Text Readability and Processing Effort in Second Language Reading
A Computational and Eye-Tracking Investigation
Bibliographic Data
| ID | 4503340 |
|---|---|
| Authors | Shingo Nahatame (0000-0002-8488-8603, University of Tsukuba Tsukuba Japan, corresponding author) |
| Year | 2021 |
| Volume | 71 |
| Issue | 4 |
| Pages | 1004-1043 |
| Publication date | 2021-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language Learning (JOURNAL) |
| Journal identifiers | ISSN: 0023-8333 • E-ISSN: 1467-9922 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/lang.12455 |
| OpenAlex | W3188438196 |
| Language | EN |
| Citations received | 14 |
| References cited | 46 |
Although text readability has traditionally been measured based on simple linguistic features, recent studies have employed natural language processing techniques to develop new readability formulas that better represent theoretical accounts of reading processes. This study evaluated the construct validity of different readability formulas, including both traditional and newer formulas, by examining their ability to predict the processing effort involved during L2 reading as evidenced by eye movements. Two studies (an experimental study and a corpus-based study) were conducted in which the readability of target texts was calculated using different formulas and then utilized to develop models that predict particular eye movement patterns during reading. These studies revealed that although traditional formulas showed reliable performance in predicting particular eye movement patterns, in many cases, the newer formulas outperformed them. These findings support the newer readability formulas as more theoretically valid and accurate measures of the processing effort involved in L2 reading
Cognitive psychology · Construct (python library) · Eye movement · Eye tracking · Linguistics · Natural language processing · Readability · Reading (process) · Sentence · Text simplification · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Psychology · Second Language Acquisition and Learning · Text Readability and Simplification
How does lexical coverage affect the processing of L2 texts?
Automated text leveling for L2 English learners
L1-Assisted L2 reading comprehension
Türkçeyi̇ İki̇nci̇ / Yabanci Di̇l Olarak Öğrenenler İçi̇n Hazirlanan Yardimci Materyalleri̇n Okunabi̇li̇rli̇ği̇ Üzeri̇ne Bi̇r İnceleme
Predicting processing effort during L1 and L2 reading
Aligning linguistic complexity with the difficulty of English texts for L2 learners based on Cefr levels
Modeling effects of linguistic complexity on L2 processing effort
Can readability formulae adapt to the changing demographics of the UK school-aged population? A study on reading materials for school-age bilingual readers
Exploring EAP English text readability and reader ability of Chinese university students through Lexile measures and metaphorical conceptualizations
A Systematic Review of Eye-Tracking Technology in Second Language Research
Bibliometric Analysis of Natural Language Processing Technology in Education
Revisiting Text Readability and Processing Effort in Second Language Reading
(Why) Are Open Research Practices the Future for the Study of Language Learning
Testing the Relationship of Linguistic Complexity to Second Language Learners' Comparative Judgment on Text Difficulty
Eye-Tracking
Insights into Second Language Reading
Automated Evaluation of Text and Discourse with Coh-Metrix
Toward a model of eye movement control in reading.
Power Analysis and Effect Size in Mixed Effects Models
The 35th Sir Frederick Bartlett Lecture
Language Comprehension as Structure Building
Making and correcting errors during sentence comprehension
Eye Movements as Reflections of Comprehension Processes in Reading
A new readability yardstick.
A theory of reading
Measuring Syntactic Complexity in L2 Writing Using Fine‐Grained Clausal and Phrasal Indices
Parafoveal word processing during eye fixations in reading
Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel
Random effects structure for confirmatory hypothesis testing
Fitting Linear Mixed-Effects Models Using lme4
A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
Handbook of Psycholinguistics
Advanced learners’ comprehension of discourse connectives
Assessing Text Readability Using Cognitively Based Indices
(Generalized Linear) Mixed-Effects Modeling
The Utility and Application of Mixed-Effects Models in Second Language Research
The Effect of Pre-reading Instruction on Vocabulary Learning
Eye movements in reading and information processing
| Unique citing works | 14 |
|---|---|
| Citations per year | 3,5 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |