Learning Object System for the Delivery of Quality Education

ABSTRACT A learning object is "any digital resource that can be reused to support learning." Learning objects are based on the notion that multiple educational institutions could share the use, and cost of the creation and management of the learning objects. The theoretical result of sharing learning objects leads to a much lower cost per educational institution. Learning objects are based on the generative and constructive learning theories that assert that learning is an active process of constructing rather than acquiring knowledge; instruction is a process of supporting that construction rather than communicating knowledge. Learning objects are also based on sound design principles of the object-oriented paradigm in computer science. These combined theories provide a framework for learning objects as: accessible, reusable, interoperable, adaptable, granular, versionable, cohesive, and loosely coupled. The purpose of this literature-based research is to explain the theory of learning objects and their benefits to organizations. This paper explains how learning objects can improve the delivery of quality education

Neural Wireframe Renderer: Learning Wireframe to Image Translations

In architecture and computer-aided design, wireframes (i.e., line-based models) are widely used as basic 3D models for design evaluation and fast design iterations. However, unlike a full design file, a wireframe model lacks critical information, such as detailed shape, texture, and materials, needed by a conventional renderer to produce 2D renderings of the objects or scenes. In this paper, we bridge the information gap by generating photo-realistic rendering of indoor scenes from wireframe models in an image translation framework. While existing image synthesis methods can generate visually pleasing images for common objects such as faces and birds, these methods do not explicitly model and preserve essential structural constraints in a wireframe model, such as junctions, parallel lines, and planar surfaces. To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint representation in a shared encoder network that must support the generation of both images and wireframes. Experiments on a wireframe-scene dataset show that our wireframe-to-image translation model significantly outperforms the state-of-the-art methods in both visual quality and structural integrity of generated images.Comment: ECCV 202

Pengembangan Lembar Kerja Peserta Didik Muatan IPA SD

This study aims to determine the development and level of validity and practicality of Science Content Student Worksheets according to expert lecturers and practitioners, as well as to train students in learning activities to improve mastery of the material. This type of research is Research and Development with methods 1) analysis phase, 2) design, 3) development, 4) implementation, and 5) evaluation. Descriptive analysis of data, namely the results of the development of Science Content Student Worksheets material for changes in the form of objects consists of a general framework that contains cover pages, prefaces, table of contents, book instructions, basic competencies, and a content framework containing learning 1, learning 2, learning 5 IPA practice. as well as the final framework containing a bibliography, and conclusions. Based on data analysis, it can be concluded that the quality of the Science Content Student Worksheets developed is included in the very good category so that it is suitable for use with a percentage of 0.83% from linguists, 0.70% from media experts, and 0.97% from material experts. Meanwhile, the teacher's response obtained a percentage of 90% and student responses obtained a percentage of 93.33% with a very good category.   Keywords: Science, Student Worksheet

Deep learning-based artificial vision for grasp classification in myoelectric hands

Objective. Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. Approach. We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at ${{5}^{\circ}}$ intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. Main results. The classification accuracy in the offline tests reached $85 \%$ for the seen and $75 \%$ for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of $84 \%$ in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to $88 \%$ . In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. Significance. The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably

COVID-19 catalyst: emergent pedagogies and a DIAgram framework

The global COVID-19 pandemic has delivered extraordinary challenges across geographies as well as practices, and clearly academia has not been spared. While the events of 2020 and 2021 have revealed some limits to teaching in the ‘old (pre-pandemic) normal’, technology-supported pedagogies have been emerging for several years. This pandemic has been a potent catalyst, not only for ad-hoc adaptation, but potentially for long-term change and improvement. The ‘old normal’ is now long passed, and approaches to learning and teaching continue to explore new ground. This article draws on the work of Built Environments Learning + Teaching (BEL+T), an academic group within the Faculty of Architecture, Building and Planning at the University of Melbourne. The BEL+T group applies creative problem-solving and design-led approaches, evidence-based research methodologies and project-focused consultancy to improve teaching quality and student engagement in built environment disciplines. The following sections introduce a learning design framework – the Delivery, Interaction, Assessment (DIA) framework – which was developed by BEL+T as a tool to communicate with and support staff throughout 2020 and 2021, and continues to be used to support teaching efforts. The translation of the elements of the DIA framework and its related ‘DIAgram’ to specific learning activities are presented in the following sections ‘on the (virtual) ground’. Some emergent pedagogies for virtual learning environments (VLEs) are outlined, exploring relationships between students, teachers, objects, sites and VLEs for learning, alongside implications for teacher presence and performance online. These key factors have influenced online approaches both before and since the onset of the pandemic. They deliver implications for emergent hybrid approaches such as dual delivery and blended synchronous learning, which are in turn driven by the needs of a still-distributed student cohort and the challenges of ongoing unpredictability

Motion-blurred Video Interpolation and Extrapolation

Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling. A broad range of research attempted to recover clean frames from blurred image sequences or temporally upsample frames by interpolation, yet there are very limited studies handling both problems jointly. In this work, we present a novel framework for deblurring, interpolating and extrapolating sharp frames from a motion-blurred video in an end-to-end manner. We design our framework by first learning the pixel-level motion that caused the blur from the given inputs via optical flow estimation and then predict multiple clean frames by warping the decoded features with the estimated flows. To ensure temporal coherence across predicted frames and address potential temporal ambiguity, we propose a simple, yet effective flow-based rule. The effectiveness and favorability of our approach are highlighted through extensive qualitative and quantitative evaluations on motion-blurred datasets from high speed videos.Comment: Accepted to AAAI 202

Analytic frameworks for assessing dialogic argumentation in online learning environments

Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchers’ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation