Simplifying Language¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 11 studies (4 causal, 3 review, 2 quant-synthesis, 2 theoretical), q1–q4 · 2 of 11 report an effect size

Description¶

Simplifying language means rewriting instructional materials — text, narration, instructions, and assessment items — so that the linguistic surface demands less of the learner: shorter sentences, common vocabulary, explicit connectives, and one idea per sentence. The goal is to free working memory for the actual content rather than for decoding prose. Simplification targets the language wrapper around content, not the conceptual difficulty of the content itself.

Design Implications¶

Simplifying language reduces extraneous cognitive load imposed by dense syntax and unfamiliar vocabulary, allowing learners to allocate resources to constructing meaning Cognitive overload degrades learning. [+S]. However, wholesale simplification can backfire: texts that are too easy or too coherent can reduce the effortful processing that drives deep learning, particularly for learners with relevant prior knowledge [~M]. The most robust finding is that targeted simplification — clarifying syntax and vocabulary while preserving conceptual challenge — outperforms both dense prose and oversimplified text [~S].

Context¶

Requirements¶

• A readability baseline (e.g., sentence length, vocabulary frequency) and a defined target audience
• Retention of technical terms where they are the learning goal, with definitions on first use
• Consistent terminology — one concept, one word — across materials (Clear Structure)
• Review by someone who did not write the text, to catch unstated assumptions

Constraints¶

• Over-simplified text can lower engagement and reduce inference-making for knowledgeable readers; coherence effects reverse with expertise [~M]
• Simplifying away all technical vocabulary prevents learners from acquiring the discipline's language, harming later reading and communication [-M]
• Simplified language does not compensate for missing background knowledge; vocabulary support without Activating Prior Knowledge yields limited gains [-M]
• Machine or formulaic simplification can distort meaning or strip necessary qualifiers, especially in technical or legal content [-W]

Implementation Variability¶

• Plain-language rewriting: short sentences, active voice, common words — suited to instructions, interfaces, and assessment items
• Scaffolded complexity: begin with simplified text, then introduce authentic disciplinary prose as proficiency grows
• Bilingual/ESL adaptation: simplified syntax plus glossaries and visuals, rather than reduced content
• Simplification of directions only: leave content texts intact but simplify task instructions and rubrics

Target Learners¶

• Beginning readers, language learners, and learners with reading-related disabilities, for whom decoding costs otherwise crowd out comprehension Automatic word recognition frees resources for comprehension. [+M]
• Novices in a technical domain who lack the vocabulary to parse dense source material
• Less beneficial — and sometimes harmful — for advanced learners, for whom simplified text is redundant and reduces productive struggle [~M]

Target Learning Goals¶

• Comprehension of factual and procedural content delivered through text or narration
• Vocabulary acquisition, when key terms are defined and reused deliberately
• Not well suited to goals requiring learners to interpret complex, ambiguous, or authentic texts as the skill itself

Instructions¶

1. Identify the linguistic demands of the material: sentence length, vocabulary rarity, syntactic embedding, and passive constructions.
2. Rewrite for clarity: one idea per sentence, active voice, explicit connectives, and consistent terminology — apply Accessible Vocabulary & Syntax.
3. Break long passages into labeled sections and lists to support Chunking Chunking reduces working memory load. [+S].
4. Remove decorative or tangential material that competes for attention, consistent with the coherence principle Irrelevant material hurts learning. [+S].
5. Preserve the conceptual challenge: keep the hard ideas intact, define necessary technical terms, and pair simplified text with tasks that demand deep processing.
6. Pilot with representative learners and check comprehension before full deployment.

Related Strategies¶

• Accessible Syntax — the sentence-level companion: restructuring grammar rather than choosing simpler words
• Activating Prior Knowledge — simplification works best when it lowers language load while prior knowledge supplies context
• Chunking — structural simplification at the level of passages and sections

Examples¶

• Plain language guidelines (U.S. government, plainlanguage.gov) — systematic rewriting of public documents into short sentences and common vocabulary; the same techniques apply to instructional materials and assessment items.
• Newsela — publishes the same news article at multiple reading levels, letting teachers match linguistic complexity to reader proficiency while keeping content constant.
• Khan Academy — uses short, plainly worded sentences in exercise hints and instructions, reserving technical vocabulary for the concept being taught.

Key Sources¶

• McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14(1), 1–43. doi:10.1207/s1532690xci1401_1
• Clark, R. C., & Mayer, R. E. (2016). E-Learning and the Science of Instruction (4th ed.). Wiley. doi:10.1002/9781119239086
• Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. doi:10.1007/978-1-4419-8126-4
• Crossley, S. A., Kyle, K., & McNamara, D. S. (2017). Sentiment analysis and social cognition engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis. Behavior Research Methods, 49(3), 803–821. doi:10.3758/s13428-016-0743-z