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Scalable Vocabulary


Simple English is the attempt to produce information with less complex words and sentence structure. The idea of simple English is to make passages easier to understand. Simple text preserves content while simplifying the other features. Simple text can be created automatically, such as having a computer try and find synonyms for unusual words that are more commonly used. Simple text can also be created by a person going through a passage and re-writing it using more basic English.


Simple English has a large subjective component, and can be difficult to judge objectively. Simplified text contains less uncommon words, has more repetition and idea overlap, less compound sentences, shorter sentences, more concrete words, and less connector words than standard passages (Crossley et al., 2007; Crossley & McNamara, 2008).


Simple text has been shown to increase reading comprehension in MID students (Fajardo et al, 2014) and in students learning English (Yano, Long, & Ross, 1994; Oh, 2001; Li, Wang, & Xu 2005). There is some concern simplified texts produce poor comprehension of more advanced skills, such as inferencing (Oh, 2001) and can teach students bad reading habits on unnatural texts (Honeyfield, 1977). However, there is also opposite evidence, as simple text has also shown to increase inferential skills (Li, Wang, & Xu, 2005). A common speculation is that while simple text increases readability, students don’t learn as much vocabulary. However, the opposite is true; vocabulary develops well in simple passages, speculated to be because of the repetition of keywords and accessibility (Yano, Long, and Ross, 1994). Students with reading difficulties are also more motivated to read simple texts and prefer them to normal passages (Chiang, 2016).


Simplification is usually compared to elaboration passages, or “hard” passages. Both simple passages and hard passages show comprehension advantages over normal text (Oh, 2001). There is also strong interaction effects between text difficulty and reader skills (Crossley, 2016). For example, simplification is generally superior when the reader is at a low level (Fajardo et al, 2014) and provides limited advantage to strong readers (Li, Wang, & Xu, 2005).


Text auto-simplifier technology has not been studied, and the information cited here is non-scientific experimentation gathered by this website’s own team. This passage is not peer reviewed and should be interpreted with caution.


It is highly recommended to use human-written simple English over computer generated. Computer simplified English tends to make errors when converting, especially with field specific terms (E.g. Krebs Cycle is often translated into Krebs Bicycle). The simple English Wikipedia offers human written, well edited passages that use simple and clear language. Because the field of human-produced simple text is well documented, this is by far the most empirically supported.


Automatic simplifiers may be useful for instructors attempting to simplify reading passages as part of an accommodation. Simplifiers which provide built in annotations, or simply identify tricky words are preferable to those that change passages.


Research Rating: The research on scalable vocabulary is peer-reviewed, experimental, more than one study. However, auto-text simplification technology is not peer reviewed. 



  • Makes content accessible

  • Human created content is very effective

  • Shows to increase comprehension


  • Automatic simplifiers are not tested, and often don’t preserve meaning

  • Human created content is laboursome for the instructor or, in the case of Wikipedia, open to edit (and therefore has all the general risks of using wikis)

To Consider

           Exactly how much simplified text effects comprehension is still in discussion. Both simplified and enriched text profied comprehension and learning advantage over plain English (Chiang, 2016). Because of this, the recommended use is to offer both simple and challenging tasks, with more simple tasks as these are more rewarding, especially during early learning.

           Simplified texts have consistently demonstrated a greater retention of surface facts (literal readings) as well (Crossley, 2016; Yano, Long, & Ross, 1994). Because of this, simple text is an excellent starting point for research and brainstorming. Finally, the weaker the reader, the simple text will benefit them. Simple text is not recommended for average to stronger readers who struggle with making advanced connections.

Special Consideration: Workflow

OS Compatibility
Internet Reliance
Optimized Use

Exact prices change frequently, which is why only approximate ranges are listed. 

$ - Under $5

$$ - Between $6 and $50

$$$ - Between $51 and $250

$$$$ - Over $250



Crossley, S. A., McNamara, D. S., (2016). Text-based recall and extra-textual generations resulting from simplified and authentic texts. Reading in a Foreign Language, 28(1), 1-19. 02642425


Chiang, M. (2016). Effects of varying text difficulty levels on second language (L2) reading attitudes and reading comprehension. Journal of Research in Reading, 39(4), 448-468. 10.1111/1467-9817.12049


Fajardo, I., Ávila, V., Ferrer, A., Tavares, G., Gómez, M., & Hernández, A. (2014). Easy‐to‐read texts for students with intellectual disability: Linguistic factors affecting comprehension. Journal of Applied Research in Intellectual Disabilities, 27(3), 212-225. 10.1111/jar.12065


Honeyfield, J. (1977). Simplification. TESOL Quarterly, 11, 431–440.


Oh, S. (2001). Two Types of Input Modification and EFL Reading Comprehension: Simplification versus Elaboration. TESOL Quarterly, 35(1), 69. doi:10.2307/3587860


Li, Y., Wang, Q., Xu, S. (2005). The effects of simplified and elaborated texts on second language reading comprehension: an exploratory study. Vigo International Journal of Applied Linguistics, 2, 45-74. ISSN 1697-0381.


Yano, Y., Long, M. H., & Ross, S. (1994). The Effects of Simplified and Elaborated Texts on Foreign Language Reading Comprehension. Language Learning, 44(2), 189-219. doi:10.1111/j.1467-1770.1994.tb01100.x


Written by Francis Wall, Last Revision January 2018

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