Year: 2025
Type: article
Abstract: This research examines the comprehensibility and syntactic density of technology in the modern era essays penned by undergraduates. Employing computational tools and a quantitative approach, the study compares readability indices (Flesch Reading Ease, Flesch-Kincaid Grade, and Gunning Fog Index) as well as syntactic complexity measures (number of sentences, average word length, and average syllable length of the text). The ten essays underwent analysis in TEXT INSPECTOR form, and exploration visualizations such as bar plots, scatter plots, and heatmaps generated in Python environment were used to map the correlation between metrics. The findings show that complexity and readability levels vary greatly in the ten analyzed sentences. For Flesch Reading Ease, scores of texts ranged from 46 to 68, which reveal moderately to relatively high readability; Flesch-Kincaid Grade and Gunning Fog Index scores demonstrated how sentence length and syllable count affected perceived text difficulty levels. Regression analysis revealed that the average number of syllables per sentence was generally a good predictor of the readability indices we derived, which elicited those polysyllabic words made text more difficult. This study adds knowledge to the existing literature concerning the nature of syntactic choices and their effects on difficulty in academic writing. The implications for practice are that specific teaching methods to promote precision should be applied to address the imbalance of linguistic complexity to communicate academic content for a wide range of academic audiences. The limitation associated with a small sample size should be elaborated on in future studies; more texts from various genres should be incorporated into the analysis to establish greater generalization.
Institutions: Institute of Southern Punjab, Sukkur IBA University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4407867930