ORCID Profile
0000-0001-7642-7121
Current Organisations
National Institute of Technology, Tiruchirappalli, India
,
Karunya University
,
University of Technology Sydney
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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.
Information Systems | Systems Theory | Learning, Memory, Cognition And Language |
Publisher: Springer International Publishing
Date: 2015
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 18-11-2022
Abstract: Pressure is mounting upon universities to ensure that our graduates are employable. Business and governments increasingly demand that graduates are equipped with skills and competencies that map into labour market needs. But students often struggle to choose courses, subjects and activities that will support their career goals and aspirations. This paper introduces an approach designed at UTS which aims to embed a skills analytics tool at key transition points for our students. The need to support such tools will a well-grounded learning design is discussed, along with the need to move beyond a “one size fits all” model for supporting EdTech tools. A solution that utilises a series of modules in the LMS is introduced.
Publisher: Wiley
Date: 30-03-2010
Abstract: The immobilization of chiral oxazaborolidine complex in the well-ordered mesochannels of SBA-15 is demonstrated by a postsynthetic approach using 3-aminopropyltriethoxysilane as a reactive surface modifier. The immobilized catalysts are characterized by various techniques, such as XRD, nitrogen adsorption, HRSEM, UV/Vis diffuse reflectance spectroscopy, and FTIR spectroscopy. The catalysts are used for the enantioselective reduction of aromatic prochiral ketones. The activity of the chiral oxazaborolidine complex immobilized SBA-15 catalysts is also compared with that of the pure chiral oxazaborolidine complex, which is a homogeneous catalyst. It is found that the activity of the chiral complex immobilized SBA-15 heterogeneous catalyst is comparable with that of the homogeneous catalyst.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Society for Learning Analytics Research
Date: 30-08-2023
Abstract: NA
Publisher: Elsevier BV
Date: 06-2012
Publisher: ACM
Date: 12-04-2021
Publisher: ACM
Date: 16-03-2015
Publisher: ACM
Date: 23-03-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 04-2018
Publisher: ACM
Date: 13-03-2017
Publisher: ACM Press
Date: 2016
Publisher: MDPI AG
Date: 13-10-2014
Publisher: Elsevier BV
Date: 09-2013
DOI: 10.1016/J.PBIOMOLBIO.2013.03.011
Abstract: Biological systems exhibit a wide range of contextual effects, and this often makes it difficult to construct valid mathematical models of their behaviour. In particular, mathematical paradigms built upon the successes of Newtonian physics make assumptions about the nature of biological systems that are unlikely to hold true. After discussing two of the key assumptions underlying the Newtonian paradigm, we discuss two key aspects of the formalism that extended it, Quantum Theory (QT). We draw attention to the similarities between biological and quantum systems, motivating the development of a similar formalism that can be applied to the modelling of biological processes.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 13-03-2023
Publisher: AI Access Foundation
Date: 14-12-2021
DOI: 10.1613/JAIR.1.12702
Abstract: In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including ex les of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including ex les of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.
Publisher: Elsevier BV
Date: 08-2009
Publisher: Springer Netherlands
Date: 20-11-2013
Publisher: Society for Learning Analytics Research
Date: 13-12-2019
Abstract: This paper presents a platform called RiPPLE (Recommendation in Personalised Peer-Learning Environments) that recommends personalized learning activities to students based on their knowledge state from a pool of crowdsourced learning activities that are generated by educators and the students themselves. RiPPLE integrates insights from crowdsourcing, learning sciences, and adaptive learning, aiming to narrow the gap between these large bodies of research while providing a practical platform-based implementation that instructors can easily use in their courses. This paper provides a design overview of RiPPLE, which can be employed as a standalone tool or embedded into any learning management system (LMS) or online platform that supports the Learning Tools Interoperability (LTI) standard. The platform has been evaluated based on a pilot in an introductory course with 453 students at The University of Queensland. Initial results suggest that the use of the RiPPLE platform led to measurable learning gains and that students perceived the platform as beneficially supporting their learning.
Publisher: Association for Computing Machinery (ACM)
Date: 13-04-2021
DOI: 10.1145/3449284
Abstract: Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capabilityrequires that pre-service nurses develop the spatial abilities they will require in their clinical placements, suchas: learning when to remain close to the patient and to other team members positioning themselves correctlyat the right time and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effectiveinteraction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and caneasily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically capturedby indoor positioning sensors can be used to address this problem for the purpose of improving nurses' reflection, learning and professional development. This paper presents i) a qualitative study that illustrateshow to elicit spatial behaviours from educators' pedagogical expectations, and ii) a modelling approachthat transforms nurses' low-level position traces into higher-order proxemics constructs, informed by sucheducatos' expectations, in the context of simulation-based teamwork training. To illustrate our modellingapproach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whompositioning traces were captured in eleven authentic nursing education classes. Low-levelx-ydata was usedto model three proxemic constructs: i) co-presence in interactional spaces, ii) socio-spatial formations (i.e.f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoorpositioning analytics can be used to address questions that educators and researchers have about teamwork inhealthcare simulation settings.
Publisher: Elsevier BV
Date: 08-2015
Publisher: IEEE
Date: 06-2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2024
Publisher: ACM
Date: 16-03-2015
Publisher: Informa UK Limited
Date: 06-12-2019
Publisher: IEEE
Date: 2014
Publisher: Informa UK Limited
Date: 12-2008
Publisher: ACM
Date: 13-03-2017
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 07-2021
Publisher: AIP
Date: 2012
DOI: 10.1063/1.3688999
Publisher: Wiley
Date: 23-04-2023
DOI: 10.1111/BJET.13321
Abstract: An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well‐known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory‐versus‐data debate in education, and extend an invitation to other investigators to join this exciting programme of research. ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems. Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts. Causal inference is a well‐developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences. An overview of causal modelling to support educational data scientists interested in adopting this promising approach. A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories. An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent. Causal models can help us to explicitly specify educational theories in a testable format. It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model. Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
Publisher: WORLD SCIENTIFIC
Date: 11-2005
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Zenodo
Date: 2017
Publisher: Royal Society of Chemistry (RSC)
Date: 2011
DOI: 10.1039/C0CP02067B
Abstract: Here we demonstrate for the first time the encapsulation of a chiral oxazaborolidine complex in the 3D mesoporous channels of an amine functionalized KIT-6 material via covalent bonding through a post-synthetic approach. The physico-chemical properties of the pure and immobilized KIT-6 catalysts were obtained by various techniques such as XRD, nitrogen adsorption, HRSEM, UV-Vis diffuse reflectance spectroscopy, and FT-IR spectroscopy. It has been found that the structural stability of the KIT-6 was not affected even after the immobilization of a significant amount of chiral ligand inside the mesoporous channels of the support. However, the values of structural parameters such as the specific surface area and the specific pore volume of the KIT-6 support was significantly lower than the pure KIT-6 support. The chemical interaction between the chiral ligand inside the mesochannels and the KIT-6 support was also confirmed by UV-Vis and FT-IR spectroscopy. The chiral catalytic performance of the immobilized catalysts for the enantioselective reduction of aromatic prochiral ketones was demonstrated and the results were compared with chiral catalyst immobilized supports with uni-dimensional porous structures, such as MCM-41 and SBA-15. Among the catalysts studied, chiral catalyst immobilized KIT-6 showed the highest performance with a high product yield and a high enantioselectivity due to its 3D porous structure with two continuous and interpenetrating systems of chiral channels and an interwoven 3D cylindrical type pores of Ia3d symmetry. The catalyst also exhibited much better recycling capability than other chiral catalyst supported mesoporous materials used in the study.
Publisher: Elsevier BV
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 04-05-2013
DOI: 10.3758/S13421-013-0312-Y
Abstract: Free-association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long-lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist-cuing, primed free-association, intralist-cuing, and single-item recognition tasks. The findings also show that when a related word is presented in order to cue the recall of a studied word, the cue activates the target in an array of related words that distract and reduce the probability of the target's selection. The activation of the semantic network produces priming benefits during encoding, and search costs during retrieval. In extralist cuing, recall is a negative function of cue-to-distractor strength, and a positive function of neighborhood density, cue-to-target strength, and target-to-cue strength. We show how these four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks, indicating that the contribution of the semantic network varies with the context provided by the task. Finally, we evaluate spreading-activation and quantum-like entanglement explanations for the priming effects produced by neighborhood density.
Publisher: ACM
Date: 13-03-2017
Publisher: World Scientific Pub Co Pte Ltd
Date: 05-2013
DOI: 10.1142/S021952591350029X
Abstract: Sophisticated models of human social behavior are fast becoming highly desirable in an increasingly complex and interrelated world. Here, we propose that rather than taking established theories from the physical sciences and naively mapping them into the social world, the advanced concepts and theories of social psychology should be taken as a starting point, and used to develop a new modeling methodology. In order to illustrate how such an approach might be carried out, we attempt to model the low elaboration attitude changes of a society of agents in an evolving social context. We propose a geometric model of an agent in context, where in idual agent attitudes are seen to self-organize to form ideologies, which then serve to guide further agent-based attitude changes. A computational implementation of the model is shown to exhibit a number of interesting phenomena, including a tendency for a measure of the entropy in the system to decrease, and a potential for externally guiding a population of agents toward a new desired ideology.
Publisher: ACM
Date: 13-03-2017
Publisher: ACM Press
Date: 2016
Publisher: ARLE (International Association for Research in L1 Education)
Date: 06-2020
Publisher: Wiley
Date: 13-08-2019
DOI: 10.1111/BJET.12868
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Ediciones Universidad Cooperativa de Colombia
Date: 10-2018
Publisher: WORLD SCIENTIFIC
Date: 06-2008
Publisher: Informa UK Limited
Date: 02-07-2020
Publisher: Springer Science and Business Media LLC
Date: 14-01-2011
Publisher: Edward Elgar Publishing
Date: 28-06-2019
Publisher: Society for Learning Analytics Research
Date: 17-09-2016
Abstract: Modern society demands renewed attention on the competencies required to best equip students for a dynamic and uncertain future. We present exploratory work based on the premise that metacognitive and reflective competencies are essential for this task. Bringing the concepts of metacognition and reflection together into a conceptual model within which we conceived of them as both a set of similar features, and as a spectrum ranging from the unconscious inner-self through to the conscious external social self. This model was used to guide exploratory computational analysis of 6090 instances of reflective writing authored by undergraduate students. We found the conceptual model to be useful in informing the computational analysis, which in turn showed potential for automating the discovery of metacognitive activity in reflective writing, an approach that holds promise for the generation of formative feedback for students as they work towards developing core 21st century competencies.
Publisher: Springer Science and Business Media LLC
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: ACM
Date: 07-03-2018
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 31-10-2017
DOI: 10.14742/AJET.3607
Abstract: Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.
Publisher: ACM Press
Date: 2016
Publisher: ACM Press
Date: 2016
Publisher: ACM
Date: 24-03-2014
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Elsevier BV
Date: 2022
Publisher: Springer International Publishing
Date: 2015
Location: India
Start Date: 2015
End Date: 2016
Funder: Office for Learning and Teaching
View Funded ActivityStart Date: 2010
End Date: 12-2012
Amount: $270,000.00
Funder: Australian Research Council
View Funded Activity