Suitability of BIM for enhancing value on PPP projects for the benefit of the public sector

Collaborative integrated working and stakeholder’s interest have been among key drivers that underpin and encourage the use of Building Information Modelling (BIM) within the AEC industry. BIM is becoming a major means to deliver projects with better improved product, and reduced risk within the construction industry. Furthermore, using BIM in areas like buildability, quality assurance, cost and scheduling can be justified through BIM-nD modelling application. What is not so obvious is how the utilisation of BIM visualisation and knowledge embedment will enhance these areas to refine and achieve better value for PPP procurement projects for the long term benefit especially during post-construction phase for the public sector. As of now there is no well-defined guidance with respect to BIM usage incorporating all of the above. Do we really need to revisit the way we specify projects within the contractual framework under PPP? This paper examines the possibility of how BIM can be utilised in the realisation of augmented formal database information management system under the PPP procurement routes with respect to operation and maintenance support. The paper concludes with additional measures that BIM can offer at the post-construction phase for the public sector at learning organisations

Development and Evaluation of an Adaptive Hypermedia System Based on Multiple Student Characteristics

Adaptive Educational Hypermedia systems (AEH) are amongst the most recent types of application to provide individualised instruction to students who undertake online courses. Such systems attempt to adapt to how individuals learn by personalizing instruction for each individual student depending upon one or more “characteristics” of the student. Prior knowledge and learning style have been identified as being prominent characteristics in this process but AEH systems implemented to date have generally been limited to only employing one of these characteristics. Such systems have also been limited in that they are specific to a particular course content and cannot be easily adapted to present different learning materials. This thesis describes the development and evaluation of a new AEH system that provides a generic template for different learning materials as well as a student model that incorporates five distinct student characteristics as an aid to learning: primary characteristics are prior knowledge, learning style and the presence or absence of animated multimedia aids (multimedia mode); secondary characteristics include page background preference and link colour preference. The use of multimedia artefacts as a student characteristic (and hence as an independent variable in this study) has not previously been implemented or evaluated. A separate non-AEH system, identical to the AEH system except for the absence of adaptation to individuals, was developed in parallel as a control. The system development consists of a requirements analysis, design and implementation. The design models including use case diagrams, conceptual design, sequence diagrams, navigation design and presentation design are expressed using Unified Modelling Language (UML). The AEH system which was developed in a generic template implemented using Java Servlets, XHTML, XML, JavaScript and HTML. The generic template is a domain-independent AEH system that has functions of both adaptivity and adaptability. The system was evaluated in an experimental research involving 67 undergraduate engineering students in the Department of Electronics at Yogyakarta State University. The learning material of Analogue Electronics was implemented into both the AEH system and non-AEH systems under seven chapter headings. The participants were randomly divided into an experimental group and a control group. During the 9 weeks of experimentation, the students studied the learning material in two randomly allocated groups, an experimental group using the AEH system and a control group using the non-AEH system. A pre-test was administered to measure initial student knowledge. The student achievement was measured at the end of each chapter of material using a chapter test and at the end of the experimentation as a whole using a post-test. Basic statistical analysis of t-test and Mann-Whitney U were conducted to investigate any difference of student achievement between the two groups. A further detailed analysis using multilevel modelling was conducted to investigate any possible effects of the adaptive parameters on the student achievement. A total of 7 hypotheses were tested during data analysis. Research findings are described as follows. Students who learned using the AEH system performed better significantly than those who learned using the NON-AEH system. The implementation of test repetition as a function of knowledge adaptation in the AEH system increased student achievement significantly. This was found to be the prominent effect. When the effect of test repetition was removed, the implementation of learning style and multimedia mode adaptation in the AEH system was still found to have significant effect upon student performance. Students whose learning style and multimedia preferences were matched with the system (AEH or non-AEH) achieved better results. In terms of the relative merit of each contributing factor toward a student’s achievement, the order of the effects was found to be (1) knowledge, (2) multimedia, and (3) learning style. Whilst repeated knowledge testing is an established cause of improved performance, the positive effects on student performance of using multimedia artefacts over choice of learning style is a new finding

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape

Condition matters: pupil voices on the design and condition of secondary schools

This research was produced by Sheffield Hallam University. The project aimed to inform the creation of a national schools Facilities Management network and an ongoing programme to research and benchmark the impact of school condition and design on pupils

Service Recommendations with Deep Learning: A Study on Neural Collaborative Engines

Background: The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering engines. Recommendation systems are data-driven infrastructures which are widely adopted to create effective and cutting-edge smart services, allowing to personalize the value proposition and adapt it to changes and variations in customers’ preferences. Method: Our research represents an exploratory investigation on the adoption of Neural Networks for Recommendation Systems, inspired by the findings of a recent study on service science that highlighted the suitability of those models for designing cutting-edge recommenders capable of overcoming stable traditional benchmarks like the Singular Value Decomposition and the k-Nearest Neighbors algorithms. Following this study, we designed a more “complex” Feed-Forward Neural Network, trained on the “Movielens 100K” dataset using the Mean-Squared Error function to approximate the model loss generated and the Adaptive Moment Estimation algorithm (Adam) for the parameters optimization. Results: The results of this study demonstrate the primary role of Feed-Forward Neural Networks for designing advanced Collaborative recommenders, consolidating and even improving the outcomes of the work that inspired our research. Conclusion: Given these assumptions, we confirm the suitability of Feed-Forward Neural Networks as effective recommendation algorithms, laying the foundations for further studies in neural-based recommendation science

Information systems for interactive learning: Design perspective

This paper aims to present and discuss educational issues and relevant research to universities and colleges in the Arabian Gulf Region. This include cultural, students’ learning preferences and the use of information and communication technology. It particularly focuses on interactive learning through the consideration of learning styles. It explores the sequential-global learning styles profile of undergraduate students as part of a continuous research in Information Systems design with a particular focus on the design of Interactive Learning Systems (ILSs). A study to examine the learning style profile of undergraduate students in a cohort of Management Information Systems at a UAE university has been conducted, and a discussion and recommendations on how these findings can be reflected on the design of ILSs are provided