| name | education-research |
| description | Supports education research including pedagogical method evaluation, learning analytics, assessment design, curriculum development analysis, and educational technology evaluation; trigger when users discuss teaching effectiveness, learning outcomes, educational interventions, or student performance data. |
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
- 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).
- 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.
- 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).
- 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.
- 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).
- 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).
- 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
