![]() The interface certainly does make the connection to the original scan (which essentially becomes your editing reference material) abundantly clear. The idea here is that because the live notation will often have some errors, a manual tidy‑up job (or at least a check) will nearly always be required. At the top of the window the corresponding stave of the scan appears in a much shallower, fixed‑size strip. Unexpectedly (at first) much of it will be greyed out, and only ever a single stave, where your mouse is pointing, appears with normal contrast at one time. Working on a ‘read’ score page, the live notation data takes up the majority of the application window. Drag and drop from the OS is not supported, and while there is an overall logic to it all it’s far from intuitive. The function of the mouse‑overs and tickboxes overlap strangely, and there are inconsistencies like being able to easily change the page order of Scans via mouse drags, but not Scores, where you might really need it. I say ‘should’ because there’s room for a lot of improvement here. Mouse‑over commands in the scans list initiate character recognition, and once complete (a matter of perhaps 5‑10 seconds per page in Printed score mode) the Scores list beneath fills up with editable notation documents. Then, conceptually, it should all be very straightforward. Here your pages exist largely in an unanalysed state, except for an initial assessment of how many systems and staves each contains: user‑modifiable, albeit with some very specific mouse techniques. Whichever way you work you’ll end up with content in the Scans panel. However, if a flatbed is a bit too 1990s for you, scores can be imported as PDFs or graphics files too, and I did quite a bit of my testing with an iOS document scanning app. Pages can also be rotated at a later point in the workflow in case of mistakes or recalcitrant bindings. The system can handle multi‑page scores, and there’s no particular need to scan them dead straight. PhotoScore offers you tips about optimum scanning settings, and these are of absolutely vital importance if you’re to enjoy success. (Not that I had performed a flatbed scan, I realised, for perhaps half a decade before undertaking this review). Clicking the Scan Pages button opens a floating window that in Mac OS, at least, gives you familiar OS‑level functionality. Most work in PhotoScore centres around its single main window. The potential time savings are huge: how is it in practice? Scans & Scores ![]() The software has some editing and basic engraving/printing facilities of its own, too, so can stand alone for many jobs. That can be passed on to Sibelius (directly), to other notation applications via MusicXML or NIFF export, or to your DAW as MIDI. It then extracts pitch, rhythm, lyrics, guitar tab and more, turning it into live notation data. ![]() The software provides a flatbed scanning interface, imports PDFs and other graphics files, and accepts handwritten symbols through touch‑enabled tablets and laptops. That’s where PhotoScore & NotateMe Ultimate, the subject of this review, comes in: it’s essentially optical character recognition (OCR) for music. The ease with which beautifully proportioned, accurately laid‑out scores can be generated and manipulated would have been unthinkable even 50 years ago.Īt the same time, they can be horribly labour‑intensive, and copying existing printed music is particularly tedious. Let Neuratron take the misery out of copying musical notation.Įngraving and scoring applications like Sibelius, Musescore, Dorico and Finale are one of the wonders of the modern age for musicians. There are tiny errors in the live notation (like the missing grace note next to the highlighted note), easy to spot against the display of the original scan at the top, but overall the accuracy is excellent. It took about 5 seconds to generate this detailed, clean notation from a printed original scan. ![]()
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Collaborative Learning: Differences Between Collaborative and Cooperative Learning: The Office of Instructional Consultation, University of California Santa Barbara.(Retrieved 18/2/07). Marshall, Understanding Media: The Extensions of Man: McGraw Hill, New York. Corporate training department Promote learning Central routing for training records Exchange information Contact us: Phone: N 19th St, 1st Floor, Tampa, FL. Paper presented at the The ICEL 2009, UiTM,Shah Alam, Malaysia. Comparison of Three Collaborative Learning Software for Secondary School. ![]() ![]() Paper presented at the Global Learn AsiaPacific 2010, Penang, Malaysia. Collaborative Learning Software for Secondary School: ATutor. Lotfi, Z., Gazerani, N., & Md Nasaruddin, F.H. Paper presented at the The 23rd AAOU Annual Conference of Open& Distance Learning, Tehran, Iran. Collaborative Learning for Secondary School: A Study on Important Features for Learners and Teachers at Secondary Schools. The role of the tutor in computer-based collaborative learning situations. Journal of Technology Education, 7, 22-30. Students who are interested in organic synthesis, medicinal chemistry, chemical biology, biochemistry, molecular biology, and enzymology, or interdisciplinary research, will enjoy research in the Guo Lab. Collaborative learning enhances critical thinking. Thus, they can learn and master a wide scope of knowledges and techniques in chemistry and biology. Collaborative learning: Cognitive and computational approaches, 1-19. What do you mean by collaborative learning. for a multiplicity of functions in the pedagogical process: ATutor, Merlot, and. References View References & Citations Mapĭillenbourg, P. According to Cozzi and Galli, prior to 1980, universities undertook. Educators quickly assemble, package, and redistribute standardized Web-based instructional content. ATutor's functionality is easily extended with feature modules. Stem Cell Summit during DECEMBER 6-9, 2016, WEST PALM BEACH, FLORIDA, USA. © 2010 Association for the Advancement of Computing in Education (AACE) Open Source Web-based free LMS, that develops and delivers online courses in minutes. Project Manager, Biomedical Research Center, Qatar University (May 2015. Orlando, Florida, USA: Association for the Advancement of Computing in Education (AACE). Zhang (Eds.), Proceedings of E-Learn 2010-World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. Collaborative Learning for Secondary School: Lotfi VCL Design. ![]() The findings gathered and analyzed in Educational Complex of Imam Khomeini Kuala Lumpur were found to satisfy the involvement of collaborative learning at home. The data collected was then subjected to statistical analysis. The instruments used to gather data were interviews and a survey which undertook the completion of two questionnaires for both learners and teachers before implementing the software. The proposed model gives an application of the tools and features to do group projects, group assessments and online classes. The purpose of the study was to design “Lotfi VCL” (Lotfi Virtual Collaborative Learning), particularly on Secondary School and Pre University learners’ participation. This research offers collaborative learning in a good learning environment to do homework, projects and assessments. Therefore consideration must be given to the development of collaborative activities outside the classrooms, which enables students to learn together. 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