 6797 4185 51 54 57 METAL 29 17 3418 3575 3145 57 46 56 QUALOPS 25 17 6611 6181 6385 43 31 62 URP 31 3 1237 1057 956 70 38 39 MIET 35 27 6817 7445 7772 73 82 83 MINING 11 32 2752 3183 3247 55 10 29 TOTAL 289 207 42162 44414 42078 663 533 604 4.2 Student Performance: 2021 Fig 2 compares 2021 pass rates for modules with tutors (blue bars) and those without (orange bars) across the various departments. The results indicate that modules with tutors consistently achieve higher pass rates, demonstrating a positive impact on student performance. In some departments, the differences are particularly striking. Mechanical Engineering Science (MES) recorded a 77.89% pass rate for tutored modules, compared to only 43.14% for non-tutored modules. Similarly, in Construction Management and Quantity Surveying (CM&QS), modules with tutors attained a 90.50% pass rate, significantly higher than the 78.38% for non- tutored modules. Other departments also showed notable improvements. Civil Engineering Technology (CET) had a 86.34% pass rate for tutored modules, compared to 77.38% for those without, while Urban and Regional Planning (URP) recorded an impressive 92.10% pass rate with tutors, versus 67.10% without. Another department where the difference was pronounced is Quality and Operations Management (QUALOPS), where tutored modules significantly outperformed nontutored ones (87.16% vs. 63.25%). Interestingly, the Mining department showed a smaller gap, with pass rates of 86.40% for tutored modules compared to 79.86% for non-tutored ones. However, this still reflects a positive impact of tutoring on student success. Fig. 2 2021 Departmental average pass rates 4.3 Student Performance: 2022 Fig. 3 compares 2022 pass rates for modules with tutors (blue bars) and those without (orange bars) across various departments. The results align with 2021 trends, showing that modules with tutors consistently outperformed those without, reinforcing the positive impact of tutor support on academic performance. While most departments followed this pattern, Civil Engineering Technology (CET) recorded an exception, where non-tutored modules had a higher pass rate (88.34%) compared to 86.30% for tutored modules. In contrast, Mechanical Engineering Sciences (MES) showed a significant improvement, with tutored modules achieving an 81.84% pass rate, compared to 45.17% for non-tutored modules. Other departments demonstrated notable differences as well. Construction Management and Quantity Surveying (CM&QS) recorded an 85.67% pass rate for tutored modules, compared to 74.62% for non-tutored ones. The Urban and Regional Planning (URP) department saw one of the most significant gaps, with tutored modules achieving 90.76%, far exceeding the 71.49% pass rate of non-tutored modules. Some departments exhibited more moderate differences but still reflected the advantages of tutoring. In Chemical Engineering (CHEMENG), tutored modules recorded an 80.92% pass rate, compared to 66.19% for non-tutored ones. Similarly, in Quality and Operations (QUALOPS), tutored modules achieved 81.51%, outperforming the 61.80% recorded for nontutored modules. Metallurgy (METAL) had the largest disparity, with tutored modules achieving an 88.82% pass rate, compared to only 40.81% for non-tutored modules. Fig. 3 2022 Departmental average pass rates 4.4 Student Performance: 2023 The 2023 pass rates, as shown in Fig. 4, continue to reflect the trend observed in previous years, with modules supported by tutors consistently achieving higher pass rates than those without. This reinforces the positive impact of tutor involvement on student success across various departments. Several departments exhibited significant disparities in pass rates. In Civil Engineering Science (CES), modules with tutors recorded an 80.01% pass rate, whereas non-tutored modules had a much lower rate of 39.14%, demonstrating the substantial impact of tutoring. Similarly, in Mechanical Engineering Services (MES), modules with tutors achieved an 81.88% pass rate, compared to just 39.51% for those without, further emphasising the effectiveness of tutoring support in improving student outcomes. Other departments also displayed notable differences. Construction Management and Quantity Surveying (CM&QS) recorded a 90.39% pass rate for tutored modules, significantly higher than the 76.45% observed in non-tutored modules. Likewise, Urban and Regional Planning (URP) saw an outstanding 94.87% pass rate for tutored modules, compared to 67.88% for those without. In some departments, the impact of tutoring remained evident but was slightly less pronounced. For instance, in Chemical Engineering (CHEMENG), modules with tutors recorded a 90.95% pass rate, compared to 74.88% for non-tutored modules. In Electrical Engineering Technology (EEET), the gap was narrower, with 90.91% pass rates in tutored modules, compared to 89.28% in those without tutors. Metallurgy (METAL) recorded a 90.76% pass rate for tutored modules, outperforming the 73.35% recorded for non-tutored modules. Similarly, Quality and Operations Management (QUALOPS) showed a significant difference, with tutored modules achieving a 91.74% pass rate, compared to 61.43% for those without tutors. Fig. 4 2023 Departmental average pass rates 4.5 An overall perspective Fig. 5 highlights the consistent positive impact of tutoring on student performance from 2021 to 2023. Tutored modules consistently outperformed non-tutored ones, with pass rates of 85% vs 67% (2021), 84% vs 61% (2022), and 88% vs 69% (2023). The largest gap (23 percentage points) occurred in 2022, while non-tutored modules slightly improved in 2023, suggesting additional influencing factors. These results underscore the importance of tutoring in enhancing student success and the need for continued investment in tutoring programs, particularly for at-risk modules. Fig. 5 Average faculty pass rates from 2021 to 2023 Table 2 presents a comparative analysis of ten randomly selected modules within the faculty, examining pass rates from 2021 to 2023 in relation to the presence or absence of tutor support. The analysis reveals that seven out of the ten modules did not have tutors in 2021, yet experienced improved student performance in 2023 following the introduction of tutors. Conversely, in the remaining three modules, the removal of tutors between 2021 and 2023 corresponded with a decline in pass rates.