Teachers' Library

A slew of tools make it easier than ever for students and educators to have conversations in the margins of the texts they’re reading. The other day I pulled out an old family cookbook and discovered several handwritten sticky notes scattered in the margins. These notes—written by me or my wife, or our relatives—included variations in cooking times (we live at a high elevation in Colorado, where cooking times do vary), ingredient substitutions (applesauce can replace sugar in brownies?), and other modifications. Family cookbooks are an example of social annotation, a practice of collaborative notetaking that feels tailor-made for digital spaces even though it’s quite old. I recently spoke with Jeremiah (Remi) Kalir, an assistant professor at the University of Colorado, Denver, about digital and web annotation. Kalir coauthored an upcoming book on this topic called Annotation with Antero Garcia, an assistant professor at Stanford University. As the book recounts, annotation is a centuri...

Tips for bringing high-interest, culturally relevant books into your classroom on a modest budget. During my time as a high school teacher, one of the greatest challenges was keeping my classroom library fresh—the process of acquiring the latest books while also replacing lost and worn-out ones. My school supplied only modest funding for this, so if teachers wanted to have the newest high-interest books in our classrooms, we had to accomplish this task mostly on our own—a scenario that, unfortunately, isn’t out of the ordinary. The urban public school where I worked did manage to keep a steady supply of books, but book deserts are a sad reality at under-resourced public schools where libraries have been closed and funding cuts have reduced the flow of independent-reading books into classrooms. Jarred Amato, an English teacher at a public high school in Nashville, made it a mission to greatly expand his classroom library several years ago. His quest—which was ultimately successful—was t...

Our AI writing scoring uses a technology called latent semantic analysis. LSA is a natural language processing technique that can analyze and score writing, based on the meaning behind words – and not just their superficial characteristics. Similarly to our speech recognition acoustic models, we first establish a language specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. Once the language model has been established, we then train the engine to score every single written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human marked items, and check that the machin...

Automated assessment, including the use of Artificial Intelligence (AI), is one of the latest education tech solutions. It speeds up exam marking times, removes human biases, and is as accurate and at least as reliable as human examiners. As innovations go, this one is a real game-changer for teachers and students. However, it has understandably been met with a lot of questions and sometimes scepticism in the ELT community – can computers really mark speaking and writing exams accurately? The answer is a resounding yes. Students from all parts of the world already take AI graded tests. PTE Academic and Versant tests – for example – provide unbiased, fair and fast automated scoring for speaking and writing exams – irrespective of where the test takers live, or what their accent or gender is. This article will explain the main processes involved in AI automated scoring and make the point that AI technologies are built on the foundations of consistent expert human judgements. So, let’s cl...