Use of Simulated Virtual Teacher Learning Environments to Educate Teachers and Coaches of Linguistically and Culturally Diverse Populations in and around Belfast
Deadline: 27 June, 2024
Level of Study
Postgraduate Research
Funding Amount
The Studentship offers tuition fees and annual stipend to support living costs for a maximum of three years, depending on residency. For 23-24 the stipend amount was £18,622 and tuition fees of £4,712
Application Status
Closed
Eligibility summary
- Level of StudyPostgraduate Research
Overview
Queen’s University Belfast in partnership with Forward South invites applications from qualified applicants for a Collaborative Doctoral Partnership (CDP) studentship, funded by the Department for the Economy (Collaborative Doctoral Partnerships (CDP) scheme), to conduct research leading to a PhD on the theme of: Responsible Artificial Intelligence to Develop Responsible Simulated Teacher Learning Environments towards educating teachers and coaches of linguistically and culturally diverse populations in and around Belfast.
This project will be supervised by Dr. Sultan Turkan, Senior Lecturer of Bilingual Education (Director of TESOL/Applied Linguistics Programme). The student will be expected to spend time at both Queen’s University Belfast and at Forward South Partnership (FSP) doing field work with bilingual students and their parents across schools connected to FSP. Also, the student will be expected to be part of a wider cohort of CDP funded students across Northern Ireland.
The studentship will be studied full time (3 years) commencing on October 1st 2024 to continue until 30 September 2027.
We encourage the widest range of potential students to study for this CDP studentship and are committed to welcoming students from different backgrounds to apply. We particularly welcome applications from minority ethnic backgrounds as they are currently underrepresented at this level in this area.
Project Overview
This project aims to develop and evaluate a Virtual Teacher Learning Environment (VTLE), drawing on AI -powered technologies, for content pre-service teachers (PSTs) at QUB to observe and practice quality teaching of content to bilingual avatars at newcomer and intermediate English proficiency levels. The numbers of immigrant children (referred as bilinguals) in NI classrooms and across the UK increase, teachers report feeling unprepared to teach content (Murphy et al., 2007; Starbuck, 2018). Undoubtedly, student teaching and field experiences constitute a significant part of Initial Teacher Education (ITE) programs (Ronfeldt, Schwartz, & Jacob, 2014). However, teacher educators face challenges in placing their PSTs in field experiences that mimics real classrooms inclusive of possible instructional scenarios, as well as vulnerable student groups such as immigrant children. In this Post-Pandemic era, the need to prepare educators for conditions outside of brick and mortar ITE environments is here to stay (Griffin et al., 2020; La Velle et al.,2020). Further, using VTLEs to supplement traditional methods such as field experiences, practicums, and internships has proven to be effective (Hixon & So, 2009; Monroe, Mendez, & Nutta, 2020), especially in providing sheltered access to bilinguals that the PSTs might not otherwise encounter in clinical training (Turkan & Kerr, Sloan, Maye, 2021).
Further Information
The research design includes the below research questions outlined under relevant research objectives (ROs) as follows: This research aims to evaluate the 1) acceptability, feasibility, and 2) preliminary effectiveness of the interface through a proof-of-concept study (Bornheimer et al., 2023). Evaluative proof-of-concept study involves a one-group pre-experimental design using quantitative and qualitative data collection before and after PSTs engaged with the observation and practice modules.
RO(1): Evaluate acceptability, feasibility.
1. To what extent is the VLE acceptable for the teacher trainees who deconstruct existing practice and approximate their practice by engaging in instructional interactions with the bilingual avatars? 2. To what extent is the VTLE feasible for the teacher trainees to complete the sessions and perform the taught skill in accordance with the feedback provided?
RO(2): Evaluate the preliminary effectiveness of the VTLE sessions in improving skills among PSTs who teach content (e.g., science)
3. To what extent do the trainees improve in terms of their preparedness for steering classroom discussions using levelled questions with newcomer and intermediate-proficiency bilinguals?
4. To what extent do the trainee performance scores change over the completed simulation sessions (minimum three)?
5. What do the trainees report on the qualitative portion of the post-survey about the effectiveness of the observation module and practice module, provided within the VTLE?
Background on Project
Prior studies on VTLEs (Turkan & Nutta, 2015; Turkan, Belur, Nutta, 2016; Turkan & Kerr, Sloan, Maye, 2021) demonstrated that trainees effectively learned to adjust the level of questions during classroom talk with the virtual bilingual student avatar puppeteered by human interactors. However, the use of interactors was unsustainable in creating a reliable virtual practice experience across multiple users. The current research aims to deliver a VTLE interface, drawing on AI technologies.
The successful candidate will be eligible to participate in events organised for all Collaborative Doctoral Partnership students who are registered with different universities and studying with cultural and heritage organisations across the UK.
Funding Towards
Living Costs / Stipend, Tuition Fees
Funding Body
The Department for the Economy
Funding Amount
The Studentship offers tuition fees and annual stipend to support living costs for a maximum of three years, depending on residency. For 23-24 the stipend amount was £18,622 and tuition fees of £4,712
A full award of stipend and fees is available to EU/UK citizens that satisfy a requirement of three years’ residency in the UK prior to the commencement of the studentship.
Number of Awards
1
Funding Body
The Department for the Economy
Eligibility
Required qualifications and responsibilities:
- A full award of stipend and fees is available to EU/UK citizens that satisfy a requirement of three years’ residency in the UK prior to the commencement of the studentship.
- The required academic qualification is a first or upper second-class degree from a university in the United Kingdom or Ireland, or qualifications and experience considered by the University as equivalent to this standard.
- An undergraduate or post-graduate degree in a related field.
- As a collaborative award, students will be expected to spend time at both the University and support Forward South Partnership with select operational tasks.
Most desired qualifications:
- A degree or double major in computer science or a related technical field.
- Previous formal or non-formal training and/or experiences in Artificial Intelligence, Virtual Reality and Immersive learning environments
Funding Type
Help with new course
Fee Status
Study Level
Postgraduate Research
Start Date
Wed, 29 May 2024 11:45:00 BST
Close Date
Thu, 27 Jun 2024 17:00:00 BST
Contact Us
To submit questions about the project and funding, contact: S.Turkan@qub.ac.uk
How to Apply
PhD Start Date: 1st October 2024
Interviews will take place week commencing 8th July. All interviews will be in person unless the applicant does not reside in and around Belfast.
To apply, please log onto Queen’s University Belfast Direct Application Portal, select the School of Social Sciences, Education and Social Work, selecting a PhD in Education, complete the required sections and submit your application by 27th June 2024. Cite reference CASTST24 in the funding section of your application.
Applications received after this date cannot be considered.
Applicants are required to upload a statement (in place of a proposal) which should outline the applicant’s:
- Previous taught or research training and experience as part of an undergraduate or postgraduate degree in a related field.
- Experiences in learning and/or teaching a second language (max 250 words)
- Previous formal or non-formal training and/or experiences in Artificial Intelligence, Virtual Reality, and Immersive learning environments (max 250 words)
Candidates must nominate two academic referees.