42083 Studio 1 Project
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 24 credit points of completed study in spk(s): C04405 Master of Information Systems OR 24 credit points of completed study in spk(s): C04401 Master of Information Systems (Extension) OR 24 credit points of completed study in spk(s): C04402 Master of Information Systems (Advanced)
Description
This IS studio subject enables students to develop professional and research skills in IS relating to their chosen career path. The studio is delivered through a combination of self-directed study and group project work. In the studio, students will develop IS knowledge and skills relating to their chosen stream of IS. Students work with mentors and facilitators throughout the session, and the individual tasks to be completed are guided by a learning contract established at the beginning of the studio.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Reduce real-life interactions into conceptual, mathematical, statistical, and computational models |
|---|---|
| 2. | Apply a single model to many different problems |
| 3. | Apply a many-model paradigm to study the behaviour and performance of a given socio-technical system |
| 4. | Design and justify potential interventions to improve the behaviour or performance of a socio-technical system |
| 5. | Reflect, reason, and communicate modelling outcomes in a way that speaks to professional engineers and those working in government organisations. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
- Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)
Teaching and learning strategies
This studio enables students to develop professional and technical skills in IS relating to their chosen career path. The studio learning objectives are achieved through a combination of self-directed study, lectures, individual reflection, and group work.
This studio is about models. It introduces students to dozens of simple models in a straightforward language and explains how to apply them. For each model introduced, we will present (i) its definition and general usage, (ii) the actual mathematical breakdown, and (iii) a real-life, relevant example of using the model.
The Studio will invite students to embrace a many-model thinking approach: the application of ensembles of models to make sense of real-world systems. The core idea is that many model thinking produces wisdom through a diverse ensemble of logical frames. By enabling students to engage with many models as frames, they will develop more nuanced, deep understandings of how systems work and how to design interventions that improve their performance and to plan future enhancements. The studio therefore enables a dynamic and engaging teaching and learning experience that confronts students with a number of socio-technical or socio-environmental systems and the challenges they face in the Anthropocene.
In small self-managed teams, students are guided through a human-centered design process that will see them apply a range of models to a specific case study. Teams are guided by mentors experienced in IS, complex systems modelling, data analytics, data visualisation, sustainability, and computational social science.
To encourage peer learning, all teams will provide constructive feedback on the work of another team at key stages during the group projects. Projects will follow the traditional stages of the modelling paradigm: scope, research, define, design, implement, and communicate. Teams will be free to decide which models (and how many) they will apply to a specific issue or problem situation. Students may choose their own case study, or they might choose one from portfolio of case studies pre-determined in consultation with industry partners (i.e., students will have to choose from a portfolio of projects).
Students will develop a reflective portfolio in which they will deliberate about what they are learning and its implications, and of the feedback they receive from peers and academics. Students will relate their learning to life and career experiences.
Assessment tasks are designed for students to demonstrate their ability to generate, reflect on, and propose solutions to a given issue or problem situation using a many-model thinking approach. In doing so, students also demonstrate their capacity to work in teams to solve problems, create solutions, communicate professionally, and manage time and tasks.
The faculty expects a commitment of nine hours per week for the Studio, three hours of which occur during scheduled Studio time. Studio time (preceded by self-study) will consist of a review of online core materials, topic presentations and interactive sessions comprising a mixture of discussions, workshop activities, case studies and informal student presentations.
Python and Jupyter notebooks will be used to develop group projects (modelling and reporting)
Github repositories will be used to track group progress (Jupyter notebooks) and to provide coordinator and peer feedback on an ongoing basis
MS Teams will be used throughout the studio as an alternative channel to offer an open and ongoing discussion forum that gives students the opportunity to interact with their peers, teaching staff, and project stakeholders. Students are able to post questions and comments about the course material. A member of staff will moderate the workspace and will answer students’ questions when appropriate. The workspace will also be used to gauge class comprehension before, during, and after seminar sessions and key project milestones. The course coordinator will consider key questions from the discussion forum prior to each class and discusses them with students during the class. Students may have an opportunity to raise further issues at the start of the class.
Content (topics)
Introduction: Many model thinking
- Models in the age of data
- Why we need many models
- The data-information-knowledge-wisdom hierarchy
Module 1: Why model?
- Types of models
- Seven uses of models
- Modelling 101: Basics of Python and Jupyter Notebooks
- Modelling 102: Basics of version control and Github
- Modelling 103: Basic principles of good data visualisation
Module 2: The Science of Many Models
Many models as independent lies
Categorisation models
Starter kit of models (only a subset will be covered in each session)
- Modelling human actors
- Normal distributions
- Power-Law distributions
- Linear models
- Concavity and Convexity
- Models of value and power
- Network Models
- Broadcast, diffusion and contagion
- Entropy: modelling uncertainty
- Random walks
- Path dependence
- Local interaction models
- Markov models
- Agent-based models
- System dynamics models
- Threshold models with feedbacks
- Spatial and hedonic choice models
- Game theory models
- Models of cooperation
- Collective action models
- Signaling models
- Learning models
- Multi-armed bandit models
- Rugged-landscape models
Module 3: Reflecting, reasoning, and communicating modelling outcomes
- Scenario testing
- Sensitivity analysis
- Uncertainty analysis
- The modelling report
Assessment
Assessment task 1: Group assignment
| Intent: | Document research project in a concise, effective, and professional manner. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1, D.1, E.1 and F.1 |
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
| Length: | Approximately 5000 words |
Assessment task 2: Reflective portfolio
| Intent: | To demonstrate evidence of enquiry and reflection on many model thinking. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 and F.1 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 3: Engagement in online discussions and peer feedback
| Intent: | To use peer communication tools to improve knowledge of many-model thinking. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1, D.1, E.1 and F.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 30% |
Minimum requirements
To pass this subject, students must achieve an overall mark of 50% or greater.
Required texts
Page, Scott E. (2018). The Model Thinker: What You Need to Know to Make Data Work for You.
Recommended texts
Shiflet, Angela B., and George W. Shiflet. Introduction to computational science: modeling and simulation for the sciences. Princeton University Press, 2014.