Education Research
- 17 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
education-research is a Claude skill that supports education research through study design, learning analytics, assessment development, and multilevel statistical analysis.
About
This skill supports education research, including evaluating pedagogical methods, learning analytics, assessment design, and curriculum analysis. A developer or researcher uses it when analyzing student-performance data, running quasi-experimental education studies, or building valid assessments. It applies multilevel modeling for nested data and reports effect sizes in educationally meaningful terms.
- Guides quasi-experimental and multilevel (HLM) analysis of nested student data
- Covers assessment design with reliability and item analysis
- Reports effect sizes translated to months of learning gain
Education Research by the numbers
- 17 all-time installs (skills.sh)
- Ranked #1,286 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
education-research capabilities & compatibility
Free; relies on open education databases and R statistical tooling.
- Capabilities
- experiment design · exploratory data analysis
- Works with
- confluence
- Use cases
- data analysis · research
- Pricing
- Free
What education-research says it does
Use hierarchical linear modeling (HLM) to account for nested data structure (students within classrooms within schools).
Translate to months of learning gain for K-12 contexts (What Works Clearinghouse approach).
npx skills add https://github.com/beita6969/scienceclaw --skill education-researchAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 17 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Use it to design and analyze education-research studies on teaching effectiveness, learning outcomes, and assessment data.
Who is it for?
Analyzing educational interventions and student data with quasi-experimental designs and hierarchical linear models.
Skip if: Building learning-management software or non-education statistical work.
When should I use this skill?
The user discusses teaching effectiveness, learning outcomes, educational interventions, or student performance data.
What you get
Delivers a defensible education study design and analysis with effect sizes interpreted as learning gains.
- Study design diagram
- Assessment statistics table
- Multilevel model results with ICC
By the numbers
- 9-item quality checklist
- 7-step methodology
Files
When to Trigger
Activate this skill when the user mentions:
- Pedagogical methods, teaching strategies, instructional design
- Learning analytics, student performance data, LMS data
- Assessment design, test validity, reliability, item analysis
- Curriculum development, learning objectives, Bloom's taxonomy
- Educational technology, e-learning, blended learning, MOOCs
- Educational interventions, quasi-experimental designs in education
- Student engagement, motivation, self-regulated learning
Step-by-Step Methodology
1. Define the research question - Specify the educational context (K-12, higher education, professional development). Identify the intervention, outcome measures, and comparison conditions. Frame using established educational theory (constructivism, connectivism, cognitive load theory). 2. Study design - Select appropriate design: RCT (gold standard but often impractical), quasi-experimental (difference-in-differences, regression discontinuity), or mixed methods. Address common challenges: nested data (students within classrooms), selection bias, contamination between groups. 3. Assessment development - Define learning objectives using Bloom's taxonomy (remember, understand, apply, analyze, evaluate, create). Develop assessment items aligned with objectives. Compute reliability (Cronbach's alpha, test-retest, inter-rater). Conduct item analysis (difficulty, discrimination index). 4. Data collection - Gather quantitative data (test scores, grades, completion rates, time-on-task from LMS logs) and qualitative data (surveys, interviews, observations, think-alouds). Ensure IRB approval for human subjects research. 5. Multilevel analysis - Use hierarchical linear modeling (HLM) to account for nested data structure (students within classrooms within schools). Report ICC (intraclass correlation) to justify multilevel approach. Include relevant covariates (prior achievement, demographics). 6. Effect size and practical significance - Report Cohen's d or Hedges' g for group comparisons. Use standards for education research: d = 0.2 (small), 0.4 (medium), 0.6 (large). Translate to months of learning gain for K-12 contexts (What Works Clearinghouse approach). 7. Evidence synthesis - Situate findings within existing evidence base. Reference systematic reviews (What Works Clearinghouse, EPPI-Centre, Campbell Collaboration). Discuss generalizability, implementation fidelity, and scalability.
Key Databases and Tools
- ERIC (Education Resources Information Center) - Education literature database
- What Works Clearinghouse (WWC) - Evidence reviews of education programs
- PISA / TIMSS / NAEP - International and national assessment data
- Google Scholar - Cross-disciplinary search
- R lme4 / HLM software - Multilevel modeling
- Canvas/Blackboard APIs - LMS data extraction
Output Format
- Study design diagram showing groups, timeline, and measurement points.
- Assessment statistics table: item number, difficulty, discrimination, point-biserial.
- Results table: outcome, groups, means/SDs, effect size (d), 95% CI, p-value.
- Multilevel model: fixed effects, random effects, ICC, variance explained.
- Practical significance translation: effect size to months of learning gain.
Quality Checklist
- [ ] Learning objectives clearly defined using established taxonomy
- [ ] Assessment items aligned with stated learning objectives
- [ ] Nested data structure handled with appropriate multilevel model
- [ ] Effect sizes reported and interpreted in educationally meaningful terms
- [ ] Implementation fidelity documented (did the intervention happen as planned?)
- [ ] Threats to validity addressed (selection, maturation, testing effects)
- [ ] IRB approval obtained for human subjects research
- [ ] Practical significance distinguished from statistical significance
- [ ] Comparison to existing evidence base (WWC, systematic reviews)
Related skills
Microsoft FoundryDeploy, evaluate, and continuously improve Microsoft Foundry agents from a single agent interface.490k1.3k
Ai Research ReproductionOrchestrate trustworthy, auditable reproduction of deep learning repositories directly from their READMEs.176k511Run TrainSafely execute selected deep learning training commands with standardized evidence capture.176k511Explore RunSafely run isolated exploratory experiments with clear recording and conservative selection before committing changes.176k511Paper Context ResolverFetch precise reproduction-critical details like dataset splits, preprocessing steps, or evaluation protocols from the original academic paper when the repo README leav141k511Repo Intake And PlanScan unfamiliar AI research repositories and receive a minimal, trustworthy reproduction target before investing significant time.140k511FAQ
How does it handle nested classroom data?
It uses hierarchical linear modeling (HLM) and reports the intraclass correlation (ICC) to justify the multilevel approach.
How are effect sizes reported?
As Cohen's d or Hedges' g, translated into months of learning gain following the What Works Clearinghouse approach.