ORCID Profile
0000-0002-6857-0582
Current Organisation
University of South Australia
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Publisher: Springer International Publishing
Date: 2017
Publisher: Inderscience Publishers
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 12-2018
Publisher: Society for Learning Analytics Research
Date: 05-08-2018
Abstract: The widespread adoption of digital e-learning environments and other learning technology has provided researchers with ready access to large quantities of data. Much of this data comes from discussion forums and has been studied with analytical methods drawn from social network analysis. However, within this large body of research there exists considerable variation in the definition of what constitutes a social tie, and the consequences of this choice are rarely described or examined. This paper presents findings from two distinct learning environments regarding different social tie extraction methods and their influence on the structural and statistical properties of the induced networks, and the association between measures of centrality and academic performance. Our findings indicate that social tie definitions play an important role in shaping the results of our analyses. The primary purpose of this paper is to raise awareness of the consequences that such methodological choices may have, and to promote transparency in future research.
Publisher: Springer Science and Business Media LLC
Date: 21-08-2019
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer New York
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Publisher: Association for Computing Machinery (ACM)
Date: 20-12-2022
DOI: 10.1145/3548686
Abstract: The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of in iduals using huge amounts of data collected from their activities. The behavioural data collection process from an in idual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD.
Publisher: ACM Press
Date: 2016
Publisher: Elsevier BV
Date: 12-2022
Publisher: Inderscience Publishers
Date: 2009
Publisher: Wiley
Date: 16-07-2019
DOI: 10.1111/BJET.12846
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: ACM
Date: 04-03-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 2006
Publisher: ACM
Date: 04-03-2019
Publisher: Elsevier BV
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Routledge
Date: 19-11-2015
Publisher: Inderscience Publishers
Date: 2013
Publisher: IEEE
Date: 07-2008
Publisher: Springer Singapore
Date: 2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 2024
Publisher: IEEE
Date: 04-2010
Publisher: IEEE
Date: 11-2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: eLife Sciences Publications, Ltd
Date: 04-2014
DOI: 10.7554/ELIFE.01808
Abstract: Cationic antimicrobial peptides (CAPs) such as defensins are ubiquitously found innate immune molecules that often exhibit broad activity against microbial pathogens and mammalian tumor cells. Many CAPs act at the plasma membrane of cells leading to membrane destabilization and permeabilization. In this study, we describe a novel cell lysis mechanism for fungal and tumor cells by the plant defensin NaD1 that acts via direct binding to the plasma membrane phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2). We determined the crystal structure of a NaD1:PIP2 complex, revealing a striking oligomeric arrangement comprising seven dimers of NaD1 that cooperatively bind the anionic headgroups of 14 PIP2 molecules through a unique ‘cationic grip’ configuration. Site-directed mutagenesis of NaD1 confirms that PIP2-mediated oligomerization is important for fungal and tumor cell permeabilization. These observations identify an innate recognition system by NaD1 for direct binding of PIP2 that permeabilizes cells via a novel membrane disrupting mechanism.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Routledge
Date: 03-10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Inderscience Publishers
Date: 2011
Publisher: Elsevier BV
Date: 02-2014
Publisher: MDPI AG
Date: 28-10-2023
DOI: 10.3390/S23218783
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Inderscience Publishers
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 1996
DOI: 10.1007/BFB0031799
Publisher: Inderscience Publishers
Date: 2012
Publisher: IEEE
Date: 04-2010
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 20-10-2005
Publisher: IEEE
Date: 12-2018
Publisher: Wiley
Date: 06-11-2017
DOI: 10.1111/BJET.12592
Publisher: Wiley
Date: 20-05-2020
DOI: 10.1111/JCAL.12453
Publisher: Springer Science and Business Media LLC
Date: 27-02-2019
Publisher: ACM
Date: 05-05-2012
Publisher: Springer Singapore
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: UNED - Universidad Nacional de Educacion a Distancia
Date: 23-09-2012
Publisher: ACM Press
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 23-09-2012
Publisher: ACM
Date: 13-03-2023
Publisher: Elsevier BV
Date: 11-2023
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 07-12-2015
Publisher: Society for Learning Analytics Research
Date: 18-02-2016
Abstract: Designing, validating and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design and psychology. Whilst each of these disciplines has established its own design methodologies, there is a need for methodological frameworks that meet the specific demands of the cross-disciplinary space defined by learning analytics. In particular there is no systematic workflow for producing learning analytics tools that are both technologically feasible and truly underpin the learning experience. In this paper, we present a set of guiding principles and recommendations derived from the LATUX workflow. LATUX is a five-stage workflow to design, validate and deploy awareness interfaces in technology-enabled learning environments. LATUX is grounded on a well-established design process for creating, testing and re-designing user interfaces. We extend this process by integrating the pedagogical requirements, to guide the design of learning analytics visualisations that can inform instructors’ pedagogical decisions or intervention strategies. The workflow is illustrated with a case study in which collaborative activities were deployed in a real classroom. Finally, the paper proposes a research agenda to support designers and implementers of learning analytics interfaces.
Publisher: Elsevier BV
Date: 2022
Publisher: ACM
Date: 13-03-2023
Publisher: Elsevier BV
Date: 05-2012
Publisher: ACM
Date: 29-04-2012
Publisher: IEEE
Date: 10-2011
Publisher: IEEE
Date: 04-2012
Publisher: Hindawi Limited
Date: 26-10-2023
DOI: 10.1155/2023/6657171
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-61350-489-5.CH009
Abstract: Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.
Publisher: Springer Berlin Heidelberg
Date: 1996
Publisher: ACM Press
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11768012_64
Publisher: ACM Press
Date: 2016
Publisher: IEEE
Date: 07-2013
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 28-03-2018
DOI: 10.14742/AJET.3207
Abstract: The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 03-2013
Publisher: ACM Press
Date: 2016
Publisher: Elsevier BV
Date: 11-2023
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2015
Publisher: National Library of Serbia
Date: 2015
Abstract: The number of information systems using adaptation rules is increasing quickly. These systems are usually focused on implement nice and complex functionality for adaptation of contents, links or presentation, so software engineering methodologies for the description of rules are required. In addition, the distributed service oriented Internet philosophy presents the challenge of combining different rules from independent Internet sources. Moreover, easy authoring, rule reuse and collaborative design should be enabled. This paper presents the AR (Adaptation Rules) model, a new software engineering model for the description of rules for adaptation. These rules can be composed as a set of smaller atomic, reusable, parametric, interchangeable and interoperable rules, with clear restrictions in their combinations. Our model enables the distribution of rules as well as rule reuse and collaboration among rule creators. We illustrate our approach with the application of this model to a hinting adaptive e-learning system that generates exercises with hints, which can be adapted based on defined rules. Advantages of the AR model are confirmed with an evaluation that has been done with teachers and learning analytics experts for adaptive e-learning.
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59140-993-9.CH080
Abstract: E-learning has evolved very rapidly in recent years, from a first stage in which a set of documents were simply made available to the students in an electronic platform, to current tools that integrate not only academic but also administrative aspects in educational institutions. Today, learning is possible virtually anywhere, anytime and new scenarios are possible thanks to the technological support available. The so-called “learning management systems” (LMS) or “learning content management systems” (LCMS) now must handle aspects ranging from tuition fees to personalized pedagogical approaches or collaborative activities.
Publisher: Wiley
Date: 29-04-2022
DOI: 10.1111/BJET.13224
Abstract: For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global “Ethical AI” movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. What is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters. Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries. A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics. What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics. It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline It describes a repeatable framework for conducting ethical Learning Analytics. Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm. The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated. The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in early 2022).
Publisher: ACM
Date: 13-03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59140-993-9.CH084
Abstract: A swarm may be defined as a population of interacting elements that is able to optimize some global objective through collaborative search of a space (Kennedy, 2001). The elements may be very simple machines or very complex living beings, but there are two restrictions to be observed: They are limited to local interactions usually the interaction is not performed directly but indirectly through the environment. The property that makes swarms interesting is their self-organizing behaviour in other words, it is the fact that a lot of simple processes can lead to complex results.
Publisher: ACM
Date: 12-04-2021
Publisher: IEEE
Date: 07-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/CBD.2014.25
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Wiley
Date: 10-10-2019
DOI: 10.1111/JCAL.12392
Publisher: IGI Global
Date: 10-2017
Abstract: Online collaborative writing tools provide an efficient way to complete a writing task. However, existing tools only focus on technological affordances and ignore the importance of social affordances in a collaborative learning environment. This article describes a learning analytic system that analyzes writing behaviors, and creates visualizations incorporating in idual engagement awareness and group ranking awareness (social affordance), and review writing behaviour history (technological affordance), to support student engagement. Studies examined the performance of the system used by university students in two collaborative writing activities: collaboratively writing a project proposal (N = 41) and writing tutorial discussion answers (N = 25). Results show that students agreed with what the visualization conveys and visualizations enhance their engagement in a collaborative writing activity. In addition, students stated that the visualizations were useful to help them reflect on the writing process and support the assessment of in idual contributions.
Publisher: ACM
Date: 23-03-2020
Publisher: Wiley
Date: 31-03-2022
DOI: 10.1111/BJET.13218
Abstract: Technological affordances have shown promising potential in advancing the delivery of corporate learning programmes designed for professional leadership development. However, there is a considerable challenge in evaluating learners' skill acquisition, with most of the past research relying on pre‐ and post‐tests or other forms of self‐reports to measure leadership development. In that sense, these approaches measure leadership development before and after the programme, while being inefficient for measuring the development during the learning process. This study collected self‐reflection answers from a professional development MOOC that allows learners to express their stepwise learning and reflect on their professional experience on leadership fronts. We developed a novel methodology and an automated system for the evaluation of leadership skills' mastery based on the depth of reflection exhibited during the learning process. We identified four groups of learners based on their course content mastery and explored the differences within groups. The results also highlight relevant insights about instructional design and provide promising avenues for future research. What is already known about this topic Professional leadership programmes have become increasingly common in workplace learning. Programmes mainly use manual/introspective measures to assess skill acquisition. What this paper adds An automated assessment system to evaluate leadership skill mastery. Evidence‐based and leadership driven inferences about skill acquisition. Use of a novel multidisciplinary methodology for complex skills assessment. Implications of practice and/or policy Assessing leadership development should include more than course grades. Assessing differences in content mastery requires evaluation of various skills. Developed assessment system provides promise for other similar domains.
Publisher: Informa UK Limited
Date: 20-07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Elsevier BV
Date: 02-2013
Publisher: Wiley
Date: 18-07-2018
DOI: 10.1111/BJET.12645
Publisher: Springer Science and Business Media LLC
Date: 25-09-2014
Publisher: Edward Elgar Publishing
Date: 10-06-2022
Publisher: Wiley
Date: 24-05-2023
DOI: 10.1111/JCAL.12829
Abstract: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work‐integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro‐level relations. The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem‐solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Applying temporal motifs as an analytics‐based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion.
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 23-12-2020
DOI: 10.14742/AJET.6370
Abstract: Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students’ sense-making of learning analytics–based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students’ current learning strategies and achievement of associated learning goals. Implications for practice or policy: Personalised feedback based on learning analytics should be informed by an understanding of students’ self-regulated learning. Instructors implementing personalised feedback should align this closely with the course curriculum. Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone. Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Inderscience Publishers
Date: 2007
Publisher: IEEE
Date: 07-2012
Publisher: ACM
Date: 16-03-2015
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Informa UK Limited
Date: 27-10-2022
Publisher: Elsevier BV
Date: 08-2011
Publisher: Elsevier BV
Date: 11-2016
Publisher: Informa UK Limited
Date: 30-06-2021
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 04-2017
Publisher: Springer International Publishing
Date: 2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: ACM
Date: 24-03-2014
DOI: 10.1145/2567574
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-756-0.CH011
Abstract: This chapter provides an overview of the use of swarm-intelligence techniques in the field of e-learning. Swarm intelligence is an artificial intelligence technique inspired by the behavior of social insects. Taking into account that the Internet connects a high number of users with a negligible delay, some of those techniques can be combined with sociology concepts and applied to e-learning. The chapter analyzes several of such applications and exposes their strong and weak points. The authors hope that understanding the concepts used in the applications described in the chapter will not only inform researchers about an emerging trend, but also provide with interesting ideas that can be applied and combined with any e-learning system.
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 10-2024
Publisher: ACM
Date: 07-03-2018
Publisher: Public Library of Science (PLoS)
Date: 19-09-2011
Publisher: Springer US
Date: 2006
Publisher: Springer International Publishing
Date: 2020
Publisher: Wiley
Date: 04-2014
DOI: 10.1111/BJET.12152
Publisher: Wiley
Date: 29-12-2010
DOI: 10.1002/SPE.1023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: ACM
Date: 16-03-2015
Publisher: IEEE
Date: 04-2011
Publisher: Springer International Publishing
Date: 05-12-2021
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 12-2012
Publisher: Society for Learning Analytics Research
Date: 05-07-2017
Abstract: The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.
Publisher: Springer International Publishing
Date: 2019
Publisher: Society for Learning Analytics Research (SoLAR)
Date: 05-2017
DOI: 10.18608/HLA17.014
Publisher: Routledge
Date: 03-10-2018
Publisher: ACM Press
Date: 2016
Publisher: Society for Learning Analytics Research
Date: 05-11-2021
Abstract: One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes - such as bigger class sizes and a more erse student population - challenged the provision of effective feedback. In light of these changes, educators have increasingly been using new digital tools to provide student feedback, given the broader adoption and availability of these new technologies. However, despite these efforts, most educators have limited insight into the recipience of their feedback and wonder which students engage with feedback. This problem is referred to as the "feedback gap," which is the difference between the potential and actual use of feedback, preventing educators and instructional designers from understanding feedback recipience among students. In this study, a set of trackable call-to-action (CTA) links were embedded in feedback messages focused on learning processes and self-regulation of learning in one fully online marketing course and one blended bioscience course. These links helped us examine the association between feedback engagement and course success. We also conducted two focus groups with students from one of the courses to further examine student perceptions of feedback messages. Our results across both courses revealed that early engagement with feedback is positively associated with passing the course and that most students considered feedback messages helpful in their learning. Our study also found some interesting demographic differences between students regarding their engagement with the feedback messages. Such insight enables instructors to ask "why" questions, support students' learning, improve feedback processes, and narrow the gap between potential and actual use of feedback. The practical implications of our findings are further discussed.
Publisher: Wiley
Date: 05-2013
Abstract: Exosomes are small extracellular 40-100 nm diameter membrane vesicles of late endosomal origin that can mediate intercellular transfer of RNAs and proteins to assist premetastatic niche formation. Using primary (SW480) and metastatic (SW620) human isogenic colorectal cancer cell lines we compared exosome protein profiles to yield valuable insights into metastatic factors and signaling molecules fundamental to tumor progression. Exosomes purified using OptiPrep™ density gradient fractionation were 40-100 nm in diameter, were of a buoyant density ~1.09 g/mL, and displayed stereotypic exosomal markers TSG101, Alix, and CD63. A major finding was the selective enrichment of metastatic factors (MET, S100A8, S100A9, TNC), signal transduction molecules (EFNB2, JAG1, SRC, TNIK), and lipid raft and lipid raft-associated components (CAV1, FLOT1, FLOT2, PROM1) in exosomes derived from metastatic SW620 cells. Additionally, using cryo-electron microscopy, ultrastructural components in exosomes were identified. A key finding of this study was the detection and colocalization of protein complexes EPCAM-CLDN7 and TNIK-RAP2A in colorectal cancer cell exosomes. The selective enrichment of metastatic factors and signaling pathway components in metastatic colon cancer cell-derived exosomes contributes to our understanding of the cross-talk between tumor and stromal cells in the tumor microenvironment.
Publisher: Emerald
Date: 05-08-2019
DOI: 10.1108/IJILT-02-2019-0024
Abstract: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.
Publisher: ACM
Date: 13-03-2020
Publisher: ACM
Date: 07-03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1996
DOI: 10.1109/43.552081
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Society for Learning Analytics Research
Date: 11-12-2018
Abstract: The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student–instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs) 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area.
Publisher: Elsevier BV
Date: 02-2016
DOI: 10.1016/J.RESMIC.2016.09.004
Abstract: Progress in next-generation sequencing technologies has facilitated investigations into microbial dynamics. An important bacterium in the dairy industry is Propionibacterium freudenreichii, which is exploited to manufacture Swiss cheeses. A healthy culture of these bacteria ensures a consistent cheese with formed 'eyes' and pleasant flavour profile, and the investigation of prophages and their interactions with these bacteria could assist in the maintenance of the standard of this food product. Two bacteriophages, termed PFR1 and PFR2, were chemically induced using mitomycin C from two different dairy strains of P. freudenreichii. Both phages have identical genomes however, PFR2 was found to contain an insertion sequence, IS204. Host range characterisation showed that PFR1 was able to form plaques on a wild type Propionibacterium acnes strain, whereas PFR2 could not. The lytic plaques observed on P. acnes were a result of PFR1 inducing the lytic cycle of a pseudolysogenic phage in P. acnes. Further investigation revealed that both PFR1 and PFR2 could infect P. acnes but not replicate. This study demonstrates the dynamic interactions between phages, which may alter their lytic capacity under certain conditions. To our knowledge, this is the first report of two phages interacting to kill their host.
Publisher: Informa UK Limited
Date: 19-04-2022
Start Date: 2016
End Date: 2018
Funder: Office for Learning and Teaching
View Funded ActivityStart Date: 2013
End Date: 2015
Funder: Office for Learning and Teaching
View Funded ActivityStart Date: 2015
End Date: 2016
Funder: Office for Learning and Teaching
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