Harnessing Technology: new modes of technology-enhanced learning: opportunities and challenges

A report commissioned by Becta to explore the potential impact on education, staff and learners of new modes of technology enhanced learning, envisaged as becoming available in subsequent years. A generative framework, developed by the researchers is described, which was used as an analytical tool to relate the possibilities of the technology described to learning and teaching activities. This report is part of the curriculum and pedagogy strand of Becta's programme of managed research in support of the development of Harnessing Technology: Next Generation Learning 2008-14. A system-wide strategy for technology in education and skills. Between April 2008 and March 2009, the project carried out research, in three iterative phases, into the future of learning with technology. The research has drawn from, and aims to inform, all UK education sectors

Computational Batik Motif Generation: Innovation of Traditional Heritage by Fractal Computation\ud

Human-computer interaction has been the cause of the emerging innovations in many fields, including in design and art, architectural, technological artifacts, and even traditional heritage. In the case of Indonesian traditional heritages, the computation of fractal designs has been introduced to develop batik design – the genuine textile art and skill that becomes a symbol of Indonesian culture. The uniqueness of Batik, which depicted in the richness of its motifs, is regarded as one of interesting aspect to be researched and innovated using computational techniques. Recent studies of batik motifs have discovered conjecture to the existence of fractal geometry in batik designs. This finding has given some inspiration of implementing certain fractal concepts, such escape-time fractal (complex plane) and iterated function system to generate batik motifs. We develop motif generator based upon the Collage Theorem by using Java TM platform. This software is equipped by interface that can be used by user to generate basic patterns, which could be interpreted and painted as batik motif. Experimentally, we found that computationally generated fractal motifs are appropriated to be implemented as batik motif. However, human made batik motifs are less detail and some of them differ significantly with the computationally generated ones for tools used to draw batik and human aesthetic constraints

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Development and evaluation of a web-based learning system based on learning object design and generative learning to improve higher-order thinking skills and learning

This research aims to design, develop and evaluate the effectiveness of a Webbased learning system prototype called Generative Object Oriented Design (GOOD) learning system. Result from the preliminary study conducted showed most of the students were at lower order thinking skills (LOTS) compared to higher order thinking skills (HOTS) based on Bloom’s Taxonomy. Based on such concern, GOOD learning system was designed and developed based on learning object design and generative learning to improve HOTS and learning. A conceptual model design of GOOD learning system, called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) model, has been proposed from the theoretical framework of this research. The topic selected for this research was Computer System (CS) which focused on the hardware concepts from the first year Diploma of Computer Science subjects. GOOD learning system acts as a mindtool to improve HOTS and learning in CS. A pre-experimental research design of one group pretest and posttest was used in this research. The samples of this research were 30 students and 12 lecturers. Data was collected from the pretest, posttest, portfolio, interview and Web-based learning system evaluation form. The paired-samples T test analysis was used to analyze the achievement of the pretest and posttest and the result showed that there was significance difference between the mean scores of pretest and posttest at the significant level a = 0.05 (p=0.000). In addition, the paired-samples T test analysis of the cognitive operations from Bloom’s Taxonomy showed that there was significance difference for each of the cognitive operation of the students before and after using GOOD learning system. Results from the study showed improvement of HOTS and learning among the students. Besides, analysis of portfolio showed that the students engaged HOTS during the use of the system. Most of the students and lecturers gave positive comments about the effectiveness of the system in improving HOTS and learning in CS. From the findings in this research, GOOD learning system has the potential to improve students’ HOTS and learning

Architectural authorship in generative design

The emergence of evolutionary digital design methods, relying on the creative generation of novel forms, has transformed the design process altogether and consequently the role of the architect. These methods are more than the means to aid and enhance the design process or to perfect the representation of finite architectural projects. The architectural design philosophy is gradually transcending to a hybrid of art, engineering, computer programming and biology. Within this framework, the emergence of designs relies on the architect- machine interaction and the authorship that each of the two shares. This work aims to explore the changes within the design process and to define the authorial control of a new breed of architects- programmers and architects-users on architecture and its design representation. For the investigation of these problems, this thesis is to be based on an experiment conducted by the author in order to test the interaction of architects with different digital design methods and their authorial control over the final product. Eventually, the results will be compared and evaluated in relation to the theoretic views. Ultimately, the architect will establish his authorial role