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Introduction to Experience Sampling (PSYC90109)
Graduate courseworkPoints: 12.5Online
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Term 2
Prof Simon Dennis
Melbourne School of Professional and Continuing Education
Further information: https://study.unimelb.edu.au/find/courses/graduate/master-of-applied-analytics
Email: continuing-education@unimelb.edu.au
Phone: + 61 3 8344 0149
Monday to Friday 8am to 9pm AEST/AEDT. Weekends and University of Melbourne observed Public Holidays 10am to 5pm AEST/AEDT.
Overview
| Availability | Term 2 - Online |
|---|---|
| Fees | Look up fees |
Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.
- Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.
Intended learning outcomes
To provide psychological professionals and researchers in the community, defence, health and government sectors the capability to:
- Collect data from both active and passive experience sampling sources such as smartphones, social media, wearable devices and the internet of things;
- Appreciate the ethical considerations and methods for mitigating security and privacy issues specific to experience sampling data;
- Use a variety of analysis methods including Bayesian models, machine learning and dynamic systems techniques appropriate to experience sampling data;
- Develop skills in the presentation of experience sampling data
Generic skills
- High level written communication skills;
- Advanced information and interpretation skills;
- Advanced analytic, integration and problem-solving skills;
- Competence in critical and theoretical thinking through essay writing and online discussions.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90130 | Critical Thinking with Analytics |
Term 3 (Online)
Term 1 (Online)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Bachelor degree or equivalent
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Note: Collection and analysis of one’s own experience sampling data is an essential component for completing the subject and it is not possible to opt out from this activity. This experience sampling data will be confidential to the individual student and will not be visible to others
Last updated: 3 November 2022
Assessment
| Description | Timing | Percentage |
|---|---|---|
Ethics case study forum discussion. Students will be required to write an initial post of 500 words and then to contribute to the discussion with at least two additional posts of 50-100 words.
| From Week 2 to Week 4 | 20% |
Data Collection Students will be required to collect their own experience sampling data and write a description of the procedure.
| Week 6 | 30% |
Data Analysis Students will create scripts and use them to analyse their own experience sampling data. They will submit a report which 1) describes the analysis methodology that was used and 2) includes the analysis scripts as appendices to the report. Length of report (excluding appendices): 1500 words
| Week 7 | 30% |
Discussion Students will discuss the outcomes of their analyses and reflect on the collection and analysis process.
| Week 8 | 20% |
Last updated: 3 November 2022
Dates & times
- Term 2 - Online
Principal coordinator Simon Dennis Mode of delivery Online Contact hours 4hr/week for 8 weeks (April‐June). Total time commitment 170 hours Pre teaching start date 22 April 2019 Pre teaching requirements During the pre-teaching period students are given the opportunity to get used to the online platform, meet the instructors/tutors and become familiar with how to access resources before the teaching period starts. Teaching period 29 April 2019 to 23 June 2019 Last self-enrol date 23 April 2019 Census date 17 May 2019 Last date to withdraw without fail 7 June 2019 Assessment period ends 30 June 2019 Term 2 contact information
Prof Simon Dennis
Melbourne School of Professional and Continuing Education
Further information: https://study.unimelb.edu.au/find/courses/graduate/master-of-applied-analytics
Email: continuing-education@unimelb.edu.au
Phone: + 61 3 8344 0149
Monday to Friday 8am to 9pm AEST/AEDT. Weekends and University of Melbourne observed Public Holidays 10am to 5pm AEST/AEDT.
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
Students will have access to electronic copies of relevant readings
Last updated: 3 November 2022