Summary. There are many advantages in having the computer individualise instruction by generating teaching material when it is needed, at a level which suits a pupil"s particular competence. This requires valid m...
Summary. There are many advantages in having the computer individualise instruction by generating teaching material when it is needed, at a level which suits a pupil"s particular competence. This requires valid models of task difficulty, and this paper describes such models and the experiments to validate them, for practice tasks in arithmetic computation. Using criteria of probability of success and rate of working for each column of an addition task in vertical format, analyses of variance of experimental data reveal main effects of digit size and number of rows. Following a formal development of the model a least squares analysis derives a function which, for the experimental data, relates those variables to the criteria. These are used by the computer to generate examples so that a pupil works at any specified level of success. Methods of implementation and decision making together with some preliminary results are given. These are extended by describing an experiment with subtraction tasks in which two competing models were used to describe pupils" success and working levels. These analyses and experiments show the complexity of the decision making which is needed for adaptive teaching and which exploit the computer"s capabilities. .overlined { text-decoration: overline; } .struck { text-decoration:line-through; } .underlined { text-decoration:underline; } .doubleUnderlined { text-decoration:underline;border-bottom:1px solid #000; } Enhanced Article (HTML) Get PDF (649K)Get PDF (649K) More content like thisFind more content: like this articleFind more content written by: PAT WOODS J. R. HARTLEY All Authors
A self-improving quadratic tutor comprising two principal components is described. One component is an adaptive teaching program where the teaching strategy is expressed as a set of production rules. The second compon...
In this work-in-progress paper, we report our experience of applying project-basedlearning (PjBL) in a web technology course, with a focus on how different feedback tactics affect students' learning outcomes. Giv...
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The design objective of the Leeds Transformation System is to transform existing programs, written in a variety of languages, into “tidier” programs. The total system was conceived of as having three phases: syntact...
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Scattered light is unavoidable during biomedical imaging, leading to downgraded images. Removing such scattered light, however, is challenging, because the imaging objects are complex and the light path is complicated...
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This practice paper introduces the project-based approach of the Center for project-basedlearning within the Department of Information Technology and Electrical Engineering of ETH Zürich. This center's appro...
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This work is largely concerned with teaching students how to plan and carry out statistical investigations. After outlining some of the problems encountered in conventional teaching, the contributions the computer can...
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive systems, their potential in 3D detection and tracking has been largely disregarded due to data sparsity and measurement noise. As a recent development, the combination of RADARs and cameras is emerging as a promising solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities, by incorporating the spatial and velocity information of the RADAR sensor. Experimental results demonstrate an absolute improvement in detection performance of 5.3% in mean Average Precision (mAP) and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between high-performance and cost-effective perception systems in autonomous driving, by capitalizing on the ubiquitous presence of RADAR in automotive applications. The code is available at: https://***/ETH-PBL/CR3DT.
Recent commentaries on developments in higher education suggest a paradox of continuing rapid growth in global demand against a background of increasing pressure on conventional resources. New groups of students are d...
Recent commentaries on developments in higher education suggest a paradox of continuing rapid growth in global demand against a background of increasing pressure on conventional resources. New groups of students are demanding flexibility and customisation, and are able to consider University and other HE providers with less concern for geographical location and more concern for their own schedules and priorities. New technologies offer some scope for meeting these demands within the new resource constraints. The idea of trading on current practice and existing tradition does not seem to be an option.
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