A checklist used by teachers to assess elementary students’ interpretation skills. It can be modified to be used by students as a self-assessment or by peers to provide feedback.

Students learn about video motion capture technology, becoming familiar with concepts such as vector components, magnitudes and directions, position, velocity, and acceleration. They use a (free) classroom data collection and processing tool—the ARK Mirror—to visualize and record 3-D motion. The Augmented Reality Kinematics (ARK) Mirror software collects data via a motion detector. Using an Orbbec Astra Pro 3D camera or Microsoft Kinect (see note below), students can visualize and record a robust set of data and interpret them using statistical and graphical methods. This lesson introduces students to just one possible application of the ARK Mirror software—in the context of a high school physics class. Note: The ARK Mirror is ported to operate on an Orbbec platform. It may also be used with a Microsoft Kinect, although that Microsoft hardware has been discontinued. Refer to the Using ARK Mirror and Microsoft Kinect attachment for how to use the ARK MIrror software with Microsoft Kinect.

Dieser Kurs bietet eine Einführung in grundlegenden Algorithmen und Datenstrukturen.

Im Kurs enthalten sind Programmbeispiele in verschiedenen Programmiersprachen. Für deren Verständnis wird die Kenntnis der jeweiligen Sprache vorausgesetzt.

Ce cours est adressé à tous les acteurs des systèmes éducatifs qui voudraient s’investiguer dans le domaine de la planification ou de la gestion des systèmes éducatifs. Il comporte trois unités d’enseignement qui vont de l’identification des critères de bon indicateur, les différentes catégories d’indicateurs de l’éducation, leurs calculs et leurs interprétations. L’acquisition de ces connaissances permettront à l’apprenant de pouvoir analyser les différents systèmes éducatifs. Ce cours est dispensé exclusivement en ligne sur la Plateforme Moodle. Un tutorat est assuré pour offrir aux apprenants différents niveaux de soutien et de permettre une collaboration entre les apprenants. Une évaluation finale sur table sera faite pour s’assurer des connaissances réellement acquises.

Here you can access PostScript/PDF and HTML versions of D.J. Arnold, Lorna Balkan, Siety Meijer, R.Lee Humphreys and Louisa Sadler Machine Translation: an Introductory Guide, Blackwells-NCC, London, 1994, ISBN: 1855542-17x.

Computation has produced new and exciting ways of studying texts. Many of these methods do not require the use of expensive programs or detailed programming knowledge, but only the know-how to combine freely accessible resources to perform various tasks. This site describes common or interesting sequences of actions, or recipes. They are organized according to the objective of the recipe. Recipes fall into the three major categories of location and identification of ideas, themes or specific terms; analysis of textual devices or themes; or the construction of new entities or corpora. The Methods Commons community benefits from shared experience and learning how others make use of recipes. You can share your experience by adding your own recipes to the collection. More information about recipe and exercise structure and authoring is available on the Recipe Structure page. We also have a Glossary that we hope you will add to.

This is the material from the UIMA tutorial held in conjunction with the 3rd UIMA@GCSL workshop.

Your list is now clean enough that you can begin analyzing its contents in meaningful ways. Counting the frequency of specific words in the list can provide illustrative data. Python has an easy way to count frequencies, but it requires the use of a new type of variable: the dictionary. Before you begin working with a dictionary, consider the processes used to calculate frequencies in a list.

This lesson uses Python to create and view an HTML file. If you write programs that output HTML, you can use any browser to look at your results. This is especially convenient if your program is automatically creating hyperlinks or graphic entities like charts and diagrams.

Here you will learn how to create HTML files with Python scripts, and how to use Python to automatically open an HTML file in Firefox.

In this two-part lesson, we will build on what you’ve learned about Working with Webpages, learning how to remove the HTML markup from the webpage of Benjamin Bowsey’s 1780 criminal trial transcript. We will achieve this by using a variety of string operators, string methods and close reading skills. We introduce looping and branching so that programs can repeat tasks and test for certain conditions, making it possible to separate the content from the HTML tags. Finally, we convert content from a long string to a list of words that can later be sorted, indexed, and counted.

In this lesson, you will learn the Python commands needed to implement the second part of the algorithm begun in the From HTML to a List of Words (part 1). The first half of the algorithm gets the content of an HTML page and saves only the content that follows the tags.

In this lesson you will first learn what topic modeling is and why you might want to employ it in your research. You will then learn how to install and work with the MALLET natural language processing toolkit to do so. MALLET involves modifying an environment variable (essentially, setting up a short-cut so that your computer always knows where to find the MALLET program) and working with the command line (ie, by typing in commands manually, rather than clicking on icons or menus). We will run the topic modeller on some example files, and look at the kinds of outputs that MALLET installed. This will give us a good idea of how it can be used on a corpus of texts to identify topics found in the documents without reading them individually.

Like in Output Data as HTML File, this lesson takes the frequency pairs collected in Counting Frequencies and outputs them in HTML. This time the focus is on keywords in context (KWIC) which creates n-grams from the original document content – in this case a trial transcript from the Old Bailey Online. You can use your program to select a keyword and the computer will output all instances of that keyword, along with the words to the left and right of it, making it easy to see at a glance how the keyword is used.

Once the KWICs have been created, they are then wrapped in HTML and sent to the browser where they can be viewed. This reinforces what was learned in Output Data as HTML File, opting for a slightly different output.

At the end of this lesson, you will be able to extract all possible n-grams from the text. In the next lesson, you will be learn how to output all of the n-grams of a given keyword in a document downloaded from the Internet, and display them clearly in your browser window.

This lesson takes the frequency pairs created in Counting Frequencies and outputs them to an HTML file.

Here you will learn how to output data as an HTML file using Python. You will also learn about string formatting. The final result is an HTML file that shows the keywords found in the original source in order of descending frequency, along with the number of times that each keyword appears.

This lesson builds on Keywords in Context (Using N-grams), where n-grams were extracted from a text. Here, you will learn how to output all of the n-grams of a given keyword in a document downloaded from the Internet, and display them clearly in your browser window.

This lesson shows how to use Python to transliterate automatically a list of words from a language with a non-Latin alphabet to a standardized format using the American Standard Code for Information Interchange (ASCII) characters. It builds on readers’ understanding of Python from the lessons “Viewing HTML Files,” “Working with Web Pages,” “From HTML to List of Words (part 1)” and “Intro to Beautiful Soup.” At the end of the lesson, we will use the transliteration dictionary to convert the names from a database of the Russian organization Memorial from Cyrillic into Latin characters. Although the example uses Cyrillic characters, the technique can be reproduced with other alphabets using Unicode.