This course helps researchers, authors, reviewers, and graduate students make academic work easier to inspect, understand, and reproduce. It is grounded in Learning Analytics but applies to empirical research more broadly. Learners work through six connected areas: methods, data, code, analysis, results, and preregistration. They learn how to improve a study before publication and how to review the evidence in a published paper. An evidence-review application can support that work, but the course does not depend on a particular product, checklist, or scoring model. The course draws a clear line between evidence reported in a paper and a successful reproduction. A tool can oraganize quotations, links, and reviewer decisions. It cannot run code, access restricted data, repeat a study, or prove that a finding will reproduce.
Explain the difference between reproducibility, replication, transparent reporting, and an automated screening result.
Review methods, data, code, analysis, results, and preregistration using clear questions that transfer across disciplines.
Find the passage, page, repository, or registration that supports a judgement, and recognize when the evidence is not enough.
Suggest realistic improvements while respecting privacy, ethics, and disciplinary context.
Use automated screening as an aid to human judgement, not as a verdict.
Published papers often make claims that readers cannot check from the paper alone. This opening module gives learners a vocabulary for that problem. It separates reproducibility from replication, explains what open science can and cannot solve, and introduces evidence-based screening.
- Define reproducibility, replication, transparency, and open science in plain academic language.
- Tell the difference between a supported judgement and a claim that still needs evidence.
- Choose Met, Not met, or Needs review and explain the choice with a paper-based source.
- Recognise when a reviewer needs to inspect a repository, follow up with authors, or leave the item unresolved.
- Write a methods section that names the methodology and makes the workflow traceable.
- Map a study’s inputs, actors, procedures, decisions, outputs, and deviations.
- Separate a short summary of a study from a description another researcher could follow.
- Choose a reporting guide that fits the research design.
By the end of this module, you will be able to:
Map a study from its research question through data generation, decisions, and results.
Distinguish a methodological label from a traceable description of what researchers actually did.
Identify missing information about sampling, procedures, instruments, decisions, and deviations.
Select reporting guidance that fits a study’s design and use it to improve a methods account.
A dataset is useful only when people can find it, understand it, and access it in a way that respects participants. Learners prepare a data package with an access statement, repository record, Licence, README, data dictionary or codebook, and collection history. They also practice writing a clear explanation for legitimate restrictions on sharing.
Distinguish open, controlled, and non-shareable data based on an access statement and privacy context.
Create a data package that includes a repository record, documentation, provenance, and a clear access or restriction statement.
Explain how metadata, licences, and persistent identifiers support responsible discovery and reuse.
Review a data-sharing statement and identify missing information about access, documentation, collection, processing, or permitted use.
A repository link is only a starting point. Learners assemble a research-code package with preparation scripts, dependencies, parameter values, relevant random seeds, version history, a README, a licence, and a stable release or archive. When code cannot be shared, they learn to explain why and to release any safe supporting material.
By the end of this module, you will be able to:
Map a repository from its inputs through data preparation and analysis to its reported outputs.
Identify the files and recorded details needed for another researcher to inspect and attempt to run an analysis.
Distinguish a repository that is reachable from evidence that a particular version of code was run.
Write a clear code-sharing or restriction statement that identifies safe supporting material to release.
Methods sections often name an approach without showing how decisions shaped the result. This module asks learners to show the route from data to finding. Quantitative studies need clear models, tests, assumptions, uncertainty, and analytic decisions. Qualitative studies need context, researcher position, coding, interpretation, and an account of how themes or claims were developed. Mixed-method studies need both strands and their connection.
Map a quantitative claim from the analysed data through transformations, tests or models, and uncertainty estimates to its interpretation.
Document qualitative context, researcher position, coding, analytic development, and the evidence supporting an interpretive claim.
Distinguish primary, secondary, and exploratory analyses when describing quantitative findings.
Explain how qualitative and quantitative strands were connected in a mixed-methods study.
A reproducible paper lets readers compare a later analysis with the original report. Learners connect tables, figures, and claims to the relevant analysis and supporting outputs. They also write limitations that name the source of uncertainty, its likely effect, and the boundary it places on the conclusion.
By the end of this module, you will be able to:
Map a textual claim to the relevant result, table or figure, analytic output, and underlying data and code materials.
Distinguish planned primary and secondary analyses from exploratory analyses in a results narrative.
Revise a results paragraph to report an estimate, uncertainty, and a decision that materially affects interpretation.
Write a limitation that identifies its source, likely consequence, and boundary on the conclusion.
Preregistration records what a study planned to test before the relevant data collection or analysis. Learners draft a time-stamped plan that covers hypotheses, design, sampling, variables, exclusions, stopping rules, and analysis. They also learn that plans can change: honest deviation reporting keeps confirmatory and exploratory work visible.
By the end of this module, you will be able to:
Draft a time-stamped study plan that distinguishes planned decisions from open questions.
Specify hypotheses or research questions, design, sampling, variables or data sources, exclusions, stopping rules, and analysis decisions when they fit the study.
Classify reported work as confirmatory, exploratory, or a combination of both.
Write a transparent manuscript note that identifies a departure from a plan, explains its reason, and states when it occurred.
The final module brings the six areas together in a supervised paper review. Learners use an evidence-review tool in a deliberate order: choose a permitted paper, inspect the extracted text, select relevant sections, examine the evidence behind each judgement, and document any override. The central habit is simple: state what the evidence supports, state what it does not, and leave uncertain cases open.
By the end of this module, you will be able to:
Select a permitted paper and identify the sections relevant to a defined review question.
Extract exact quotations and record a page, section, table, figure, or other source location for each judgement.
Distinguish whether a reported data, code, or preregistration link is reachable, accessible, documented, reusable, or executable.
Record a Met, Not met, or Needs review judgement and an evidence-based reason for any override.