Learning With Texts Download ##TOP##
Lauro Beriault
Full disclosure: This post contains affiliate links. ?Please note Learning With Texts is no longer hosted at Fi3M, though it has been continued as a SourcForge project. We also recommend LingQ as an alternative with similar features.
I have Windows 10
I installed the Easy PHP Devserver 16.1.1 as they told to do with Windows 10. Maybe I should have got the 14...
I installed the ZIP file
I moved it in the right folder and unzipped it in the \www directory and renamed the lwt_v_whatever to lwt
I renamed the file connect_easyphp.inc.php to connect.inc.php.
I started PHP. The app icon appeared in the task bar.
I want to start the LWT and right-click on the PHP icon.
I've been using learning with texts for some time now and find it a great application. However, I'd like to be able to use it on the go. Has anyone had any success deploying it to a remote server, on heroku for example? I'd like to migrate my local db too, so I can just get started on the remote server where I am now. Thanks!
I've developed a small tool, Lute ("Learning Using Texts"): a free, open source PHP-Apache-MySQL project for learning languages through reading that you install on your personal machine. Here's a brief demo.
Basically, you load texts (and audio) into a database (copy/paste from any source), and every word of your text is displayed according to its "learnt" status, based on texts you've read previously. Click on one or more words, and you can search them in up to 3 online dictionaries, set their status, etc.
I had such a problem, It was not due to LWT but to the webhoster (000webhost.com). Some statistics tracking code had been inserted into the page, with the result that my site was so slow that it was all but unusable. After I disabled that tracking code, everything was ok. You may not have such a problem with another hoster or, obviously, if you run a local server.
I tried LWT a few months ago and found manually segmenting the text to yield seriously diminishing returns (especially when I wasn't familiar enough with the text to know exactly where I should be segmenting).
The solution was to use something automated that would segment the text and insert spaces between words (I use DimSum). DimSum's segmentation isn't perfect but one of the nicer things about LWT is that you can manually choose which characters make up the "word/phrase" you are interested in learning.
use a program like Wenlin, use "Make transformed copy" > "Segment Hanzi", and replace the " " with a single space. Now import the result into LWT (while setting the LWT language option "Make each character a word" to No).
set the LWT language option "Make each character a word" to Yes and import a not too big dictionary with the most important words (max. 10,000 to 20,000 words/expr.) like the list of the (new) HSK6 words or the big list of the highest level of the old HSK. e,g, from here: -- Now the most important words are automatically recognized by LWT. If you unset SHOW ALL you will not see the single characters and the numbers of the multi-character expressions.
I have installed XAMPP, EasyPHP and MAMP as local "web" servers with the default settings. These personal web servers are only accessible in your local network (behind your router's firewall) so there is little or no risk if your firewall is configured correctly. It is difficult to say something about security risks in YOUR environment. Please refer to the help pages and forums of the package you have installed.
I can't speak for LWT, but for CTA at least it can be used as a feeder program to something like Anki - that is, it can be used to analyse Chinese texts and then generate lists of words you should learn. You then you can use Anki to do the actual studying/revision of those words.
Forget it. I've found a standalone tool that copies LWT perfectly and provides no security risk, as you don't have to run a server to use it. It also happens to highlight words with colors, so no complaints anymore.
The great thing about specialist and enthusiast conferences is the confluence of similar minds. Through socialising with others, we learn as much from fellow attendees as from speakers. And so, it was through a chance encounter with a new conference friend that I learnt about Learning With Texts, a free, browser-based software for learning foreign languages through reading.
There is some indication that people differ regarding their visual and verbal cognitive style. The Object-Spatial Imagery and Verbal Questionnaire (OSIVQ) assumes a three-dimensional cognitive style model, which distinguishes between object imagery, spatial imagery and verbal dimensions. Using eye tracking as a means to observe actual gaze behaviours when learning with text-picture combinations, the current study aims to validate this three-dimensional assumption by linking the OSIVQ to learning behaviour. The results largely confirm the model in that they show the expected correlations between results on the OSIVQ, visuo-spatial ability and learning behaviour. Distinct differences between object visualizers, spatial visualizers and verbalizers could be demonstrated.
Welcome to a new series where we feature great books for language learners and teachers. This month we are featuring the book, Academic Reading Circles, by Tyson Seburn (@Seburnt). We caught up with author, Tyson Seburn, who shared these thoughts about the book:
Academic Reading Circles (ARC) is an intensive reading approach whose components work on the basis that language learners develop deep textual comprehension better through initial collaboration than if tackled alone. L1 readers take for granted the many aspects of a text that combine together to create meaning, but language learners do not. In ARC, they engage with a text through different lenses that draw attention to specific types of information (see graphic below), and they co-construct knowledge discovered from these lenses for a clearer overall picture of the meaning and significance of the text. These lenses provide learners with focussed tasks to accomplish while reading individually, which come together during collaborative in-class group work. ARC prepares learners to gain more from their texts, which in turn enables them to utilise these texts better in their coursework. ARC is the result of adaptations to an existing reading skills framework, research into reading strategies, and a great deal of trial and error. At its core, it is what it claims to be: a group of readers circled around a common text used for an academic purpose.
We also use third-party cookies that help us analyze how you use this website, store your preferences, and provide the content and advertisements that are relevant to you. These cookies will only be stored in your browser with your prior consent.
The color-coding really helps to organize my attention to words as I read. Strings of unhighlighted words should be no problem. Red words are those I previously thought were too obscure to bother learning. Green words are newly learned ones. Yellow and orange are prime candidates for learning now!
Contextual input, such as learning with texts or movies, makes it easier for us as learners to digest and remember words. We can also internalize grammar through repetition without the same boredom that tags alongside workbook drills.
You can also change your reading settings, which are found under the ellipses button. Reader settings include:
- Font size
- Font style
- Page width
- Line spacing
- Highlighting settings
- Widgets and sidebar settings
- Text-to-speech options
In contrast to studies using word generation, studies using text generation have failed to produce a learning benefit consistently and reliably (e.g., Einstein et al., 1990; Maki et al., 1990, Exp. 2; McDaniel et al., 1986, Exp.1, 2002, Exp. 2a; Schindler et al., 2017; Thomas & McDaniel, 2007, Exp.1). These inconsistent findings suggest that text generation is not necessarily beneficial for all learners and under all circumstances, and thus raise the question of contextual factors and conditions that possibly moderate the occurrence and magnitude of the text generation effect.
One framework addressing this question is the contextual framework by McDaniel and Butler (2011; see also Einstein et al., 1990; McDaniel & Einstein, 1989, 2005). It comprises the ideas of material appropriate processing (MAP, McDaniel et al., 1986; McDaniel & Einstein, 1989; see also Einstein et al., 1984) and transfer appropriate processing (TAP, Morris et al, 1977) and describes desirable difficulties such as text generation as the result of a complex interaction of learning material, difficulty intervention, learning assessment tasks, and learner characteristics. According to the framework, desirable difficulties can be expected to have an additional value for learning only if they stimulate cognitive processes that are relevant for learning (and for the specific learning test) and are not already stimulated by the learning material or initiated by the learners themselves.
Although these meta-analyses included only studies with simpler study materials such as words, sentences, or numbers, similar findings (i.e., lower effect sizes for free recall compared to recognition and cued recall) might occur for text generation (although it is noteworthy that recognition tasks are rarely used in text generation studies).
The general purpose of the present study was to provide a systematic quantitative review of the text generation effect. First, we sought to provide an estimate of the magnitude of the text generation effect based on all available studies. Second, we aimed to examine whether occurrence or magnitude of the text generation effect varies as a function of study and sample characteristics, characteristics of the intervention and the texts used for learning, and characteristics of the learning test. Identifying moderators of the text generation effect is important for assessing its generalizability and for practical applications of generation in educational settings. Moreover, some of the moderators included in the meta-analysis, in particular text genre, generation task, and their interaction, are relevant for theoretical accounts of the generation effect such as the contextual framework and the material appropriate processing framework (McDaniel & Einstein, 1989; McDaniel et al., 1986). Therefore, a third aim was to examine the genre-by-generation task interaction which is of particular theoretical relevance for the text generation effect.
df19127ead