<strong>SDGenie (beta)</strong> is a tool from CABI that helps researchers plan, track, and evaluate evidence synthesis projects. It centralizes project...
SDGenie (beta): Teaching Researchers to Plan, Track, and Evaluate Evidence Synthesis — A Practical Curriculum and Pedagogy
SDGenie (beta) is a tool from CABI that helps researchers plan, track, and evaluate evidence synthesis projects. It centralizes project planning, automates tracking, and supports transparent assessment of methods and outcomes to improve efficiency and rigor in systematic reviews and other syntheses.
Key Takeaways
- SDGenie (beta) centralizes project planning, tracking, and evaluation for evidence synthesis.
- Teaching researchers requires clear learning outcomes, scaffolded curriculum, and active assessment tied to real projects.
- Use blended pedagogy: short lectures, worked examples, deliberate practice, and peer review.
- Track progress with dashboards and rubrics. Use tools and templates to cut bias and increase reproducibility.
- Avoid common errors: vague outcomes, overcomplex tools, missing metadata, and weak assessment.
What is SDGenie (beta)?
SDGenie (beta) is a project-centric tool designed to guide evidence synthesis teams through planning, tracking, and evaluating systematic reviews and related methods. It packages templates, workflow steps, and evaluation prompts. It keeps protocol elements, screening progress, extraction status, and bias assessments in one place. The tool asks the right questions. It records decisions. It timestamps changes. It aims to make syntheses traceable and teachable.
This matters for educators. SDGenie becomes part of a curriculum. It is a platform for skills practice. It holds learner work. It yields data for assessment. When teaching research methods, you need tools that show process, not only outcomes. SDGenie fits that need.
Core Benefits and Importance
- Transparency and Reproducibility. SDGenie stores protocols, decisions, and version history. That guards against hindsight bias and omissions.
- Efficiency. Templates and guided steps cut wasted time. Teams repeat best practices without reinventing the workflow.
- Teaching and Assessment. Educators can map tasks to learning outcomes and grade using artifacts saved in the system.
- Project Management. Dashboards show screening rates, extraction progress, and reviewer agreement. This helps instructors coach teams and identify bottlenecks.
- Data for Meta-Learning. The system logs learner choices and timings. That data informs curriculum refinement and targeted remediation.
Step-by-Step Implementation Guide for Teaching SDGenie and Evidence Synthesis
This section walks a course designer, instructor, or research lead through a practical sequence. The aim: teach evidence synthesis using SDGenie (beta) as both tool and teaching artifact.
- Define course goals and learning outcomes.
- Start with clear outcomes. Use action verbs: "write a protocol," "conduct boolean searches," "extract data using a standard form," "assess risk of bias using a tool (e.g., Cochrane RoB)," "generate a forest plot," "interpret heterogeneity."
- Map outcomes to assessment. Rubrics must match outcomes. If the outcome is reproducible screening, grade inter-rater agreement and documented reconciliation.
- Link outcomes to student engagement. Make tasks authentic and tied to real questions.
- Build a curriculum scaffold.
- Sequence skills from simple to complex. Begin with research question framing (PICO or equivalent). Move to search strategy, then screening, then extraction, bias assessment, synthesis, and reporting.
- For each module, pair a short lecture (15–25 minutes) with a hands-on lab in SDGenie. The lecture explains the principle. The lab enforces the practice.
- Include checkpoints. Require a protocol draft, a completed sample search, a screened subset, an extraction form, and a draft synthesis.
- Prepare materials and templates.
- Use SDGenie templates for protocols, screening forms, extraction tables, and bias assessment. Customize to the course needs.
- Provide exemplar protocols and annotated examples. Show a good protocol and a poor one. Discuss differences.
- Provide rubrics for each deliverable. Attach rubrics to the SDGenie artifacts where possible.
- Teach methods and pedagogy together.
- Combine direct instruction on methodology with practice. Teach critical appraisal alongside bias assessment tools like Cochrane RoB or PRISMA guidance (PRISMA).
- Use deliberate practice. Give short tasks with immediate feedback. Let students repeat until performance stabilizes.
- Use peer review. Have teams swap extracts and codebooks. Ask peers to critique and justify decisions.
- Use assessment and feedback that measures process and product.
- Grade artifacts and logs. Score not only the final review but the decisions recorded in SDGenie: inclusion criteria changes, search term evolution, reasons for exclusion.
- Run inter-rater reliability checks. Teach Cohen’s kappa and percent agreement. Make students compute it on small batches.
- Assign reflective journals. Ask for a short rationale for major decisions. The reflections reveal understanding.
- Integrate ethics, bias, and fidelity checks.
- Teach about conflicts of interest, publication bias, and selective reporting. Map these risks into the assessment rubrics.
- Require students to run sensitivity analyses and document limitations.
- Practice scaling and real-world application.
- Move from classroom sets to live data. Have teams run a full small-scope synthesis in SDGenie across 8–12 weeks.
- Invite external peer review or open your repository for public comment. This teaches scholarly exchange.
- Use course data to refine the curriculum.
- Harvest logs and common errors. If many teams misunderstand extraction fields, redesign the lesson and the template.
- Adjust pacing based on screening velocity and feedback.
Pedagogy and Curriculum Design Principles (Applied)
- Keep lessons short. Students learn faster with short, clear objectives and routine practice.
- Use worked examples. Show step-by-step how to complete an extraction or justify an inclusion decision.
- Use spaced practice across the term. Return to screening and extraction later to shore up retention.
- Promote active learning. Labs in SDGenie are active learning. Students make decisions and record them.
- Foster peer instruction. Students explain decisions to one another. That raises understanding.
- Ensure formative feedback. Quick, specific feedback is better than delayed, broad comments.
Assessment Strategies
- Use mixed assessment: formative quizzes, graded artifacts (protocol, search strategy), process metrics (screening rates), and final synthesis product.
- Use rubrics for technical items (e.g., search sensitivity) and for judgment items (e.g., interpretation of heterogeneity).
- Include a reproducibility check: have a separate team reproduce a key step without access to the protocol draft and measure fidelity.
Tools and Integration
- SDGenie sits alongside other tools. Use reference managers (Zotero, EndNote), screening tools (Rayyan when needed), statistical software (R, RevMan), and visualization tools.
- Teach tool choice. Let students compare workflows and report trade-offs.
Comparison Table: SDGenie vs Traditional Evidence Synthesis Workflow
| Feature |
SDGenie (beta) |
Traditional Workflow (ad hoc files and spreadsheets) |
| Centralized protocol and versioning |
Yes |
Often No |
| Built-in templates for screening/extraction |
Yes |
Variable; instructor must provide |
| Time-stamped decision log |
Yes |
Rare; often missing |
| Dashboard for progress and inter-rater agreement |
Yes |
Manual calculation or separate tools |
| Teaching integration (labs, rubrics) |
Designed for it |
Instructor-built, inconsistent |
| Reproducibility support |
High |
Low to moderate |
| Data export for R/RevMan |
Yes |
Possible but manual |
Common Mistakes to Avoid (When Teaching and Using SDGenie)
1. Vague Learning Outcomes.
If outcomes are fuzzy, assessment will be harder. Define what you expect clearly. Say who will do what. Give criteria. The protocol should reflect those criteria.
2. Overloading Students with Tools.
Do not overwhelm. Students do not need five new platforms at once. Start in SDGenie and add one extra tool at a time. Teach the purpose of each.
3. Treating SDGenie as a Black Box.
Teach the logic, not the clicks. Explain why each field matters. Show the methodological rationale behind extraction fields and bias assessments.
4. Ignoring Process Data.
Process data are teaching gold. Logs show misconception, fragility, and poor practices. Use them to give timely remediation.
5. Poor Rubrics.
Rubrics that reward style over substance will mislead. Rubrics must map to learning outcomes and to artifacts saved in SDGenie.
6. Failing to Teach Critical Appraisal.
A tool cannot replace judgment. Teach how to read methods and detect risk of bias. Use real examples and counterexamples from published work (see Cochrane and Joanna Briggs Institute guidance).
Frequently Asked Questions
Q: What is the best way to introduce SDGenie to a class of mixed-ability students?
A: Start with a short demo. Then give each student a one-page task. Use pair work. Let stronger students coach weaker ones. Keep objectives tiny. Grade formative tasks to push practice.
Q: How do I assess inter-rater reliability in a course setting?
A: Use small batches. Have pairs screen the same 50 records. Compute percent agreement and Cohen’s kappa. Discuss discrepancies in class. Grade both agreement and the quality of reconciliation notes.
Q: Does SDGenie replace statistical tools like R or RevMan?
A: No. SDGenie organizes and documents the workflow. Export your cleaned data to R or RevMan for meta-analysis. Teach students both: organizing the project and running the analysis.
Q: Can SDGenie support open pedagogy and student publications?
A: Yes. The system stores decisions and artifacts suitable for public archiving. Use it to teach open science principles and to prepare classroom syntheses for publication.
Practical Course Example (12-week module)
Weeks 1–2: Framing the question. Teach PICO, scoping searches, and protocol writing. Use SDGenie protocol template. Assign protocol draft.
Weeks 3–4: Search strategies and documentation. Teach Boolean operators and controlled vocabulary. Have students run a pilot search and upload search strings into SDGenie.
Weeks 5–6: Screening. Teach inclusion criteria, pilot screening, and reconciliation. Run inter-rater tests in SDGenie and compute reliability.
Weeks 7–8: Data extraction. Teach extraction templates, units of analysis, and data cleaning. Use worked examples and blind checks.
Weeks 9–10: Risk of bias and synthesis. Teach RoB tools, forest plots, and heterogeneity measures. Run a small quantitative synthesis in R or RevMan.
Weeks 11–12: Reporting and reflection. Teach PRISMA reporting. Assign final report and reflective piece. Use a rubric that includes process fidelity.
Measurement and Iteration
- Track student mastery across cohorts. Use pre/post tests and artifact scoring.
- Use logged decisions to find recurring misunderstandings. Adjust instruction and templates.
- Keep the curriculum lean. Remove activities that do not produce measurable skill gains.
Subtle Moral and Intellectual Frame
Teaching research is a moral work. It asks students to seek truth with humility. The practice of documenting decisions and admitting uncertainty is a small act of honesty. A student who records why they excluded a study grows in stewardship. There is quiet virtue in method. This is not religion. It is discipline. It echoes older calls to witness and record the facts with care.
Conclusion
SDGenie (beta) is more than a set of features. It is an opportunity. It lets teachers make process visible. It forces decisions to be recorded. It supplies data for assessment. Use it to teach method, not just technique. Build a curriculum that pairs short instruction, hands-on labs, deliberate practice, and clear rubrics. Track both product and process. Teach students to document their choices. Teach them to justify those choices. Make reproducibility a habit. Do this and students will leave able to do real syntheses that stand scrutiny.
Resources and Further Reading
- PRISMA statement and reporting guidelines: PRISMA
- Cochrane methods: Cochrane
- Joanna Briggs Institute: JBI
- Campbell Collaboration: Campbell
- Bloom's taxonomy and course design principles: Vanderbilt CFT
Internal Links
Category: Learning Strategies
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