Decomposed level 1

CMM Segment (Line Title)	Risk Source	Risk Name (Decomposed)	Impact	Typical Responses
Initial	Hardware shortage	Lack of suitable devices (PC/laptop absence)	Students forced to use mobile phones only; unable to run LMS, write essays, or access complex simulations	Device loan programs, emergency hardware grants, partnerships with local libraries for computer access
Initial	Connectivity instability	Unreliable or no Internet access	Frequent disconnections during live sessions; inability to download/upload materials; missed assessments	Low- bandwidth mobile data packages, offline content packs (ZIP/USB), asynchronous submission windows, ISP subsidies
Initial	Environmental constraint	No quiet study space at home	High cognitive load from noise/distractions; inability to participate in oral exams or group discussions	Provision of quiet study rooms on campus, noise- cancelling headphone loans, scheduled “quiet hours” in libraries
Initial	Socioeconomic/ability gap	Digital literacy & accessibility gap	Students cannot navigate LMS, use collaboration tools, or access content due to disability Lack of skills	Mandatory onboarding tutorials, simplified UI modes, screen-reader testing, peer tech-support mentors
Initial	Process immaturity	Inconsistent course delivery	Confused learners, missed objectives, reputation damage	Minimum delivery standards, instructor quick- guides, peer mentoring, centralized templates
Initial	Assessment design weakness	High cheating / academic integrity Inc.	Erosion of standards, unfair grading, regulatory issues	Authentic assessments, randomized banks, honor pledges, remote proctoring, forensic review
Managed	Scheduling & workload	Time-management failure	Lower completion, stress, poorer outcomes	Pacing guides, check-ins, automated reminders, smaller milestones, study skills modules
Managed	Tech reliability & support	Platform downtime & poor support	Missed classes/assessments, frustration	SLA with provider, redundant channels, extended support, status page, contingency procedures
Managed	Data & privacy weak	Student data exposure	Legal/regulatory fines, trust loss	Privacy settings enforcement, staff training, data minimization, backups, encrypted storage
Defined	Pedagogy gap	Poor instructional design (online)	Low engagement, passive learning, poor transfer	ID review mandatory, online pedagogy training, standardized outcomes mapped to activities, peer review
Defined	Social/engagement factors	Student isolation & weak soft skills	Lower participation, mental health issues, weaker collaboration	Synchronous group tasks, virtual community spaces, peer mentoring, wellbeing resources
Defined	Accessibility oversight	Non-compliance with accessibility	Exclusion of learners with disabilities, legal risk	Accessibility checklist in sign-off, captioning, screen-reader testing, remediation plan
Quantitatively Managed	Analytics blind spots	Incomplete/misinterpreted analytics	Wrong interventions, wasted resources	Consistent metrics (engagement, timeliness), instructor dashboards, model validation, data governance
Quantitatively Managed	Quality variability	Inconsistent outcomes across sections	Accreditation risk, student dissatisfaction	Statistical monitoring, standard rubrics, targeted faculty coaching
Quantitatively Managed	Security threats	Cyberattacks (phishing, account takeover)	Service disruption, data breach, exam compromise	MFA, phishing training, incident response playbooks, pen-testing, patch cadence
Optimizing	Innovation risk	Poorly tested new tools (AI, XR)	Wasted investment, learner confusion, bias issues	Pilot programs, ethical review, phased rollouts with metrics, vendor due diligence
Optimizing	Change fatigue	Overload from continuous updates	Reduced adoption, staff burnout	Stakeholder co-design, prioritized roadmap, communication calendar, change impact assessments
Optimizing	Long-term wellbeing	Chronic screen-time & mental decline	Reduced retention, health complaints, poorer learning	Blended modality targets, enforced breaks, wellbeing curriculum, survey monitoring and support referrals

Back to Risk Model