Assessment in large public universities is often treated as an academic matter rather than as a form of organizational work. When assessment depends on fragmented routines and individual discretion, institutions struggle with transparency, consistency and managerial oversight. This study investigates how Business Process Management (BPM) can be used to govern assessment operations as an organizational work process in higher education, without intervening in academic judgment.
The study draws on a mixed-methods case at a large public technical university in Vietnam, combining institutional process data, system logs, semi-structured interviews with academic and administrative staff (n = 15) and a student survey (n = 16,664). Quantitative data were analyzed using reliability testing and exploratory factor analysis, while qualitative data were examined thematically.
BPM-based governance improved assessment operations on multiple dimensions. End-to-end assessment cycle time was reduced by more than 40%, manual workload fell by approximately 50% and student perceptions of fairness and transparency were high (mean = 4.3/5). Four mechanisms shaped these outcomes: coherent leadership, formalized workflows, enhanced data visibility and the separation of teaching and assessment roles. At the same time, the reform generated tensions as standardized digital workflows altered established professional practices.
Results show how assessment operations can be governed as organizational work using BPM principles in public universities, strengthening accountability without displacing academic judgment.
1. Introduction
1.1 Assessment of learning outcomes: academic practice and organizational work
Assessment in higher education is widely understood as a form of academic labor grounded in disciplinary knowledge and professional judgment. Decisions concerning marking, standards interpretation and alignment between assessment tasks and intended learning outcomes are enacted through shared academic understandings of quality rather than through fully specified organizational procedures. Foundational work on assessment highlights that such judgments depend on tacit standards, disciplinary norms and contextual interpretation, which resist complete codification (Sadler, 1989; Boud and Falchikov, 2007; Wilson et al., 2024). More recent scholarship similarly emphasizes assessment as a professional and social practice in which evaluative judgment is developed and exercised within communities of practice, rather than imposed through procedural control (Tai et al., 2018). Assessment therefore, remains fundamentally a pedagogical practice through which academic judgment is enacted and academic standards are sustained (Brown, 2015). From a work-applied management perspective, such operational conditions constitute a legitimate focus for process design and governance intervention, particularly in contexts facing growing accountability and transparency pressures (Gulledge and Sommer, 2002). Taken together, these challenges point not to deficiencies in academic judgment itself, but to limitations in the organizational conditions under which assessment is conducted. Addressing these limitations requires attention to how assessment operations are designed, coordinated and governed at the institutional level. Accordingly, this study focuses on assessment operations as organizational work, rather than on assessment as academic practice. It examines how BPM can be used to strengthen the organizational conditions under which academic assessment is carried out—improving coordination, transparency and oversight—while leaving professional judgment and pedagogical decision-making intact.
1.2 Business process management as a governance approach
BPM offers a structured approach for governing organizational work processes. Originally developed in the industrial and service sectors, BPM has increasingly been applied in public organizations to enhance transparency, accountability and performance control. Its focus lies not in automation alone, but in aligning workflows, roles, decision rules and performance indicators within a coherent process architecture.
In professional organizations such as universities, BPM is particularly relevant because it provides a way to balance professional autonomy with organizational accountability. By making work processes explicit and visible, BPM enables managers to understand how work is carried out in practice, where risks and bottlenecks emerge and how evidence can support managerial decision-making. At the same time, BPM does not prescribe the substance of professional judgment. Instead, it structures how such judgments are enacted, documented and reviewed.
Despite this potential, BPM has been applied mainly to administrative and support functions in higher education. Its use in governing core academic work processes, especially assessment, remains limited in management-oriented research, which continues to approach assessment largely from pedagogical, quality assurance, or technological perspectives.
1.3 Research gap, purpose and contributions
The limited attention to assessment operations as organizational work points to a broader gap in work-applied management literature. While BPM is well established in studies of operational and support processes, there is relatively little empirical research on its application to core professional work in large public universities.
This paper addresses that gap by examining how BPM was implemented to manage assessment as an organizational work process in a large public technical university in Vietnam. Using a mixed-methods design, the study analyses how BPM-based digital workflows reshaped roles, clarified responsibilities, enhanced process visibility and supported managerial control in a highly decentralized academic environment. It also explores how these changes enabled organizational learning through systematic reflection on process data.
The paper makes three contributions. First, it reframes assessment as a knowledge-intensive organizational work process with clear managerial implications. Second, it provides empirical evidence of how BPM can function as a governance mechanism in professional organizations beyond administrative domains. Third, it offers practical insights for managers seeking to strengthen accountability and performance while respecting professional autonomy.
2. Research framework and literature review
2.1 Business process management: foundations and relevance to higher education
This section outlines BPM's conceptual roots and why it matters for higher-education governance. BPM originated in Total Quality Management (TQM) and Continuous Quality Improvement (CQI), offering a structured approach to modeling, executing, monitoring and improving organizational processes (van der Aalst, 2013; Dumas et al., 2018). BPM is not merely a technical approach; rather, it emphasizes governance through the integration of process design, digital platforms and human resources to enhance coordination, accountability and organizational responsiveness (Weske, 2007; Jeston, 2022).
In university contexts, BPM supports the systematic organization of managerial, operational and support processes in a transparent and comprehensible manner (Wiechetek et al., 2017; Sujanawati et al., 2021; Bubenik et al., 2022). It also provides a means of linking core academic functions—such as curriculum design, student assessment and quality assurance—with accreditation requirements while preserving institutional autonomy (Anh et al., 2022). BPM is increasingly recognized as a key driver of digital transformation in public universities, particularly in the redesign of large-scale administrative and academic service processes (Ammirato et al., 2024). In parallel, simulation-based Business Process Reengineering approaches have been applied to optimize resource-intensive university operations, such as teaching assignments and scheduling. These studies highlight the importance of formal process modeling in reducing lead times and coordination costs (Renna and Colonnese, 2025).
Although BPM research has expanded rapidly over the past 2 decades, its application in education, particularly in developing contexts, remains limited (Anh et al., 2022). Extending BPM principles across the full assessment lifecycle, from planning and administration to grading and feedback, offers a practical pathway for improving fairness, transparency and accountability in student assessment (Gulledge and Sommer, 2002). This paper examines a university-wide BPM-enabled assessment reform at reform at Hanoi University of Industry (HaUI), a large public technical university in Vietnam, together with the subsequent transfer of this model to other Vietnamese universities.
2.2 Learning outcome assessment in Vietnamese universities: pedagogical foundations and organizational change
Assessing student learning achievement is central to improving academic quality and educational effectiveness in higher education. Contemporary assessment theory emphasizes the importance of constructive alignment between assessment practices, intended learning outcomes and disciplinary standards (Biggs and Tang, 2011). While assessment practices are increasingly mediated by digital and algorithmic systems, assessment remains inherently pedagogical in nature. Assessment tasks and criteria are designed to reflect educational intent and disciplinary norms and their interpretation is shaped by tacit standards and contextual judgment exercised within academic communities (Sadler, 1989). In digitally mediated contexts, technologies may support, shape and calibrate judgment through the provision of rubrics, exemplars, comparative data, analytics and, in some cases, automated scoring, operating as part of broader socio-technical arrangements in which standards, validation processes and accountability are institutionally defined.
In Vietnam, the adoption of digital assessment-related practices remains uneven, reflecting variations in institutional capacity, digital infrastructure and governance arrangements (MOET, 2022; Le and Nguyen, 2021). Persistent challenges include fragmented digital systems, limited resources and weak data governance, which constrain transparency, consistency and institutional oversight of assessment practices—particularly when assessment is delivered at scale across multiple programmes and organizational units (Nguyen, 2024). These conditions make it difficult to sustain alignment between pedagogical intent, technological implementation and accountability requirements in the absence of coherent organizational coordination.
Accordingly, this paper conceptualizes BPM-enabled assessment reform as a process of organizational change rather than as a transformation of assessment practice itself. Drawing on Fullan's (2007) framework, leadership is understood as shaping collaborative and enabling structures that support data-informed learning and continuous improvement (Hargreaves and Fullan, 2012). Informed by educational change theory (Kezar and Eckel, 2002; Kezar, 2014), the analysis focuses on four mechanisms underpinning the HaUI case: leadership coherence, process standardization, data visibility and structural decoupling between teaching and assessment roles. Together, these mechanisms illustrate how BPM-based assessment reform strengthens governance and coordination conditions for assessment at scale, while leaving pedagogical judgment distributed across academic and socio-technical arrangements.
2.3 Institutional isomorphism and the legitimacy of digital assessment
The diffusion of HaUI's BPM-based assessment model to several other universities in Vietnam, including Hai Phong University (HPU), Dong Nai University of Technology (DNTU) and Hanoi University of Industry and Trade (HAUIT), raises important questions about the drivers of adoption. DiMaggio and Powell's (1983) theory of institutional isomorphism provides a useful lens for interpreting this process. Coercive isomorphism, arising from policy mandates, accreditation requirements, or societal expectations, may explain alignment with HaUI's practices, even in the absence of immediate efficiency gains. Mimetic isomorphism may also operate, as institutions replicate perceived best practices in conditions of uncertainty.
In this context, BPM functions not only as a technical intervention but also as a symbol of institutional modernity and legitimacy (Raczyńska and Krukowski, 2019). Adoption of BPM-based frameworks may signal compliance with external standards and position universities as forward-looking within an increasingly competitive higher education landscape.
Moreover, BPM aligns closely with the principles of New Public Management (NPM), which emphasize measurable outcomes, procedural accountability and the use of digital tools for performance management (Kregel et al., 2022). In assessment contexts, BPM supports output measurement by standardizing how learning outcomes are evaluated and recorded, while enabling process control through formalized tasks, timelines and responsibilities. From this perspective, BPM-enabled assessment reform can be understood as part of a broader shift towards NPM-informed governance in Vietnamese higher education, extending beyond a single institutional context.
3. Research methodology
3.1 Research context
The study was conducted at HaUI, one of Vietnam's largest multidisciplinary technical universities. The institution operates across three campuses, enrolls more than 35,000 students and delivers 82 academic programmes. Each year offers over 2,000 courses and administers close to one million individual assessments across more than 30 formats. This operational scale provides a suitable empirical setting for examining assessment as organizational work, particularly in relation to coordination and quality assurance at scale.
HaUI's programmes follow the Conceive – Design – Implement – Operate (CDIO) framework and since 2020, its engineering programmes have aligned with international accreditation standards such as the Accreditation Board for Engineering and Technology (ABET). These frameworks require systematic evidence of continuous improvement in curriculum design and assessment. Prior to BPM implementation, the university had also introduced a structural separation between teaching and assessment through an independent testing governance unit to strengthen fairness and accountability.
Institutional documentation and stakeholder accounts indicate several recurring operational bottlenecks in assessment processes. Examination scheduling was resource-intensive due to large enrollments, a credit-based registration system and manual coordination across multiple units. Assessment operations were fragmented, as invigilation and grading were largely managed independently by course lecturers without standardized procedures, increasing coordination costs and supervisory risks. In addition, heterogeneous tools and data formats for recording results contributed to processing delays, calculation errors and limited transparency. Together, these conditions created constraints on data reliability and governance oversight.
In response to these operational pressures, the university adopted BPM as an institution-wide framework to redesign and digitally enable its assessment system. The combination of scale, accreditation commitments and identifiable process constraints makes HaUI an appropriate case for analyzing BPM-enabled governance in higher education.
3.2 Research design
This study employed a mixed-method approach with a qualitative emphasis to examine how BPM is applied to student assessment in Vietnamese higher education. A single embedded case study design was adopted and complemented by a large-scale student survey. In addition, supplementary operational data from partner institutions were collected to provide indicative evidence of model transferability, without constituting a multi-case design. Hanoi University of Industry was selected as the case due to its early and comprehensive adoption of BPM in assessment workflows. HaUI occupies a dual position as both an internal innovator and a facilitator of external transfer. The BPM framework initially developed at HaUI has subsequently been adopted by several other institutions in Vietnam, which vary in size, governance structures and levels of digital maturity. This dual role makes HaUI a particularly informative case, offering insight not only into institutional transformation, but also into how BPM-enabled assessment reform can scale and adapt across Vietnam's higher education system.
3.3 Data collection
Data were collected over a eight-month period (March–October 2024) from three primary sources.
Document analysis. A range of materials was examined, including internal reports, the BPM-enabled digital assessment management system logs, official guidelines issued by the Ministry of Education and Training (MOET) and institutional process flowcharts and external accreditation feedback reports (e.g. ABET review documentation, 2023). These sources were used to map how BPM was designed and implemented in HaUI.
Semi-structured interviews. Fifteen key stakeholders were interviewed, comprising ten lecturers and five administrative staff members (two from academic departments, two from the Testing Center and one from the IT unit). The interviews explored implementation processes, perceived outcomes, transfer experiences and key challenges (see Supplementary material – Appendix A for the interview protocol).
Student survey. The target population included 18,802 students across five schools and two faculties at HaUI. A total of 16,664 undergraduate students completed the survey, yielding a response rate of 88.6%. The survey was conducted after grading but prior to grade release, allowing students to assess procedural fairness, transparency and timeliness while minimizing outcome-related bias. Table 1 presents the demographic profile of the respondents.
Demographic profile of student respondents
| Category | Frequency | % |
|---|---|---|
| Male | 10,882 | 65.3 |
| Female | 5,782 | 34.7 |
| Year 1 | 1,246 | 7.5 |
| Year 2 | 5,747 | 34.5 |
| Year 3 | 4,599 | 27.6 |
| Year 4 | 3,934 | 23.6 |
| Year 5 | 1,136 | 6.8 |
| School of Electrical & Electronic Engineering | 3,894 | 23.4 |
| School of Economics | 4,339 | 26.0 |
| School of Mechanical & Automotive Engineering | 3,350 | 20.1 |
| School of Foreign Languages & Tourism | 1,574 | 9.4 |
| School of Information & Communication Technology | 2,294 | 13.8 |
| Faculty of Chemical Engineering | 654 | 3.9 |
| Faculty of Garment Technology & Fashion Design | 559 | 3.4 |
The nine-item survey captured students' perceptions of fairness, transparency, timeliness and procedural clarity in the assessment process using a five-point Likert scale (see Supplementary material – Appendix B for the full list of items). Following a pragmatic mixed-methods strategy, operational data were combined with stakeholder perspectives. This embedded single-case design supports analytical, rather than statistical, generalization of institutional processes.
Preliminary quantitative analysis confirmed the reliability and construct validity of the survey instrument. Internal consistency was acceptable (Cronbach's α = 0.779) and sampling adequacy were strong (KMO = 0.847). Exploratory factor analysis (EFA) identified a two-factor structure explaining approximately 71% of the total variance. In addition, two researchers independently coded all 15 interview transcripts and achieved a high level of inter-coder agreement (κ = 0.78).
3.4 Data analysis
Qualitative analysis. Interview transcripts were analyzed using thematic content analysis. Inductive and deductive coding were combined to identify recurring patterns related to BPM processes, transfer mechanisms and perceived outcomes.
Quantitative analysis. Survey data were analyzed using SPSS and AMOS. After data cleaning, the dataset was randomly split into two sub-samples. EFA was conducted on the first sub-sample, followed by CFA on the second sub-sample to validate the measurement model. One-way ANOVA was used to examine differences across faculties.
3.5 Reliability and triangulation
Findings were validated through triangulation of interview data, system logs and survey responses. To enhance credibility, member-checking sessions were conducted with selected participants and external quality assurance experts were consulted. Evidence of successful transfer of BPM practices to other universities provided an additional layer of validation, supporting the model's potential scalability beyond the HaUI context.
3.6 Ethical considerations
All participants provided informed consent prior to data collection. Anonymity and confidentiality were maintained throughout the study. Ethical approval was granted by the HaUI Institutional Review Board in accordance with national research ethics regulations.
4. Case study: BPM in assessment at the case institution
4.1 Implementing BPM for assessment
The reform rests on three pillars: (1) process modeling, (2) a digital assessment management system and (3) role clarity and implementation capacity.
Process modeling. The redesign followed two principles: operational decoupling and comprehensive formalization. Operational decoupling means that assessment is managed independently of instruction, with safeguards to reduce conflicts of interest—for example, cross-departmental invigilation. Comprehensive formalization refers to the explicit specification of tasks, handover points, data flows and stakeholder responsibilities. Cross-functional process maps were developed for the full assessment lifecycle, covering planning, administration, grading, appeals and archiving. The assessment workflows were modeled following BPMN 2.0 principles, including role-based swimlanes, event sequencing and decision gateways. These maps serve as quality-assurance blueprints and as executable logic for the digital system. Figure 1 presents the assessment process as designed and implemented at HaUI. Additional standard operating procedures are provided in Supplementary material – Appendix C.
The flowchart titled “Process of Learning Assessment” is organized into vertical sections labeled “Examination Preparation”, “Conduct of Examination”, “Examination Marking”, and “Result Compilation”, and horizontal swim lanes labeled on the left as “Assessment Center”, “School or Faculty of Training”, “Lecturer”, and “Student”. At the top left, Oval text box 1 reads, “1. Database Setup”. A downward arrow from Oval text box 1 leads to Rectangular text box 2 that reads, “2. Formative Assessment Preparation”. A downward arrow from Rectangular text box 2 leads to Rectangular text box 3 that reads, “3. Teaching and Formative Assessment”. A downward arrow from Rectangular text box 3 leads to Rectangular text box 4 that reads, “4. Taking Formative Assessment”. An arrow from Rectangular text box 4 leads to Rectangular text box 5 that reads, “5. Examination Eligibility Review”. A downward arrow from Rectangular text box 5 leads to Diamond text box 6 that reads, “6. Correction”. A branch labeled “Yes” leads upward from Diamond text box 6 back to Rectangular text box 5. A branch labeled “No” leads rightward from Diamond text box 6 to a curved rectangular box that reads, “14. View and Follow”. Above Rectangular text box 5, Rectangular text box 7 reads, “7. Examination Planning”. An upward arrow from Rectangular text box 7 leads to Rectangular text box 8 that reads, “8. Exam Room Assignment List”. A rightward arrow from Rectangular text box 8 leads to Rectangular text box 9 that reads, “9. Request for Proctor”. A downward arrow from Rectangular text box 9 leads to Rectangular text box 10 that reads, “10. Request for Exam Paper”. A downward arrow from Rectangular text box 10 leads to Rectangular text box 11 that reads, “11. Exam Paper Nomination”. A rightward arrow from Rectangular text box 9 also connects toward Rectangular text box 13 that reads, “13. Exam Preparation”. A downward arrow from Rectangular text box 13 leads to Rectangular text box 12 that reads, “12. Proctor Nomination”. A rightward arrow from Rectangular text box 13 leads into the next section. In the “Conduct of Examination” section, Rectangular text box 15 reads, “15. Proctoring Assignment”. A downward arrow from Rectangular text box 15 leads to Rectangular text box 16 that reads, “16. Conduct of Examination”. A downward arrow from Rectangular text box 16 leads to Rectangular text box 17 that reads, “17. Proctor if Assigned”. A downward arrow from Rectangular text box 17 leads to Rectangular text box 18 that reads, “18. Taking the Examination”. In the “Examination Marking” section, Rectangular text box 19 reads, “19. Receipt of Examination Papers”. A downward arrow from Rectangular text box 19 leads to Rectangular text box 20 that reads, “20. Processing of Exam Identification Codes”. A downward arrow from Rectangular text box 20 leads to Rectangular text box 21 that reads, “21. Examiner Assignment”. A downward arrow from Rectangular text box 21 leads to Rectangular text box 22 that reads, “22. Exam Marking”. A downward arrow from Rectangular text box 22 leads to Rectangular text box 23 that reads, “23. Entry of Scores into the System”. A rightward arrow from Rectangular text box 23 leads to Rectangular text box 24 that reads, “24. Compilation or Announcement of Scores”. A downward arrow from Rectangular text box 24 leads to Diamond text box 25 that reads, “25. View and Check”. A branch labeled “No Complain” leads rightward from Diamond text box 25 to Rectangular text box 27 that reads, “27. Finalization of Results”. A branch labeled “Complain” leads upward from Diamond text box 25 to Rectangular text box 26 that reads, “26. Re-marking”, and then upward to Rectangular text box 27. A rightward arrow from Rectangular text box 27 leads to Rectangular text box 28 that reads, “28. Review for Analysis”. A rightward arrow from Rectangular text box 28 leads upward to Oval text box 29 that reads, “29. Archiving of Records”. Multiple connecting arrows link the steps sequentially across the swim lanes and sections from left to right.Assessment operations workflow. Source: Authors, based on institutional documentation (2023)
The flowchart titled “Process of Learning Assessment” is organized into vertical sections labeled “Examination Preparation”, “Conduct of Examination”, “Examination Marking”, and “Result Compilation”, and horizontal swim lanes labeled on the left as “Assessment Center”, “School or Faculty of Training”, “Lecturer”, and “Student”. At the top left, Oval text box 1 reads, “1. Database Setup”. A downward arrow from Oval text box 1 leads to Rectangular text box 2 that reads, “2. Formative Assessment Preparation”. A downward arrow from Rectangular text box 2 leads to Rectangular text box 3 that reads, “3. Teaching and Formative Assessment”. A downward arrow from Rectangular text box 3 leads to Rectangular text box 4 that reads, “4. Taking Formative Assessment”. An arrow from Rectangular text box 4 leads to Rectangular text box 5 that reads, “5. Examination Eligibility Review”. A downward arrow from Rectangular text box 5 leads to Diamond text box 6 that reads, “6. Correction”. A branch labeled “Yes” leads upward from Diamond text box 6 back to Rectangular text box 5. A branch labeled “No” leads rightward from Diamond text box 6 to a curved rectangular box that reads, “14. View and Follow”. Above Rectangular text box 5, Rectangular text box 7 reads, “7. Examination Planning”. An upward arrow from Rectangular text box 7 leads to Rectangular text box 8 that reads, “8. Exam Room Assignment List”. A rightward arrow from Rectangular text box 8 leads to Rectangular text box 9 that reads, “9. Request for Proctor”. A downward arrow from Rectangular text box 9 leads to Rectangular text box 10 that reads, “10. Request for Exam Paper”. A downward arrow from Rectangular text box 10 leads to Rectangular text box 11 that reads, “11. Exam Paper Nomination”. A rightward arrow from Rectangular text box 9 also connects toward Rectangular text box 13 that reads, “13. Exam Preparation”. A downward arrow from Rectangular text box 13 leads to Rectangular text box 12 that reads, “12. Proctor Nomination”. A rightward arrow from Rectangular text box 13 leads into the next section. In the “Conduct of Examination” section, Rectangular text box 15 reads, “15. Proctoring Assignment”. A downward arrow from Rectangular text box 15 leads to Rectangular text box 16 that reads, “16. Conduct of Examination”. A downward arrow from Rectangular text box 16 leads to Rectangular text box 17 that reads, “17. Proctor if Assigned”. A downward arrow from Rectangular text box 17 leads to Rectangular text box 18 that reads, “18. Taking the Examination”. In the “Examination Marking” section, Rectangular text box 19 reads, “19. Receipt of Examination Papers”. A downward arrow from Rectangular text box 19 leads to Rectangular text box 20 that reads, “20. Processing of Exam Identification Codes”. A downward arrow from Rectangular text box 20 leads to Rectangular text box 21 that reads, “21. Examiner Assignment”. A downward arrow from Rectangular text box 21 leads to Rectangular text box 22 that reads, “22. Exam Marking”. A downward arrow from Rectangular text box 22 leads to Rectangular text box 23 that reads, “23. Entry of Scores into the System”. A rightward arrow from Rectangular text box 23 leads to Rectangular text box 24 that reads, “24. Compilation or Announcement of Scores”. A downward arrow from Rectangular text box 24 leads to Diamond text box 25 that reads, “25. View and Check”. A branch labeled “No Complain” leads rightward from Diamond text box 25 to Rectangular text box 27 that reads, “27. Finalization of Results”. A branch labeled “Complain” leads upward from Diamond text box 25 to Rectangular text box 26 that reads, “26. Re-marking”, and then upward to Rectangular text box 27. A rightward arrow from Rectangular text box 27 leads to Rectangular text box 28 that reads, “28. Review for Analysis”. A rightward arrow from Rectangular text box 28 leads upward to Oval text box 29 that reads, “29. Archiving of Records”. Multiple connecting arrows link the steps sequentially across the swim lanes and sections from left to right.Assessment operations workflow. Source: Authors, based on institutional documentation (2023)
Digital assessment management system. The BPM-enabled digital assessment management system operationalizes the process models. It centralizes real-time data, enforces role-based access and automates high-volume tasks such as eligibility checks, scheduling, room and invigilator assignment, score consolidation and archiving. Crucially, governance rules are embedded in system logic. For example, the rule that lecturers must not invigilate their own courses is hard-coded into the scheduling algorithm, preventing manual override. In this way, the system functions both as an operational tool and as an enforcement mechanism for institutional accountability.
The system integrates with other platforms, including the learning management system, online testing tools and campus-wide systems for academic records, student information, facilities and human resources. Integration reduces duplicate entries and supports consistent identifiers across the Learning Management System (LMS), records and scheduling. A live monitoring dashboard provides visibility into ongoing sessions, system alerts and operational performance. The analytic module produces score distribution summaries and flags outliers for follow-up review. Figure 2 presents an example of the analytic dashboard.
The table and line graph present statistical results of exam scores. At the top, a note is present in a foreign language. Below the note is a table. Column header: Column 1, “S T T”. Column 2, “Foreign language text”. Column 3, “Foreign language text”. Column 4, “Foreign language text (percentage)”, with subcolumns “0”, “1”, “2”, “3”, “4”, “5”, “6”, “7”, “8”, “9”, “10”. Row 1: Column 1, “1”. Column 2, “2017 to 2018”. Column 3, “299”. Column 4 subcolumns: “2.34”, “2.68”, “6.35”, “10.03”, “15.38”, “35.45”, “18.06”, “8.03”, “1.34”, “0.33”, “0.00”. Row 2: Column 1, “2”. Column 2, “2018 to 2019”. Column 3, “396”. Column 4 subcolumns: “2.02”, “2.53”, “7.83”, “12.37”, “19.19”, “21.97”, “17.68”, “12.63”, “3.54”, “0.25”, “0.00”. Row 3: Column 1, “3”. Column 2, “2019 to 2020”. Column 3, “833”. Column 4 subcolumns: “1.20”, “2.04”, “6.72”, “12.48”, “17.29”, “19.33”, “20.89”, “11.52”, “6.72”, “1.80”, “0.00”. At the bottom is a line graph with a title in a foreign language. The vertical axis is labeled in a foreign language and ranges from 0 to 40 with increments of 10. The horizontal axis is labeled in a foreign language and ranges from 0 to 10 with increments of 1. Four plotted lines appear. The first line begins at approximately (0, 2.34), slightly rises to (1, 2.68), increases through (2, 6.35) and (3, 10.03), rises to (4, 15.38), peaks at (5, 35.45), then decreases to (6, 18.06), (7, 8.03), (8, 1.34), (9, 0.33), and ends at (10, 0.00). The second line begins at (0, 2.02), rises to (1, 2.53), increases through (2, 7.83) and (3, 12.37), rises to (4, 19.19), peaks at (5, 21.97), decreases to (6, 17.68), then declines through (7, 12.63), (8, 3.54), (9, 0.25), and ends at (10, 0.00). The third line begins at (0, 1.20), rises to (1, 2.04), increases through (2, 6.72) and (3, 12.48), rises to (4, 17.29), increases to (5, 19.33), peaks at (6, 20.89), then decreases through (7, 11.52), (8, 6.72), (9, 1.80), and ends at (10, 0.00). The fourth plotted line follows a similar distribution from score 0 to score 10 and intersects near the middle scores around 4 to 6 before declining toward 10. Note: All numerical data values are approximated.Course assessment analytic interface. Source: Authors, based on institutional system (2024)
The table and line graph present statistical results of exam scores. At the top, a note is present in a foreign language. Below the note is a table. Column header: Column 1, “S T T”. Column 2, “Foreign language text”. Column 3, “Foreign language text”. Column 4, “Foreign language text (percentage)”, with subcolumns “0”, “1”, “2”, “3”, “4”, “5”, “6”, “7”, “8”, “9”, “10”. Row 1: Column 1, “1”. Column 2, “2017 to 2018”. Column 3, “299”. Column 4 subcolumns: “2.34”, “2.68”, “6.35”, “10.03”, “15.38”, “35.45”, “18.06”, “8.03”, “1.34”, “0.33”, “0.00”. Row 2: Column 1, “2”. Column 2, “2018 to 2019”. Column 3, “396”. Column 4 subcolumns: “2.02”, “2.53”, “7.83”, “12.37”, “19.19”, “21.97”, “17.68”, “12.63”, “3.54”, “0.25”, “0.00”. Row 3: Column 1, “3”. Column 2, “2019 to 2020”. Column 3, “833”. Column 4 subcolumns: “1.20”, “2.04”, “6.72”, “12.48”, “17.29”, “19.33”, “20.89”, “11.52”, “6.72”, “1.80”, “0.00”. At the bottom is a line graph with a title in a foreign language. The vertical axis is labeled in a foreign language and ranges from 0 to 40 with increments of 10. The horizontal axis is labeled in a foreign language and ranges from 0 to 10 with increments of 1. Four plotted lines appear. The first line begins at approximately (0, 2.34), slightly rises to (1, 2.68), increases through (2, 6.35) and (3, 10.03), rises to (4, 15.38), peaks at (5, 35.45), then decreases to (6, 18.06), (7, 8.03), (8, 1.34), (9, 0.33), and ends at (10, 0.00). The second line begins at (0, 2.02), rises to (1, 2.53), increases through (2, 7.83) and (3, 12.37), rises to (4, 19.19), peaks at (5, 21.97), decreases to (6, 17.68), then declines through (7, 12.63), (8, 3.54), (9, 0.25), and ends at (10, 0.00). The third line begins at (0, 1.20), rises to (1, 2.04), increases through (2, 6.72) and (3, 12.48), rises to (4, 17.29), increases to (5, 19.33), peaks at (6, 20.89), then decreases through (7, 11.52), (8, 6.72), (9, 1.80), and ends at (10, 0.00). The fourth plotted line follows a similar distribution from score 0 to score 10 and intersects near the middle scores around 4 to 6 before declining toward 10. Note: All numerical data values are approximated.Course assessment analytic interface. Source: Authors, based on institutional system (2024)
Role clarity and capacity. Personnel were reorganized into three coordinated groups: a central assessment unit responsible for end-to-end oversight and quality assurance; academic staff responsible for test item development, invigilation and grading; and an IT support team responsible for system stability, security and integration. This separation means that while lecturers design exam content and may grade student work, scheduling and oversight are handled independently, reinforcing the decoupling of teaching and assessment roles. The division of labor reflects core BPM principles of cross-functional coordination and end-to-end accountability. Clear role allocation also ensures that system capabilities are used as intended and that accountability remains traceable.
Overall, the combination of a defined process architecture, a dedicated digital platform and aligned human capacity enabled HaUI to operationalize assessment governance at scale. The approach streamlined operations and embedded fairness, transparency and continuous improvement into routine assessment work.
4.2 Outcomes and observations
4.2.1 Overall results
Applying BPM to assessment at HaUI improved both process performance and internal quality assurance. System documentation and interview data indicate that objectivity improved following the introduction of cross-unit invigilation, anonymized scripts, coded identifiers and double-blind grading. Participants noted that these measures reduced subjectivity and supervisory risks (Lecturer_02; Admin_01). The 2023 ABET review also highlighted the structural separation of teaching and assessment and the digitalization of workflows as exemplary features. System log data and institutional performance reports indicate that operational performance improved following BPM implementation. Reported violations declined, assessment cycle time shortened and staff effort required for assessment operations decreased by approximately 50%. Process analytics show that the average turnaround time for a complete assessment cycle fell from more than 17 days to around 7 days between March and October 2023. In a post-implementation student survey (n = 16,664), the mean satisfaction score was 4.3 out of 5. Students rated “system effectiveness” slightly higher (mean = 4.4) than “initial impressions/perceptions” (mean = 4.1). Interviewed lecturers and administrators also reported gains in time efficiency, transparency and workload reduction. Figure 3 summarizes the number of students assessed and the average time required per assessment cycle under the new system.
The combined bar chart and line graph presents monthly statistics with a data table below. The vertical axis on the left ranges from 0 to 80000 with increments of 10000. The vertical axis on the right ranges from 0 to 20 with increments of 2. The horizontal axis lists months “March”, “April”, “May”, “June”, “July”, “August”, “September”, and “October”. Bars represent “Number of candidates”. The line represents “Average time to complete the assessment activity (day)”. The bar at “March” reaches 13365. The bar at “April” reaches 6417. The bar at “May” reaches 40607. The bar at “June” reaches 70487. The bar at “July” reaches 13770. The bar at “August” reaches 24078. The bar at “September” reaches 19148. The bar at “October” reaches 34846. The line begins at “March” with value 17.19. The line decreases to “April” at 16.73. The line decreases to “May” at 15.38. The line increases to “June” at 16.2. The line decreases to “July” at 14.83. The line decreases to “August” at 12.35. The line decreases to “September” at 10.42. The line decreases to “October” at 6.68. Below is the tabular data for the same values.Number of examinees and assessment completion time. Source: Authors, based on system logs (2024)
The combined bar chart and line graph presents monthly statistics with a data table below. The vertical axis on the left ranges from 0 to 80000 with increments of 10000. The vertical axis on the right ranges from 0 to 20 with increments of 2. The horizontal axis lists months “March”, “April”, “May”, “June”, “July”, “August”, “September”, and “October”. Bars represent “Number of candidates”. The line represents “Average time to complete the assessment activity (day)”. The bar at “March” reaches 13365. The bar at “April” reaches 6417. The bar at “May” reaches 40607. The bar at “June” reaches 70487. The bar at “July” reaches 13770. The bar at “August” reaches 24078. The bar at “September” reaches 19148. The bar at “October” reaches 34846. The line begins at “March” with value 17.19. The line decreases to “April” at 16.73. The line decreases to “May” at 15.38. The line increases to “June” at 16.2. The line decreases to “July” at 14.83. The line decreases to “August” at 12.35. The line decreases to “September” at 10.42. The line decreases to “October” at 6.68. Below is the tabular data for the same values.Number of examinees and assessment completion time. Source: Authors, based on system logs (2024)
During an ABET external review in late 2023, evaluators highlighted two exemplary practices: (a) structural separation of teaching and assessment, and (b) comprehensive digitalization of assessment processes. The model has since been transferred to other institutions, indicating feasibility beyond a single site.
4.2.2 Quantitative analysis
To strengthen construct validation, given the large sample size, a split-sample approach was adopted. After data cleaning, the full dataset (N = 16,664) was randomly divided into two approximately equal sub-samples. Exploratory Factor Analysis (EFA) was conducted on Sample 0 (n ≈ 8,300), while Confirmatory Factor Analysis (CFA) was performed on Sample 1 (n ≈ 8,300).
The nine-item scale (Q1–Q9) showed strong internal consistency (Cronbach's α = 0.779; 0.792 when standardized). All items met acceptable item–total correlation thresholds; therefore, no items were removed. Table 2 reports the reliability statistics.
Reliability statistics
| Cronbach's alpha | Cronbach's alpha (standardized) | Number of items |
|---|---|---|
| 0.779 | 0.792 | 9 |
Table 3 reports the Kaiser–Meyer–Olkin (KMO) measure and Bartlett's Test of Sphericity for Sample 0, confirming the suitability of the data for exploratory factor analysis. The KMO value was 0.845, indicating adequate sampling adequacy. Bartlett's Test of Sphericity was statistically significant (χ2 = 44,617.64, df = 36, p < 0.001), confirming that the correlation matrix was appropriate for factor extraction in the exploratory phase.
KMO and Bartlett's test (sample 0)
| Kaiser-Meyer-Olkin measure of sampling adequacy | 0.845 | |
|---|---|---|
| Bartlett's test of Sphericity | Approx. Chi-Square | 44617.642 |
| df | 36 | |
| Sig. | 0.000 |
To validate this structure, a CFA was conducted on Sample 1 using AMOS. The measurement model demonstrated a good overall fit (χ2/df < 3; CFI >0.95; TLI >0.94; RMSEA <0.06). All standardized factor loadings were statistically significant (p < 0.001) and exceeded recommended thresholds (>0.60). Composite reliability (CR) values exceeded 0.80 and Average Variance Extracted (AVE) values were above 0.50 for both constructs, indicating satisfactory convergent validity. Discriminant validity was also supported.
Within-factor correlations was strong and statistically significant (p < 0.01). Correlations between the two factors were weak or slightly negative, supporting the two-factor structure. One-way ANOVA indicated significant differences across groups (e.g. faculties) for Q1 (F = 16.437, p < 0.001), Q2 (F = 12.524, p < 0.001), Q3 (F = 13.128, p < 0.001) and Q6 (F = 2.246, p = 0.036). Differences for the remaining items were not statistically significant (p > 0.05).
Detailed CFA results, factor loadings and full ANOVA outputs are provided in Supplementary material – Appendix D.
4.2.3 Qualitative analysis
Fifteen semi-structured interviews were conducted to examine staff perceptions of the BPM-enabled reform. Using a combination of inductive and deductive coding, three themes emerged: implementation process, outcomes achieved and limitations and challenges.
Implementation process. Participants emphasized that standardized procedures and digital coordination strengthened assessment governance. Moving from unit-specific routines to a shared cross-functional workflow was widely seen as a major improvement. One lecturer noted, “For the first time, we had a clear, end-to-end process for everything from test design to result archiving” (Lecturer_07). An administrator observed that “the BPM platform connects data between lecturers and the Testing Center, making it easier to track progress at each stage” (Admin_01). Participants also noted that implementation required training and adjustment, especially to align expectations across academic and administrative units.
Outcomes achieved. Interviewees reported improvements in efficiency, transparency and user experience. Many highlighted reduced manual workload and faster turnaround. An administrator stated, “Result processing time was reduced by nearly half compared to before” (Admin_01). A lecturer added, “Every step is logged in the system, which makes errors or misconduct unlikely” (Lecturer_02). Several participants noted that students received results sooner and expressed greater confidence in the fairness of the process.
Limitations and challenges. Participants pointed to limited digital literacy among some lecturers, occasional technical issues, resistance to standardized workflows and ongoing needs for system enhancement. These constraints reflect broader capacity challenges in digital transformation across Vietnamese higher education.
Figure 4 summarizes the relative prominence of the three themes.
The vertical axis labeled “Number of Mentions” ranges from 0 to 40 in increments of 5. The horizontal axis is labeled “Implementation Process”, “Achieved Outcomes”, and “Limitations and Challenges”. The bar values from left to right are as follows: Implementation Process, 39. Achieved Outcomes, 40. Limitations and Challenges, 33.Frequency of theme mentions. Source: Authors
The vertical axis labeled “Number of Mentions” ranges from 0 to 40 in increments of 5. The horizontal axis is labeled “Implementation Process”, “Achieved Outcomes”, and “Limitations and Challenges”. The bar values from left to right are as follows: Implementation Process, 39. Achieved Outcomes, 40. Limitations and Challenges, 33.Frequency of theme mentions. Source: Authors
Within the implementation theme, process standardization and technology integration were the most frequent sub-themes. Within outcomes, time savings dominated. Within challenges, technical issues were cited most often. This pattern aligns with the survey results: students reported high satisfaction with system performance, but more variation in early user experience.
To strengthen trustworthiness, we triangulated interview evidence with system logs and survey data. Member-checking with selected participants confirmed that interpretations reflected their views. Expert debriefing with external quality assurance specialists further refined the thematic structure. Convergence between interview insights and survey patterns supports the robustness of the results.
4.3 Cross-institutional validation: evidence from transferred implementations
To assess transferability, follow-up data were collected from the operational event logs of the BPM-enabled digital assessment systems implemented at three Vietnamese universities that adopted the model during 2023–2024: Dongnai University of Technology (DNTU), Hai Phong University (HPU) and Hanoi University of Industry and Trade (HAUIT). These institutions differ in size, governance and digital maturity, providing a basis for external validation. As shown in Table 4, analysis of system-generated log data indicates efficiency gains comparable to those at HaUI. On average, exam-cycle duration decreased by 41.2% (range = 35–48%), manual workload decreased by approximately 38% and student satisfaction with fairness and transparency remained high (around 4.28 out of 5).
Comparative operational results across three universities (January–December 2024)
| Indicator | DNTU | HPU | HAUIT | |||
|---|---|---|---|---|---|---|
| Jan–Jun 2024 | Jul–Dec 2024 | Jan–Jun 2024 | Jul–Dec 2024 | Jan–Jun 2024 | Jul–Dec 2024 | |
| Average completion time for assessment (days) | 15 | 10 | 11 | 9 | 9 | 7 |
| Rate of grade justification (% of courses) | 0.19 | 0.10 | 0.14 | 0.10 | 0.51 | 0.29 |
| Rate of grade review requests (% of total tests) | 0.10 | 0.01 | 0.23 | 0.26 | 0.24 | 0.16 |
| Number of examinees | 44,577 | 45,475 | 113,174 | 64,016 | 18,630 | 16,192 |
Interview data from administrative staff in the Testing Center indicate fewer post-exam disputes, improved auditability and stronger accountability after implementation. Only limited context-specific adaptations were required, mainly additional staff training and learning management system integration, while the core BPM architecture remained unchanged. These results suggest that the approach functions as a replicable governance framework rather than a context-dependent solution.
5. Discussion
5.1 Reframing assessment operations as organizational work
The results reinforce the view that assessment operations in higher education should not be viewed as a purely academic or pedagogical activity, but rather as a form of organizational work embedded in institutional operating processes. When implemented at scale, assessment requires coordination among multiple units and actors across different points in time and decision-making contexts, thereby generating significant operational complexity. Treating assessment merely as a matter of professional autonomy obscures its character as a work system and limits managerial capacity to ensure consistency, accountability and continuous improvement.
By reframing assessment as a work enactment process, this study aligns with work-applied management perspectives that emphasize how professional work is carried out through routines, workflows and organizational structures. Evidence from this case demonstrates that treating assessment operations as work enables process design, monitoring and organizational learning without reducing it to a purely technical activity.
5.2 BPM as a governance mechanism in professional organizations
A central contribution of this research is its demonstration of how BPM operates as a governance mechanism rather than merely as a technical or efficiency-driven tool. In the case examined, BPM did not prescribe the academic substance of assessment decisions. Instead, it structured how those decisions were planned, executed, documented and reviewed. This distinction is critical in professional organizations where autonomy and expertise underpin legitimacy.
The introduction of BPM-based workflows clarified roles and responsibilities across academic and administrative units, reduced ambiguity in task ownership and enhanced process visibility for managers at different levels. Such visibility enabled managerial oversight that was previously difficult to achieve in decentralized, paper-based systems. Importantly, governance was exercised through transparency and evidence, rather than direct intervention in professional judgment. This supported a more balanced relationship between managerial control and academic autonomy.
These findings extend BPM scholarship by demonstrating its relevance to core professional work processes and not only to administrative or support functions. They also contribute to public sector management debates by showing that process-oriented governance can strengthen accountability without undermining professional values.
5.3 BPM and organizational learning in assessment practices
Beyond governance, the study highlights the role of BPM in enabling organizational learning. The systematic capture of process data—such as timelines, compliance rates, exception handling and feedback loops—created opportunities for reflection that were previously unavailable. Managers and quality assurance units were able to identify recurring bottlenecks, variations across programs and areas of risk. As a result, assessment operations shifted from a reactive activity to a source of institutional learning.
This process-based feedback aligns with work-applied learning theories that emphasize learning through reflection on work practices rather than through formal training alone. In this sense, BPM functioned as an infrastructure for organizational learning, embedding improvement cycles in everyday work rather than as a periodic or externally driven exercise.
The evidence suggests that digital transformation initiatives in higher education are most effective when grounded in process thinking and explicitly linked to organizational learning mechanisms.
5.4 Implications for managing scale and complexity
The scale of the institution underscores the managerial significance of these findings. With tens of thousands of students and a very large volume of assessment-related activities each year, informal coordination mechanisms become unsustainable. BPM provided a means to manage scale by standardizing critical process elements while preserving flexibility where professional judgment was required.
This balance between standardization and discretion is particularly relevant for large public universities facing increasing regulatory scrutiny and performance demands. The results show that process-based approaches help organizations manage complexity by making work visible, manageable and improvable, rather than relying solely on trust and individual expertise.
While the empirical case is grounded in the Vietnamese public university context, the organizational mechanisms identified—such as workflow formalization, role separation, auditability and data visibility—address challenges of scale and coordination that are common to large higher education institutions across different national systems.
5.5 Positioning the study within work-applied management literature
From a theoretical perspective, this study contributes to work-applied management literature by linking BPM, professional work and organizational learning. It demonstrates how managerial interventions at the level of work processes can reshape practices, roles and learning dynamics in knowledge-intensive organizations. In doing so, the study responds to calls for more empirical research on how management tools operate in real work settings, particularly within the public sector and higher education contexts.
6. Managerial implications for practice
The findings offer several practical implications for managers, leaders and quality professionals in higher education and comparable professional organizations.
6.1 Treat assessment operations as a managed work process
Managers should recognize assessment as a core organizational work process rather than leaving it solely to individual professional discretion. This does not diminish academic freedom; rather, it acknowledges that large-scale assessment requires deliberate process design, coordination and monitoring. Clear process ownership and well-defined workflows can substantially reduce operational risk and inconsistency.
6.2 Use BPM to enhance visibility, not micromanagement
BPM should be used to increase process visibility and support evidence-based oversight, not to micromanage professionals. Transparent and traceable workflows enable managers to identify systemic issues and support improvement without intervening in the substance of professional decisions. This approach helps maintain trust while strengthening accountability.
6.3 Clarify roles and responsibilities across units
One of the most tangible benefits observed was clearer role definition among lecturers, program coordinators, departments and quality assurance units. BPM initiatives should formalize role boundaries and handover points, reducing reliance on informal communication and individual memory, especially in large or high-turnover organizations.
6.4 Embed organizational learning in everyday work
Quality assurance and improvement should not be treated as periodic or externally driven tasks. Managers can use BPM data to support continuous organizational learning through regular reflection on process performance, exceptions and outcomes. Dashboards and reports should be designed not only for compliance, but also to encourage learning and dialogue.
6.5 Align digital transformation with managerial capability
Digital tools do not improve management unless they are embedded within coherent process and governance frameworks. Assessment platforms should be explicitly linked to managerial concerns such as risk management, workload distribution and performance monitoring. BPM provides an effective bridge between digital infrastructure and managerial capability.
6.6 Implications beyond higher education
Although this paper focuses on a university context, its implications extend to other public sector professional organizations where work is knowledge-intensive and discretion-based. BPM can support governance, organizational learning and performance improvement in domains such as healthcare and public administration that face similar challenges of scale, accountability and professional autonomy.
7. Limitations
This study has several limitations that should be acknowledged. First, it is based on a single case selected through a theory-driven approach. As such, the findings support analytical rather than statistical generalization. In addition, HaUI's relatively high level of digital maturity may not reflect conditions at many other Vietnamese universities, which may introduce a degree of selection bias.
Second, the student survey relies on self-reported perceptions. Although survey results were triangulated with system logs and interview data to mitigate common-method bias, this limitation cannot be fully eliminated. Although the survey was deliberately designed and administered to minimize outcome-related bias, students' expectations or subjective perceptions of their own performance may still have indirectly influenced their evaluations of fairness and transparency in the assessment process.
Third, the observed operational improvements—such as reduced processing time and lower error rates—primarily reflect short-to medium-term outcomes. Further longitudinal research is needed to assess whether BPM contributes to longer-term effects, including sustained academic integrity or improved learning outcomes.
Finally, while data from partner universities strengthen the credibility of the study, these data were drawn from internal reports with differing formats and digital infrastructures. Accordingly, the cross-institutional findings should be interpreted as indicative rather than conclusive evidence of generalizability. Future research could apply standardized metrics across multiple institutions to enable more systematic and robust comparisons.
It should also be noted that this study does not evaluate changes in academic judgment, marking quality, or disciplinary standards; its contribution lies in analyzing the organizational coordination and governance conditions surrounding assessment.
8. Conclusion
As universities navigate ongoing digital transformation, they face increasing pressure to deliver assessment systems that are fair, transparent, scalable and aligned with institutional accountability requirements. This article demonstrates that BPM provides a governance-oriented approach to address these challenges. By treating assessment operations as an integrated, end-to-end organizational process supported by digital infrastructure, BPM links pedagogical intent with managerial oversight and creates a foundation for institutional reform.
The HaUI's case illustrates how BPM can be operationalized through a coherent process architecture and a customized digital platform. The reform improved objectivity, reduced process variation, shortened turnaround times and enhanced the auditability of assessment activities. These outcomes strengthened internal quality assurance and supported compliance with international accreditation frameworks, including ABET and other outcome-based standards.
Importantly, the model proved transferable beyond a single institution. Its adoption by several partner universities indicates that BPM functions not as a one-off technical solution, but as a scalable governance framework. Evidence from these institutions underscores the importance of leadership commitment, staff capacity and alignment between digital systems and governance structures for successful transfer.
From a theoretical perspective, the study extends BPM research into the organizational infrastructure supporting core academic work, showing how process standardization, role clarity and data visibility can enhance fairness and support organizational learning. From a practical standpoint, it offers a replicable framework for universities seeking to modernize assessment practices, strengthen accountability and align with global quality standards.
Overall, the case evidence suggests that BPM can serve both as a tool for institutional improvement and as a strategic framework for broader digital transformation in higher education. The lessons derived from the design, implementation and dissemination of this model provide actionable guidance for policymakers, university leaders and quality assurance professionals working to improve fairness, efficiency and transparency in student assessment.
The supplementary material for this article can be found online.
