|Location||University of Queensland, Faculty of Engineering Architecture and Information Technology|
|Eligibility||Australian residents only|
PhD Scholarship in Data Science and Educational Data Mining
PhD Scholarship in Data Science and Educational Data Mining:
Developing a Neuro-Adaptive Learning platform
Job Number: 504522
School of Information Technology and Electrical Engineering
It is an exciting time to get involved with the School of Information Technology and Electrical Engineering, located on UQ’s St. Lucia campus. The School is ramping up its investment in teaching, research and engagement to create an inspiring, diverse and flexible workplace. The direction is backed by a bold, new strategic vision to ensure the School is at the forefront of meaningful research outcomes and pedagogy across its core impact areas of health, data, automation and energy. Boasting strong student enrolments in professionally accredited programs, combined with world-class researchers and facilities, the School is focused on strengthening its position in the global computer science and engineering communities. By attracting the brightest minds and fostering a truly innovative and collaborative work environment, the School will develop global solutions to contemporary issues and mentor the leaders of tomorrow.
Details of the School may be accessed on its website at http://www.itee.uq.edu.au/
UQ is a research intensive University ranked within the top 50 universities in the world. The Pyrometallurgy Innovation Centre undertakes fundamental and applied research, funded through competitive Government grants and by industry partners world-wide. There are excellent opportunities to establish Australian and international industry and academic contacts and careers in high temperature metal processing and recycling.
Adaptive learning is one of the most prominent techniques for providing an efficient, effective, and customised learning experience for students. Adaptive learning platforms dynamically adjust the level or type of instruction based on individual student abilities or preferences. Adaptive learning platforms commonly model learners solely based on their knowledge state, ignoring the cognitive and emotional state of leaners, which have been proven to have a critical role in learning. This is mostly due to limitations in current learning technologies as they have no direct way to assess learners’ cognitive and emotional state.
In recent years, neuro-physiological methods and tools have been used for measurement of user’s cognitive load and emotional processes. For example, eye tracking technologies or heart rate variability devices have been used for detection of a variety of reactions and emotions such as satisfaction, sadness, mind wandering, stress and frustration. Neuro-physiological tools are increasingly becoming more reliable, portable and affordable, thus providing a potential avenue for adoption in many new domains and applications, offering new research opportunities. In this project, we seek to utilise neuro-physiological tools for development of a neuro-adaptive learning platform.
The project aims to:
- develop neuro-enhanced learner models for use in adaptive learning platforms that can simultaneously capture the knowledge state and cognitive state of learners.
- develop recommender systems that can use neuro-enhanced learner models for recommending learning activities best tailored to the needs of each learner.
The central hypotheses under investigation in this project are the following:
- Neuro-adaptive learning platforms are more successful than traditional adaptive learning platforms in promoting meta-cognitive activities such as reflection, planning and self-regulation.
- Neuro-adaptive learning platforms can accurately detect lack of motivation and engagement in learners.
- Neuro-adaptive learning platforms can be used for identifying learning activities that are perceived to be engaging and exciting by learners.
- Neuro-adaptive learning platforms are more successful than traditional adaptive learning platforms in recommending learning activities that improve learning outcomes of learners.
Students with a strong background in computer science or information systems with a desire to work in transdisciplinary teams will be best suited for the project. Preference will be given to candidates who can provide evidence of the following:
- Experience in designing and conducting quantitative, qualitative or mixed-method studies.
- Familiarity with one or more of the following areas:
- educational theory, instructional design, learning sciences
- machine learning, data mining, learning analytics, recommendation systems
- cognitive science, psychology, human computer interaction (HCI), user-centred design
The project is financed through a UQ Alliance PhD stipend together with the Technical University of Denmark (DTU). As part of this project the PhD candidate will closely collaborate with the PhD candidate recruited at DTU. A research stay of 6 month at the partner university is planned for the PhD candidates both at DTU and UQ resulting in a period of co-location of at leat 12 months.
The scholarship for the PhD degree is subject to academic approval to the PhD program at the School of ITEE at UQ. For information about the general requirements for enrolment, please see the UQ PhD Guide webpage.
The stipend is equivalent to the APA rate, $ 27,082 per annum (2018 rate, indexed annually). The scholarship will be for three (3) years with the possibility of a six (6)- month extension. Continuation of the scholarship is dependent on the candidate remaining in good standing according to the UQ PhD program rules. The scholarship is available to both domestic and international students. For the candidates who have been successful in obtaining any government-funded scholarship or UQ-funded scholarship or any other external scholarship, a top-up scholarship may be awarded.
To submit an application for this role, please complete an online application here: https://apply.uq.edu.au/. All applicants will be required to supply the following documents:
- A cover letter that addresses how you meet the requirements for the PhD program and addressing the desired skills and attributes related to this project;
- A curriculum vitae detailing education, professional experience, research experience, publications, and relevant competencies;
- Academic transcript for all post-secondary study undertaken, complete or incomplete, including the institution grading scale;
- Award certificates for all completed post-secondary study
- Evidence for meeting UQ’s English language proficiency requirements; and
- The name and contact details of two referees who can best comment on your prior research experience. UQ will contact your referees directly, but you will need to enter their details into the application form.
Short listed candidates will be invited for an online interview with the project leaders from UQ and DTU.
Application closing date
18 June 2018
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