The University of Sydney Page 1

Sydney Data Stories

Embedding current

(interdisciplinary) research

in undergraduate curricula

Dr Di Warren | School of Mathematics & Statistics

Dr Samantha Clarke | Educational Innovation

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Abstract

In higher education, there is extensive literature around the complex relationship between research and teaching,resulting in an ongoing, contentious conversation. While the value of the symbiotic teaching-research nexus islargely unquestioned by senior academic administrators (Neumann, 1992), the extent to which it issuccessfully implemented at a departmental level is varied (Elken & Wollscheid, 2016). The result of anysiloing of research and teaching is that undergraduate students tend to view their lecturers as “teachers” ratherthan “researchers, and experience a personal disconnect between their learning experience and the researchculture of their department. Not only does this impoverish the undergraduate curricula, but the curriculum itselfcan end up siloed, rather than demonstrating interdisciplinary transfer.

In this paper, we discuss the “Sydney Data Stories”, which is a new initiative across the University of Sydney whichaims to champion and nurture the teaching-research nexus in data science. Given the highly collaborativenature of data science, the “Sydney Data Stories” showcases researchers from a diverse range ofdisciplines, allowing current research questions with their associated data to be embedded in theundergraduate curricula. Using a case study from a new data science course, we consider the benefits of thisinnovative, integrated approach for both students and lecturers, in terms of motivation, engagement andexcellence.

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Overview

1. New data science curriculum

2. The teaching-research nexus

3. Embedding interdisciplinary research in data science

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1. New data science curriculum

– In 2018, the University of Sydney launched a new undergraduate curriculum, including an interdisciplinary major in data science, co-hosted by the Schools of Mathematics and Statistics and Information Technology.

– The first course in the data science major is DATA1001 “Foundations of Data Science” which is the flagship unit in a first-year suite of data science and statistics offerings, taken by almost 3000 students per year.

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Data Science in undergraduate teaching

“The standard intro (stats) course has passed its use-by date. It reveals far too little of the exploding world of data and does it far too slowly …

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Data Science in undergraduate teaching

“Beginning experiences of data analysis should feel like driving a shiny sports car at breakneck speed along the Riviera, sliding around hairpin bends overlooking thrilling vistas.

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Data Science in undergraduate teaching

“We spend too much time in working in windowless workshops with our heads stuck under the hood .”

Chris Wild (2015)

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2. The teaching-research nexus

The ideal …

“the tangible nexus, the intangible nexus and the global nexus”

Neumann (1992)

Not always realised (especially at scale)

“the idea of a synergetic relationship coexists on an intellectuallevel, this idea however, sometimes appears to be challenged atthe department level where research and teaching can betreated as distinct activities competing for resources (i.e. time andfunding)”

Elken & Wollscheid (2016)

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Integrating research in undergraduate teaching

2 dimensions (Healey, 2005)

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Integrating research in undergraduate teaching

4 factors in student perception (Visser-Wijnveen et al, 2016)

- Motivation

- Reflection

- Participation

- Current research

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Integrating research in undergraduate teaching

6 activities (Brew & Mantai, 2017)

- Undergraduate learning (“everything students do at university is research”)

– Individual work, study and uncoordinated skills development

– Coordinated skills development through individual and group work

– Research-based scholarly experience/tasters

– Scholarly practice within courses

– Integration into the scholarly community (holiday scholarships or internships)

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3. Embedding interdisciplinary research in data science

Researcher Topic

Prof. David James Precision Health – a revolution in health care

Dr Phoebe Meagher Australian Shark Attack File

Dr Emi Tanaka Mighty oaks from little acorns grow: what's in store for statisticians tomorrow?

Dr Helen McGuire Big data helping explore our amazing technicoloured immune system

A/Prof. Tara Murphy A petabyte per night: Managing the astronomy data deluge

Prof. Robert HazenThe co-evolution of rocks and life: data-driven insights from the 'The New

Mineralogy'

Dr Shaunna MorrisonMars Science Laboratory: Exploring the habitability and geologic history of

the red planet

Prof. Jean Yang The challenges and rewards of collaborative research

Dr Liana PozzaX-ray guns and pretty maps: Improving contaminated site assessment in

urban areas

Mr Saweng Lam Industry speaker: Quantium

Dr Danny Liu Connecting People with Data

2018 Sydney Data Stories Masterclass Speakers

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A petabyte per night: Managing the astronomy data deluge

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Australian Shark Attack File

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Initial feedback

“Another great feature of the course is

the data science masterclasses. While

the masterclasses are an optional set

of lectures for those who are

interested in engaging more with the

unit, I find these masterclasses not

only reinforce some of the concepts

we learn in lectures and labs, but also

demonstrates how what we are

learning is applicable in a variety of

fields, and what the field of data

science has to offer.”

DATA1001 Student feedback, June 2018

In the 2018 Semester 1 unit survey, when

asked the question:

“What have been the best aspects of this

unit of study?”

“The data masterclasses were enjoyable to

attend and gave interesting insight into the

ways in which data can be used. The Week 9

and Week 12 talks were very good. (The

Week 6 talk, however, was much too

complicated for a first-year audience.)”

“The last 2 data masterclasses.”

“Data master-classes.”

DATA1001 Student feedback, May 2018

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Some pedagogical benefits

1. Students see their lecturer as interested in all research

2. Students see another lecturer interested in their learning

3. Top students get to meet their future-self

4. All students see a wealth of future pathways

5. Students experience a completely authentic assessment, built around current research

6. Students recognise the reputation of their institution

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