Dissemination of IT for the Promotion of Materials Science (DoITPoMS) is a web-based
educational software resource designed to facilitate the teaching and learning of
Materials science, at the tertiary level for free.[1][2]
The FDTL project was aimed at building on expertise concerning the use of
Information Technology (IT) to enhance the student learning experience and to disseminate these techniques within the Materials Education community in the UK and globally.[7][8] This was done by creating an archive of background information, such as video clips, micrographs, simulations, etc, and libraries of
teaching and learning packages (TLPs) that covers a particular topic, which were designed both for independent usage by students and as a teaching aid for educators. A vital feature of these packages is a high level of user interactivity.[9][1]
DoITPoMS has no commercial sponsors and no advertising is permitted on the site.[10][11] The background science to the resources within DoITPoMS has all been input by
unpaid volunteers, most of whom have been academics based in universities. A single person retains responsibility for a particular resource, and these people are credited to the site.[1][10] While the logo of
University of Cambridge does appear on the site, is content is available freely and licensed under
CC BY-NC-SA 2.0 UK.[12][6]
Format and usage
A small subset of the 900 images in the micrograph library[13]
The set of resources currently available on the site comprises Libraries of TLPs (~75), Micrographs (~900), Video clips (~150), Lecture demonstration packages (5), and Stand-alone simulations (2).[6] These all have slightly different purposes, and the modes of usage cover a wide range.[14][10] In each TLP, several simulations typically allow the user to input data to visualise the characteristics of particular effects or phenomena. This to enable students to explore areas in their way and facilitates the creation of exercises by educators.[15][16] Each TLP has a set of questions at the end, designed to test whether the main points of the TLP have been understood.[4][1][17]
^Warmuzek, M (2021). "Application of the convolutional neural network for recognition of the metal alloys microstructure constituents based on their morphological characteristics". Computational Materials Science. 199: 110722.
doi:
10.1016/j.commatsci.2021.110722.