Data Science and AI Educators' Programme: Mentoring notes

Data Science and AI Educators' Programme: Mentoring notes === ###### tags: `mentoring meeting` `DS-AI-Educators'-Programme` :::info - **Call time and day**: TBC with the group - **Meeting chair**: [Luisa Cutillo] - **Meeting note-taker** [Everyone] - **Meeting time-keeper** [Tom Albone] - **Meeting time-keeper** [Amal Algamdey] - **Call joining link**: [MS teams] ::: The mentoring sessions are an opportunity for you to discuss your learning, any challenges you are currently experiencing and any challenges you might face moving forward. We encourage you to use these sessions as a safe space to share best practice and support one another in your development as Data Science and AI educators. To make the most of this opportunity, it is important that you meet weekly. Please try to find a mutually convenient time each week to meet for between 30 minutes and 1 hour. It may be useful to assign a meeting chair, note-taker and time-keeper. You can use the blue box, above, to take note of who will fulfill these roles. Below, you will find a suggested structure for each week's call. There may be sessions where you have other organic conversations, without the need for a structure. This is perfectly fine - the structure is there as a prompt, should you need to use it. **Table of contents** [TOC] ## Week 1 : w/c 1 May 2023 05/05/2023 **Roll call** - - - - ### Agenda and Notes for Week 1 - Introductions - get to know each other - Luisa, lecturer in the school of Mathematics, University of Leeds. - Andreas, MSc Data Science at LSE, Microsoft - Tom, Data Science Lecturer at [Data Science Campus] (https://datasciencecampus.ons.gov.uk/), Office for National Statistics - Amal, MSc Computation at UCL, Researcher at Foster+Partners - Tell your peers about yourself - where you work/why you applied to the programme etc. - Luisa: I attended the program last year and really enjoyed it. Hence thjis year I am here as a mentor. I am Turing Fellow and LITE fellow on a project related to Data Science and AI education, particularly interested in skills gaps. - Andreas: starting to teach Data Science at LSE next fall, will be a great preparation - Tom: I work in the Capability team in the [Data Science Campus](https://datasciencecampus.ons.gov.uk/), training core data science skills to those who are employed in the Civil Service and wider public sector. We also do a lot of outreach to schools and communities. I joined the programme to become a better educator of DS&AI. - Amal: I work on developing AI application for AEC. - Set some personal goals: what do you hope to achieve through participation in this programme? Where would you like to be by the end of this programme? - Tom: My existing teaching skills come from Primary Education, although I have been training adults for nearly a year. I wanted to undertake formal training so that my understanding of how to teach DS&AI specifically to professionals was better. I want concrete strategies and to be able to place myself in the mindset of trainees to best empower themselves to use new skills. I'll hopefully still be in the same role, however with a clearer focus on how to tailor training programmes to meet the needs of different cohorts whilst also becoming a leader in embedding these approaches within our organisation. - What are your plans to take this programme/new learning into your own community? - Tom: Along with my colleague who is also on the course, I will use this in my own design and delivery of training programmes, enhancing how I engage with stakeholders and mentoring colleagues and participants in how to learn by teaching, whether this is through formal courses or informal conversations and mentoring. - What teaching or 'educator' experience do you have and how do you hope the programme will add to this experience? - Tom: I have a background as a Primary Teacher and have often delivered bespoke pieces of training to new starters in different analytical roles. I hope that the programme will remind me of some of the best practice in pedagogy but also give me some new insights that I might not be aware of, specifically in relation to DS&AI. _**Meeting logistics & Reminders**_ - Decide on a time and a day that works to meet every week. If you cannot find a mutual day every week (and in advance), agree to co-ordinate on a week-by-week basis the best time to meet. Will you set up a poll? Who will co-odinate this? _**Task after this call**_ - Next week's cohort call is on 'Identifying Learner Needs'. Please take time to reflect on how you identify learner needs and any questions you might have. ## Week 2 : 8 May 2023 **Roll call** - - - - ### Agenda and Notes for Week 2 Quick check in - how are you all doing? Spend some time reflecting on the learning from Week 1 (Carpentries pedagogy sessions). Think about the following questions as prompts for discussion: - What did you learn that you didn't know before? [TAKE NOTES HERE] - Was there anything unusual or interesting that came up during the sessions? [TAKE NOTES HERE] - Was there a concept that you found challenging? Why? [TAKE NOTES HERE] - Are there any concrete examples that you could apply in your role as an educator? [TAKE NOTES HERE] _**Task after this call**_ - Next week's cohort call will involve a problem-solving clinic around common challenges in teaching Data Science and AI. Please come prepared to discuss some of the challenges you face in your role as an educator. ## Week 3 : 15 May 2023. date 19/05/2023 **Roll call** - - - - ### Agenda and Notes for Week 3 Quick check in - how are you all doing? This week, we would like you to focus on the 'Building Skill With Feedback' module from Week 1. The session discussed how formative assessment tools can be used to help identify learner misconceptions during a course/lesson/programme etc. With your group, please discuss the following and take notes where appropriate. _You should use your own teaching/instructing/educating experience to inform your discussion and give concrete, useful examples where possible._ - Consider a lesson that you have taught (or plan to teach). What potential misconceptions might your learner encounter? - Tom: Effective Programming (Command Line and Version Control with Git). Misunderstand why CL is necessary to understand Version Control. Misunderstand different concepts between Git and Github and Git Bash etc. - How can you ensure that your material and delivery mitigates this misconception? - Tom: Explains the links between the different concepts clearly. Keep mentioning the links throughout delivery. Create anonymous poll for some quick feedback to clear up misconceptions - identify the misconception is the first and important setp - How could you use a formative assessment strategy(such as multiple choice questions) to mitigate this misconception? - Design to test for misconception - Again, considering your own experiences and practice, what other examples of formative assessment could you use? Think about how this would apply to a lesson that you plan to teach (or have taught). - word cloud, self-marking exercises,... _**Task after this call**_ For next week's call, you will be looking at memory and cognitive load in the context of a session/lesson/training that you have led (or participated in). Please bring this teaching material to your next mentoring session. An overview with approximate timings of the session, plus the content covered will suffice. ## Week 4 : 22 May 2023. Call day 26/05/2023 **Roll call** - - - - ### Agenda and Notes for Week 4 Quick check in - how are you all doing? This week, we would like you to focus on the 'Memory and Cognitive Load' module from Week 1. The session discussed the benefits of guided practice and formative assessment, as well as some tips for consolidating content. With your group, please discuss the following and take notes where appropriate. - Looking at the materials you have brought with you today, imagine yourself as a learner in this lesson. Is the information presented in short chunks, followed by an opportunity for consolidation of the concept(s) taught (through some kind of formative assessment)? If not, work together to re-jig the session, adding opportunities for consolidation, where possible. - [TAKE NOTES HERE] Andreas: starting to teach soon, I will try to embed all this advice - In order to reduce cognitive load on a learner, we can also use a strategy called scaffolding. This introduces small, easily-digestible concepts step-by-step until they reach the final 'learning goal'. How could you scaffold _your_ session to support your learners? - [TAKE NOTES HERE] Andreas: start with the basics, spread concepts over course period - How could you incorporate some guided practice into your session? What is the value of guided practice? - [TAKE NOTES HERE] Andreas: pass out relevant examples to students _**Task after this call**_ Before next week, please prepare a short, 5-minute session to deliver to your group. This will be an opportunity to 'teach' and receive feedback from your peers. You can teach any concept you like, providing that, by the end of your 5-minutes, your learners will have grasped the concept! You will each receive feedback on both your delivery _and_ your content, so please ensure that you have started to consider/incoporate pedagogical elements that you have learned so far. If you would like to pair up to deliver a 10-minute session together, you may. ## Week 5 : 29 May 2023 -Meeting date 02/06/2023 **Roll call** - - - - ### Agenda and Notes for Week 5 Quick check in - how are you all doing? This week, we would like you to focus on the 'Teaching is a Skill' module from Week 1. The session was an opportunity to practice teaching and receive feedback on your delivery. Today, you will repeat the exercise, this time thinking about giving feedback on delivery _and_ content. You should consider elements such as memory and cognitive load, opportunities for assessment as well as the overall delivery. - [Andreas code teaching about strings in python] - [FEEDBACK HERE] - Positive: The notebook was structured but not already populated. We could see the commands edited live, this gave time to catch up with the concepts introduced. The pitch of the material was appropriate and the explanations clear and not rushed. - Negative: there was no mention about syntax for comments (not coding lines) or the tool used. Prepopulating examples reduces cognitive load during initial explanation as students don't have to follow the typing and listen to explanations. Furthermore trainer does not have to memorise/copy syntax from somewhere as they explain. This is good for exercises and solutions later. - [NAME] - [FEEDBACK HERE] - [NAME] - [FEEDBACK HERE] - [NAME] - [FEEDBACK HERE] - [NAME] - [FEEDBACK HERE] ## Week 6 : 5 June 2023 -< meeting on the 9th June **Roll call** - - - - ### Agenda and Notes for Week 6 Quick check in - how are you all doing? This week, we would like you to focus on last week's 'Making Learning Memorable' cohort call. - What did you learn that you didn't know before? - Tom: Gamification and games as an approach to teaching. The technical and time debt incurred up front in developing these. - Andreas: instant gratification through little games or exercises - Was there anything unusual or interesting that came up during the sessions? - Tom: The use of a physical box for problem solving - Was there a concept that you found challenging? Why? - Tom: Used of educational games to teach. Resources in civil service tight, often face challenges with participants not even having access to free open source software needed. - Are there any concrete examples that you could apply in your role as an educator? - Tom: Use of collaborative, cloud based platforms for teaching. Currently moving towards this approach along with other delivery partners. ## Week 7 : 12 June 2023 **Roll call** - - - - ### Agenda and Notes for Week 7 Quick check in - how are you all doing? This week, we would like you to focus on motivational and positive learning environments. This might include, though not be limited to, aspects such as growth mindset, encouraging participation and creating a positive, safe learning environment. Today, you're going to think about what has motivated you to become an 'educator' and write a short explanation. This should take about 5 minutes. When you've finished, share it with the group - you can also save this as part of your teaching philosophy for future reference. - [Tom] - Experience of learning at school was mixed, some great teachers some not so great. Always enjoyed volunteering with programmes working with younger children at university so wanted to reach the type of children who may have a similar experience and struggle. Career in teaching primary turned out not exactly to be the challenge I wanted but after leaving and gaining more technical skills I kept taking part in educational voluntary programmes and became even more passionate about teaching the skills I'd learnt to children and my peers. After spending a couple of years training to be a data scientist I found that I enjoyed training and mentoring colleagues the most which resulted in me joining the campus faculty. - Andreas - Sharing what I've learned with others. Very gratifying. Also helping people from different disciplines learn data science (similar to me). Doing something that (I think) I'm good at and that I enjoy doing - explaining and answering question. Care about others doing well, not judgmental. Find out if an academic career is right for me, but can also help me in my professional career. Going back to university and meeting professors and students. Different way of working. - [NAME] - [TAKE NOTES HERE] - [NAME] - [TAKE NOTES HERE] - [NAME] - [TAKE NOTES HERE] - What do you notice about the motivations you have to teach? - Is there some diversity amongst your group? Is this a good thing? - Why would it be important for the learner to embrace their errors? - Why would it be important for the educator to embrace their errors too? ## Week 8 : 19 June 2023 **Roll call** - - - - ### Agenda and Notes for Week 8 Quick check in - how are you all doing? This week, we would like you to focus on the last two weeks of 'Embedding Ethics' cohort calls. - What did you learn that you didn't know before? - [TAKE NOTES HERE] - Was there anything unusual or interesting that came up during the sessions? - [TAKE NOTES HERE] - Was there a concept that you found challenging? Why? - [TAKE NOTES HERE] - Are there any concrete examples that you could apply in your role as an educator? ## Week 9 : 26 June 2023 **Roll call** - - - - ### Agenda and Notes for Week 9 Quick check in - how are you all doing? This week, we are going to think about 'Preparing to Educate'. You will never know everything about your learners. Thinking deeply about them as people will help you to prepare and create the most inclusive environment for everyone. Today you will have two activities: - Take a moment to imagine a learner who might attend your class/workshop/programme/session etc. What is their background? What problems do they face? What do they have to gain from attending this session? Create a learner profile and share this with your group. How are you going to meet their needs? - Andreas: Social Science undergrad student - basic math background, no coding experience, interested in data science but probably not as their primary field. Problems: A lot of new concepts at once, new way of thinking (ie algorithmically). How to solve: mix of theory and practice, asking questions, looking for misconceptions. Minimize time between the two. - Tom: Civil Servant Analyst without programming experience but knowledge of data and statistics. They will learn how to use open source data science tools such as R/Python, Git for version control to make their analysis more reproducible and auditable. Meet their needs by teaching practical workshops using open source software or alternative web platforms, demonstrating common analytical workflows with interactive exercises. Mostly starting from first principles (assuming no prior experience). - Take a moment to think of a learning objective from a workshops/class/session that you have led (or intend to lead). - Suppose a learner has mastered this objective and wanted to try someting more cognitively challenging on the exact same topic. Take some time to brainstorm what objective they could work towards next. Share this with the group. - Andreas: eg linear regression - if too easy for someone, they can think of some of the limitations, or examples of when or when not it's the right approach. Maybe giving them something more advanced to read, or a more challenging exercise. Problem: avoid them doing the next lession or they'll be bored quickly again. - Tom: To be able to use R/Python to summarise and group Dataframes. This activity often splits a cohort as it can be quite challenging to take what they've already learnt about using the language and apply it to complex groupings of data. We often have to stay on first principles for quite a long time, so for those more capable there's often a large number of exercises aimed at creating more complex groupings and summaries, with questions that can only be answered by thinking deeply about what's included in the data without specifying variables to group on or aggregates to use. - Suppose a learner struggled to meet the specified objective. What might they be missing? How would you support them? Indentify one more fundamental thing the learner needs to be able to do in order to be successful in meeting this objective. - Tom: They might be missing an understanding of how the logic of grouping and summarising works, either in general or within Python/R. This could be conceptual, syntax or both. As I teach mostly virtually, I'd support them in the main session because often people are reluctant to speak up when they have a misconception so it can be useful to quieter members of the group. The people who aren't struggling have usually been encouraged to move on if they like with the learning materials. We have the capacity to do breakouts or a quick private teams call with someone if they require it so the session doesn't come to a complete standstill. - Andreas: Asking them to explain how they currently understand the topic, even if wrong, that way you can find out what may be missing and try to go over that again. It's difficult without knowing specifically where the misconceptions are. If it's something from way back in the class, more difficult to solve (at least immediately). Maybe have a 1:1 session wiht them. ## Week 10 : w/c 3 July 2023 **Roll call** - - - - ### Agenda and Notes for Week 10 Quick check in - how are you all doing? This week, we are going to think about working with a team/collaboratively to develop training materials (TBC - details to follow soon) - Tom: remote work to develeop and deliver training material in research and public institutions. The big advantage of remote working for this kind of activities is inclusivity and diversity. - working with people lets you acces other people opinins and ideas without having to think of everything by yourself. - the negative part is to compromise and be able to persuade on ways that you are sure by exeprince that will work. - It is essential to keep aims and roles very clear from the beginning. The team needs a leader. - Andreas: has got more students perspective and is curious of implementing himself in his new educator role. ## Week 11 : w/c 10 July 2023 **Roll call** - - - - ### Agenda and Notes for Week 11 Quick check in - how are you all doing? This week will be about preparing for the graduation. Please use the time to work on your graduation presentations, using your peers as a sounding board if needed. The graduation sessions are a brilliant opportunity for you to have a final reflection and networking session, in which we can celebrate some of your highlights and think ahead to what comes next. Therefore, in preparation for the sessions, we ask that you kindly prepare a short, 3-minute presentation about your time on the Data Science and AI Educators’ Programme. Slides are optional. We have prepared a selection of prompts to help you cover some of the information that you might like to share with your peers: - What is/are your highlight/s from the programme? - How will you apply some of your learning(s) from the programme to your line of work? - How do you intend to use the programme to impact your community? - How can other participants from the programme collaborate with you/get to know you better once the programme has finished? ## Week 12 : w/c 17 July 2023 **Roll call** - - - - ### Agenda and Notes for Week 12 Quick check in - how are you all doing? This will be your final week of the programme. Take a look back at the goals you set for yourself at the beginning of the programme. Have you met them? If there are any that you didn't meet, or only partially met, what steps can you take to ensure that you work towards meeting the goal? Can your peers support you in this? - [name=Tom] - I set myself some goals to refresh my understanding of pedagogy, bringing it more up to date given changes in working practice, switch to training adults and focus on DS&AI. Met most of these as now have practical examples of how to create learner profiles, teach to those who are traditionally not from a computing/statistical background and on collaboration in developing learning materials. - Some of the goals around ethics were only partially met. I feel this was due to the course having too much of an academic feel and not balanced enough between the former and careers in industry/wider public service (such as local/national government, ngos, non-profit, contractors etc). One lecture was all about how every course should have liberal arts baked into it which was completely useless for someone who is training professionals with very limited time on their hands and quite specific training needs. - [NAME] - [TAKE NOTES HERE] - [NAME] - [TAKE NOTES HERE] - [NAME] - [TAKE NOTES HERE] - [NAME] - [TAKE NOTES HERE]