ORCID Profile
0000-0002-8375-1816
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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) | Workforce Transition and Employment
Publisher: ACM
Date: 13-03-2023
Publisher: Association for Computing Machinery (ACM)
Date: 06-09-2024
DOI: 10.1145/3622784
Publisher: Elsevier BV
Date: 02-2017
DOI: 10.1016/J.ZOOL.2016.11.002
Abstract: All syngnathid fishes are characterized by a tail with a vertebral column that is surrounded by dermal Plates - four per vertebra. Seahorses and pipehorses have prehensile tails, a unique characteristic among teleosts that allows them to grasp and hold onto substrates. Pipefishes, in contrast, possess a more rigid tail. Previous research (Neutens et al., 2014) showed a wide range of variation within the skeletal morphology of different members in the syngnathid family. The goal of this study is to explore whether the ersity in the three-dimensional (3D) shape of different tail types reflects grasping performance, and to what degree grasping tails occupy a different and more constrained ersity. For this, a 3D morphometrical analysis based on surfaces was performed. Four different analyses were performed on the tail skeleton of nine species exhibiting different levels of tail grasping capacities (four pipehorse, three seahorse, one pipefish and one seadragon species) to examine the intra-in idual variation across the anteroposterior and dorso-ventral axis. In the two interspecific analyses, all vertebrae and all dermal plates were mutually compared. Overall, intra-in idual variation was larger in species with a prehensile tail. The analysis on the vertebrae showed differences in the length and orientation of the hemal spine as well as the inclination angle between the anterior and posterior surface of the vertebral body. This was observed at an intra-in idual level across the anteroposterior axis in prehensile species and at an inter-in idual level between prehensile and non-prehensile species. Across the anteroposterior axis in prehensile tails, the overall shape of the plates changes from rectangular at the anterior end to square at the posterior end. Across the dorso-ventral axis, the ventral dermal plates carry a significantly longer caudal spine than the dorsal ones in all prehensile-tailed species. It can therefore be concluded that prehensile tails exhibit a larger anteroposterior and dorso-ventral shape variation than non-prehensile ones. However, the hypothesis that there is a more constrained shape variation among prehensile species compared to non-prehensile ones had to be rejected.
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 13-03-2023
Publisher: Society for Learning Analytics Research
Date: 22-07-2019
Abstract: The design of effective learning analytics extends beyond sound technical and pedagogical principles. If these analytics are to be adopted and used successfully to support learning and teaching, their design process needs to take into account a range of human factors, including why and how they will be used. In this editorial, we introduce principles of human-centred design developed in other, related fields that can be adopted and adapted to support the development of Human-Centred Learning Analytics (HCLA). We draw on the papers in this special section, together with the wider literature, to define human-centred design in the field of learning analytics and to identify the benefits and challenges that this approach offers. We conclude by suggesting that HCLA will enable the community to achieve more impact, more quickly, with tools that are fit for purpose and a pleasure to use.
Publisher: ACM
Date: 13-03-2023
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier BV
Date: 06-2017
Publisher: Society for Learning Analytics Research
Date: 09-04-2021
Abstract: Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
Publisher: Informa UK Limited
Date: 19-01-2020
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 28-12-2020
DOI: 10.14742/AJET.6388
Abstract: Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers’ perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers’ actions align with emerging learning analytics technologies, including the practices of pre-service teachers who are developing their perspectives on data use in classroom in their initial teacher education programme. This may lead to an integration gap in which technology and data literacy align poorly with expectations of the role of data and enabling technologies. This paper describes two participatory workshops that provide ex les of the value of human-centred approaches to understand teachers’ perspectives on, and expectations for, data as evidence. These workshops focus on the design of pre-service teachers enrolled in teacher education programmes (N = 21) at two Australian universities. The approach points to the significance of (a) pre-service teachers’ intentions to track their students’ dispositions to learning and their ability to learn effectively, (b) the materiality of learning analytics as an enabling technology and (c) the alignment of learning analytics with learning design, including the human-centred, ethical and inclusive use of educational data in the teaching practice. Implications for practice or policy: Pre-service teachers ought to be given opportunities to engage and understand more about learning design, learning analytics and the use of data in classrooms. Professional experience placements for pre-service teachers should include participatory data sessions or learning design workshops. Teacher education academics in universities must be provided with ongoing professional development to support their preparation work of pre-service teachers’ data literacy, learning analytics and the increasing presence of data.
Publisher: Informa UK Limited
Date: 10-11-2020
Publisher: Wiley
Date: 04-05-2020
DOI: 10.1111/JCAL.12444
Publisher: Elsevier BV
Date: 11-2015
Publisher: Wiley
Date: 02-03-2021
DOI: 10.1111/JCAL.12542
Abstract: Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations.
No related organisations have been discovered for Roberto Martinez-Maldonado.
Start Date: 07-2021
End Date: 08-2024
Amount: $229,171.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2022
End Date: 07-2025
Amount: $389,011.00
Funder: Australian Research Council
View Funded Activity