Dr Karen Blackmore
School of Design Communication and IT (Information Technology)
- Phone:+61 2 492 15206
Karen Blackmore received her BInfoTech(SpatialInfo) With Distinction in 2001 and her PhD in 2008 from Charles Sturt University, Australia. Her PhD research was cross-disciplinary in nature and focused on agent-based modelling of business strategies and their associated changing resource needs. Specifically, this work involved the use of data mining, clustering and visualisation to identify and explore patterns in a large longitudinal data set. Her postdoctoral research work was conducted at the University of Newcastle, in collaboration with Hunter Councils. This work focused on the use of self-organising maps and data analysis techniques to model the environmental impacts of climate change. This work was awarded LGSA’s Environment Award for Energy Saving and Climate Projection Winner C Division & Overall Category Winner 2009. She also has a research track record in the areas of business strategy modelling, data mining, information visualisation, pattern recognition, computer games and education.Research Expertise
My major areas of research interest include: * Agent based models of complex adaptive systems * Application of data mining and pattern recognition techniques to understand patterns in global climate model data * Spatial and aspatial models of social and physical systems * Cross-disciplinary research issues My early research focus centred on data mining and spatial data modelling. For example, I explored the use of rule based classifiers, neural networks, genetic algorithms and fuzzy logic to find patterns in “Missing Persons Data” (Blackmore, et al. 2005; Blackmore & Bossomaier 2003a, 2003b; Blackmore & Bossomaier 2002a, 2002b; Blackmore et al. 2002). Data mining, clustering and statistical modelling also featured significantly in my PhD and Postdoctoral research. My PhD research involved modelling and analysing patterns associated with changing resource needs in organisations. A number of publications have arisen from this work (Blackmore et al. 2003; Blackmore & Nesbitt 2009; Blackmore & Nesbitt 2012). More recently, I have published results from my postdoctoral work that uses Self-Organising Maps (SOMS) and statistical downscaling to model regional climate variability (Goodwin, Freeman & Blackmore 2010; Goodwin & Blackmore forthcoming). In addition to the above academic publications, during 2008 to 2010 I was principal or co-author on eleven (11) reports (six allocated ISBN numbers) associated with my industry based postdoctoral studies and my employment with the University of Newcastle’s Centre for Urban and Regional Studies (CURS). The postdoctoral work was in collaboration with the Hunter Central Coast Regional Environment Strategy (HCCREMS). This work has been applied and used as the basis for the development of climate change adaptation strategies by local government authorities within the Hunter and Central Coast region. My work with CURS was conducted under an ARC Linkage grant and focussed on inter-agency data sharing and involved spatial data analysis of social vulnerability. The work was conducted in collaboration with the University of Western Sydney and the Department of Premier and Cabinet. Lastly, I have a research record and interest in areas relating to teaching and learning. I have investigated the complex factors associated with plagiarism in courses offered through partner or offshore campuses (Moffatt & Blackmore 2005, 2006) and issues in cross-disciplinary research higher degree research (Blackmore & Nesbitt 2008). In my role with Planning, Quality and Reporting at the University of Newcastle, I authored numerous research reports on a range of topics related to improving the student experience and developing strategies to improve the University’s performance in global ranking schemes. One of these reports formed the basis of a current University project aimed at reducing student attrition. Additionally, my work titled “Fuzzy Data Mining Approaches to Predicting Student Success and Retention” was presented at Australasian Association for Institutional Research Annual Forum held in November, 2012.
I have teaching experience at a University level in a range of IT areas. This experience encompasses different modes of delivery (eg. Internal and Distance Education) and ranges in level from 1st year to Masters and Graduate Certificate programs. I have delivered courses in the following areas:
• Computer Games Production • Database Management Systems • Database Systems • Principles of Database Development • ICT Fundamentals • Managing Internet Marketing Information • Market Research • Geographic Information Systems • Digital Image Analysis • Strategic Information Management • Commerce on the Information Superhighway • Introduction to the Senses • Relationship Marketing • Introduction to Remote Sensing
I am committed to the delivery of high quality teaching and engage in continuing professional development activities (eg. Tertiary Teaching Colloquium and education research publications) to ensure my skills in this area are appropriate and relevant to the needs of students. The quality of my teaching has been evidenced through positive student and peer feedback, both in terms of the way I deal with students and the quality of the materials I develop to support my teaching.
I have been an active member of school based marketing and research committees, as well as being a member of Faculty level Marketing committees. My involvement in the marketing committees stems from expertise in this area and also an interest in making courses more attractive to, and reaching, potential students.
Macquarie University - Continued research building on from postdoctoral work to derive regional climate change projections. Research involves the use of self organising maps (SOMs) to produce synoptic types, statistical analysis of weather station data, statistical downscaling and rule based classification. Ongoing work focuses on spatial modelling of shoreline changes and analysis of complex global climate data.
- PhD, Charles Sturt University
- Bachelor of Information Technology, Charles Sturt University
- Complex systems
- Computer Game Production
- Conceptual modelling
- Data mining
- Database Management
Fields of Research
|080109||Pattern Recognition and Data Mining||70|
|090903||Geospatial Information Systems||15|
|Title||Organisation / Department|
|Lecturer||University of Newcastle
School of Design Communication and IT
|Dates||Title||Organisation / Department|
|1/05/2009 - 1/12/2012||Research Fellow||Macquarie University
Department of Environment and Geography - Environmental Science
|1/01/2008 - 1/05/2010||
|University of Newcastle
|1/01/2006 - 1/01/2008||Lecturer||Charles Sturt University
School of Information Technology
For publications that are currently unpublished or in-press, details are shown in italics.
Chapter (3 outputs)
Smith SP, Blackmore K, Nesbitt K, 'A Meta-analysis of Data Collection in Serious Games Research', Serious Games Analytics: Methodologies for Performance Measurement, Assessment, and Improvement, Springer, New York 31-55 (2015)
|2006||Moffatt S, Blackmore KL, 'National anti plagiarism strategies: A shared responsibility in transnational university partnerships?', Breaking down boundaries: International experience in open, distance and flexible learning, Open and Distance Learning Association of Australia, Adelaide, Australia 1-12 (2006) [B1]|
|2005||Blackmore K, Bossomaier T, Foy S, Thomson D, 'Data mining of missing persons data', , SPRINGER-VERLAG BERLIN 305-314 (2005) [B1]|
Journal article (8 outputs)
Gu X, Blackmore KL, 'A systematic review of agent-based modelling and simulation applications in the higher education domain', Higher Education Research and Development, (2015)
This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a ... [more]
This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a Â¿bottom-upÂ¿ modelling paradigm in which system-level behaviour (macro) is modelled through the behaviour of individual local-level agent interactions (micro). This approach of considering the behaviour of systems of interacting Â¿agentsÂ¿ has been applied to a wide variety of domains. Of particular interest, are the ways that ABMS applications have been used to further understand the dynamics of the HE domain. We conduct a systematic review of literature to analyse publications by year, role of the simulator, development stage of the models, and any associated validation. We also identify areas for future work, which includes an emphasis on validating existing and future models, detailed description of simulations to allow replication and further development, and the use of agent-based models in other contexts within the increasingly complex HE domain.
Gu X, Blackmore K, Cornforth D, Nesbitt K, 'Modelling Academics as Agents: An Implementation of an Agent-Based Strategic Publication Model', Journal of Artificial Societies and Social Simulation, 18 10-10 (2015)
Goodwin ID, Freeman R, Blackmore K, 'An insight into headland sand bypassing and wave climate variability from shoreface bathymetric change at Byron Bay, New South Wales, Australia', Marine Geology, 341 29-45 (2013) [C1]
The headland sand bypassing mechanisms in the Eastern Australian longshore sand transport system are investigated at Cape Byron, in response to wave climate variability. The mecha... [more]
The headland sand bypassing mechanisms in the Eastern Australian longshore sand transport system are investigated at Cape Byron, in response to wave climate variability. The mechanisms are interpreted from shoreface bathymetric change between surveys in 1883, 2002 and 2011 CE. They involve a split in the sand transport to follow a nearshore path along the inner bar and a cross-embayment path connecting the up-coast and down-coast outer bars. The relative magnitude of the net sand transported via the two pathways is controlled by a rotation in directional wave conditions. Two bypassing mechanisms were interpreted: (i) a predominantly cross embayment transport during unimodal east-southeast wave climate such as those interpreted for the period prior to 1883; and, (ii) a split transport between the inner nearshore and cross-embayment paths during a bimodal dominant south-south-easterly and sub-dominant east-north-easterly wave climate such as in the 2000s. The net sand transport bypassing Cape Byron was dominated by a connected outer bar system prior to 1883 and conversely, a stronger inner bar system during the 1960s to 2000s. This is manifest in the 10Â° rotation in seabed morphology and shoreline planforms. These changes are in accordance with decadal climate variability described by the Interdecadal Pacific Oscillation (IPO). The switching between headland sand bypassing mechanisms on interannual to decadal timescales determines the geometry of the bypass strand with the downcoast littoral zone and has important implications for understanding the shoreline rotation and the application of the headland-bay beach concept to predicting planform curvature in open compartments. Â© 2013.
King RAR, Blackmore KL, 'Physical and political boundaries as barriers to the continuity of social vulnerability', Applied Geography, 44 79-87 (2013) [C1]
The dynamics of social vulnerability are of key interest to many government agencies and departments. Identifying the geographic distribution of vulnerability within regions, and ... [more]
The dynamics of social vulnerability are of key interest to many government agencies and departments. Identifying the geographic distribution of vulnerability within regions, and analysing how localised areas of social need change over time, is a key information requirement for decision-making, and the resultant allocation of resources. Typically, the delineation of areas for the determination of social vulnerability occurs using a combination of political and census boundaries. In many instances, the boundaries of these areas align to natural geographic features such as rivers or lakes. In other cases, a boundary is aligned to a man-made structure such as a road. The boundary may also be arbitrarily positioned based on some measure of distance and not align to any physical feature. In this research, we identify the various boundary types present in a political region. Using two measures of social vulnerability, we assess these boundaries as barriers to the continuity of social vulnerability. From our results, we identify motorways/highways and watercourses as potential barriers. We find no significant effects with lesser road structures suggesting there is no "wrong side of the street". These results have implications for decision-makers and emphasise the need to recognise the "softness" of boundaries, and consider the relationships between areas, when allocating resources. Â© 2013 Elsevier Ltd.
Blackmore K, Nesbitt K, 'Verifying the Miles and Snow strategy types in Australian small- and medium-size enterprises', Australian Journal of Management, 38 171-190 (2013) [C1]
In this paper we set out to verify the existence of Miles and Snow strategy types in Australian small- and medium-size enterprises (SMEs) through objective classification. Austral... [more]
In this paper we set out to verify the existence of Miles and Snow strategy types in Australian small- and medium-size enterprises (SMEs) through objective classification. Australian SMEs, in particular, are interesting as they are reported to have some unique characteristics, with as many as 70% following a low growth or life-style pathway. While numerous empirical studies have been conducted to validate the existence and characteristics of the Miles and Snow strategy types in different domains for both larger and smaller enterprises, these studies typically rely on a subjective, 'self-typing' approach. In this study we employ a more objective approach by identifying measures from existing survey data that capture the strategic dimensions proposed by Miles and Snow. We use these objective measures in a K-means cluster analysis to identify four different strategic groups. Three of the groups correspond to the three 'viable' Miles and Snow strategy types of Defender, Prospector and Analyser; however, we also identify a 'Static' strategy type that constitutes 42% of SMEs in the sample. Â© The Author(s) 2012.
Blackmore KL, Nesbitt KV, 'Defending against turbulent conditions: Results from an agent-based simulation', International Journal of Business Studies, 17 127-148 (2009) [C1]
|Show 5 more journal articles|
Conference (11 outputs)
Ng P, Nesbitt K, Blackmore K, 'Sound improves player performance in a multiplayer online battle arena game', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015)
Sound in video games is often used by developers to enhance the visual experience on screen. Despite its importance in creating presence and improving visual screen elements, soun... [more]
Sound in video games is often used by developers to enhance the visual experience on screen. Despite its importance in creating presence and improving visual screen elements, sound also plays an important role in providing additional information to a player when completing various game tasks. This preliminary study focuses on the use of informative sound in the popular multiplayer online battle arena game, Dota 2. Our initial results indicate that team performance improves with the use of sound. However, mixed results with individual performances were measured, with some individual performances better with sound and some better without sound.
Blackmore K, Nesbitt KV, Smith SP, 'IE2014: Proceedings of the 2014 Conference on Interactive Entertainment', Proceedings of the 2014 Conference on Interactive Entertainment, Newcastle, NSW (2014) [E4]
Gu X, Blackmore K, 'The Publishing Game: An Analysis of "Game" Related Academic Publishing Patterns', Proceedings of the 2014 Conference on Interactive Entertainment, Newcastle, NSW (2014) [E1]
Ng P, Nesbitt K, Blackmore K, 'Informative Sound and Performance in a Team Based Computer Game', Entertainment ComputingÂ¿ICEC 2014 (2014) [E1]
Blackmore K, Nesbitt K, Cornforth D, 'Simulating stable, trending and turbulent operating environments', Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, School of Design, Communication and IT, University of Newcastle, Newcastle, Australia (2013) [E1]
Blackmore KL, Nesbitt KV, 'Simulating the performance of small-medium enterprises in different market conditions', 2012 International Conference on Applied and Theoretical Information Systems Research Proceedings, Taipei (2012) [E1]
Blackmore KL, Nesbitt KV, 'Identifying risks for cross-disciplinary higher degree research students', Computing Education 2008: Proceedings of the Tenth Australasian Computing Education Conference (ACE2008), Wollongong, NSW (2008) [E1]
Blackmore K, Bossomaier TRJ, 'Comparison of See5 and J48.PART Algorithms for Missing Persons Profiling', Proceedings of the First International Conference on Information Technology and Applications (ICITA 2002) (2002)
Algorithms to derive rules from data sets can obtain differing results from the same data set. The J48.PART and the See5 schemes use similar methodologies to derive rules, however... [more]
Algorithms to derive rules from data sets can obtain differing results from the same data set. The J48.PART and the See5 schemes use similar methodologies to derive rules, however, differences appear in the number and constitution of rules produced to predict outcomes for missing persons cases. See5 generates fewer rules to obtain the same level of accuracy as J48.PART. Analysis of the input-output space using a measure of concept variation indicates missing persons profiling is characteristic of a difficult classification problem, resulting in fragmentation problems. This provides explanation for the differences that occur in the number and constitution of rules.
|Show 8 more conferences|
Report (8 outputs)
Mee KJ, McGuirk P, O'Neill P, Blackmore K, King R, 'Indicators of Social Vulnerability: Comparison of SDAP Composite Score, Hunter Region, 2008 and Census Composite Score, Hunter Region, 2006', Department of Premier and Cabinet Hunter Region, 16 (2010)
McGuirk P, Mee K, O'Neill P, Blackmore K, King R, Dimeski B, Askew L, 'Indicators of Social Vulnerability: SDAP Composite Score2006, 2007, 2008 Hunter Region Section 3', Department of Premier and Cabinet Hunter Region, 23 (2010)
O'Neill P, McGuirk P, Mee K, Blackmore K, King R, Dimeski B, 'Indicators of Social Vulnerability: Change in SDAP Composite Score 2006-2008', Department of Premier and Cabinet Hunter Region, 41 (2010)
|Show 5 more reports|
Grants and Funding
|Number of grants||1|
Click on a grant title below to expand the full details for that specific grant.
20131 grants / $4,085
Spatial Data Analytics: Addressing critical application problems concerning the environment and human society, and the interactions between them, using spatial data analytic approaches$4,085
Funding body: University of Newcastle
|Funding body||University of Newcastle|
|Project Team||Doctor Karen Blackmore|
|Scheme||New Staff Grant|
|Type Of Funding||Internal|
|Commenced||Research Title / Program / Supervisor Type|
Transaction Cost Economics and the Emergence of Entrepreneurial Opportunity
Economics, Faculty of Business and Law
Enhancing Player's Performance by Using Sound in Computer Games
Information Technology, Faculty of Science and Information Technology
Analysis of Influential Factors in Academic Publication System using Agent-Based Modelling and Simulation
Information Technology, Faculty of Science and Information Technology
November 25, 2014
A PhD or research masters opportunity exists focusing on using game technology in climate change scenarios under the supervision of Dr Karen Blackmore
November 25, 2014
Dr Karen Blackmore
School of Design, Communication and IT (DCIT)
School of Design Communication and IT
Faculty of Science and Information Technology
|Phone||+61 2 492 15206|
|Fax||+61 2 492 15896|
Callaghan, NSW 2308