ORCID Profile
0000-0002-8236-3133
Current Organisation
Monash University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Educational Technology and Computing | Specialist Studies in Education | Learning Sciences
Teaching and Instruction Technologies | Learner and Learning Processes | Application Software Packages (excl. Computer Games) |
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 2022
Publisher: ACM
Date: 23-03-2020
Publisher: ACM
Date: 13-03-2023
Publisher: Elsevier BV
Date: 2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 2022
Publisher: ACM
Date: 21-10-2023
Publisher: Springer International Publishing
Date: 2018
Publisher: Wiley
Date: 04-05-2021
DOI: 10.1111/BJET.13102
Abstract: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning. What is already known about this topic Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education. Student success in K‐12 online education is substantially lower than in conventional schools. Students experienced various difficulties related to the delivery of online learning. What this paper adds Provide empirical evidence for the online learning experience of students in different school years. Identify the different needs of students in primary, middle, and high school. Identify the challenges of delivering online learning to students of different age. Implications for practice and/or policy Authority and schools need to provide sufficient technical support to students in online learning. The delivery of online learning needs to be customised for students in different school years.
Publisher: Springer Science and Business Media LLC
Date: 17-03-2015
Publisher: Springer Science and Business Media LLC
Date: 22-01-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 09-07-2017
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 13-03-2017
Publisher: Wiley
Date: 27-04-2023
DOI: 10.1111/BJET.13333
Abstract: Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. What is already known about this topic Human–human tutoring is acknowledged as an effective instructional method. Polite expression can help strengthen the relationship between tutors and students. Polite expression can promote students' learning achievements in many educational contexts. What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues. Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues. Politeness alone was insufficient to predict the students' performance. Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort. The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress. To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students.
Publisher: ACM
Date: 13-07-2016
Publisher: Springer International Publishing
Date: 2019
Publisher: Wiley
Date: 06-08-2023
DOI: 10.1111/BJET.13370
Abstract: Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. What is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks. Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks. Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring. What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation. A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks. Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts. Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks. The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved. Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: ACM
Date: 13-03-2017
Publisher: Elsevier BV
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: ACM
Date: 13-03-2023
Start Date: 08-2022
End Date: 07-2025
Amount: $389,011.00
Funder: Australian Research Council
View Funded Activity