HE-D01 — Digital Transformation of Universities

Course Code: HE-D01  |  School: School of Higher Education  |  Cluster: Institutional Development

Level: Advanced  |  Estimated duration: 6 weeks (approximately 40 hours)  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

Digital transformation in universities is usually described as a technology programme and is almost always an organisational one. Institutions procure a learning platform, a student information system and an analytics layer, and then discover that the difficulty lies in academic workload models, data definitions, committee structures and the professional identity of teaching staff. This course begins from that observation and treats technology as the least difficult component.

The course is deliberately sceptical about the transformation literature. Christensen’s disruption thesis, applied to higher education with considerable confidence, has not predicted well; the massive open online course wave of the early 2010s did not displace the university; and Martin Weller’s history of educational technology documents a long sequence of technologies that were each going to change everything. Neil Selwyn’s critical work is used to keep the analysis honest.

What remains after the scepticism is substantial. Digital infrastructure genuinely changes what a university can do at what cost, and institutions that manage it well have real advantages in access, flexibility and evidence. Learners build a transformation strategy grounded in institutional capability, sequenced realistically, and costed including the organisational change that technology procurement usually omits.

Learning outcomes

On completion, a successful learner will be able to:

1. Assess an institution’s digital maturity across infrastructure, capability, governance and culture.
2. Evaluate claims about technological disruption in higher education against the historical record.
3. Design a digital transformation strategy sequenced by dependency and institutional readiness.
4. Analyse the organisational, workload and identity barriers that determine whether technology adoption succeeds.
5. Specify institutional systems and integration requirements, including data architecture and interoperability.
6. Assess the governance, privacy, procurement and vendor dependency risks of digital investment.
7. Build a business case for digital investment that includes the full cost of organisational change.

Who this course is for

Senior institutional leaders, chief information officers and digital directors, deans, heads of learning technology, planning staff, and change managers responsible for institution-wide systems projects.

Prerequisites

Familiarity with how a university operates administratively and academically. This is an advanced course and assumes prior institutional experience; HE-F02 The University as an Institution provides useful grounding.

Syllabus

Module 1 — Diagnosing Digital Maturity

Transformation strategies fail when they are built for the institution the leadership imagines rather than the one that exists. This module establishes the baseline honestly across four dimensions.

Lessons. 1.1 Infrastructure, systems and technical debt  ·  1.2 Staff and student digital capability  ·  1.3 Governance, data ownership and decision rights  ·  1.4 Culture, trust and the history of previous failed projects

Deliverable. A diagnostic note assessing one institution’s digital maturity across four dimensions, including an honest account of a previous failed technology project.

Module 2 — Disruption Claims and the Historical Record

Every generation of educational technology has been announced as transformative, and the record of those announcements is instructive. This module reads the claims against what happened.

Lessons. 2.1 Christensen’s disruption thesis applied to higher education  ·  2.2 The MOOC wave: predictions, outcomes and what was learned  ·  2.3 Weller’s history of educational technology cycles  ·  2.4 Distinguishing durable change from hype in current claims

Deliverable. An analytical brief evaluating one current transformation claim against the historical pattern of educational technology adoption.

Module 3 — Systems, Data and Architecture

Institutional systems fail at their joins, and most universities carry a decade of partially integrated procurement. The module specifies architecture rather than products.

Lessons. 3.1 Core systems: student records, learning platform, finance, research  ·  3.2 Integration, interoperability and the single source of truth  ·  3.3 Data architecture, definitions and governance  ·  3.4 Vendor lock-in, procurement strategy and exit planning

Deliverable. A design artefact: a target systems and data architecture for one institution, with an explicit migration path from the current state.

Module 4 — The Organisational Barrier

The determining variables in technology adoption are workload, academic autonomy and professional identity, none of which appear in procurement documents. This module addresses them directly.

Lessons. 4.1 Workload models and the real cost of digital teaching  ·  4.2 Academic autonomy and the resistance to standardised platforms  ·  4.3 Professional identity and the framing of technology as deskilling  ·  4.4 Support, training and the failure of one-off staff development

Deliverable. An evaluation report analysing the organisational barriers to one specific digital initiative, with evidence from staff perspectives.

Module 5 — Governance, Risk and the Business Case

Digital investment decisions are frequently made on vendor projections and unfunded change assumptions. The module builds a business case that includes what those omit.

Lessons. 5.1 Privacy, data protection and student surveillance risk  ·  5.2 Algorithmic systems, procurement scrutiny and accountability  ·  5.3 Total cost of ownership including organisational change  ·  5.4 Benefits realisation, monitoring and the discipline of stopping

Deliverable. An implementation plan comprising a phased transformation roadmap with a full business case, risk register and stopping criteria.

Module 6 — Capstone: A Digital Transformation Strategy

The capstone produces a strategy that a governing body could approve and an institution could actually execute, including the things it declines to do. Realism is weighted above ambition.

Lessons. 6.1 Setting the baseline and the strategic case  ·  6.2 Sequencing by dependency and readiness  ·  6.3 Costing the change, not just the technology  ·  6.4 Presenting to governance

Deliverable. A 3,000-word digital transformation strategy for one named institution, including maturity assessment, target architecture, phased roadmap, business case, risk register and explicit exclusions.

Assessment

Pass mark 70 per cent. All assessed components must be attempted. Every mark in this course is issued by a human assessor; no assessment outcome is generated automatically.

Rubric criteria

Each assessed artefact is marked against four criteria at four levels (distinction, pass with merit, pass, fail).

1. Diagnostic honesty. The institutional baseline, including past failures, is assessed candidly.
2. Critical judgement. Technology claims are tested against evidence rather than accepted from vendors or advocates.
3. Architectural coherence. Systems, data and integration proposals are technically consistent and migratable.
4. Change realism. Organisational barriers, costs and timelines are treated as the primary constraint.

Reading list

Core. Selwyn, N. (2016) Is Technology Good for Education? Cambridge: Polity Press.

• Bates, A. W. (2019) Teaching in a Digital Age: Guidelines for Designing Teaching and Learning, 2nd edn. Vancouver: Tony Bates Associates.
• Weller, M. (2020) 25 Years of Ed Tech. Edmonton: Athabasca University Press.
• Christensen, C. M. and Eyring, H. J. (2011) The Innovative University: Changing the DNA of Higher Education from the Inside Out. San Francisco: Jossey-Bass.
• Laurillard, D. (2012) Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology. New York: Routledge.
• Williamson, B. (2017) Big Data in Education: The Digital Future of Learning, Policy and Practice. London: Sage.

Academic integrity and use of AI

Generative AI may be used in this course as a drafting, translation and critique aid, and its use must be disclosed. Every submission carries a short use-of-AI statement naming the tools used, the tasks they performed and the checks the learner applied to the output. Using AI to fabricate data, invent sources, impersonate an interview participant, or produce an artefact the learner cannot explain in a live viva is prohibited.

All citations are verified before submission; a reference that cannot be located by the assessor is treated as fabricated. Fabrication, plagiarism and undisclosed ghost-authorship result in failure of the component and referral to the academic integrity panel. Marks in this course are issued by human assessors, and any learner may be asked to defend a submission orally before a mark is confirmed.