Data Science and AI Educators' Programme [2023 Proposal]

# Data Science and AI Educators' Programme [2023 Proposal] --- ###### tags: `data-science-AI-educators'-programme` [TOC] --- ## Aims and objectives of the programme (reorder into direct and indirect. How do we measure?) **Our aims** 1. To identify and overcome barriers to data science and AI training practices to support quality enhancement, both nationally and internationally. 4. To empower educators with the confidence and knowledge to continually enhance their practice. 5. To foster a community that supports knowledge exchange and sharing of best practices. 6. To stimulate the development of open and inclusive curricula. 7. To support the uptake of data science and AI training across a range of disciplines. **Your objectives** 1. Develop your professional skills - Receive customised, example-based and pedagogy-specific training in a supportive, mentor and expert-led environment - Develop skills in one, or more, of the following key areas: - Curriculum development, drawing from existing resources at the Turing and beyond - Adopting and championing existing open-source curricula - Pedagogical approaches to technical training in data science and AI - Apply skills learned to a wide range of learner groups, including your teams and communities - Receive peer-based mentoring and expert consultation over several weeks to allow time to reflect and develop skills - Become a mentor in later rounds of the programme, should you choose to - Acquire useful and transferable skills: even non-teaching fields demand skills that can be learned through teaching, especially as part of a team and broader community of practice 2. Build your confidence as a Data Science and AI Educator - Develop confidence to bring training and resources to communities, including colleagues, team members or students - Receive feedback in a safe and supportive environment 3. Be a part of a nationwide network of AI educators - Become part of an inclusive and extensive network of data science and AI educators, where there is an opportunity to foster knowledge, share best practices, make connections and partake in collaborations 4. Co-develop this programme by sharing your feedback --- :::info ## Proposal ::: ## _Overview of actions_ | Action | Outcome | Notes | | ------ | --------- | -------- | | Re-run online with in-person elements | In Progress | | |Improve links with DSEIG| In Progress | | | Funding proposal | In Progress | Going into Skills budget for 2023 - 2024 | | Create guidebook/platform for DS and AI Educator resources | Not started | | | Review and amend curriculum | In Progress | | | Review mentoring/community building setup | In Progress | | | Apply for Carpentries membership | On hold | Bespoke membership doesn't meet our needs // Build a case for non- bespoke membership // Build a case for Trainer training for AD | | Create Moodle course | On hold | | | Funding proposal for illustrations/infographics | In progress | Going into Skills budget for 2023 - 2024 | | Contextualise the programme for different audiences | On hold | | | Gain endorsement from universities/academic institutions | In progress | MN to action | | Write academic paper | On hold | | ## _Target audience_ #### 2021 - 2022 audience overview | ALL APPLICATIONS | | | ACCEPTED APPLICATIONS | | | | ---------------- | --- | --- | --------------------- | --- | --- | | University | 58 | 75% | University | 36 | 75% | | School | 2 | 3% | School | 1 | 2% | | Industry | 11 | 14% | Industry | 6 | 13% | | Institute | 3 | 4% | Institute | 2 | 4% | | Government | 2 | 3% | Government | 2 | 4% | | Other | 1 | 1% | Other | 1 | 2% | #### 2022 - 2023 proposal Propose to target a **university audience** - topics can then predominently reflect university educators' needs. Propose to target a **UK only** audience to allow for in-person networking. E.g. Early career to well-established ## _Curriculum and learning objectives_ ::: info **definitions** - educators = target participants for the programme - teaching = activity (not training) - learners = target learner group ::: | 2021 - 2022 | | 2022 - 2023 PROPOSAL | | _Session duration_ | |:-----------:| --------------------------------------------------------------- |:--------------------:| -------------------- |:----------------:| | 1 | Carpentries Pedagogy | 1 | Carpentries Pedagogy | _2 x 0.5 days_ | | 2 | Collaborative development and delivery of a new course | 2 | Identifying learner needs | _1.5 hours_ | | 3 | Developing, launching and hosting your training project | 3 | Challenges (and overview) of teaching DS and AI | _1.5 hours_ | | 4 | Designing training for live delivery vs.asynchronous delivery | 4 | Post-pandemic teaching: what does it look like? | _1.5 hours_ | | 5 | Ethics in the context of training | 5 | Making learning memorable | _1.5 hours_ | | 6 | Widening participation in teaching (panel) | 6 | Embedding Ethics into teaching: the background | _1.5 hours_ | | 7 | Challenges with teaching DS and AI (panel) | 7 | Embedding Ethics into teaching: let's get practical | _1.5 hours_ | | 8 | Continuous evalutation of user/learner feedback | 8 | Assessment and feedback | _1.5 hours_ | | 9 | Making learning memorable | 9 | Collaborative development and delivery of teaching materials | _1.5 hours_ | | 10 | Collaboration between industry and academia (panel) | 10 | Working together to embed data science (and data-driven methods) across disciplines | _1.5 hours_ | | 11 | Product management, sustainability, legacy and entrepreneurship | 11 | Making teaching relevant to real-world applications: alignment between industry and academia | _1.5 hours_ | | 12 | Graduation | 12 | Graduation | _3 x 1hr sessions_ | --- **Key to Curriculum Topics:** :::success Topic is ready for final approval ::: :::warning Topic needs minor reviews ::: :::danger Topic needs considerable thought ::: --- **Curriculum Topics:** :::success **1: Carpentries Pedagogy Sessions** Learning Outcomes: - Day 1 - Intro and overview - [Memory and cognitive load](https://carpentries.github.io/instructor-training/05-memory/index.html) (how cognitive load affects learning, how to design with memory constraints in mind): 45 mins - [Building skill with feedback](https://carpentries.github.io/instructor-training/06-feedback/index.html) (how to get feedback and how to use feedback to improve): 20 mins - [Teaching is a skill](https://carpentries.github.io/instructor-training/11-practice-teaching/index.html) (use peer-peer lesson practice to give thoughtful and useful feedback and to incorporate others' feedback into own practice): 1 hour - Homework - Day 2 - [Live coding is a skill](https://carpentries.github.io/instructor-training/17-live/index.html): 65mins - [Preparing to teach](https://carpentries.github.io/instructor-training/18-preparation/index.html) (creating learner profiles, critically analyse a learning objective, identify checkpoints for formative assessment): 45mins - [Equity, inclusion and accessibility](https://carpentries.github.io/instructor-training/09-eia/index.html) (why is it important and how to enhance it in lessons): 40mins **What's new/different?** - 'Teaching is a skill' - 'Preparing to teach' - 'Equity, inclusion and accessibility' **Replaces:** - 'Motivation and building positive learning environments' - 'Expertise and instruction' - 'Working with your team' ::: :::success **2. Identifying learner needs** Learning Outcomes: - To identify learner needs/gaps and be able to integrate them into propsals for department heads - To practise persona mapping - To find and adapt pre-existing resources to meet learner needs/gaps _Activity:_ - Assessment or exercise during the call aimed at [learner persona profiling](https://www.shiftelearning.com/blog/bid/302513/The-Ultimate-Cheat-Sheet-for-Creating-Learner-Personas) - Brainstorm where people get their resources and where are resources missing? **For action:** - Matt to map against UKFPS ::: :::success **3. Challenges of teaching DS and AI** Learning Outcomes: - To understand wider and systemic challenges facing DS and AI educators (panel discussion) - To know strategies to overcome the wider challenges (panel discussion) - To be aware of day-to-day challenges that educators face and develop solutions for these (practical/breakout room) **What's new/different?** - Format slightly different. Less of a formal panel session with the addition of an activity/problem-solving clinic (below) _Activity:_ - Participants brainstorm challenges before the session - the challenges they face will form the breakout room activities where educators work together to share best practice/find solutions to the challenges they face - Icebreaker to share highlights of being a DS and AI Educator - There will be a follow-up problem-solving clinic towards the end of the programme to address any further challenges that come up during the programme. This could be during the peer-mentoring sessions and will allow participants to reflect on any strategies they implemented after this cohort call. ::: :::success **4. Post-pandemic teaching: what does it look like?** Learning Outcomes: - To understand different approaches to engaging students - To share best practices of how teaching has adapted - To identify what works well and what needs improvement - To be able to apply methods to larger cohorts of students **What's new/different?** - New topic, focusing on the online / virtual aspects / the benefits of doing different aspects or teaching approaches - Better engagement with a large cohort _Activity:_ - Example walk-through in redesigning a live course to be suitable for self-paced learning - Discussion session - where has online learning gone since the pandemic? What's the middle ground? ::: :::success **5. Making learning memorable** Learning Outcomes: - To discuss different ways to make learning memorable and be able to apply this to your own practice - To analyse a case study of immersive learning _Activity:_ - Using the '7 ways to make learning memorable' and an area of participants' practice, can they adapt/embed/create an area of their practice to reflect one of these 7 methods? ::: :::success **6. Embedding Ethics into teaching: the background** Learning Outcomes: - To discuss what is meant by ethics - what is our common understanding? - To understand what ethics means in the context of data science and AI _and_ in the context of teaching - To be able to convince others of the value of ethics in your field - A piece on ethical considerations for inclusive/accessible/representative practice? **What's new/different?** - The previous ethics topic had more of a 'project focus', looking at multiple areas of Turing Commons; such as lessons learned, how it was built etc. - This run will have a _wider overview_ as to _how_ educators might be able to embed ethics into a curriculum, with practical takeaways and a focused case studies. - The name of the topic has also changed. _Activity:_ - Multiple breakout rooms to discuss and share ideas on the key learning outcomes - Discussion: What could we do to help embed ethics? What is stopping you from embedding ethics into your DS/AI modules? Will be helpful from a strategy perspective. ::: :::success **7. Embedding ethics into teaching: let's get practical** Learning Outcomes: - To understand how to embed ethics in your curriculum - To know considerations for ethics at all stages of teaching (planning, delivery and assessment) and be able to begin to apply this - To examine detailed case study/studies on embedding ethics into project lifecycles - To know how to bridge the gap between ethics subject knowledge and actual, real-world implementation _Activity_: - Problem-solving clinic: Educators to take a module/course/programme that they teach or have planned and critically analyse, in groups. What currently works well from an ethics perspective? How could ethics be further embedded into the module/course/programme? - If you were to spend another 30/60/90 minutes on this topic, this is what you'd do next i.e. giving them next steps to continue learning **Notes:** - Peer mentoring activity this week could be a diversity toolkit clinic // or activity led by Maxine on diverse data sets - Deeper dive into using Turing Commons and bringing it to your learners (TTT) has potential as a case study ::: :::success **8. Assessment and feedback** Learning Outcomes: - To know different types of assessment and when to use them - To be able to reflect on my own practice, identifying areas of challenge and strategies to overcome these **What's new/different:** - Keep this session as a reflection session - Use [asynchronous 'reflection-style' activity](https://hackmd.io/o0jc-jHkQNynCuaZp9vIDg) from the last run to structure this call. Participants will attend the call live and complete activities in breakout rooms. _Activity:_ - Available in above link ::: :::success **9. Collaborative development and delivery of teaching materials** Learning Outcomes: - To understand the collaborative aspect of designing a course/programme - To know how to develop materials from different perspectives e.g. lesson vs. series of lessons vs. whole curriculum - To deep dive into building open source training resources for future use _Activity:_ **Notes:** - Clau might be useful for LO #3. ::: :::success **10. Working together to embed data science (and data-driven methods) across disciplines** - To understand how data skills are applied to non-cognate disciplines - To be able to embed data skills into non-cognate disciplines with confidence - To share best practice inc. materials _Activity:_ - Main format will be panel sessions of cognate/non-cognate educators to discuss the implementation of data skills across subjects - Break out rooms will be split into cognate/non-cognate disciplines to discuss current approaches/pain points and to share best practices - Second break out room will be mixed disciplines to cover the above points **Notes:** - Challenges of embedding data science into educators' practice will vary depending on level of teaching e.g. junior teacher vs course director - DCMS published report on this: [National Data Skills Pilots](https://www.officeforstudents.org.uk/publications/evaluation-of-national-data-skills-pilots-final-report/) which could form basis for the call - 2-way street: knowledge exchange mutually beneficial e.g. cognate discplines might want to understand how their skills are applied in another discipline ::: :::success **11. Making teaching relevant to real-world applications: alignment between industry and academia** Learning Outcomes: - To understand why (most often) training shouldn't be siloed as only theoretical and academical, and why it needs to be useful when applied to real world industry needs - To examine case studies of academia using industry real-world applications in practice - To discuss how to bridge the gap between academia and industry _Activity:_ - Some kind of 'how to' for real-world problems/application - Where to find case studies relevant to the teaching topic and how to integrate into curriculum: discussion and practice in breakout rooms ::: :::success **12. Graduation** These [calls](https://hackmd.io/HeWzjj6-STeHSXk0LYAQmA) were a highlight! ::: #### Curriculum Proposal Overview - All cohort calls should be 1.5 hours long, to include well planned breakout activities - Learning objectives are clear and updated - Order of sessions changed - Pedagogy sessions amended to focus more on 'successful/most interesting' modules - Clear speaker instructions to focus on the processes / methodology etc within area of expertise, and less on the product or project they manage - Clear slides for each call ## Mentoring sessions #### Peer-mentoring session 2021 - 2022 - 8 groups of 5 - 2 groups of 4 - Randomly allocated (each group was a mix of gender identities and area of work) #### Peer-mentoring session 2023 - Rename as mentoring sessions - Groups of 5 people; can choose between online or in person sessions - Aim: complement the cohort calls through reflections and discussions - Application form: add Qs regarding location and about in person availability - Initial networking session in-person at the Turing office to establish peer groups. Offer online option too. - Accessibility considerations for those that can't afford travel - [name=Mishka] is there a real need for this, or all online would be better use of our resource? - Use previous participants & DSEIG volunteers as mentors for the participants - Create feedback opportunities (early on in the programme) to assess effectiveness of peer-mentoring sessions - Use mentors to check in on progress and attendance of the groups - Organise mentor training session (Nick M) - Web presence to showcase the mentors contributing to the programme? #### Onboarding / upskilling session Timing: 1-1.5h, around mid-April Materials: to be shared Who would deliver the training: Ideas - private Slack channel for mentors - office hours for mentors - consider offering mentors a certificate for their contributions ## _Speakers_ | Session | Session name | Proposed speaker(s) | Suggested by | |:-------:| ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |:------------:| | 1 | Carpentries Pedagogy | reach out to Carpentries trainers | | | 2 | Identifying learner needs | | | | 3 | Challenges of teaching DS and AI (panel) | | | | 4 | Post-pandemic teaching: what does it look like? | [Richard Waites](https://www.linkedin.com/feed/update/urn:li:activity:6999826196444147712/) | Mishka | | 5 | Making learning memorable | David PS, Helen (JISC) | | | 6 | Embedding Ethics into teaching: the background | Chris B, Maxine M, | | | 7 | Embedding Ethics into teaching: let's get practical | | | | 8 | Assessment and feedback | | | | 9 | Collaborative development and delivery of teaching materials | | | | 10 |Working together to embed data science (and data-driven methods) across disciplines Developing, launching and hosting your training project | | | | 11 | Making teaching relevant to real-world applications: alignment between industry and academia | | | | 12 | Graduation | | | --- :::info ## Project plan ::: ## _Overall Timeline_ | Activity | Date | Actionee | Status | | ---------------------------------------------------- | ------------------------- | -------- | -------- | | Review action plan | September 2022 | AM | Complete | | Review proposal and sign off | November 2022 | ALL | Complete | | Find speakers & mentors | December to February 2022 | ALL | Ongoing | | Plan community/peer-mentoring event | December 2022 | MN | Complete | | Budget prep | December 2022 | MN | Complete | | Prepare all comms (call doc, EF page etc) | January 2023 | AM | Ongoing | | Prepare EIA | January 2023 | AM | Ongoing | | Prepare evaluation approach and work on Aims/Obj | February 2023 | AM | Ongoing | | Applications open | 27 Feb 2023 | AM | | | Pre-programme webinar and FAQ | w/c 6 March 2023 | AM | | | Pre-programme webinar and FAQ | w/c 20 March 2023 | AM | | | Promote at AI UK | 21/22 March 2023 | ALL | | | Applications close | 26 March 2023 | AM | | | Review applications - Skills and mentors to review | w/c 27 March | ALL | | | Communicate outcomes | 5 Apr 2023 | AM | | | Programme begins | 19 April 2023 | ALL | | | Programme ends | 5 Jul 2023 | | | | Programme evaluation | July 2023 | | | | Activity | Date option 1 | | --------------------------------------------------- | ------------- | | Applications open | 27 Feb | | Pre-programme webinar and FAQ | w/c 6 March | | Pre-programme webinar and FAQ | w/c 20 March | | Promote at AI UK | 21/22 March | | Applications close | 26 March | | Review applications - Skills and mentors to review | w/c 27 March | | Communicate outcomes | 5 Apr | | Programme begins | 19 April | | Programme ends | 5 Jul | | Programme evaluation | July | ## Session timeline | Session | Date | | | ---------------------------------------------------------------- | ---------------- | ----- | | Carpentries pedagogy day 1 | Wednesday 19 Apr | 9-12 | | Carpentries pedagogy day 2 | Thursday 20 Apr | 9-12 | | CC1: Identifying learner needs | Wednesday 26 Apr | 13:30 | | CC2: Challenges of teaching DS and AI | Wednesday 3 May | 13:30 | | CC3: Post pandemic teaching: what does it look like? | Wednesday 10 May | 13:30 | | CC4: Making learning memorable | Wednesday 17 May | 13:30 | | CC5: Embedding ethics into teaching: the background | Wednesday 24 May | 13:30 | | CC6: Embedding ethics into teaching: let's get practical | Wednesday 31 May | 13:30 | | CC7: Assessment and feedback | Wednesday 7 Jun | Asynch| | CC8: Collaborative development and deliver of teaching materials | Wednesday 14 Jun | 13:30 | | CC9: Working together to embed data science across disciplines | Wednesday 21 Jun | 13:30 | | CC10: Making teaching relevant to real-world applications | Wednesday 28 Jun | 13:30 | | Graduation | July | | ## Evaluation - Incorporate TULs / TNDEAs to get their impact feedback once the programme has finished running. What impact has the programme had in their department? - Beforehand reach out: give chance to input - focus group? - AM meeting with Chris (impact lead) to decide between pre/post programme survey VS exit survey - include ongoing evaluation e.g. weekly poll for cohort call progress and attendance with mentor ## Actions - [x] [name=Mishka] build contributor package - [x] [name=Mishka] speak to Yo Y about a mentoring sessions / reuse materials and we deliver the training (alternatively go to Nick M) - [x] [name=Mishka] write email brief for UPs/ TNDAs - [x] [name=Ayesha] ## Comms plan - targetted - previous DSAIEG participants - UPs & TNDAs (with MN) - Education IG - wide - Slack, Twitter, LinkedIn - Skills newsletter (speak to Bridget)