A university can have a secure online examination system and still have a fragmented assessment operation.
Questions may be stored in one system. Student information may come from another. Learning outcome mappings may live in spreadsheets. Examination scheduling may depend on manual coordination. Practical assessments may be managed separately. Accreditation evidence may need to be assembled after the assessment is complete.
That is why choosing the right digital assessment platform features is no longer simply an examination technology decision.
Universities need to evaluate whether a digital assessment platform can connect assessment design, governance, academic integrity, delivery, evaluation, analytics, learning outcomes, institutional reporting and continuous improvement.
For academic leaders, examination teams, quality and accreditation professionals, CIOs, IT teams and procurement leaders, the more important question is not:
How many features does the platform have?
It is:
Can the platform help the institution design, govern, deliver, evaluate, analyze and improve assessment across its entire lifecycle?
This distinction matters because assessment is not just an examination event. It is a source of evidence about student learning, assessment quality, programme outcomes and institutional performance.
This guide explains the 12 essential capability areas universities should evaluate when selecting a digital assessment platform for universities, why each capability matters, and what institutions should ask vendors to demonstrate before making a decision.
The essential digital assessment platform features for universities should include assessment authoring, intelligent question banks, blueprinting and learning outcome alignment, assessment governance, multi-modal assessment, secure and flexible delivery, evaluation and moderation, assessment analytics, competency and programme-level evidence, accreditation reporting, interoperability, data portability, accessibility, scalability and reliability.
However, there is no universal feature checklist.
A capability becomes valuable when it addresses a genuine institutional requirement.
A medical university may prioritize OSCE, OSPE and workplace-based assessment. A large multi-campus university may prioritize intelligent scheduling, test-center management and offline delivery. An institution preparing for accreditation may place greater emphasis on outcome mapping, evidence traceability and institutional reporting.
The right approach is therefore to evaluate the platform against the university’s assessment lifecycle, not simply against a product brochure.
Digital assessment has reached a more complex stage.
Universities are now balancing academic integrity, accessibility, learner experience, artificial intelligence, institutional policy, interoperability, security and financial pressures while also managing increasingly diverse assessment models.
That means a platform that simply delivers an online examination may solve only one part of the institutional problem.
Consider a typical university assessment journey.
A faculty member creates questions.
The questions are reviewed.
The assessment is mapped to learning outcomes.
A blueprint is created.
Students are scheduled.
Rooms, devices and proctors are allocated.
The assessment is delivered.
Students submit responses.
Evaluators mark them.
Results are reviewed.
Item performance is analyzed.
Learning outcomes are assessed.
Programme-level evidence is generated.
The institution identifies areas for improvement.
That is an assessment lifecycle.
A modern digital assessment system should help connect these stages rather than treating each one as a separate activity.
A useful institutional model is:
Plan → Design → Prepare → Deliver → Secure → Evaluate → Analyze → Report → Improve
An even simpler strategic framework is:
This framework provides a better way to evaluate assessment technology.
For example:
Question → Blueprint → Assessment → Student response → Evaluation → Item analysis → Learning outcome evidence → Programme reporting → Continuous improvement
The value of a connected platform comes from preserving these relationships.
Assessment data should not become disconnected as it moves through the institution.
The question should remain connected to its metadata.
The question should remain connected to the assessment.
The assessment should remain connected to the learning outcomes.
The result should remain connected to the evidence.
The evidence should inform improvement.
This is the foundation of connected assessment.
The first capability universities should evaluate in digital assessment software is assessment design and authoring.
Universities may have hundreds of faculty members creating assessments across multiple faculties, departments, courses and programmes.
If assessment creation depends on disconnected tools and manual processes, maintaining consistency, quality and governance becomes increasingly difficult.
A capable assessment authoring environment should support:
Assessment creation and configuration
Multiple question types
Structured question authoring
Reusable assessment content
Templates
Question metadata
Media-based questions where appropriate
Version control
Author and reviewer workflows
Controlled publication
But the objective is not simply to make it faster to create an exam.
The larger objective is to create a controlled academic process.
For example, a university may want a standardized assessment structure across a programme while allowing faculty members to create discipline-specific content.
The platform should support both institutional consistency and academic flexibility.
Ask:
Can multiple academic users collaborate?
Can assessments be reviewed and approved?
Is version history available?
Can assessment content be reused?
Can changes be tracked?
Can permissions be configured?
Can the university standardize assessment templates?
A strong vendor demonstration should show an assessment moving from authoring → review → approval, rather than simply showing a question editor.
A question bank should not simply be a digital folder containing old examination questions.
When properly structured, it can become a long-term institutional assessment asset.
Questions can be associated with metadata such as:
Course
Topic
Difficulty
Question type
Cognitive level
Learning outcome
Competency
Author
Review status
Version
Assessment history
This creates a more useful question lifecycle:
Create → Review → Approve → Use → Analyze → Revise → Reuse or Retire
That lifecycle connects question bank management with assessment quality and assessment intelligence.
For example, if a question consistently produces unexpected results, academic teams can investigate whether the issue relates to ambiguity, content coverage, difficulty or another factor.
Ask the vendor:
“Show us the complete lifecycle of one question.”
The demonstration should cover:
Question creation → metadata → review → assessment use → performance → item analysis → revision
A platform with a large question bank is not automatically a platform with a mature question-management capability.
The real value comes from what the institution can do with those questions over time.
One of the most important digital assessment platform features for higher education is blueprinting.
An assessment blueprint defines the intended structure of an assessment before questions are assembled.
A blueprint may specify:
Topics
Number of questions
Marks
Difficulty levels
Cognitive levels
Learning outcomes
Content distribution
Assessment weightage
Imagine an assessment designed to measure several Course Learning Outcomes.
Without a blueprint, question selection can become heavily dependent on individual judgment.
With a blueprint, the academic team can define the intended assessment structure before delivery.
This becomes particularly important for Outcome-Based Education.
A university should be able to answer:
Why is this question included in this assessment?
The answer should connect to the academic purpose of the assessment.
A mature platform should allow institutions to connect:
Question → CLO → PLO → Programme → Evidence
This creates traceability.
Ask vendors:
Can questions be mapped to learning outcomes?
Can assessment blueprints be created and enforced?
Can coverage be checked before delivery?
Can blueprint information feed analytics?
Can outcome mappings appear in reports?
Can institutions maintain multiple frameworks?
A blueprint should not simply exist as a configuration screen.
It should remain useful throughout the assessment lifecycle.
As assessment operations become more complex, governance becomes as important as functionality.
Universities need to know:
Who created an assessment?
Who reviewed it?
Who approved it?
Which version was approved?
Who changed it?
Which policy applied?
Who scheduled it?
Who evaluated it?
What exceptions occurred?
Who authorized those exceptions?
This is the role of assessment governance.
Governance turns institutional policies and responsibilities into operational controls.
A governance-driven assessment environment should support:
Role-based permissions
Approval workflows
Version control
Audit trails
Policy configuration
Exception management
Assessment ownership
Moderation workflows
Controlled administrative intervention
The objective is not to eliminate academic judgment.
It is to ensure that academic decisions happen within a transparent and accountable process.
Do not ask only:
“Does the platform have audit logs?”
Ask:
“Show us the audit trail for a real assessment from creation through result publication.”
That demonstrates whether governance is genuinely embedded in the platform.
One of the biggest mistakes in evaluating assessment technology is assuming that university assessment means online written examinations.
Universities assess learning in many different ways.
Depending on discipline and programme, assessment may include:
Computer-based written examinations
Paper-based examinations
Paper-to-digital assessment
Practical examinations
Clinical examinations
OSCE
OSPE
Workplace-based assessment
Team-based readiness assessment
Portfolio-based evaluation
Continuous assessment
Competency assessment
This matters because different assessment modalities produce different types of evidence.
A university that conducts clinical education may need structured observation and station-based evaluation.
An engineering programme may need practical or technical assessment.
A professional programme may need workplace-based evidence.
A university may also need to preserve paper-based assessment while progressively digitizing its workflows.
Therefore, an institution should ask:
“Can this platform support the assessment modalities we actually use?”
A platform may be excellent at online MCQ delivery but provide limited value for an institution that needs practical, clinical and workplace assessment.
The strongest assessment environments allow institutions to bring different assessment evidence into a connected academic view.
That is a much more powerful proposition than simply supporting more question types.
Practical and clinical assessment often requires workflows that are fundamentally different from written examinations.
For example:
Student → Station → Evaluator → Rubric → Observation → Score → Feedback
A platform supporting OSCE or OSPE should therefore be able to manage more than question delivery.
It may need to support:
Stations
Circuits
Evaluator assignment
Structured scoring
Timing
Observation
Feedback
Results
Reporting
DigiAssess’s OSSAI capability is designed around practical and clinical station-based assessment workflows.
Competency cannot always be demonstrated through one examination.
It may require evidence collected over time.
For example:
Observation → Evidence → Assessor feedback → Reflection → Competency progression
This is particularly relevant to clinical, laboratory, internship, research and professional education.
DigiAssess’s DigiPortfolio capability addresses longer-term workplace and competency assessment, including evidence, reflections, assessor feedback and competency progression.
The key principle is:
A university should choose assessment technology based on how it demonstrates learning, not merely how it delivers exams.
Assessment delivery is where the platform becomes visible to students, but flexibility should not mean sacrificing control.
Universities may need different delivery environments depending on assessment type and risk.
These can include:
Onsite assessment
Remote assessment
Online assessment
Offline assessment
Hybrid assessment
BYOD
Controlled test centers
Distributed assessments
High-stakes examinations
The appropriate model depends on institutional context.
A controlled test center may be appropriate for a high-stakes professional examination.
A formative assessment may be appropriate for remote delivery.
An institution operating across regions with variable connectivity may require offline capabilities.
A mature online assessment platform should allow universities to select the appropriate delivery model for each assessment.
Universities should not evaluate delivery only under ideal conditions.
Ask:
What happens if connectivity fails?
Can an assessment continue offline?
How are responses stored?
How does synchronization occur?
What happens when the student reconnects?
Can administrators intervene?
Can an assessment be paused or resumed?
What happens if a device becomes unavailable?
The dedicated Examly environment within the DigiAssess ecosystem is designed around online, offline, BYOD and test-center delivery models, including encrypted offline storage and later synchronization.
The broader procurement principle is:
Test the platform under failure conditions, not only under ideal conditions.
Security is one of the most visible categories of digital assessment features, but security should not be confused with academic integrity.
A mature assessment environment may use multiple layers:
Authentication
→ identity verification
Secure delivery
→ assessment content protection
Access controls
→ controlled participation
Randomization
→ reduced predictable exposure
Monitoring
→ behavioural evidence
Proctoring
→ supervision where appropriate
Audit trails
→ preserved evidence
Governance
→ institutional response
This layered model is especially important as universities conduct assessments across onsite, remote and hybrid environments.
AI-powered proctoring can be useful in some contexts, but universities should not treat it as a magic solution.
The right questions include:
How are potential incidents detected?
How are false positives handled?
Can authorized staff review flagged events?
What evidence is retained?
Who can access incident data?
What institutional policy governs the response?
Can human reviewers override automated decisions?
The principle is:
Academic integrity is a governance outcome, not merely a technology feature.
A secure platform should therefore provide the institution with visibility, control and accountability rather than simply attempting to prevent misconduct through technical restrictions.
Digital assessment does not end when the student submits a response.
The institution still needs to:
Evaluate → Moderate → Finalize → Report → Provide feedback
A capable digital assessment software solution should support evaluation workflows appropriate to the assessment type.
Depending on context, that may include:
Automated scoring where appropriate
Manual marking
Rubrics
Structured evaluation
Digital marking
Moderation
Grade review
Feedback
Result processing
Automation should not be treated as the objective by itself.
Some assessments require expert academic judgment.
The platform should instead help make evaluation:
Consistent
Traceable
Efficient
Reviewable
Evidence-based
For practical assessment, evaluators may need structured rubrics.
For clinical assessment, they may need to record observations across stations.
For workplace assessment, evidence may need to be collected over time.
Therefore, universities should evaluate whether the platform supports different evaluation models, rather than asking only whether it supports automated grading.
Traditional reporting answers:
What happened?
Assessment intelligence asks:
What does the evidence suggest, and what should we examine or improve?
This distinction is becoming increasingly important.
Useful assessment analytics may include:
Student performance
Cohort performance
Grade distribution
Item performance
Item difficulty
Item discrimination
Question quality
Outcome performance
Blueprint coverage
Assessment trends
Programme-level patterns
But analytics should support investigation rather than automatically determine causation.
For example, if an item performs poorly, possible explanations could include:
Ambiguous wording
Inappropriate difficulty
Insufficient content coverage
Blueprint imbalance
Misalignment with learning outcomes
Genuine conceptual difficulty
The purpose of assessment intelligence is therefore not simply to produce more charts.
It is to help academic teams ask better questions.
Reporting:
The average score was 64%.
Assessment intelligence:
Which outcomes performed below target, which assessment components contributed to that pattern, and what should the academic team investigate?
This is where assessment technology can begin contributing to continuous improvement.
A university needs more than individual examination scores.
Academic leaders often need to understand whether students are progressing toward course, programme and professional outcomes.
A useful evidence chain is:
Question → Assessment → CLO → PLO → Programme → Competency → Student progression → Institutional evidence
This is where learning outcome mapping and competency assessment become strategically important.
For Outcome-Based Education, the institution should be able to understand how assessment activity contributes to intended learning outcomes.
For competency-based programmes, the evidence may come from multiple sources:
Written examinations
Practical assessments
Clinical observations
Workplace assessment
Portfolio evidence
Structured feedback
The institution therefore needs a way to bring these evidence streams together.
The goal is not simply to calculate another score.
It is to understand:
What evidence do we have that the learner achieved the intended outcome or competency?
That is a much more meaningful institutional question.
Reporting is often treated as the final stage of assessment.
For universities, it should instead become the bridge to improvement.
Useful reporting may exist at several levels:
A report may show a result.
Evidence should help explain:
What was assessed?
Why was it assessed?
Which outcomes were involved?
Which policy governed it?
Which assessment version was used?
How was it evaluated?
What did the results show?
What action followed?
This is particularly important for accreditation and quality assurance.
Accreditation evidence should ideally be generated from normal academic processes rather than manually reconstructed months later.
DigiAssess reporting capabilities include consolidated performance reporting, learning outcome analysis, individual student reports, improvement or progress reporting and accreditation or compliance-oriented outputs.
That creates an important shift:
Accreditation readiness should be built into assessment operations, not treated as a reporting exercise at the end of the academic year.
A university’s assessment environment rarely exists independently.
It may need to interact with:
Student Information Systems
ERP platforms
LMS platforms
Identity systems
Quality systems
Reporting environments
Therefore, integration and interoperability should be part of the core procurement discussion.
But asking:
“Does your platform have an API?”
is not enough.
Ask:
What data can be exchanged?
In which direction?
How frequently?
How are failures handled?
How are authentication and authorization managed?
Can historical data be migrated?
What happens when the university changes systems?
Data portability is another important consideration.
Final grades are not the only institutional data that may matter.
Universities may need access to:
Questions
Question metadata
Assessments
Blueprints
Outcome mappings
Attempts
Marks
Feedback
Reports
Audit evidence
A platform should therefore be evaluated not only for what it can import, but also for what the institution can retrieve and reuse.
Where relevant, institutions should also ask vendors about support for established assessment interoperability approaches and standards.
Accessibility should not be treated as a checkbox.
Universities should evaluate accessibility across:
Authoring → Student assessment → Evaluation → Reporting
Questions should include:
Can students navigate the assessment using a keyboard?
Does the interface work with assistive technologies?
Are assessment questions accessible?
Can alternative formats be supported where required?
Can appropriate accommodations be configured?
Is authentication accessible?
Are proctoring workflows compatible with institutional accessibility requirements?
Institutions should request evidence rather than relying on a generic “accessible” claim.
For web-based systems, WCAG 2.2 provides a recognized framework for evaluating accessibility across areas including keyboard operation, input assistance, authentication and compatibility with assistive technologies.
Finally, universities should request evidence about:
Concurrent users
Peak examination loads
Performance
Availability
Disaster recovery
Monitoring
Incident response
Offline synchronization
Support processes
“Scalable” is a claim.
Load-test evidence is evidence.
Not every capability should carry equal weight during procurement.
This classification is not universal.
A university conducting primarily formative assessments may not require sophisticated proctoring.
A medical university may consider OSCE and workplace assessment essential.
A multi-campus institution may place much greater value on scheduling, seat allocation and test-center management.
The correct procurement approach is therefore:
Match platform capabilities to institutional requirements, assessment risk, academic models and operational realities.
For large universities, assessment delivery is also a resource-management problem.
Consider coordinating:
Thousands of students
Multiple campuses
Limited rooms
Computer availability
Proctor availability
Different assessment durations
Seat capacity
Student conflicts
Special requirements
Different assessment windows
This can quickly become spreadsheet-intensive.
That is why intelligent assessment scheduling can be strategically valuable.
A mature platform may need to consider:
Student availability
Faculty availability
Proctor availability
Room capacity
Device availability
Test-center capacity
Assessment duration
Infrastructure readiness
Scheduling conflicts
Institutional constraints
Test-center management can also include:
Capacity
Devices
Seating
Proctor allocation
Infrastructure readiness
Standby resources
Real-time operational visibility
DigiAssess’s governance-first material specifically describes AI-assisted scheduling, test-center management, seat allocation and resource constraints as part of the assessment ecosystem.
The key procurement question is:
“Can the platform manage the operational complexity behind large-scale assessment?”
For an institution conducting thousands of assessments, that question may be more important than whether the platform has another question type.
Digital transformation does not always mean abandoning paper immediately.
Many universities still operate assessments that involve:
Paper examinations
Handwritten responses
Physical answer sheets
Practical documentation
Mixed digital and non-digital workflows
A useful digital assessment platform should therefore be able to coexist with transitional assessment models.
Paper-to-digital workflows can help institutions move toward greater digital visibility without requiring every assessment process to become fully computer-based overnight.
This is especially relevant when institutions need to preserve existing academic practices while improving:
Data capture
Reporting
Outcome mapping
Analysis
Traceability
Institutional evidence
The strategic goal is not:
“Make everything digital.”
It is:
“Make assessment evidence more connected, measurable and useful.”
Not necessarily.
An LMS can support many routine assessments and remains an important part of the academic technology environment.
The question is whether the LMS provides everything the institution needs for its assessment model.
A typical LMS environment is centered around:
Learning → Content → Course activity → Course assessment
A broader assessment environment may need to support:
Design → Govern → Deliver → Evaluate → Analyze → Evidence → Improve
A university may need specialist assessment capabilities when it requires:
High-stakes examination controls
Advanced assessment governance
Blueprinting
Learning outcome traceability
Item analysis
Test-center management
Complex scheduling
Practical or clinical assessment
Workplace-based assessment
Accreditation evidence
Institution-wide assessment intelligence
The decision should therefore not be:
“LMS or assessment platform?”
It should be:
“Which assessment capabilities should remain within our existing environment, and which require a specialist assessment platform?”
This allows institutions to build a connected academic technology architecture without replacing systems unnecessarily.
AI should not be treated as one isolated feature.
It is increasingly becoming a cross-cutting capability across the assessment lifecycle.
AI can assist with question creation, assessment preparation, content structuring and authoring workflows.
AI can assist with appropriate evaluation workflows, rubric support and feedback processes where institutionally suitable.
AI can assist with anomaly detection, monitoring and proctoring workflows.
AI can help identify patterns in assessment data and surface areas that may require academic investigation.
Natural-language interfaces can make assessment information easier for students, faculty and institutional leaders to explore.
The important question is not:
“Does the platform use AI?”
It is:
“Where does AI create value, and what governance controls surround it?”
Universities should ask:
What exactly does the AI do?
What data does it use?
Is human review required?
Can a decision be overridden?
How are false positives handled?
What evidence is retained?
How is privacy addressed?
How are AI-generated outputs governed?
What happens when the AI is uncertain?
The principle is:
AI should strengthen assessment governance and decision-making, not remove institutional accountability.
This may be the most important part of the procurement process.
Do not spend the entire vendor demonstration watching isolated feature screens.
Give the vendor a realistic institutional scenario.
For example:
“Show us how one assessment moves from question creation to programme-level outcome reporting.”
Then ask the vendor to demonstrate:
Create the assessment.
Add questions.
Apply metadata.
Create a blueprint.
Map learning outcomes.
Apply institutional policies.
Schedule students.
Allocate required resources.
Deliver the assessment.
Handle an operational exception.
Evaluate responses.
Moderate results.
Analyze item performance.
Review outcome attainment.
Generate programme-level evidence.
This reveals something a product brochure cannot:
A platform can have hundreds of features and still create fragmented processes if those capabilities operate independently.
The strongest assessment technology should allow data and evidence to remain connected as the assessment progresses.
Ask:
Can we follow one assessment from design to evidence?
Look for:
Design → Governance → Delivery → Evaluation → Analytics → Evidence
If the workflow breaks into multiple disconnected systems, investigate why.
Ask:
Can we determine who did what, when and under which policy?
Test:
Approval
Modification
Scheduling
Evaluation
Exception handling
Result publication
The goal is institutional accountability.
Ask:
Can we trace an assessment activity to its intended learning outcome?
Ideally, demonstrate:
Question → CLO → PLO → Programme → Report
This is particularly important for Outcome-Based Education and accreditation.
Do not test only the ideal scenario.
Ask:
What happens if connectivity fails?
What happens if a device disconnects?
What happens if an assessment needs to be paused?
What happens if a student requires an approved adjustment?
What happens if a test center loses capacity?
What happens if multiple campuses experience operational problems?
A robust platform should have defined workflows for these situations.
Ask:
“If we needed to migrate away from the platform in the future, what assessment data could we export?”
Ask specifically about:
Assessment content
Questions
Metadata
Outcomes
Attempts
Marks
Feedback
Reports
Audit records
This can reveal the difference between a sustainable institutional platform and a highly dependent technology environment.
Before selecting a digital assessment platform, universities should request evidence alongside functionality.
The principle is simple:
Do not evaluate a feature only from a checkbox. Evaluate it through evidence.
If a vendor says:
“Yes, we support intelligent scheduling.”
Ask them to schedule a realistic institutional scenario.
If they say:
“Yes, we support assessment analytics.”
Ask them to analyze a real assessment.
If they say:
“Yes, we support outcome mapping.”
Ask them to demonstrate:
Question → CLO → PLO → Programme report.
Evidence makes vendor comparison meaningful.
DigiAssess approaches assessment as a connected institutional workflow rather than only as an online examination tool.
Its documented assessment lifecycle can be viewed through three broad stages.
The foundation includes:
Institutional onboarding
Academic structure
User and system integration
Learning outcome frameworks
Assessment configuration
Assessment blueprints
Question banks
The assessment then moves through:
Assessment preparation
Governance
Scheduling
Centralized or decentralized delivery
Onsite or remote assessment
Online or offline execution
Authentication
Proctoring
Examination conduction
Evaluation
The resulting evidence can include:
Item analysis
Consolidated performance reporting
Learning outcome analysis
Individual student performance
Improvement and progress reporting
Accreditation and compliance evidence
This creates a connected chain:
Assessment Design → Governance → Delivery → Evaluation → Analytics → Outcomes → Evidence → Improvement
The broader DigiAssess ecosystem extends this approach across different assessment modalities, including written assessment, paper-to-digital workflows, practical and clinical assessment, workplace-based assessment, team-based assessment and portfolio-based competency assessment.
It also brings together capabilities such as policy-driven governance, intelligent scheduling, test-center management, AI-powered proctoring, assessment analytics and institutional reporting.
This creates an important strategic distinction.
DigiAssess is not positioned simply as a tool for putting an examination online.
It is positioned as an assessment governance and intelligence ecosystem designed to connect assessment activity with institutional evidence.
That distinction matters because the future of university assessment is not only about delivering exams digitally.
It is about making assessment:
Governed.
Traceable.
Measurable.
Intelligent.
Evidence-driven.
Continuously improvable.
Selecting a digital assessment platform is a strategic decision for a university.
The strongest platform is not necessarily the one with the most features.
It is the one whose capabilities work together to support the institution’s actual assessment requirements.
That means asking:
Can we design assessments effectively?
Can we manage and govern assessment content?
Can we align assessments with learning outcomes?
Can we apply institutional policies consistently?
Can we deliver assessments securely across the environments we need?
Can we support written, practical, clinical, workplace and other assessment modalities where required?
Can we evaluate and moderate results efficiently?
Can we understand item, student, course and programme performance?
Can we connect assessment evidence to learning outcomes and competencies?
Can we produce reliable evidence for quality assurance and accreditation?
Can the platform integrate with our existing digital ecosystem?
Can it remain accessible, reliable, scalable and portable over time?
Most importantly:
Can assessment move from an isolated examination event to meaningful institutional evidence and continuous improvement?
That is the difference between simply digitizing an exam and building a connected assessment capability.
For universities evaluating digital assessment platform features, the starting point should therefore not be a vendor’s feature list.
Start with the assessment lifecycle.
That is the foundation for a more connected, transparent and intelligence-driven approach to assessment in higher education.
