154 research outputs found
Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles
Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing.
Three core components directly affect the overall productivity in construction sector, i.e. labour productivity, raw material productivity, and machine or equipment productivity.
With a focus on construction machinery, three factors influence productivity at excavation sites; i.e. 1) machine-based productivity and its configuration, 2) site layout and environmental conditions, and 3) operators’ behaviour.
Operators’ competence and motivation represent two key parameters that affect their behaviour.
On one side, gamification has attracted a growing area of interest both in literature and practice, seeking to place a layer of entertainment and pleasure to the top of serious activities (with a focus on improving the applicant’s motivation and behaviour). On the other side, telematics systems are utilized to collect operational data of the machine, and calculate its productivity rate. Telematics data are presented to operators (via a built-in screen available in the cabin of the machine) to provide real-time feedback about machine performance. In addition, these data can support machine owners to perceive operators’ behaviour on a real-time basis. To conclude, telematics systems are providing real-time data which can be a great input into gamification.
A guideline is proposed in this dissertation that helps gamification designers to develop more transparent gamification models. This guideline is utilized to introduce a gamification model that gamifies telematics data with a focus on enhancing operators’ behaviour (machine productivity) in loading and transferring activities. The model was implemented at two sites(one recycling and one mining site) and could encourage operators (who were operating wheel-loaders and dump-trucks) to prevent redundant activities like texting, phoning, and even eating while operating the machine. Subsequently, it enhanced overall machine productivity up to 37% during the site observation.
To summarize, a gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially lead to a more proper operators’ behaviour at work and subsequently can improve overall productivity rate at construction sites
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Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group
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Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles
Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing.
A gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially address this issue
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Measuring knowledge sharing processes through social network analysis within construction organisations
The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable
to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes
Using Active Learning to Teach Critical and Contextual Studies: One Teaching Plan, Two Experiments, Three Videos.
Since the 1970s, art and design education at UK universities has existedas a divided practice; on the one hand applying active learning in thestudio and on the other hand enforcing passive learning in the lecturetheatre. As a result, art and design students are in their vast majorityreluctant about modules that may require them to think, read and writecritically during their academic studies. This article describes, evaluatesand analyses two individual active learning experiments designed todetermine if it is possible to teach CCS modules in a manner thatencourages student participation. The results reveal that opting foractive learning methods improved academic achievement, encouragedcooperation, and enforced an inclusive classroom. Furthermore, andcontrary to wider perception, the article demonstrates that activelearning methods can be equally beneficial for small-size as well aslarge-size groups
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Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
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