ORCID Profile
0000-0002-5843-101X
Current Organisation
University of Tasmania
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Publisher: IEEE
Date: 2007
Publisher: Emerald
Date: 18-04-2017
DOI: 10.1108/ITSE-02-2016-0006
Abstract: This study aims to evaluate factors influencing undergraduate students’ acceptance of a computer-aided learning resource using the Phantom Omni haptic stylus to enable rotation, touch and kinaesthetic feedback and display of names of three-dimensional (3D) human anatomical structures on a visual display. The software was developed using the software development life cycle, and was tested by students enrolled in various bachelor degrees at three stages of development within the technology acceptance model, action research and design research methodology frameworks, using mixed methods of quantitative and qualitative analysis. The learning system was generally well-accepted, with usefulness (72 ± 18, mean ± standard deviation, 0-100 visual analogue scale) rated higher ( p 0.001) than ease of use (57 ± 22). Ease of use ratings declined across the three versions as modules were added and complexity increased. Students with prior experience with 3D interfaces had higher intention to use the system, and scored higher on identification of anatomical structures. Students with greater kinaesthetic learning preferences tended to rate the system higher. Haptic feedback was considered the best aspect of the system, but students wanted higher spatial resolution and lower response times. Previous research relating to haptic devices in medical and health sciences has largely focused on advanced trainees learning surgical or procedural skills. The present research suggests that incorporating haptic feedback into virtual anatomical models may provide useful multisensory information in learning anatomy at the undergraduate level.
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 08-04-2019
Publisher: IADIS Press
Date: 16-07-2019
Publisher: Springer Science and Business Media LLC
Date: 25-05-2021
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 02-10-2021
Abstract: This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.
Publisher: IEEE
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11751649_105
Publisher: IEEE
Date: 2007
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-7998-2637-8.CH009
Abstract: This article presents a study on emotions of students and their reactions towards learning and watching video clips with different personality traits, with the help of existing facial expression analyzing applications. To demonstrate this, the user's expressions are recorded as video while watching the movie trailer and doing the quiz. The results obtained are studied to find which emotion is most prevalent among the users in different situations. This study shows that students experience seemingly different emotions during the activity. This study explores the use of affective computing for further comprehension of student emotion in learning environments. While previous studies show that there is a positive correlation between emotion and academics, the current study demonstrated the existence of the inverse relation between them. In addition, the study of the facial analysis of movie trailer confirmed that different people have different ways of expressing the feeling. Results of the study will help to further clarify connection between various personality traits and emotions.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11403937_25
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 09-10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 27-03-2021
DOI: 10.3390/S21072344
Abstract: Emotion recognition plays an important role in human–computer interactions. Recent studies have focused on video emotion recognition in the wild and have run into difficulties related to occlusion, illumination, complex behavior over time, and auditory cues. State-of-the-art methods use multiple modalities, such as frame-level, spatiotemporal, and audio approaches. However, such methods have difficulties in exploiting long-term dependencies in temporal information, capturing contextual information, and integrating multi-modal information. In this paper, we introduce a multi-modal flexible system for video-based emotion recognition in the wild. Our system tracks and votes on significant faces corresponding to persons of interest in a video to classify seven basic emotions. The key contribution of this study is that it proposes the use of face feature extraction with context-aware and statistical information for emotion recognition. We also build two model architectures to effectively exploit long-term dependencies in temporal information with a temporal-pyramid model and a spatiotemporal model with “Conv2D+LSTM+3DCNN+Classify” architecture. Finally, we propose the best selection ensemble to improve the accuracy of multi-modal fusion. The best selection ensemble selects the best combination from spatiotemporal and temporal-pyramid models to achieve the best accuracy for classifying the seven basic emotions. In our experiment, we take benchmark measurement on the AFEW dataset with high accuracy.
Publisher: IADIS Press
Date: 16-07-2019
Publisher: World Scientific Pub Co Pte Lt
Date: 10-2019
DOI: 10.1142/S0218001419400159
Abstract: Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild s ling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.
Publisher: MDPI AG
Date: 03-11-2022
Abstract: Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.
Publisher: Elsevier BV
Date: 04-2023
Publisher: ACM Press
Date: 2016
Publisher: MDPI AG
Date: 23-12-2022
Abstract: Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 s le students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.
Publisher: IEEE
Date: 2005
No related grants have been discovered for Soonja Yeom.