Do Students With Specific Learning Disorders With Impairments in Reading Benefit From Linguistic Simplification of Test Items in Science?

Abstract

Previous research illustrated that reading comprehension and science performance correlate highly. Because students with specific learning disorders with impairments in reading (SLD-IR) show deficits in reading comprehension, they may struggle to perform in science. As language in science is characterized by linguistic complexity, the question arises whether students with SLD-IR can be supported by reducing linguistic complexity. The aim of this preregistered study was to investigate whether students with SLD-IR benefit more from linguistic simplification in science than their peers without SLD-IR. The sample consisted of 70 students (age, M = 12.67; 50% female) with n = 35 having SLD-IR. Applying a multilevel logistic regression model, we found neither a main effect of linguistic simplification nor an interaction effect (differential boost) on science performance. However, students with SLD-IR performed significantly lower in science. Implications include further investigation on how to support students with SLD-IR in their science performance.

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

“…Lingualyzer furthermore provides summary statistics on a paragraph and sentence level and is unique in providing information consistently at three different text levels (i.e., document, paragraph, and sentence). These summarization statistics provide more localized information and could therefore potentially be very useful in for example (linguistic) stimuli creation, validation and analysis (Cruz Neri & Retelsdorf, 2022a;Dodell-Feder et al, 2011;Trevisan & García, 2019).…”

“…What's more, the regularities in language are different enough between different language users such that it is possible to identify the author of a piece of text (Juola, 2008;Türkoğlu et al, 2007). Quantitative text analysis is also used for stimulus creation (Cruz Neri & Retelsdorf, 2022a), validation (Trevisan & García, 2019) and analysis (Dodell-Feder et al, 2011). Finally, the resulting quantification is used in computational and statistical models to infer and understand latent properties of texts, such as the truth value of political statements (Mihalcea & Strapparava, 2009;Rashkin et al, 2017), whether social media texts contains humor or irony (Barbieri & Saggion, 2014;Reyes et al, 2012) or hate speech (Fortuna & Nunes, 2018), and the readability of a text (McNamara et al, 2012).…”

Metacognition & learning process: Using think-aloud protocol (TAP) to understand students and their teacher’s reflection processes during a problem-solving situation

“…According to the attentional control theory by M. W. Eysenck and N. Derakshan [48], these negative emotions negatively affect learner's ability to control his/her attention and consequently a difficulty in engaging his/her cognitive resources in solving the problem situation. Cruz Neri et al [49] insist on careful reading exercises before starting the answering process, also motivation in mathematics is seen as an indispensable factor for a better performance.…”

LATIC–A linguistic analyzer for text and item characteristics

“…As noted above, LATIC as a tool offers users to automatically tag and count parts of speech. Furthermore, LATIC calculates further characteristics as well as traditional readability indices, which are regularly used in research [ 10 , 19 , 48 ].…”

“…It is widely known that reading processes depend on reader characteristics, such as reading skills [ 8 – 10 ], but also on text and item characteristics [ 11 , 12 ]. Regarding the latter, especially the linguistic complexity has gained a lot of attention in the past decades [ 13 , 14 ].…”