Text Complexity Classification Data Mining Model Based on Dynamic Quantitative Relationship between Modality and English Context

With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. This article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment. Secondly, based on the specific process of text mining, namely, data collection, data processing, and data visualization, it is proposed to subdivide the user demand analysis into three stages of text complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology. After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. Then, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology. Experimental results show that the collected quantitative relationship information is identified and expressed in order to realize the conversion of quantitative relationship information into product features. The market data and text data can be integrated to help improve the model performance and the use of text data can further improve predictions for accuracy.

Left Out—Feelings of Social Exclusion Incite Individuals with High Conspiracy Mentality to Reject Complex Scientific Messages

We investigated linguistic factors that affect peoples’ trust in science and their commitment to follow evidence-based recommendations, crucial for limiting the spread of COVID-19. In an experiment ( N = 617), we examined whether complex (vs. simple) scientific statements on mask-wearing can decrease trust in information and its sources, and hinder adherence to behavioral measures. In line with former research on social exclusion through complex language, we also examined whether complexity effects are mediated via feelings of social exclusion. Results indicate that negative effects of text complexity were present, but only for participants with a strong conspiracy mentality. This finding informs how to increase trust in science among individuals with a high conspiracy mentality, a population commonly known for its skepticism towards scientific evidence.

Analyzing Cognitive Demands of a Scientific Reasoning Test Using the Linear Logistic Test Model (LLTM)

The development and evaluation of valid assessments of scientific reasoning are an integral part of research in science education. In the present study, we used the linear logistic test model (LLTM) to analyze how item features related to text complexity and the presence of visual representations influence the overall item difficulty of an established, multiple-choice, scientific reasoning competencies assessment instrument. This study used data from n = 243 pre-service science teachers from Australia, Canada, and the UK. The findings revealed that text complexity and the presence of visual representations increased item difficulty and, in total, contributed to 32% of the variance in item difficulty. These findings suggest that the multiple-choice items contain the following cognitive demands: encoding, processing, and combining of textually presented information from different parts of the items and encoding, processing, and combining information that is presented in both the text and images. The present study adds to our knowledge of which cognitive demands are imposed upon by multiple-choice assessment instruments and whether these demands are relevant for the construct under investigation—in this case, scientific reasoning competencies. The findings are discussed and related to the relevant science education literature.

Automated Complexity Assessment of English Informational Texts for EFL Pre-service Teachers and Translators

Automated text complexity assessment tools are of enormous practical value in solving the time-consuming task of analyzing English informational texts for their complexity at the pre-reading stage. The present study depicts the application of the automated text analysis system the TextEvaluator as an effective tool that helps analyze texts on eight dimensions of text complexity as follows: syntactic complexity; academic vocabulary; word unfamiliarity; word concreteness; lexical cohesion; interactive style; level of argumentation; degree of narrativity, with further summarizing them with an overall genre-dependent complexity score. This research examines the complexity dimensions of English informational texts of four genres – legal, linguistic, news, and medical – that are used for teaching reading comprehension to EFL (English as a foreign language) pre-service teachers and translators at universities in Ukraine. The data obtained with the help of the TextEvaluator has shown that English legal texts are the most difficult for reading comprehension in comparison to linguistic, news, and medical texts. In contrast, medical texts are the least challenging out of the four genres compared. The TextEvaluator has provided insight into the complexity of English informational texts across their different genres that would be useful for assembling the corpora of reading passages scaled on specific dimensions of text complexity that predict text difficulty to EFL pre-service teachers and translators.

The impact of reading fluency level on interactive information retrieval

Given an information need and the corresponding set of documents retrieved, it is known that user assessments for such documents differ from one user to another. One frequent reason that is put forward is the discordance between text complexity and user reading fluency. We explore this relationship from three different dimensions: quantitative features, subjective-assessed difficulty, and reader/text factors. In order to evaluate quantitative features, we wondered whether it is possible to find differences between documents that are evaluated by the user and those that are ignored according to the complexity of the document. Secondly, a task related to the evaluation of the relevance of short texts is proposed. For this end, users evaluated the relevance of these short texts by answering 20 queries. Documents complexity and relevance assessments were done previously by some human experts. Then, the relationship between participants assessments, experts assessments and document complexity is studied. Finally, a third experimentation was performed under the prism of neuro-Information Retrieval: while the participants were monitored with an electroencephalogram (EEG) headset, we tried to find a correlation among EEG signal, text difficulty and the level of comprehension of texts being read during the EEG recording. In light of the results obtained, we found some weak evidence showing that users responded to queries according to text complexity and user’s reading fluency. For the second and third group of experiments, we administered a sub-test from the Woodcock Reading Mastery Test to ensure that participants had a roughly average reading fluency. Nevertheless, we think that additional variables should be studied in the future in order to achieve a sound explanation of the interaction between text complexity and user profile.

Automated Complexity Assessment of English Informational Texts for EFL Pre-service Teachers and Translators

Automated text complexity assessment tools are of enormous practical value in solving the time-consuming task of analyzing English informational texts for their complexity at the pre-reading stage. The present study depicts the application of the automated text analysis system the TextEvaluator as an effective tool that helps analyze texts on eight dimensions of text complexity as follows: syntactic complexity; academic vocabulary; word unfamiliarity; word concreteness; lexical cohesion; interactive style; level of argumentation; degree of narrativity, with further summarizing them with an overall genre-dependent complexity score. This research examines the complexity dimensions of English informational texts of four genres – legal, linguistic, news, and medical – that are used for teaching reading comprehension to EFL (English as a foreign language) pre-service teachers and translators at universities in Ukraine. The data obtained with the help of the TextEvaluator has shown that English legal texts are the most difficult for reading comprehension in comparison to linguistic, news, and medical texts. In contrast, medical texts are the least challenging out of the four genres compared. The TextEvaluator has provided insight into the complexity of English informational texts across their different genres that would be useful for assembling the corpora of reading passages scaled on specific dimensions of text complexity that predict text difficulty to EFL pre-service teachers and translators.