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    <title>DEV Community: NewGen Books</title>
    <description>The latest articles on DEV Community by NewGen Books (@newgenbooks).</description>
    <link>https://dev.to/newgenbooks</link>
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      <title>DEV Community: NewGen Books</title>
      <link>https://dev.to/newgenbooks</link>
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      <title>How we measure the real CEFR level of a book, and where it still fails</title>
      <dc:creator>NewGen Books</dc:creator>
      <pubDate>Tue, 03 Nov 2026 21:40:28 +0000</pubDate>
      <link>https://dev.to/newgen-books/how-we-measure-the-real-cefr-level-of-a-book-and-where-it-still-fails-1nba</link>
      <guid>https://dev.to/newgen-books/how-we-measure-the-real-cefr-level-of-a-book-and-where-it-still-fails-1nba</guid>
      <description>&lt;p&gt;Most graded readers have a level printed on the cover. Almost nobody can tell you how it was decided.&lt;/p&gt;

&lt;p&gt;We publish free books at &lt;a href="https://books.dev" rel="noopener noreferrer"&gt;books.dev&lt;/a&gt;, each in the original English and many in simpler editions, and we wanted a number on every edition that we could defend. Here is how our meter works today, how its first version failed, and what it still can't do.&lt;/p&gt;

&lt;h2&gt;
  
  
  Why a readability formula wasn't enough
&lt;/h2&gt;

&lt;p&gt;Classic formulas such as Flesch-Kincaid look at the length of words and sentences. We compared them with teachers' difficulty judgments on a public set of passages, and they underrate fiction: a novel can read as much easier than teachers find it. Short words can hide a lot. For a library of novels, that bias sits exactly where we need accuracy.&lt;/p&gt;

&lt;h2&gt;
  
  
  What the meter combines
&lt;/h2&gt;

&lt;p&gt;The score blends three signals:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;strong&gt;A CEFR text classifier&lt;/strong&gt; (weight 0.70)&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Vocabulary&lt;/strong&gt; (0.25): average word level, the level at which 95% of the text is covered, and rare lemmas per 1,000 words&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Syntax&lt;/strong&gt; (0.05): 90th percentile sentence length, parse tree depth, subordinate clauses&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The sum then passes through isotonic regression, a monotonic mapping to the final score. We show it with tenths, such as B1.5.&lt;/p&gt;

&lt;h2&gt;
  
  
  Version 1 was calibrated against itself
&lt;/h2&gt;

&lt;p&gt;The first version had hand-set weights, and its vocabulary and syntax parts were fitted to the classifier's own answers. Only two reference points had a known level. It looked fine until we tested it on exam texts with official levels: both C1 and C2 came out around B2.8. The scale was compressed at the top.&lt;/p&gt;

&lt;p&gt;Version 1.1 was fitted to texts of known level instead. The important decision was how to choose the weights. Cross-validating open corpora against each other picked almost pure classifier (0.95), which looks great on those corpora and generalizes worse. So we held out one exam board at a time, trained on everything else, and tested on the board the model had never seen. The mean absolute error on 200 unseen exam texts fell from 0.70 to 0.51 of a level.&lt;/p&gt;

&lt;p&gt;The calibration texts belong to their owners, so we keep them private and publish only our own results.&lt;/p&gt;

&lt;h2&gt;
  
  
  Exams are not novels
&lt;/h2&gt;

&lt;p&gt;Version 1.1 still had an open question: does a scale fitted on exam papers carry over to fiction? Against those teacher judgments it reached a Spearman correlation of 0.70, and it placed fiction 0.134 lower than non-fiction of equal difficulty. Version 1.2 adds that offset for fiction. It's a small correction, but it comes from a measurement rather than a hunch.&lt;/p&gt;

&lt;h2&gt;
  
  
  What it looks like on a real book
&lt;/h2&gt;

&lt;p&gt;&lt;em&gt;Alice's Adventures in Wonderland&lt;/em&gt; measures C1.2 in the original. Our adapted editions measure B2.1 and B1.5. A reader sees all three before choosing one.&lt;/p&gt;

&lt;h2&gt;
  
  
  What it still can't do
&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;strong&gt;It can't separate C1 from C2.&lt;/strong&gt; No version does, so C2 gets a bare label with no tenths rather than a false precision.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Short texts are noisy.&lt;/strong&gt; The error on a single short passage is about half a level.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;It measures the text, not the reader.&lt;/strong&gt; Background knowledge and interest change how hard a book feels, and no number on a page captures that.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you've worked on readability, I'd like to know what you would test next. The library is free at &lt;a href="https://books.dev" rel="noopener noreferrer"&gt;books.dev&lt;/a&gt;.&lt;/p&gt;

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      <category>nlp</category>
      <category>machinelearning</category>
      <category>showdev</category>
      <category>books</category>
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