Adaptive project planning and management based on best practices - COBRA system

Planning a project with proper considerations of all necessary factors and managing a project to ensure its successful implementation will face a lot of challenges. Initial stage in planning a project for bidding a project is costly, time consuming and usually with poor accuracy on cost and effort predictions. On the other hand, detailed information for previous projects may be buried in piles of archived documents which can be increasingly difficult to learn from the previous experiences. Project portfolio has been brought into this field aiming to improve the information sharing and management among different projects. However, the amount of information that could be shared is still limited to generic information. This paper, we report a recently developed software system COBRA to automatically generate a project plan with effort estimation of time and cost based on data collected from previous completed projects. To maximise the data sharing and management among different projects, we proposed a method of using product based planning from PRINCE2 methodology. (Automated Project Information Sharing and Management System -�COBRA) Keywords: project management, product based planning, best practice, PRINCE2

e-learning in project management using simulation models: a case study based on the replication of an experiment

Current e-learning systems are increasing their importance in higher education. However, the state of the art of e-learning applications, besides the state of the practice, does not achieve the level of interactivity that current learning theories advocate. In this paper, the possibility of enhancing e-learning systems to achieve deep learning has been studied by replicating an experiment in which students had to learn basic software engineering principles. One group learned these principles using a static approach, while the other group learned the same principles using a system-dynamics-based approach, which provided interactivity and feedback. The results show that, quantitatively, the latter group achieved a better understanding of the principles; furthermore, qualitatively, they enjoyed the learning experience

Investigating familiar interfaces to help older adults learn computer applications more easily

Many older adults wish to gain competence in using a computer, but many application interfaces are perceived as complex and difficult to use, deterring potential users from investing the time to learn them. Hence, this study looks at the potential of ‘familiar’ interface design which builds upon users’ knowledge of real world interactions, and applies existing skills to a new domain. Tools are provided in the form of familiar visual objects, and manipulated like real-world counterparts, rather than with buttons, icons and menus found in classic WIMP interfaces. This paper describes the formative evaluation of computer interactions that are based upon familiar real world tasks, which supports multitouch interaction, involves few buttons and icons, no menus, no right-clicks or double-clicks and no dialogs. Using an example of an email client to test the principles of using “familiarity”, the initial feedback was very encouraging, with 3 of the 4 participants being able to undertake some of the basic email tasks with no prior training and little or no help. The feedback has informed a number of refinements of the design principles, such as providing clearer affordance for visual objects. A full study is currently underway.

A conventionally managed project: a systems based case study

The technique of linear responsibility analysis is used for a retrospective case study of a private development consisting of an extension to an existing building to provide a wholesale butchery facility. The project used a conventionally organized management process. The organization structure adopted on the project is analysed using concepts from the systems theory, which are included in Walkers theoretical model of the structure of building project organizations. This model proposes that the process of building provision can be viewed as systems and sub-systems that are differentiated from each other at decision points. Further to this, the sub-systems can be viewed as the interaction of managing system and operating system. Using Walkers model, a systematic analysis of the relationships between the contributors gives a quantitative assessment of the efficiency of the organizational structure used. The project's organization structure diverged from the models propositions resulting in delay to the project's completion and cost overrun but the client was satisfied with the project functionally.

Knowledge sharing: Context, confusion and controversy

Project managers in the construction industry increasingly seek to learn from other industrial sectors. Knowledge sharing between different contexts is thus viewed as an essential source of competitive advantage. It is important therefore for project managers from all sectors to address and develop appropriate methods of knowledge sharing. However, too often it is assumed that knowledge freely exists and can be captured and shared between contexts. Such assumptions belie complexities and problems awaiting the unsuspecting knowledge-sharing protagonist. Knowledge per se is a problematic esoteric concept that does not lend itself easily to codification. Specifically tacit knowledge possessed by individuals, presents particular methodological issues for those considering harnessing its utility in return for competitive advantage. The notion that knowledge is also embedded in specific social contexts compounds this complexity. It is argued that knowledge is highly individualistic and concomitant with the various surrounding contexts within which it is shaped and enacted. Indeed, these contexts are also shaped as a consequence of knowledge adding further complexity to the problem domain. Current methods of knowledge capture, transfer and, sharing fall short of addressing these problematic issues. Research is presented that addresses these problems and proposes an alternative method of knowledge sharing. Drawing on data and observations collected from its application, the findings clearly demonstrate the crucial role of re-contextualisation, social interaction and dialectic debate in understanding knowledge sharing.

Teachers as researchers and teachers as software developers: how use-case analysis helps build better educational software

A series of government initiatives has raised both the profile of ICT in the curriculum and the expectation that high quality teaching and learning resources will be accessible across electronic networks. In order for e-learning resources such as websites to have the maximum educational impact, teachers need to be involved in their design and development. Use-case analysis provides a means of defining user requirements and other constraints in such a way that software developers can produce e-learning resources which reflect teachers' professional knowledge and support their classroom practice. It has some features in common with the participatory action research used to develop other aspects of classroom practice. Two case-studies are presented: one involves the development of an on-line resource centred on transcripts of original historical documents; the other describes how 'Learning how to Learn', a major, distributed research project funded under the ESRC Teaching and Learning Research Programme is using use-case analysis to develop web resources and services.

Freeze-frame: a new infant inhibition task and its relation to frontal cortex tasks during infancy and early childhood

The current study investigated a new, easily administered, visual inhibition task for infants termed the Freeze-Frame task. In the new task, 9-month-olds were encouraged to inhibit looks to peripheral distractors. This was done by briefly freezing a central animated stimulus when infants looked to the distractors. Half of the trials presented an engaging central stimulus, and the other half presented a repetitive central stimulus. Three measures of inhibitory function were derived from the task and compared with performance on a set of frontal cortex tasks administered at 9 and 24 months of age. As expected, infants' ability to learn to selectively inhibit looks to the distractors at 9 months predicted performance at 24 months. However, performance differences in the two Freeze-Frame trial types early in the experiment also turned out to be an important predictor. The results are discussed in terms of the validity of the Freeze-Frame task as an early measure of different components of inhibitory function. (C) 2007 Elsevier Inc. All rights reserved.

Relating computer tasks to existing knowledge to improve accessibility for older adults

Routine computer tasks are often difficult for older adult computer users to learn and remember. People tend to learn new tasks by relating new concepts to existing knowledge. However, even for 'basic' computer tasks there is little, if any, existing knowledge on which older adults can base their learning. This paper investigates a custom file management interface that was designed to aid discovery and learnability by providing interface objects that are familiar to the user. A study was conducted which examined the differences between older and younger computer users when undertaking routine file management tasks using the standard Windows desktop as compared with the custom interface. Results showed that older adult computer users requested help more than ten times as often as younger users when using a standard windows/mouse configuration, made more mistakes and also required significantly more confirmations than younger users. The custom interface showed improvements over standard Windows/mouse, with fewer confirmations and less help being required. Hence, there is potential for an interface that closely mimics the real world to improve computer accessibility for older adults, aiding self-discovery and learnability.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

A method for assessing confidence in requirements analysis

Context: During development managers, analysts and designers often need to know whether enough requirements analysis work has been done and whether or not it is safe to proceed to the design stage. Objective: This paper describes a new, simple and practical method for assessing our confidence in a set of requirements. Method: We identified 4 confidence factors and used a goal oriented framework with a simple ordinal scale to develop a method for assessing confidence. We illustrate the method and show how it has been applied to a real systems development project. Results: We show how assessing confidence in the requirements could have revealed problems in this project earlier and so saved both time and money. Conclusion: Our meta-level assessment of requirements provides a practical and pragmatic method that can prove useful to managers, analysts and designers who need to know when sufficient requirements analysis has been performed.

Learning across business sectors: context, embeddedness and conceptual chasms

While the construction industry is frequently encouraged to learn from other business sectors, the difficulties of transferring knowledge from one context to another are rarely acknowledged. The problematic nature of knowledge is addressed with particular emphasis on the concept of contextual embeddedness. From this point of view, the process of 'knowledge transfer' depends upon a prolonged process of socialization between actors from both the 'receiving' and 'sending' contexts networking. It is contended that a significant conceptual chasm exists between the exhortations of industry leaders to learn from other sectors and the theoretical complexities associated with knowledge transfer. An ongoing research project is described that seeks to facilitate knowledge sharing between construction and aerospace. A novel approach to knowledge sharing based upon soft systems methodology (SSM) (Mode 2) is described and justified. Initial findings from the first cycle of the research are discussed and used to highlight the importance of context in the implementation of supply chain management.

What (science for) adaptation to climate change in Colombian agriculture? A commentary on “A way forward on adaptation to climate change in Colombian agriculture: perspectives towards 2050” by J. Ramirez-Villegas, M. Salazar, A. Jarvis, C. E. Navarro-Valcines.

Climate change is putting Colombian agriculture under significant stress and, if no adaptation is made, the latter will be severely impacted during the next decades. Ramirez-Villegas et al. (2012) set out a government-led, top-down, techno-scientific proposal for a way forward by which Colombian agriculture could adapt to climate change. However, this proposal largely overlooks the root causes of vulnerability of Colombian agriculture, and of smallholders in particular. I discuss some of the hidden assumptions underpinning this proposal and of the arguments employed by Ramirez-Villegas et al., based on existing literature on Colombian agriculture and the wider scientific debate on adaptation to climate change. While technical measures may play an important role in the adaptation of Colombian agriculture to climate change, I question whether the actions listed in the proposal alone and specifically for smallholders, truly represent priority issues. I suggest that by i) looking at vulnerability before adaptation, ii) contextualising climate change as one of multiple exposures, and iii) truly putting smallholders at the centre of adaptation, i.e. to learn about and with them, different and perhaps more urgent priorities for action can be identified. Ultimately, I argue that what is at stake is not only a list of adaptation measures but, more importantly, the scientific approach from which priorities for action are identified. In this respect, I propose that transformative rather than technical fix adaptation represents a better approach for Colombian agriculture and smallholders in particular, in the face of climate change.

High-latitude particle precipitation and its relationship to magnetospheric source regions

Two central issues in magnetospheric research are understanding the mapping of the low-altitude ionosphere to the distant regions of the magnetsphere, and understanding the relationship between the small-scale features detected in the various regions of the ionosphere and the global properties of the magnetosphere. The high-latitude ionosphere, through its magnetic connection to the outer magnetosphere, provides an important view of magnetospheric boundaries and the physical processes occurring there. All physical manifestations of this magnetic connectivity (waves, particle precipitation, etc.), however, have non-zero propagation times during which they are convected by the large-scale magnetospheric electric field, with phenomena undergoing different convection distances depending on their propagation times. Identification of the ionospheric signatures of magnetospheric regions and phenomena, therefore, can be difficult. Considerable progress has recently been made in identifying these convection signatures in data from low- and high-altitude satellites. This work has allowed us to learn much about issues such as: the rates of magnetic reconnection, both at the dayside magnetopause and in the magnetotail; particle transport across the open magnetopause; and particle acceleration at the magnetopause and the magnetotail current sheets.

Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as: measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.

Action learning: how learning transfers from entrepreneurs to small firms

The paper presents research with small and medium enterprise (SME) owners who have participated in a leadership development programme. The primary focus of the paper is on learning transfer and factors affecting it, arguing that entrepreneurs must engage in ‘action’ in order to ‘learn’ and that under certain conditions they may transfer learning to their firm. The paper draws on data from 19 focus groups undertaken from 2010 to 2012, involving 51 participants in the LEAD Wales programme. It considers the literatures exploring learning transfer and develops a conceptual framework, outlining four areas of focus for entrepreneurial learning. Utilising thematic analysis, it describes and evaluates what (actual facts and information) and how (techniques, styles of learning) participants transfer and what actions they take to improve the business and develop their people. The paper illustrates the complex mechanisms involved in this process and concludes that action learning is a method of facilitating entrepreneurial learning which is able to help address some of the problems of engagement, relevance and value that have been highlighted previously. The paper concludes that the efficacy of an entrepreneurial learning intervention in SMEs may depend on the effectiveness of learning transfer.