Computing descriptive metrics and propositions in reading texts and recalls

Abstract

Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). This study addresses the problem of computational techniques to perform a multi-factor text analysis aimed at assessing text metrics and the amount of information in two contrasting texts. Assessing recalls in general and estimating the scope of information reproduced in recalls in particular are equally challenging. We introduce a computational linguistic tool that measures 28 linguistic parameters enabling conventional level of language assessment. The results are indicative of the tool distinguishing two versions of the low (OT51) versus high (MR51) cohesion of the texts but not the recalls. The results also showed that ubiquitously used descriptive metrics and readability indices inappropriately distinguish between reading texts and their recalls. Overall, the research advances our understanding of the relationship between conventional (quantitative, lexical) and semantic metrics providing foundations for more effective algorithms of assessing and profiling academic texts types.