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| Meanwhile, the poor Babel fish, by effectively removing all barriers to communication between different races and cultures, has caused more and bloodier wars than anything else in the history of creation. ~ The Hitchhiker's Guide to the Galaxy |
One of the few things those of us involved in building human/computer interfaces know for certain is that the cognitive load of the interface matters. The greater the cognitive load, the longer it takes someone to complete a process or read content on the web page. The longer it takes someone to complete a process, the less likely they are to complete the process. Larger e-commerce companies with large datasets, like PayPal and eBay, know how likely a person is to complete the checkout process given how long it takes them.
Part of the avoidance of evaluating this last piece - how "readable" content is - is due to the fact that it's generally very difficult to assess "readability". In US English there are two formulas that can be used for evaluation: the Flesch Reading-Ease Score (FRES) and the Flesch-Kincaid Grade Level Formula. These two evaluative tools provide comparable scores for English language in other regions and have been pretty widely used; however, they do not apply to other languages.
The difficulty of assessing languages other than English presents unique challenges to assessing the accessibility of web pages with regard to internationalization. Because of my interest in both internationalization and accessibility, their convergence around another of my loves - language - makes this a very interesting topic for me...and, frankly, the main reason why I'm sharing this with you.
We tend to pay a lot of attention to the Flesch-Kincaid Grade Level Formula in the US, but that's not really a measure of accessibility, because it's not a measure of how easy or difficult it is to read, but a measure against a "typical" student's comprehension...and the whole idea of "typical" should be an anathema within the accessibility community. The Flesch Reading-Ease Score, on the other hand, is not a comparison of the content to other "typical" content, but a measure of the content itself, by analyzing the number of words and sentences and the number of syllables in the words used.
The number of syllables in word, in languages other than English, are not necessarily a measure of complexity, however. As a hypothetical example, if we compare the English (three syllable) phrase "take this drug" with the Spanish (nine syllable) phrase "tomar este medicamento", we get a FRES of 119.19 for English (which maps to roughly third grade, or an 8 year-old) and a FRES of -50.01 for Spanish, well beyond the reading ease of the Harvard Law Review. Even if we used the English phrase "take this medication", the FRES is still 34.59, a whopping 80 points greater than the Spanish phrase...or roughly the difference between fourth grade (US) and college graduate reading levels.
Granted, this is an extremely small, hypothetical example; however, there is simply no way to justify the difference between those two phrases. Even though many standards in the US specify readability requirements using the FRES, there must be a better way to calculate the readability, especially across languages.
If we look outside the US, and about ten years before the Flesch-Kincaid Grade Level Formula was developed, we find the Läsbarhetsindex. This formula is considerably easier to use than either Flesch-Kincaid readability tests - it's the number of words divided by the number of sentences plus the number of "long words" multiplied by one hundred and divided by the number of words. A "long word" in the Läsbarhetsindex is a word with more than 5 letters. The Läsbarhetsindex formula gives a score for "take this medication" of 36 and the score for "tomar este medicamento" is also 36.
While there is a general mapping of Läsbarhetsindex to educational levels in the author's native Sweden, such a mapping does not exist for other countries...which makes sense because every educational system is different. This, however, doesn't affect our use of this measure for accessibility, because as was mentioned before, for accessibility we need to evaluate the content, not necessarily compare the content to an educational level.
Although the definition of a "long word" may vary, depending on the language used, the remainder of the formula should work for measuring the readability of content for accessibility purposes.
To aid in your evaluation work, I've created a library that will soon be published via npm as roking-a11y. In the meantime - before it's published on npm - you can get the source in the roking-a11y repo on my GitHub profile.
I'm also looking into how else this can be used and extended. One of my plans is to perhaps modify the length of a "long word" based on a language code, and perhaps add guidance for specific score ranges. If you have ideas about how else this might be helpful, please leave a comment - I don't track issues or feature requests in GitHub. If you've done research in this area, I'd especially like to hear from you.
Happy coding.
We tend to pay a lot of attention to the Flesch-Kincaid Grade Level Formula in the US, but that's not really a measure of accessibility, because it's not a measure of how easy or difficult it is to read, but a measure against a "typical" student's comprehension...and the whole idea of "typical" should be an anathema within the accessibility community. The Flesch Reading-Ease Score, on the other hand, is not a comparison of the content to other "typical" content, but a measure of the content itself, by analyzing the number of words and sentences and the number of syllables in the words used.
The number of syllables in word, in languages other than English, are not necessarily a measure of complexity, however. As a hypothetical example, if we compare the English (three syllable) phrase "take this drug" with the Spanish (nine syllable) phrase "tomar este medicamento", we get a FRES of 119.19 for English (which maps to roughly third grade, or an 8 year-old) and a FRES of -50.01 for Spanish, well beyond the reading ease of the Harvard Law Review. Even if we used the English phrase "take this medication", the FRES is still 34.59, a whopping 80 points greater than the Spanish phrase...or roughly the difference between fourth grade (US) and college graduate reading levels.
Granted, this is an extremely small, hypothetical example; however, there is simply no way to justify the difference between those two phrases. Even though many standards in the US specify readability requirements using the FRES, there must be a better way to calculate the readability, especially across languages.
If we look outside the US, and about ten years before the Flesch-Kincaid Grade Level Formula was developed, we find the Läsbarhetsindex. This formula is considerably easier to use than either Flesch-Kincaid readability tests - it's the number of words divided by the number of sentences plus the number of "long words" multiplied by one hundred and divided by the number of words. A "long word" in the Läsbarhetsindex is a word with more than 5 letters. The Läsbarhetsindex formula gives a score for "take this medication" of 36 and the score for "tomar este medicamento" is also 36.
While there is a general mapping of Läsbarhetsindex to educational levels in the author's native Sweden, such a mapping does not exist for other countries...which makes sense because every educational system is different. This, however, doesn't affect our use of this measure for accessibility, because as was mentioned before, for accessibility we need to evaluate the content, not necessarily compare the content to an educational level.
Although the definition of a "long word" may vary, depending on the language used, the remainder of the formula should work for measuring the readability of content for accessibility purposes.
To aid in your evaluation work, I've created a library that will soon be published via npm as roking-a11y. In the meantime - before it's published on npm - you can get the source in the roking-a11y repo on my GitHub profile.
I'm also looking into how else this can be used and extended. One of my plans is to perhaps modify the length of a "long word" based on a language code, and perhaps add guidance for specific score ranges. If you have ideas about how else this might be helpful, please leave a comment - I don't track issues or feature requests in GitHub. If you've done research in this area, I'd especially like to hear from you.
Happy coding.
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