Using Data to Improve Student Outcomes Course

Participants can map the data sources required for a student outcomes analysis.

• Student information system records for enrolment, demographics and programme status
• Learning management system data covering access, submissions and activity patterns
• Assessment, attendance, behaviour and learner-support data source mapping
• Unique learner identifiers and record-linkage risks across institutional systems
• Data lineage from collection point through transformation to management report
• Common Education Data Standards concepts for consistent education data exchange

Participants can assess whether student data is fit for a defined decision purpose.

• Data quality dimensions: completeness, accuracy, consistency, validity, timeliness and uniqueness
• Profiling missing values, duplicate records, invalid codes and implausible dates
• Reconciliation checks between enrolment, attendance, assessment and completion records
• Documenting business rules for withdrawals, transfers, deferrals and programme changes
• Sampling source records to test reported performance against underlying evidence
• Data-quality issue logs with severity, owner, corrective action and deadline

Participants can apply proportionate governance controls to student data use.

• Purpose limitation and data minimisation for student outcome analysis
• Role-based access controls for sensitive learner information and case notes
• Consent, lawful basis and retention schedules under applicable privacy requirements
• De-identification techniques for reports, workshops and external data sharing
• Bias risks in learner categories, risk scores and intervention decisions
• Governance roles: data owner, steward, custodian, analyst and decision-maker

Participants can establish credible baselines and targets for improvement priorities.

• Baseline periods selected to account for calendar cycles and cohort maturity
• Rate calculations using correct numerators, denominators and inclusion rules
• Internal benchmarking between programmes, campuses, modes and learner groups
• External benchmarks interpreted against comparable learner populations and definitions
• Target-setting methods using historical trends, capacity and intervention assumptions
• Confidence intervals and small-number cautions for performance comparisons

Participants can interpret patterns in student data without overstating conclusions.

• Frequency tables revealing distributions, concentrations, outliers and data-entry anomalies
• Median, mean, range and percentile measures for student performance data
• Trend charts separating normal variation from material shifts in outcomes
• Control charts for monitoring attendance, pass rates and withdrawal patterns
• Cross-tabulations comparing outcomes by programme, cohort and learner characteristic
• Chart selection rules for rates, counts, distributions and time-series evidence

Participants can track how defined learner cohorts progress through key stages.

• Cohort definitions based on entry date, programme, mode and learner status
• Student lifecycle stages from enquiry through enrolment, completion and progression
• Survival curves showing retention patterns across terms or academic years
• Transition matrices for movement between modules, levels and programme statuses
• Funnel analysis locating learner losses at critical progression points
• Comparing entry cohorts while controlling for elapsed study time

Participants can design indicators that identify students requiring timely follow-up.

• Attendance thresholds calibrated by delivery mode, programme intensity and learner profile
• Learning platform engagement indicators: access, participation, submission and feedback activity
• Combining missed assessments, absence and late work into risk flags
• False-positive and false-negative trade-offs in early-warning indicator design
• Escalation workflows linking risk flags to named support actions
• Response-time measures for outreach, referral, contact and intervention completion

Participants can use assessment evidence to locate learning barriers and performance gaps.

• Assessment blueprints linking learning outcomes, tasks, criteria and weightings
• Item analysis using facility values and discrimination indices
• Moderation evidence for consistency, fairness and standards in marking
• Grade distributions examined for clustering, compression and unexpected variation
• Diagnostic assessment patterns informing curriculum support and teaching response
• Feedback turnaround measures linked to learner progress and resubmission outcomes

Participants can identify and respond to outcome gaps without stigmatising learner groups.

• Equity indicators disaggregated by protected characteristics and educational disadvantage
• Intersectional analysis examining combined learner characteristics and outcome differences
• Practical significance alongside statistical significance in equity-gap interpretation
• Deficit framing avoided through barrier-focused analysis and learner voice
• Accessibility and reasonable-adjustment data incorporated into service improvement reviews
• Equity impact assessments for proposed policies, interventions and resource decisions

Participants can investigate plausible causes of an identified outcome problem.

• Problem statements specifying affected learners, outcome gap, period and magnitude
• Five Whys analysis separating symptoms from contributing process failures
• Fishbone diagrams covering curriculum, teaching, support, systems and external factors
• Process mapping the student journey around identified failure points
• Triangulating quantitative patterns with staff insight, learner feedback and case records
• Hypothesis registers recording evidence, assumptions, tests and confidence levels

Participants can segment learners for proportionate support without creating harmful labels.

• Segmentation criteria based on needs, behaviours, circumstances and study stage
• Needs-based personas grounded in evidence rather than demographic stereotypes
• Risk matrices combining likelihood, impact, urgency and support capacity
• Prioritisation rules for universal, targeted and intensive support tiers
• Caseload dashboards balancing learner need, adviser capacity and response times
• Reviewing segment definitions when outcomes or learner circumstances change

Participants can design targeted interventions with clear mechanisms and measurable results.

• Theory of change linking intervention activities to student outcome mechanisms
• PDSA cycles for testing support changes before wider implementation
• Intervention design specifying population, offer, channel, timing and responsible owner
• Implementation fidelity measures distinguishing poor delivery from weak intervention design
• Equity checks ensuring support access does not widen existing gaps
• Success measures covering reach, uptake, experience, outcomes and unintended effects

Participants can evaluate whether an intervention contributed to improved student outcomes.

• Before-and-after comparisons with clear baseline, follow-up and cohort definitions
• Comparison groups selected for credible intervention-effect interpretation
• Difference-in-differences logic for evaluating phased programme changes
• Contribution analysis when randomised trials are impractical or inappropriate
• Process evaluation examining reach, fidelity, participant experience and delivery barriers
• Evaluation reports separating findings, limitations, recommendations and next decisions

Participants can specify reports that support timely management action.

• Dashboard user stories defining decisions, users, questions and action triggers
• KPI hierarchies connecting board measures with operational follow-up indicators
• Exception reporting highlighting material changes, thresholds and unresolved risks
• Visual design principles for accessible charts, tables and performance commentary
• Narrative reporting that states finding, implication, action and accountable owner
• Dashboard governance covering refresh schedules, version control and metric changes

Participants can run structured reviews that convert evidence into monitored actions.

• Performance review agendas structured around outcomes, causes, actions and decisions
• RACI matrices assigning accountability for data, interventions and follow-up
• Action logs recording owner, milestone, dependency, measure and status
• Escalation criteria for persistent underperformance, safeguarding concerns and resource constraints
• Meeting packs combining KPI trends, learner insight and intervention evidence
• Review cadences aligned to academic calendars, assessment cycles and governance meetings

Participants can assemble an evidence-based plan for a priority student outcome challenge.

• Priority problem selection using impact, urgency, feasibility and strategic alignment
• Stakeholder mapping across academic teams, support services, learners and governors
• Improvement-plan structure covering baseline, diagnosis, intervention and evaluation
• Resource estimates for staffing, systems, communications and learner-support capacity
• Risk registers addressing data limitations, implementation barriers and unintended consequences
• Ninety-day implementation roadmaps with milestones, owners and review points

Participants can communicate student-outcome evidence clearly to operational and senior audiences.

• Executive briefing structures for decisions, options, risks and recommended actions
• Technical appendices documenting definitions, methods, limitations and data-quality findings
• Staff communications explaining measures without blame or misleading simplification
• Learner-facing messages using transparent, respectful and accessible language
• Challenge questions anticipated from governors, regulators and senior leaders
• Presentation rehearsal techniques for defending findings and responding to uncertainty

Participants can establish routines that sustain evidence use beyond a single project.

• Data literacy expectations for managers, teachers, advisers and support teams
• Communities of practice for sharing measures, findings and improvement lessons
• Standard operating procedures for recurring outcome reviews and action tracking
• Data stewardship arrangements supporting definition control and quality remediation
• Professional judgement balanced with quantitative evidence and learner voice
• Maturity assessments identifying next improvements in people, process and technology

Participants can build, review and present a complete student outcomes improvement plan supported by evidence.

• Define a priority outcome problem using baseline evidence and cohort boundaries
• Assess data quality, ethical constraints and limitations affecting proposed analysis
• Analyse learner patterns through disaggregation, lifecycle measures and root-cause hypotheses
• Specify a targeted intervention using theory of change and PDSA cycles
• Create dashboard measures, governance arrangements and a ninety-day action roadmap
• Present recommendations, receive peer review and revise the complete improvement plan