COSC471-24S1 (C) Semester One 2024

Special Topic

15 points

Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024


Special Topic



This course explores the use of cognitive task performance models/formalisms as well as physiological measurement tools and techniques to assess human cognitive workload. Computational methods are used for modelling and predicting cognitive load in various subtasks. Physiological indicators of cognitive demands are identified and sensor technology and response measure are analysed. Students will engage in empirical analysis and prediction of cognitive task workload.

This course introduces and applies cognitive task performance modelling, computational analysis of cognitive workload, physiological response measurement and analysis, as well as cognitive workload assessment based on physiological measures. The course relates this specific form of systems analysis to design implications and methods.

Course content / Hōtaka (subject to minor changes):
- Human information processing; cognitive constructs
- Mental workload measurement; basic cognitive task analysis
- Computational cognitive task modeling (basic and advanced)
- Computational cognitive task analysis
- Physiological indicators of cognitive demand
- Physiological response sensor technology
- Physiological sensor and PC integration
- Physiological data collection (various task conditions)
- Computational cognitive task modeling application
- Design of experiment and data collection for cognitive workload assessment
- Basic statistical analysis on cognitive workload data
- System design implications of cognitive workload analysis

Learning Outcomes

Learning Outcomes / Hua Akoranga:
As a student in this course, I will develop the ability to…”
- Perform cognitive task analysis using established (GOMS) methods;
- Code cognitive task performance models for (working memory demand) analysis;
- Integrate physiological sensors with conventional desktop computing equipment;
- Collect physiological response data with COTS (EmotiBit) sensors;
- Conduct workload measurement through use of physiological response measures;
- Design and execute a two-condition experiment to compare human workload responses in variations on cognitive task circumstances; and
- Conduct basic descriptive and inferential statistical analyses on cognitive task model and physiological sensor data outputs for cross-validation of human workload outcomes.

Transferable Skills / Pūkenga Ngaio:
“As a student in this course, I will develop the following skills…”
- Capability to use different coding languages and techniques to represent human work;
- Capability to integrate Bluetooth and Wi-Fi sensors with PC/Mac devices;
- Capability to work with an engineering team to design a basic experiment and collect human performance data; and
- Capability to use common statistical languages to analyse empirical datasets.

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Employable, innovative and enterprising

Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Globally aware

Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.


Approval by the Head of Department

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 14:00 - 16:00 Ernest Rutherford 141
19 Feb - 31 Mar
22 Apr - 2 Jun
Lab A
Activity Day Time Location Weeks
01 Thursday 16:00 - 17:00 Jack Erskine 101
19 Feb - 31 Mar
29 Apr - 2 Jun

Course Coordinator

For further information see Computer Science and Software Engineering Head of Department

Professor David Kaber, Jack Erskine room JE247

David Kaber is currently a Jack Erskine Visiting Fellow in the Department of Computer Science and Software Engineering at the University of Canterbury (New Zealand). Kaber is also the Dean’s Leadership Professor and immediate past-chair of the Department of Industrial and Systems Engineering at the University of Florida (UF). Prior to joining UF, Kaber was a distinguished professor of industrial engineering at North Carolina State University where he also served as the Director of Research for the Ergonomics Center of North Carolina. Kaber’s primary area of research interest is human-systems engineering with a focus on human-automaton interaction, including design and analysis for situation awareness in complex human in-the-loop systems. Domains of study for his research have included physical work systems, industrial safety systems, robotic systems, transportation systems and healthcare. Kaber is a Fellow of IEEE and previous Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. He is a Fellow of Institute of Industrial Engineers and a Fellow of the Human Factors & Ergonomics Society. Kaber is also a Certified Human Factors Professional (BCPE) and a Certified Safety Professional (BCSP).

Textbooks / Resources

Readings / Pukapuka Ako:

“Engineering Psychology & Human Performance” – 5E, Table of Contents
- See “lecture schedule” for specific sections. You do not need to read the entire book (but you could).

“Guide to GOMS Model Usability Evaluation using NGOMSL”
- Foundational for basic cognitive task analysis.

“Cogulator: A Primer”
- Fundamental to being able to use Cogulator.

“Getting Started with EmotiBit”
- Guide to setting-up and using EmotiBit sensor.

“Working with EmotiBit Data”
- Document on how to do basic data collection and analysis with EmotiBit sensor.

“R Users Guide” – University of Wisconsin - Madison
- Guide for basic use of R studio for statistical analysis.

Statistics Lab Manual – Chapter 5
(Siju Swamy, Associate professor at Saintgits College of Engineering)
- Focused chapter on correlation analysis and use of R.


Students with Disabilities:
Students with disabilities should contact the Equity & Disability Service office Please also communicate with the course coordinator (Kaber) at least one week before the start of any course activity for which you may have a special requirement.

Indicative Fees

Domestic fee $1,110.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Computer Science and Software Engineering .

All COSC471 Occurrences

  • COSC471-24S1 (C) Semester One 2024