CC8: Collaborative development and delivery of teaching materials### Agenda | Agenda | Speaker | Time | | --------------------------------------------------------- | ------------ | ------------- | | Intro and overview | Ayesha | 10:30 - 10:40 | | Embedding data science across disciplines: a case study | Serge Sharoff| 10:40 - 11:00 | | Working together to embed data science across disciplines (including breakout sessions) | Eilis Hannon | 11:00 - 11:55 | | Wrap-up | Ayesha | | --- ## Feedback and Q&A ### Questions from participants :question: Place your questions here and we will pick these up in the session :smile_cat: - What kinds of practical sessions do you run with the language students? Do you teach them coding and developing solutions or just teach theory? - _Answered by speaker in the session_ - - - ### Useful Links/references :bookmark_tabs: - [Insert title of link in square brackets](insert URL in round brackets) - - - - --- ## Breakout activity :speaking_head_in_silhouette: ### Group 1 :::success _Part 1_ - What does 'non-cognate' discipline mean? "The term "non-cognate student" typically refers to a student who is pursuing a degree or studying in a field that is unrelated or different from their previous academic background or qualifications. In this context, "cognate" refers to a subject or field that is closely related or similar to the student's prior academic experience. For example, if a student with a background in engineering decides to pursue a graduate degree in psychology, they would be considered a non-cognate student because engineering and psychology are unrelated fields. The term is commonly used in academic settings, particularly when discussing admissions or program requirements. Being a non-cognate student may have implications for the student's course selection, prerequisites, and potential challenges in adapting to the new field of study. In some cases, non-cognate students may need to complete additional coursework or prerequisites to gain the necessary knowledge and skills to succeed in their chosen field." (ChatGPT!) - What is the typical learner profile of a learner from a non-cognate discipline? - Is there a typical profile? - Maybe people wanting to change careers - The need to expand people's skillset - People can come with professional and work experience - What are the effects of that on the learning environment? - People bringing specific viewpoints and perspectives - Often come with questions related to their previous experiences - Need to review glossary and terms so people are all on the same page - Varying backgrounds and skills can make teaching challenging ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: --- ### Group 2 :::success _Part 1_ - What does 'non-cognate' discipline mean? - Studying a different (not-similar) field and dicipline - - - What is the typical learner profile of a learner from a non-cognate discipline? - Fear towards the new dicipline - Sometime we meet enthusiastic learners - they want to address their domain problems in their dicipline however they do not want to get into the nitty-gritty technical details - Require basic foundational skills to apply data science techniques - - What are the effects of that on the learning environment? - Positively, novel/new interdiciplinary subject areas appears - Richer conversations as data science knowledge/skills are embedded into other domains of knowledge - - ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: --- ### Group 3 :::success _Part 1_ - What does 'non-cognate' discipline mean? - very confusing term, hard to draw the line as to what is 'non-cognate'. Maybe try other defintions like 'non-STEM' - Not obtaining an accredited degree, but taking a course to learn a new skill - - - What is the typical learner profile of a learner from a non-cognate discipline? - self-disciplined - limited STEM understanding - come in different profiles - - - What are the effects of that on the learning environment? - with no evaluation, self-motivated and less stressful environment - Curious - - ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: --- ### Group 4 :::success _Part 1_ - What does 'non-cognate' discipline mean? - a discipline outside of the context/traditional boundaries of the main field of study - - - - What is the typical learner profile of a learner from a non-cognate discipline? - is there a typical learner profile in this case? 'non-cognate' seems to imply 'non-typical' - but there does seem to be a relationship between certain 'non-cognate' disciplines (e.g. linguistics/language studies and data science-->think LLMs) - at the same time, most professions and fields deal with data, so is this still non-cognate? - - What are the effects of that on the learning environment? - ... - What can be tricky for non-cognate learners is the distinction between the fields that we are teaching (e.g. text mining vs data science, are they the same) - It can be difficult to design materials to keep things interesting for students with varying experience and prior knowledge. ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: --- ### Group 5 :::success _Part 1_ - What does 'non-cognate' discipline mean? - Wordcels instead of shape-rotators - Not a traditional quantitative subject (maths, statistics, computing) - - - What is the typical learner profile of a learner from a non-cognate discipline? - Scared of maths- bad psychological associations with failure in school, angry that 'nerds' are doing so well in the modern tech world, holds maths and statistic in contempt to protect ego - Very varied backgrounds (some very comfortable and confident with maths and coding, others have none) - Some very resistant and don't want to learn - In some countries, there is a very negative attitude towards these quantitative subjects, people are happy to publicly admit 'I can't do - - What are the effects of that on the learning environment? - Remedial lessions on terminology and concepts- no assumed knowledge - Use relevant applications and examples for the students' discipline - Allow students to progress at own pace, and provide extension material for those progressing fastest - Have to persuade students of the subject's worth before you can start teaching. Need to convince them they are worthwhile skills. ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: --- ### Group 6 :::success _Part 1_ - What does 'non-cognate' discipline mean? - a non-related discipline - e.g. lab scientists or humanists - Not all of STEM - - What is the typical learner profile of a learner from a non-cognate discipline? - required part of a programme of study - may not be something they've engaged with something themselves - - - What are the effects of that on the learning environment? - can potentially be frustrating for students, learning that learning is non-linear - need to get over initial hump of difficulties - - ::: :::info _Part 2_ - Look at the possible shared delivery models, below. - What are the opportunities and weaknesses of these delivery models? - ... - - - What barriers do you foresee with any of these? - ... - - - - How do they influence the learning environment/learning conditions? - ... - - - ![](https://hackmd.io/_uploads/Sk9-OhbFn.png) </br> ![](https://hackmd.io/_uploads/rJrEu2bY2.png) </br> ![](https://hackmd.io/_uploads/HJIB_nbKn.png) </br> ![](https://hackmd.io/_uploads/Sk1UDszKh.png) ::: ---