The Effect of Computer-Assisted Learning on Students’ Long-Term Development

2019 ◽  
Author(s):  
Nicola Bianchi ◽  
Yi Lu ◽  
Hong Song
Keyword(s):  
Computer Assisted ◽  
Computer Assisted Learning

Effectiveness of computer‐assisted learning in sports dentistry: Studies over a multiple‐year period and at two universities

2021 ◽  
Author(s):  
Nana Shiota ◽  
Atsuhiro Kinoshita ◽  
Masayo Sunaga ◽  
Gen Tanabe ◽  
Kairi Hayashi ◽  
...  
Keyword(s):  
Computer Assisted ◽  
Computer Assisted Learning

scholarly journals Computer-assisted sign language translation: a study of translators’ practice to specify CAT software

2021 ◽  
Author(s):  
Marion Kaczmarek ◽  
Michael Filhol
Keyword(s):  
Sign Language ◽  
Language Translation ◽  
Computer Assisted ◽  
Translation Process ◽  
Sign Languages ◽  
Subjective Data ◽  
Do So

AbstractProfessional Sign Language translators, unlike their text-to-text counterparts, are not equipped with computer-assisted translation (CAT) software. Those softwares are meant to ease the translators’ tasks. No prior study as been conducted on this topic, and we aim at specifying such a software. To do so, we based our study on the professional Sign Language translators’ practices and needs. The aim of this paper is to identify the necessary steps in the text-to-sign translation process. By filming and interviewing professionals for both objective and subjective data, we build a list of tasks and see if they are systematic and performed in a definite order. Finally, we reflect on how CAT tools could assist those tasks, how to adapt the existing tools to Sign Language and what is necessary to add in order to fit the needs of Sign Language translation. In the long term, we plan to develop a first prototype of CAT software for sign languages.

The Finkleman Preparation: A Computer Simulation for Teaching Undergraduate Students of Pharmacology

1994 ◽  
Vol 22 (6) ◽  
pp. 474-480
Author(s):  
David Dewhurst ◽  
Ian Hughes ◽  
Richard Ullyott
Keyword(s):  
Undergraduate Students ◽  
Teaching And Learning ◽  
Learning Objectives ◽  
Computer Assisted ◽  
Learning Program ◽  
Computer Assisted Learning ◽  
Sympathetic Nerve Stimulation ◽  
Chart Recorder ◽  
Experimental Protocols ◽  
Evoked Contractions

An interactive computer-assisted learning program is described, which simulates a number of experiments which can be performed on the isolated, innervated duodenum of the rabbit (the Finkleman preparation). This preparation is one of the classical pharmacological preparations used to demonstrate to undergraduate students the effects of selected drugs: those acting on adrenoceptors or intestinal smooth muscle, or those affecting responses to sympathetic nerve stimulation. The program runs on any IBM compatible PC, and makes use of text and high resolution graphics to provide a background to the experiments and to describe the methodology. A screen display which emulates a chart recorder presents simulated results (spontaneous or evoked contractions of the gut), derived from actual data, in response to the selection by students of predetermined experimental protocols from a menu. The program is designed to enhance or replace the traditional laboratory-based practical using this preparation, whilst achieving the majority of the same teaching and learning objectives.

scholarly journals A Century of French Railways: The Value of Remote Sensing and VGI in the Fusion of Historical Data

2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Robert Jeansoulin
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Computer Assisted ◽  
Railway Network ◽  
Reconstruction Process ◽  
Railway Stations ◽  
Eu Member States ◽  
Public Data ◽  
Public Datasets

Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago.The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually. VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) three-steps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for ‘gares’ where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset—in geojson format under open license—encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before. This work demonstrates: (a) it is possible to reconstruct transport data from the past, at a national scale; (b) the value of remote sensing and of VGI is considerable in completing public sources from an historical perspective; (c) data quality can be monitored all along the process and (d) the geocoded outcome is ready for a large variety of further studies with statistical data (demography, density, space coverage, CO2 simulation, environmental policies, etc.).

Effects of timing of formative feedback in computer‐assisted learning environments

2020 ◽  
Vol 36 (5) ◽  
pp. 718-728
Author(s):  
Carmen Candel ◽  
Eduardo Vidal‐Abarca ◽  
Raquel Cerdán ◽  
Marie Lippmann ◽  
Susanne Narciss
Keyword(s):  
Learning Environments ◽  
Formative Feedback ◽  
Computer Assisted ◽  
Computer Assisted Learning
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