Internal research initiatives 2020
The Virtuoso Sense & Respond framework will provide the foundation for the collection, analysis, and utilisation of data as a key component throughout the educational lifecycle, including batch, real-time, and event-driven data processing in addition to affordances for data science and machine learning.
Individual Learning Pathways will provide each student with an extensive framework for managing their specific learning needs via a suite of dedicated tools for engaging key stakeholders, including teachers, parents, and students themselves.
The Assessment Rubrics Framework will accommodate the provision of well-targeted and specific feedback in ongoing support of student learning across a range educational contexts.
The provision of features within Virtuoso for identifying and accommodating learning prerequisites at a granular level will support the management of individual learning progressions and inform subsequent learning design.
A flexible and configurable system for the elicitation of student feedback will support educators to better adapt to student learning needs whilst providing students with voice and agency on an ongoing basis.
Haven is an experiential learning and assessment platform that leverages immersive technologies including virtual and augmented reality, 360-degree video, and mobile devices, to enable teachers and students to readily utilise immersive learning experiences in pursuit of specific educational outcomes within Virtuoso.
This project aims to develop a comprehensive emotional view of the student through the identification of their individual flow zone, which considers the emotional intensity in the relationship between academic performance and emotional quality of each individual.
An extensive framework for supporting teacher professional development based on student learning progressions and bringing together educators in the ongoing and contextualised development of teaching competencies.
This project aims to better understand how educators engage with data and how corresponding design guidelines can be established to support impactful data-driven decision making in educational settings.
This project seeks to dynamically identify reading difficulties, including appropriate interventions, supported by automatic speech recognition. This aligns with the United Nations 4th Sustainable Development Global Goal, which is to ensure inclusive and equitable quality education for all by 2030.
Previous research collaborations
These programs have been developed to support research collaborations between industry and universities.
By Prof. Mark McMahon and Dr Michael Garrett
Cinglevue develops a prototype in concert with Edith Cowan University and engaged with teachers at CEDP to compare how their classification of learning outcomes (taken from the Australian Curriculum) compared to those provided by the Instructional Activity Matrix within Virtuoso. Findings demonstrate the overall efficacy of the Matrix, which could provide teachers with a fast and standardised classification of a given instructional statement which could be used to inform lesson or assessment design. Feedback provided by the teachers involved has also been used to enhance the solution in terms of broadening the scope of instructional statements that can be accurately classified.
Funding by CSIRO SIEF STEM+ Business Fellowship Program Cinglevue collaborated with researchers at the University of Technology Sydney (UTS) to undertake research concerning the implementation of an underlying system for leveraging vast quantities of educational data to intelligently guide student learning, aid allocation of educational resources, and help provide informed recommendations based on individual learning needs. This entails the research and development of an Artificial Intelligence (AI) framework/architecture that will provide Cinglevue’s Virtuoso platform with these capabilities based on machine learning/deep learning approaches. Cinglevue’s contribution to this project has funded the involvement of a post-doctoral research fellow at UTS in addition to three PhD students over a two year period.
Part of the Innovation Connections
Cinglevue worked with Edith Cowan University to develop an accessible psychometric assessment tool to provide educators with detailed insight into the psychological characteristics of students, specifically those that impact academic performance. Unlike traditional measures, Cinglevue’s psychometric assessment tool is self-completed by students via an online interface and subsequently interpreted by classroom teachers, eliminating the need for dedicated psychologists to be involved in the administration or interpretation of test results.
This allows the tool to be utilised at scale to evaluate the individual differences of all students within a school, and in doing so, provide the ability for collective analysis at the group, class, school, or district level to identify broad trends or insights in concert with other forms of information.