Instructor Notes

About This Course

Essential Digital Skills is a foundation course in practical digital research work. The teaching goal is not to turn participants into policy specialists. It is to help them make better day-to-day decisions about collaboration, governance, data management, interpretation, and responsible AI use.

The four core sessions cover digital tools, digital delivery, governance and policy, and research data management. Broader data interpretation and dedicated AI teaching are separate follow-on courses.

The course works best when learners leave with:

  • a clearer mental model of the university research ecosystem
  • better judgement about where work should live and how it should be documented
  • stronger awareness of risk, roles, and escalation points
  • reusable habits they can apply immediately in their own projects

Delivery Pattern

Most sessions in this course already follow a useful rhythm:

  1. Short concept framing.
  2. Example or comparison.
  3. Group activity or reflection task.
  4. Debrief focused on practical decisions.

Maintain that pattern where possible. Learners often understand the concept more clearly after they have had to defend a choice, rank a risk, or improve a weak example.

Session-Level Emphasis

Digital tools

Use Digital Tools to clarify the role of Microsoft 365, SharePoint, RIS, UniCore, ORCID, and basic digital security practices. Learners often know these systems exist but not how they fit together.

The lesson also includes literature discovery/reference management and writing tools. Allow 115 minutes overall, including a 10-minute break after Digital Security. All six topics use 10 minutes teaching. Research Identity has a ten-minute audit; the other original topics each have one five-minute task. Sections 5 and 6 each have two five-minute tasks. This totals 60 minutes teaching and 45 minutes activities, including feedback.

Before delivery:

  • Print or project the supplied search question, reference comparison and writing pilot. All four new tasks work on paper or in notes, without accounts or installations.
  • Use two five-minute tasks per section. Collect a search plan, checked reference and reading note, annotated structure plan, and reviewed sentence with a co-author agreement.
  • Put pair reports and feedback inside the task time. There are no separate room checks in these two sections.
  • Show worked feedback after each task. The reference records and writing pilot are fictional; do not present them as published evidence.
  • Keep any live EndNote/Zotero or Word/LaTeX demonstration optional and separate from the required tasks. Use the learner additional material for practical import, citation, export and document-editing follow-up.

Use a visible five-minute timer and take two brief reports. Each task has a supplied input and can be completed independently. Do not spend the task installing software or troubleshooting a writing integration.

The writing block includes two brief AI slides derived from the separate AI course. The invented rewrite works without AI accounts or uploading research material; debrief changes in meaning, unsupported claims, verification and recording the contribution.

Delivery and coordination

Use Digital Delivery to make project planning feel lighter and more actionable. The aim is not to impose formal agile doctrine, but to give teams language for iteration, coordination, and shared ownership.

Governance and policy

Use Data Governance & Policy to distinguish ethics, lawful basis, data classification, and institutional compliance. Learners often collapse these into one vague idea of “policy”.

Research data management

Use Research Data Management to connect DMPs, folder structures, metadata, storage, retention, and FAIR reuse. Keep bringing the discussion back to actual files, responsibilities, and workflows.

Optional Follow-on Teaching

For deeper output-level FAIR assessments and choices about publication, preregistration, methods, credit and responsible access, signpost FAIR and Open Research Practices. Its two 100-minute lessons build on the RDM session; keep storage, backups, file organisation and DMP instruction in EDS rather than extending the FAIR course into another management course.

Dedicated AI teaching now sits in Responsible Use of Generative AI. If learners want deeper coverage of prompting, validation, documentation, and defensible AI use, that course is the natural follow-on.

Data collection, exploration, interpretation and impact belong to Methods for Data Science. Keep these routes separate from the core EDS session list. Use learner additional material to consolidate the four taught themes without adding new prerequisites.

Common Sticking Points

  • Confusion between governance principles and operational decisions.
  • Assuming consent solves all data protection questions.
  • Treating DMPs as proposal paperwork rather than live workflow planning.
  • Underestimating documentation and metadata needs.
  • Treating AI fluency as evidence or authority.
  • Unclear boundaries between personal working storage and collaborative storage.
  • Limited understanding of who owns which decision in a project.

Facilitation Moves That Usually Help

  • Ask learners to justify tool/storage choices against risk level.
  • Use “what would you do next week?” prompts to force concrete planning.
  • Surface trade-offs explicitly (openness vs sensitivity, speed vs rigor).
  • Ask “what would another team member need to know to use this safely?”
  • Push groups from abstract principles to named platforms, roles, and actions.
  • When an answer is vague, ask what document, system, or person would hold that decision in reality.
  • Turn comparison tasks into ranking tasks where possible. Ranking forces prioritisation.

Managing Policy Questions

Learners will often ask for definitive answers about institutional policy. When the answer depends on context:

  • be honest about uncertainty
  • distinguish principle from local implementation
  • signpost the relevant service or approval route
  • avoid improvising a confident answer when escalation is the right move

This is particularly important for:

  • ethics approval thresholds
  • personal and special category data
  • external sharing and cloud services
  • retention and preservation decisions
  • AI use with sensitive or unpublished material

Room Setup and Activity Design

  • Mixed-role groups usually work well because they expose different assumptions.
  • If the room is quiet, start with individual reflection before group discussion.
  • If the room is very mixed in confidence, ask pairs to nominate a reporter rather than relying on open plenary discussion.
  • Encourage groups to write down one recommended action and one unresolved question after each activity.

Good Evidence of Learning

You are looking for learners who can:

  • explain why a tool or storage choice is appropriate
  • identify when a governance issue needs escalation
  • improve a weak DMP or documentation example
  • check a citation against its source and improve a weak reading note
  • propose a small, realistic workflow improvement
  • describe AI use in terms of task, source boundary, and verification

Follow-Up Suggestions

If you have time at the end of a session, ask learners to leave with one sentence in each of these forms:

  • “We should stop…”
  • “We should start…”
  • “We should clarify…”

This produces better follow-up actions than a generic “Any questions?” close.

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