---
# System prepended metadata

title: 'Data Science and AI Educators'' Programme: Mentoring notes'
tags: [DS-AI-Educators'-Programme, mentoring meeting]

---

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**: Yang Lu
- **Meeting note-taker** Everyone
- **Meeting time-keeper** [enter name]
- **Call joining link**: [enter Zoom link here]


:::

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

**Roll call**
**Attendees:**
- Qian Fu
- Moe Elbadawi
- Yang Lu 
- 


### Agenda and Notes for Week 1

- Introductions - get to know each other
  - Yang: Senior leturer in Computer Science at YSJU
  - Qian: Research Fellow in data integration, analytics and management, at the University of Birmingham
  - Moe:  Research fellow at UCL, specialising in digital pharmaceutics
- Tell your peers about yourself - where you work/why you applied to the programme etc.
  - Moe: I work at UCL School of Pharmacy. I applied to this programme to further hone my teaching skills in DS and AI. I am also interested in learning about pedagogy techniques relevant to DS and AI
  - Qian: Working at the Birmingham Centre for Railway Research and Education (BCRRE), University of Birmingham; have developed a few open source tools that make it easier to work with rail data in reproducible and open pipelines written in Python. / To improve my teaching skills and gain transferable skills and confidence in my teaching and research career
- 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?
  - pedagogiacal skills in teaching programming and data science courses
  - simplifying the course contents and delivering effectively
  - motivating students especially who do not have programming experience to learn programming and run data science algorithms
- What are your plans to take this programme/new learning into your own community?
    - Moe: I plan to implement my new found knowledge into my current teaching material. In addition, I will share my experience with colleagues to help them keep abreast of recent developments.
    - Qian: To adapt the curricula and resources to customise the teaching materials and design courses tailored to learners' needs, including those who may be new to data science and AI; and to share my knowledge and experience and foster a culture of continuous learning.
- What teaching or 'educator' experience do you have and how do you hope the programme will add to this experience?
    - Qian: I have few teaching experience; hope the programme will provide tailored training, mentorship, and expert guidance to equip me with the pedagogical approaches to effectively teach technical concepts to diverse learners
- 

_**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?
    - We will meet on 10th May at 3.30 pm for next week

_**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**

- Qian Fu
- Moe Elbadawi
- Yang Lu 
- 
### 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?
      - Different types of learners and how to design the course to keep it interesting to people at differet levels
  - Was there anything unusual or interesting that came up during the sessions?
      - Giving feedback 
      - Summative feedback and formative feedback
  - Was there a concept that you found challenging? Why?
      - Dark mode for teaching
  - Are there any concrete examples that you could apply in your role as an educator?
      - Mind map
      - Giving feedback to others
      - Managing the course for different types of learners
      - EDI issues

_**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

**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?
  - Misunderstanding about the Machine learning, mixing with robotics; 
  - Underestimating the difficulty of coding for machine learning cases;
  - Thinking linear algebra as the essential element by beginners;
  - Thinking lecturers know everything; 
  - 
- How can you ensure that your material and delivery mitigates this misconception?

  - Investing certain amount of time/efforts on preparation to even just one lecture/session so as to understand everything correctly

- How could you use a formative assessment strategy(such as multiple choice questions) to mitigate this misconception? 

  - Investing certain amount of time/efforts on preparation to even just one lecture/session so as to understand everything correctly

- 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).
  - Q&A session; review the material; no reponse at the beginning; a bit warm-up by lecturers; 
  - small-group tutorial; demo solutions to math questions; informal discussion on question solving

_**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

**Roll call**

- Qian Fu
- Moe Elbadawi
- Yang Lu 

### 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.
  - Challenges of transition to online meetings/teaching
  - how confident/comfortable of doing it
  - what you do differently if you it (pandemic) again 
- 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?
  - Live coding: remove/hide some lines of codes
  - Demo - manual calculation vs computer environment
- How could you incorporate some guided practice into your session? What is the value of guided practice?
  - HTML Materials - following instructions - not working well with students ;(
  - with scaffolding co-lab - happy; but not working :(
  - Improved with scaffolding co-lab - removing key methods to find out :)

_**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 : 5 June 2023

**Roll call**

- Qian Fu
- Moe Elbadawi
- Yang Lu 


### Agenda and Notes for Week 5

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?
    - Having an extra TA in the class to help dealing with ad-hoc questions; instructors can effectively organise live-coding sessions without getting distracted.
  - Was there anything unusual or interesting that came up during the sessions?
    - Moe found that students showing better learning capacity are those who actively apply Chatgpt in the self-learning
    - We may not always trust what ChatGPT provides us; what contents generated by ChatGPT can be 100% trustable - things that you are able to verify - domain knowledge and expertise are essential
  - Was there a concept that you found challenging? Why?
    - Teaching programming to new learners (zero knowledge about Python)
    - IDE setting-up is very crucial - suggest do spend time on this and only proceed when there is no question on IDEs 
  - Are there any concrete examples that you could apply in your role as an educator?
    - Our backgounds can influence the IDE selection - for instance, Anaconda/VS may not work well for educators whose work is completely data-driven, while it may work for ppls with CS background.

## Week 6 : 19 June 2023

**Roll call**

- Qian Fu
- Moe Elbadawi
- Yang Lu 
- 
### Agenda and Notes for Week 6

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.


- [Qian Fu]
  - Decide to use Jupyter Notebook and live coding to introduce class in python
  - starting with Fuction example which can help students to review - easier to understand new content
  - By showing similarities and differences between Function and Class - students can make comparison and that won't make them feel too stretched.
  - Suggestion - 1. may add semantic meanings in the questions, code lines and also explaination (e.g. task1, task2) to help students review
  - 2. Pros and cons - when/whether to adopt method/class
- [NAME]
  - [FEEDBACK HERE]
- [NAME]
  - [FEEDBACK HERE]
- [NAME]
  - [FEEDBACK HERE]
- [NAME]
  - [FEEDBACK HERE]
  -
## Week 7 : 30 June 2023

**Roll call**

- Qian Fu
- Moe Elbadawi
- Yang Lu

### 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.


- Qian
  - Co-supervising PhD student; challenging early career researchers on subject knowledge; bad attitutes;
  - Feeling offended if students do sth without permission and causing damages to reputation; 
  - Suggestions: communication is the key to the process; PhD Handbook would be very useful in this sort of conversation - finding the "common ground" of understanding betweenn educators and students and showing they what should/should not do in the teaching/learning environment as well as supervison relationship.
- Moe
  - Coming across the same situation as well. May be seeing it objectively can help; people may see things differently
- [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?
    - Learning from the errors; we are all humans!!! 

## Week 8 : 19 June 2023

**Roll call**

- Moe
- Qian
- Yang
- 


### 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?
    - Moe: misuse the materials
        - AI to make decision on medicines (it may be harmful to people)
        - Why do we need the ethics; students may not be interested; tangible classes and materials; highlight the consequences 
    - Qian: understand ethics is important; the way we discuss ethics and teaching ethics may not be important, especially to undergraduates due to the lack of research experience with industry and academic institutes.
  - Was there anything unusual or interesting that came up during the sessions?
  - Was there a concept that you found challenging? Why?
     - How do we make it interesting to students!
     - How to design the teaching methods and modules to get them a bit more engaged; understanding the importance.
  - Are there any concrete examples that you could apply in your role as an educator?
      - e.g. companies don't use dropbox;
      - refer to the student national AI guidelines; XXX principles

## Week 9 : 26 June 2023

**Roll call**

- Moe
- Qian
- Yang
-  

### 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?
  - [TAKE NOTES HERE]
- 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.
  -  [TAKE NOTES HERE]
  -  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.
  -  [TAKE NOTES HERE]

## Week 10 : w/c 3 July 2023

**Roll call**
- Moe
- Qian
- Yang
- 

### 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)

- Moe: Collaborating with 2 PhD students. They can find the gaps in the each others' teaching;
    - Challenge: delivery to students; subject knowledge; 
    - Opportunity: good for their career
- Qian: developing a python package - how the package can be used
    - Challenge: initially I have to think from a student perspective (python beginner) while creating the materials about the package; Self-learning process; Now a US researcher joining and can share insights; need the feedback from lear
    - Opportunity: welcome to participating the development of the package and documentation
        -  py-helper (small but useful tool); 
        -  opens-math-data (one command line only: geographic data download for free)

GitHub: https://github.com/mikeqfu
pydriosm
https://pydriosm.readthedocs.io/en/latest/
pyhelpers
https://pyhelpers.readthedocs.io/en/latest/
pyrcs
https://pyrcs.readthedocs.io/en/latest/
## 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]
  - [TAKE NOTES HERE]
- [NAME]
  - [TAKE NOTES HERE]
- [NAME]
  - [TAKE NOTES HERE]
- [NAME]
  - [TAKE NOTES HERE]
- [NAME]
  - [TAKE NOTES HERE] 