HE-F04 — Introduction to Academic Research

Course Code: HE-F04  |  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

Research is a craft with public standards, and most of what goes wrong in it goes wrong early: in a question that cannot be answered, a design that cannot support the claim, a literature review that summarises rather than argues, or an ethical arrangement made after the fact. This course addresses the beginning of the process, where the return on careful work is highest.

It is deliberately design-focused rather than technique-focused. Learners are not taught statistical procedures or coding software; they are taught to formulate an answerable question, to establish what is already known and where the genuine gap lies, to choose a design that can actually support the inference they want to make, and to anticipate the ethical and practical obstacles before committing resources.

The course applies equally to quantitative, qualitative and mixed approaches, and treats the choice between them as a consequence of the question rather than a matter of allegiance. It concludes with a full research proposal of the standard expected by a funding panel or a doctoral admissions committee, together with a pre-registered analysis intention or its qualitative equivalent.

Learning outcomes

On completion, a successful learner will be able to:

1. Formulate a research question that is specific, answerable with available means, and connected to an identified gap in existing knowledge.
2. Conduct and document a systematic literature search, and synthesise the results into an argument rather than an annotated list.
3. Select a research design appropriate to the question, and state the inferences the design can and cannot support.
4. Identify threats to validity, reliability and trustworthiness in a proposed design and specify measures to address them.
5. Prepare an ethics submission that identifies risks to participants, consent arrangements, data protection measures and conflicts of interest.
6. Plan data management across collection, storage, retention and sharing, in line with open science expectations and legal obligations.
7. Write a full research proposal with question, literature justification, design, method, timeline, resources and dissemination plan.

Who this course is for

The course serves master’s and doctoral students, early-career researchers, academic staff supervising research for the first time, and analysts in policy, health and development organisations who commission or conduct applied studies. It suits any discipline in the social sciences, humanities, health or applied professional fields.

Prerequisites

Undergraduate-level academic literacy and access to a research library or database. Learners should come with a topic they intend to pursue, since every assessed task builds toward a proposal on that topic.

Syllabus

Module 1 — From topic to answerable question

The module works through the transformation of an interest into a question: narrowing scope, specifying population and outcome, distinguishing descriptive, explanatory, evaluative and interpretive questions, and testing answerability against available time, access and skill. It treats an unanswerable question as the most common and most expensive error in research.

Lessons. 1.1 Topic, problem and question. 1.2 Question types and what each demands. 1.3 Scope, feasibility and access. 1.4 Testing a question before committing.

Deliverable. Diagnostic note (800 words) stating a question with justification of its answerability.

Module 2 — Literature: searching, appraising, synthesising

This module teaches systematic searching with documented strategy, critical appraisal of sources, and synthesis that produces an argument about the state of knowledge. It distinguishes the narrative review, the systematic review and the scoping review, and addresses reference management and the identification of predatory or unreliable sources.

Lessons. 2.1 Search strategy and documentation. 2.2 Appraising evidence quality. 2.3 Synthesis as argument. 2.4 Predatory publishing and source reliability.

Deliverable. Analytical brief (1,000 words): a synthesised review establishing the gap.

Module 3 — Research design

The module surveys designs and matches them to questions: experimental and quasi-experimental, survey and cross-sectional, longitudinal, case study, ethnographic, documentary and archival, and mixed-methods configurations. For each it states the inference supported and the characteristic failure mode.

Lessons. 3.1 Designs for causal claims. 3.2 Designs for description and measurement. 3.3 Designs for interpretation and meaning. 3.4 Mixed methods: integration rather than addition.

Deliverable. Design artefact: full design specification with justification and stated limitations.

Module 4 — Quality: validity, reliability, trustworthiness

This module addresses what makes findings credible. It covers internal and external validity and their threats, measurement reliability, sampling and its consequences for generalisation, and the qualitative criteria of credibility, transferability, dependability and confirmability, including reflexivity as method rather than confession.

Lessons. 4.1 Threats to internal validity. 4.2 Sampling and generalisation. 4.3 Measurement and construct validity. 4.4 Trustworthiness criteria and reflexivity.

Deliverable. Evaluation report (1,000 words) auditing the proposed design against quality threats.

Module 5 — Ethics, data and governance

The module covers research ethics as practice rather than paperwork: risk to participants, informed consent including where literacy or power relations complicate it, anonymity and confidentiality, vulnerable populations, data protection obligations, secure storage and retention, and the declaration of conflicts of interest.

Lessons. 5.1 Risk assessment and proportionality. 5.2 Consent in unequal settings. 5.3 Data protection and secure handling. 5.4 Conflicts of interest and funder influence.

Deliverable. Implementation plan: complete ethics submission and data management plan.

Module 6 — Writing and defending the proposal

The final module assembles the parts. It covers proposal structure and the expectations of funders and examiners, realistic timelines and resourcing, risk and contingency, dissemination and open access, and the practice of defending a design against methodological objection.

Lessons. 6.1 Proposal structure and reviewer expectations. 6.2 Timeline, budget and contingency. 6.3 Dissemination and open access. 6.4 Defending the design.

Deliverable. Final capstone: complete research proposal (3,000 words) with ethics and data annexes.

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. Question quality. Whether the question is specific, answerable with the means described, and genuinely connected to an identified gap.
2. Literature command. Whether the review argues rather than summarises, appraises source quality, and documents a reproducible search.
3. Design coherence. Whether the design can support the intended inference, whether limitations are stated frankly, and whether alternatives were considered.
4. Ethical and practical readiness. Whether risks, consent, data handling and resourcing are addressed concretely enough for the study to actually proceed.

Reading list

Core. John W. Creswell & J. David Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th edition (Thousand Oaks: SAGE, 2018).

Wayne C. Booth, Gregory G. Colomb & Joseph M. Williams, The Craft of Research, 4th edition (Chicago: University of Chicago Press, 2016).

Robert K. Yin, Case Study Research and Applications: Design and Methods, 6th edition (Thousand Oaks: SAGE, 2018).

Yvonna S. Lincoln & Egon G. Guba, Naturalistic Inquiry (Beverly Hills: SAGE, 1985).

Mark D. Wilkinson et al., ‘The FAIR Guiding Principles for Scientific Data Management and Stewardship’, Scientific Data, 3 (2016), 160018.

Council for International Organizations of Medical Sciences, International Ethical Guidelines for Health-related Research Involving Humans, 4th edition (Geneva: CIOMS, 2016).

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.