HE-F03 — Teaching and Learning in Higher Education

Course Code: HE-F03  |  School: School of Higher Education  |  Cluster: Level 1 — Foundations of Academic Practice

Level: Foundation  |  Duration: 6 weeks · 24–30 learning hours  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

University teaching is the only professional activity of comparable consequence that most practitioners are permitted to undertake without training. Disciplinary expertise is assumed to transfer to instruction, and it does not. The result is a sector in which a great deal of teaching is conducted by intelligent people repeating the methods they happened to experience as students.

This course provides the evidence base and the design discipline that disciplinary training omits. It covers what is actually known about adult learning, the principle of constructive alignment between outcomes, activities and assessment, the design of active learning at scale, feedback that changes subsequent performance, and inclusive practice for genuinely diverse cohorts.

The orientation is empirical rather than ideological. Learning styles, generational theories and other popular claims are examined against the evidence and, where the evidence does not support them, discarded. What remains is a compact set of practices with defensible support: retrieval practice, spacing, worked examples, elaborative interrogation, structured peer instruction and criterion-referenced feedback. Learners redesign one of their own units and evaluate the result.

Learning outcomes

On completion, a successful learner will be able to:

1. Explain the principal evidence-based accounts of how adults learn, and distinguish supported findings from popular claims that the evidence does not sustain.
2. Write module and programme learning outcomes that are specific, observable and assessable at an appropriate cognitive level.
3. Design a unit in which outcomes, learning activities and assessment tasks are constructively aligned, and justify each alignment decision.
4. Select and sequence active learning methods appropriate to discipline, cohort size and delivery mode.
5. Design feedback that is timely, criterion-referenced and actionable, and explain how it will be used by students before the next assessed task.
6. Apply inclusive and accessible design principles to teaching materials, activities and assessment.
7. Evaluate the effect of a teaching change using appropriate evidence, and report the result honestly including where it did not work.

Who this course is for

The course is intended for lecturers and tutors at any career stage, doctoral candidates preparing to teach, academic developers, programme leaders responsible for teaching quality, and professional staff supporting learning and teaching. It is discipline-neutral and suits both laboratory and seminar traditions.

Prerequisites

Current or imminent responsibility for teaching a unit, module or seminar series, since the assessed work requires redesigning something real. No prior education coursework is assumed.

Syllabus

Module 1 — What is known about learning

The module surveys the reliable findings of learning science: the role of prior knowledge, working memory limits and cognitive load, the testing and spacing effects, interleaving, and the conditions under which transfer occurs. It also examines widely believed claims with weak support and explains why they persist.

Lessons. 1.1 Prior knowledge and misconception. 1.2 Cognitive load and worked examples. 1.3 Retrieval, spacing and interleaving. 1.4 Claims the evidence does not support.

Deliverable. Diagnostic note (800 words) auditing one existing unit against learning-science principles.

Module 2 — Outcomes and constructive alignment

This module teaches the writing of outcomes that can actually be assessed, the use of cognitive taxonomies without mechanical application, and the alignment of activity and assessment to outcome. It addresses the common failure in which stated outcomes promise analysis and synthesis while assessment rewards recall.

Lessons. 2.1 Writing observable outcomes. 2.2 Taxonomies and cognitive level. 2.3 Aligning activity to outcome. 2.4 Detecting misalignment in existing units.

Deliverable. Analytical brief (1,000 words) diagnosing alignment failure in a real unit.

Module 3 — Designing active learning

The module covers methods that put students to work: structured peer instruction, problem and case-based learning, studio and laboratory design, simulation, and the management of discussion. It gives particular attention to large-cohort settings, where active methods are most often abandoned as impractical.

Lessons. 3.1 Peer instruction and concept testing. 3.2 Problem, case and project designs. 3.3 Managing discussion and participation. 3.4 Active learning at scale.

Deliverable. Design artefact: a fully specified redesigned unit with session plans and materials.

Module 4 — Assessment and feedback

This module distinguishes assessment of, for and as learning. It covers criterion referencing, rubric design, marking reliability and moderation, the design of authentic tasks, and feedback that students can act on. It also addresses assessment security in an era of generative AI without retreating to invigilated recall.

Lessons. 4.1 Criterion referencing and rubric design. 4.2 Moderation and marking reliability. 4.3 Authentic and programmatic assessment. 4.4 Feedback that changes performance.

Deliverable. Evaluation report (1,000 words) on the quality of feedback in one existing unit.

Module 5 — Inclusive and accessible practice

The module addresses the diversity of real cohorts: prior educational disadvantage, disability and accessibility requirements, language of instruction, and the design choices that reduce unnecessary barriers. It treats inclusion as design rather than accommodation, and examines the evidence on what actually narrows attainment gaps.

Lessons. 5.1 Universal design for learning. 5.2 Accessibility of materials and platforms. 5.3 Teaching in a second language of instruction. 5.4 Attainment gaps: what closes them.

Deliverable. Implementation plan (1,000 words) for inclusive redesign with resource requirements.

Module 6 — Evaluating and improving teaching

The final module covers how teaching is judged and how it should be improved: student evaluation instruments and their known biases, peer observation, learning analytics and their limits, scholarship of teaching and learning as a research practice, and the construction of an evidenced teaching portfolio.

Lessons. 6.1 Student evaluations and their biases. 6.2 Peer observation done well. 6.3 Small-scale evaluation design. 6.4 The evidenced teaching portfolio.

Deliverable. Final capstone: redesigned unit, evaluation design and reflective commentary (3,000 words).

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. Evidence use. Whether design decisions are justified by cited learning research rather than by preference or tradition.
2. Alignment quality. Whether outcomes, activities and assessment genuinely correspond, and whether misalignments are identified and corrected.
3. Practical feasibility. Whether the redesign can be delivered with the staffing, time and facilities actually available, and whether workload implications are stated.
4. Reflective honesty. Whether the learner reports what did not work, distinguishes evidence from impression, and avoids overclaiming from small evaluations.

Reading list

Core. John Biggs & Catherine Tang, Teaching for Quality Learning at University, 4th edition (Maidenhead: Open University Press, 2011).

Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett & Marie K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching (San Francisco: Jossey-Bass, 2010).

Scott Freeman et al., ‘Active Learning Increases Student Performance in Science, Engineering, and Mathematics’, Proceedings of the National Academy of Sciences, 111:23 (2014), pp. 8410–8415.

David Nicol & Debra Macfarlane-Dick, ‘Formative Assessment and Self-Regulated Learning’, Studies in Higher Education, 31:2 (2006), pp. 199–218.

Paul A. Kirschner, John Sweller & Richard E. Clark, ‘Why Minimal Guidance During Instruction Does Not Work’, Educational Psychologist, 41:2 (2006), pp. 75–86.

CAST, Universal Design for Learning Guidelines, version 2.2 (Wakefield: CAST, 2018).

Academic integrity and use of AI

AI tools may be used in this course for locating literature, organising evidence, drafting non-analytical sections and criticising the learner’s own work. They may not be used to generate the analysis, the evaluation or the recommendation. Every submission carries a use-of-AI statement naming the tools used and the purpose of each use.

All citations must be verified against the primary source, and institutional facts — regulations, standards, accreditation criteria, published data — must be cited to the issuing body. Fabricated references and invented statistics result in failure and referral. Where learners write about their own institution they must not disclose confidential personnel or student data, and illustrative cases must be anonymised.