1. Pass criteria (must-have)

• Education question first: the central question is about learning/teaching computing (informatics) or computing used in education with a clear informatics / computational-thinking educational contribution.
• Computing-education linkage: the paper makes an explicit connection to programming, computational thinking, curriculum, pedagogy, assessment, teacher education, equity/ethics, or evidence-based evaluation of a computing-learning tool.
• Scholarly contribution: the manuscript makes an original research or synthesis contribution grounded in relevant literature and supported by a transparent method.

2. What INFEDU means by research

In INFEDU, a manuscript counts as research when it makes a clear, non-trivial knowledge claim about learning/teaching computing or about a clearly scoped computing in education problem, and supports that claim with a transparent and defensible method.

Research in INFEDU is not limited to one method family. Within Research Articles, contributions may be empirical, design-and-evaluation, methodological/measurement, or theoretical/conceptual. Within Reviews, contributions may be systematic, scoping, systematic mapping, or meta-analytic.

Regardless of genre, the manuscript should make a visible chain from problem → aim → novelty/contribution → method → evidence/synthesis → interpretation, and it should include a real interpretive Discussion that states what the paper shows, what it only suggests, and what it cannot conclude.

Paradigm-appropriate research warrant

INFEDU evaluates a manuscript by the kind of knowledge it claims to produce. Methodology is the transparent account of how that claim was warranted; it is not a hierarchy in which large-N experiments automatically outrank interpretive, design-oriented, local, or conceptual work.

• Causal and population claims require designs capable of supporting causal or population inference.
• Interpretive claims require contextual depth, reflexivity, traceable evidence, conceptual coherence, and attention to alternative or negative cases.
• Pragmatist and design-based claims require an authentic problem, explicit design rationale, enactment or worked application, consequences, reflection, and adaptation logic.
• Conceptual and theoretical claims require transparent source selection or argument construction, differentiation from nearest scholarship, counterarguments, boundaries, and operational implications.

Sample size, control groups, statistical significance, saturation, member checking, and inter-rater reliability are claim-dependent tools, not universal pass criteria. Authors should state whether their evidence is confirmatory, associational, interpretive, illustrative, formative/design-informative, or conceptual.

3. Research Articles

Use Research Article for original contributions that generate new knowledge directly. This includes empirical studies, design & evaluation studies, methodological / measurement studies, and theoretical / conceptual studies.

Research questions and a true Discussion

A publishable Research Article must do more than restate findings. INFEDU often returns manuscripts when the paper reports results but does not include a genuine interpretive Discussion, or when binary “Does X improve Y?” questions are not matched by a design that can justify that level of inference.

At minimum, a publishable Discussion should:

• answer each research question or aim explicitly;
• interpret what the results mean for learning/teaching computing;
• compare findings with prior computing/informatics education research;
• consider alternative explanations, confounds, and boundary conditions;
• derive implications that remain traceable to measured evidence.

Minimum reporting checklist for Research Articles

• Design clarity: state the study design and the intended inference level.
• Unit of analysis and independence: clarify clustering, repeated measures, or nesting where relevant.
• Measures/instruments/materials: provide enough detail for editorial review and reproducibility.
• Quantitative reporting: define the sample, explain missing data handling, and report uncertainty for inferential claims.
• Qualitative reporting: describe sampling, data sources, analysis steps, and trustworthiness procedures.
• Ethics and privacy: report approval/exemption basis, consent/assent where applicable, and data-protection safeguards.
• Required statements: conflicts of interest, funding, data/material availability, and generative-AI disclosure where applicable.

4. Reviews

Use Review for systematic, scoping, mapping, meta-analytic, critical, integrative, or other transparent syntheses that produce new knowledge for informatics/computing education. INFEDU does not prioritize reviews whose main contribution is only a more recent search date, a larger corpus, checklist compliance, bibliometric counting, or descriptive categorization of an already well-reviewed topic.

Contribution-first threshold

A publishable review should make clear what an informed INFEDU reader can understand, decide, design, teach, assess, or investigate differently because this synthesis exists. The contribution may be conceptual, explanatory, methodological, corrective, boundary-setting, resource-oriented, or practice-oriented. Immediate classroom recommendations are not mandatory, but the intellectual or educational consequence must be explicit and traceable to the evidence.

Authors should identify the nearest existing reviews and state the smallest genuine increment of the present review. A new review is justified when it changes an important conclusion, resolves a contradiction, exposes a limitation in prior synthesis, develops a defensible new organization of the evidence, or creates a reusable evidence resource that materially improves research or educational judgement.

What INFEDU expects

• Clear review problem and questions. State the consequential informatics/computing-education problem and explicit review question(s).
• Nearest-review differentiation. Compare the manuscript with the closest prior reviews and explain what is genuinely new rather than relying on topicality or search recency.
• Transparent protocol. Report the review type, databases/sources, search dates and strings, eligibility criteria, screening workflow, extraction/coding process, quality or evidence-strength handling, and synthesis method as appropriate to the review design.
• Evidence-role traceability. Distinguish what each source actually provides—for example learner outcomes, perceptions, system performance, implementation evidence, conceptual argument, or contextual support. Do not count a system designed to support learning as evidence of a learner outcome unless that outcome was measured.
• Critical synthesis. Move beyond frequencies and theme lists to interpretation: convergences, contradictions, mechanisms, boundary conditions, evidence gaps, or decision-relevant distinctions.
• Bounded implications. Recommendations for educators, researchers, curriculum designers, or policymakers should follow from the strongest evidence actually available and should state important conditions and uncertainties.

What is not sufficient on its own

• a recent search end date or a larger number of included studies;
• PRISMA or other reporting-checklist compliance;
• descriptive bibliometrics, frequency tables, or thematic categorization;
• a list of research gaps followed by generic calls for “more research”;
• generic educational recommendations not derived from the reviewed evidence;
• low textual similarity without a semantic comparison with the nearest reviews.

AI-assisted review research

Generative AI or AI-assisted tools may support writing or review procedures only under full author responsibility. Methodologically consequential use (for example searching, screening, extraction, classification, coding, translation, quality appraisal, or synthesis) must be disclosed in the manuscript with enough information to evaluate and reproduce the procedure where feasible. Authors must verify AI-assisted outputs against the original sources and remain responsible for source accuracy, eligibility decisions, coding, interpretation, citation integrity, confidentiality, and the final synthesis. AI assistance does not reduce INFEDU's originality, evidence, transparency, or synthesis requirements.

Editorial screening note. Review manuscripts may be screened before external peer review for semantic overlap with existing reviews and for a small number of load-bearing source-to-claim relationships. A technically rigorous review may be rejected at initial editorial screening when its distinct contribution or evidential basis is not sufficiently established.

5. Reporting, transparency, and ethics

• Originality and prior dissemination: disclose conference versions, preprints, chapters, or other related prior dissemination, and explain what is new.
• Methods transparency: describe the method clearly enough for editorial assessment and peer review.
• Human data: state the ethics basis, consent/assent where applicable, and data provenance.
• Generative AI disclosure: disclose AI-assisted writing, translation, editing, transcription, coding, or analysis where relevant; authors remain responsible for accuracy and integrity.
• Data/materials statement: include a data/material availability statement even if access is restricted or not applicable.

See also: Instructions for authors, Submit your article, and Research Ethics.

6. Often out of scope

• General e-learning or satisfaction surveys with no clear computing/informatics learning focus.
• Generic higher-education analytics not tied to a computing-learning context.
• Tool/app descriptions with usability-only evaluation and no meaningful educational evidence.
• Pure opinion pieces presented as research.
• Narrative reviews without a reproducible search and synthesis protocol.
• Bibliometric/scientometric ranking manuscripts without a direct, explicit computing-education contribution and education-relevant implications.

7. Editorial notes

1. Quantitative empirical studies: sample size should be justified appropriately and uncertainty should be reported for inferential claims. If the study is underpowered or highly context-bound, it should be framed accordingly and claims should remain bounded.
2. Theoretical and methodological work: these remain fully admissible within Research Article when they use a defensible conceptual or methodological approach, provide operational clarity, and avoid overclaiming.
3. Conference proceedings and prior dissemination: INFEDU may consider substantially extended journal versions, but authors should disclose prior dissemination, cite the earlier version, and explain what is genuinely new.
4. Special issues: some expectations may be adapted when a special issue explicitly defines additional or narrower framing requirements.