Word Difficulty Research Articles

Dual-Language Sentiment Analysis: A Comprehensive Evaluating SVM, Logistic Regression, XGBoost, and Decision Tree Using TF-IDF On Arabic and English Dataset

Sentiment analysis (SA) is a growing area of study that straddles a number of disciplines, including machine learning, data mining, and natural language processing. It is focused on the automatic extraction of viewpoints presented in a certain text. Many studies have been conducted in the area of sentiment analysis because to its broad uses, particularly on texts in English, whereas other languages like Arabic have gotten less attention. The Arabic language presents several difficulties, such as its rich morphology and the difficulty of tracing words back to their original roots. Arabic comments have been analyzed and categorized into good and negative attitudes using a framework. With the aim of evaluating any tweet, opinion, purpose, or reputation, such as a university, company, mobile, and others, the research analyzes the comments made by users of the social networking site Twitter. It does this by using classification technology and machine learning, which are among the fundamental tasks of the data mining process used in the larger process, which is to explore knowledge . This search helps the user to access the evaluation of other users through their tweets and comments on the social networking site for an opinion immediately and automatically, and then the process of uploading and evaluating the opinions using appropriate algorithms for this purpose as(Decision Tree classifier DTC , XGboost, Logistic Regression LR, Support Vector Machine SVM )with Term Frequency-Inverse Document Frequency TF_IDF

Rasch modelling vs. item facility: implications on the validity of assessments of Asian EFL/ESL vocabulary knowledge and lexical sophistication modelling

This study examines how second/foreign language (L2) word difficulty estimates derived from item response theory (IRT) and classical test theory (CTT) frameworks are virtually identical in the context of vocabulary testing. This conclusion is reached via a two-stage process: (a) psychometric assessments of both approaches and (b) L2 word difficulty modelling with lexical sophistication. Using data collected from first/native language (L1) Japanese EFL learners, both approaches led to similar conclusions in terms of the psychometric properties of the construct. Furthermore, CTT- and IRT/Rasch-derived estimates for word difficulty yielded nearly identical results in the predictive models. Although the “CTT-vs-IRT” debates in the past few decades have concluded with a middle ground agreed upon in most educational settings, this study acts as a useful demonstration to L2 vocabulary researchers who appear to rely heavily on Rasch/IRT analysis. The findings have practical applications to the area of L2 word difficulty research. Namely, that Rasch (an IRT procedure) alone might not be sufficient for validation, although it could be preferable when conducting inferential testing because of its potential utility for reducing chances of Type II errors.

Difficulty in artificial word learning impacts targeted memory reactivation and its underlying neural signatures.

Sleep associated memory consolidation and reactivation play an important role in language acquisition and learning of new words. However, it is unclear to what extent properties of word learning difficulty impact sleep associated memory reactivation. To address this gap, we investigated in 22 young healthy adults the effectiveness of auditory targeted memory reactivation (TMR) during non-rapid eye movement sleep of artificial words with easy and difficult to learn phonotactical properties. Here, we found that TMR of the easy words improved their overnight memory performance, whereas TMR of the difficult words had no effect. By comparing EEG activities after TMR presentations, we found an increase in slow wave density independent of word difficulty, whereas the spindle-band power nested during the slow wave up-states - as an assumed underlying activity of memory reactivation - was significantly higher in the easy/effective compared to the difficult/ineffective condition. Our findings indicate that word learning difficulty by phonotactics impacts the effectiveness of TMR and further emphasize the critical role of prior encoding depth in sleep associated memory reactivation.

Difficulty in artificial word learning impacts targeted memory reactivation and its underlying neural signatures

Sleep associated memory consolidation and reactivation play an important role in language acquisition and learning of new words. However, it is unclear to what extent properties of word learning difficulty impact sleep associated memory reactivation. To address this gap, we investigated in 22 young healthy adults the effectiveness of auditory targeted memory reactivation (TMR) during non-rapid eye movement sleep of artificial words with easy and difficult to learn phonotactical properties. Here, we found that TMR of the easy words improved their overnight memory performance, whereas TMR of the difficult words had no effect. By comparing EEG activities after TMR presentations, we found an increase in slow wave density independent of word difficulty, whereas the spindle-band power nested during the slow wave up-states – as an assumed underlying activity of memory reactivation – was significantly higher in the easy/effective compared to the difficult/ineffective condition. Our findings indicate that word learning difficulty by phonotactics impacts the effectiveness of TMR and further emphasize the critical role of prior encoding depth in sleep associated memory reactivation.

Estimating word difficulty using stratified word familiarity

Estimating word difficulty using stratified word familiarity

A research of word difficulty clustering based on improved k-means++ algorithm

Abstract Nowadays, Wordle, a daily puzzle game offered by the New York Times, is now becoming prevalent around the world by storm, and difficulty analysis of words has become a hot topic. However, the difficulty analysis of words is highly sensitive to the feature extraction of words, which undoubtedly creates great difficulties. Based on this, this paper proposes an improved K-means++ algorithm for clustering analysis of word difficulty, which is simulated and verified in the U.S. college students’ mathematical modeling C problem data. The results show that this paper clusters the 359 words in the data into 6 classes A, B, C, D, E, and F, and determines the class of word ERIE as B based on the distance between ERRIE and the word attributes of the six clustering centers. The DB index value of the model is as high as 0.815, and the results of the analysis of variance (ANOVA) of the clustering index are very significant, which proves the high accuracy of the model.

Relative complexity in a model of word difficulty: The role of loanwords in vocabulary size tests

Recent studies have shown that the frequency effect, although long used as a guide to word difficulty, fails to explain all variance in learner word knowledge. As such, a “more than frequency” conclusion has been offered to explain how lexical sophistication accounts for word difficulty. This study presents a multiple regression model of word-learning difficulty from a data set of monolingual Japanese first language (L1) learners. Vocabulary Size Test (VST) scores of 2,999 L1 Japanese university students were converted to logit scores to determine the word-learning difficulty of 80 target words. Five lexical sophistication variables were found to correlate with word-learning difficulty (frequency, cognate status, age of acquisition, prevalence, and polysemy) above a practical significance threshold. These were subsequently entered into a regression model with the logit scores as the dependent variable. The model (R2 = .55) indicates that three lexical sophistication variables significantly predicted VST scores: frequency (ß = -.28, p = .029), cognateness (ß = -.24, p = .005), and prevalence (ß = 0.22, p = .040). Despite suggestions that complexity studies be interpreted considering what is understood about the construct of linguistic complexity, researchers have rarely made explicit the differences between absolute and relative complexity variables. As some variables can be shown to vary in complexity according to the L1 population, these must be considered in discussions of test generalizability. Although frequency will continue to be the primary criterion for the selection of lexical items for teaching and testing, the cognate status of words can be used to predict the potential learning burden of the word more precisely for learners of different L1 backgrounds.

What is the best predictor of word difficulty? A case of data mining using random forest

Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly, applied linguists have questioned the use of frequency as the principal criterion in the development of wordlists and vocabulary tests. Despite being informative, previous studies on the topic have been limited in the way the researchers measured word difficulty and the statistical techniques they employed for exploratory data analysis. In the current study, meaning recall was used as a measure of word difficulty, and random forest was employed to examine the importance of various lexical sophistication metrics in predicting word difficulty. The results showed that frequency was not the most important predictor of word difficulty. Due to the limited scope, research findings are only generalizable to Vietnamese learners of English.

Surgical cavity dilatation after resection of IDH-mutated astrocytoma of the mesial temporal lobe: illustrative cases.

Space-occupying tumor bed cysts may exceptionally happen after the resection of diffuse low-grade glioma. Their mechanism and management remain debated. The authors report two cases of tumor bed cysts occurring after the resection of a left temporal diffuse low-grade glioma with two different evolutions. The first patient showed a spontaneous decrease in the cyst volume and did not report any symptoms. In contrast, the second patient showed a progressive increase in the cyst volume and reported headaches and difficulties in finding words. Endoscopic cyst fenestration was performed and led to symptom relief and normalization of the surgical cavity. A tumor bed cyst is a rare complication of temporal low-grade glioma resection. Its formation is due to entrapment of the choroid plexus in the temporal horn widely opened into the surgical cavity. Endoscopic cyst fenestration should be offered only in symptomatic cases. https://thejns.org/doi/10.3171/CASE23674.

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

The optimization and application of an English word memory algorithm based on reinforcement learning involve refining a system that utilizes reinforcement learning techniques to enhance vocabulary retention. By leveraging reinforcement learning, the algorithm can adapt its strategies for presenting and reviewing English words based on users' responses and performance. This adaptive approach enables personalized and effective learning experiences, wherein the algorithm adjusts the frequency and timing of word repetitions to maximize memorization efficiency. Applications of this optimized algorithm span language learning platforms, educational apps, and personalized tutoring systems, offering learners tailored support to strengthen their English vocabulary acquisition and retention skills. This paper introduces the Spider Swarm Optimized English Word Memory Algorithm (SSOEWMA) algorithm, a novel approach aimed at optimizing language processing and learning tasks. Leveraging principles of reinforcement learning and swarm intelligence, SSOEWMA demonstrates its effectiveness in enhancing various aspects of language processing, including word difficulty assessment, sentence estimation, memory retention, and student performance evaluation. Through a series of experiments and evaluations, we showcase SSOEWMA ability to optimize dataset attributes, accurately estimate sentence characteristics, and improve memory retention performance. Simulation results achieved an average word recall accuracy of 85% across multiple evaluations. Through its utilization of reinforcement learning principles and swarm intelligence, SSOEWMA significantly improved memory retention performance, reducing recall latency by an average of 20% compared to baseline measures. Additionally, the algorithm demonstrated strong learning efficiency, converging to optimal solutions with a speed increase of 30% compared to traditional methods.

Decision tree based prediction system for word difficulty classification

In the era of information technology in education, personalised learning is becoming increasingly important, especially in the English learning process. An important aspect of facilitating personalised learning in English reading is the use of a reliable objective word difficulty classification system to quickly capture and understand reading difficulties and core concepts. The aim of this study was to use decision tree modelling to predict the general proficiency of the public in words with different attributes. In addition, a K-Means clustering algorithm was used to categorise words into five classes based on their level of difficulty. By adopting this approach, the prediction of word difficulty becomes both fast and objective, and by testing the accuracy of the model, it was found that our model achieved an accuracy of 0.95. Accurate classification of word difficulty will play an important role in facilitating personalised learning in English reading and improve the efficiency of English reading.

Profiling English sentences based on CEFR levels

Abstract The study aims to demonstrate the procedure for constructing the CEFR-based Sentence Profile (CEFR-SP), a dataset with the CEFR levels assigned for sentences, and to identify the characteristics at each level. Basic statistics such as word length and sentence length are presented for each CEFR level for 7,511 carefully selected sentences, and statistical tests are conducted between adjacent levels to identify criterial features. The findings reveal significant differences in word length between adjacent levels, while word difficulty is not significant in discriminating levels at either end (A1–A2, C1–C2). Sentence length and depth are also not significant discriminators for higher levels (B2–C1, C1–C2). Notably, sentence-level data generally exhibit discriminative values compared to text-level statistics, indicating their direct capture of characteristics at each CEFR level.

Analisis Kesulitan Membaca Permulaan Siswa Sekolah Dasar

Early reading difficulties in elementary school students are an important problem in the educational process. The aim of this research is to determine the factors that influence initial reading difficulties, as well as the types of difficulties students face in initial reading based on several related studies. Various factors such as the environment, psychology, and internal maturity of students can be the main causes of reading difficulties. Types of difficulties include a lack of understanding of letters, difficulty pronouncing words, and limitations in reading sentences or paragraphs. This research uses qualitative methods through literature study. The data analysis technique used in this research is content analysis. The results illustrate the various difficulties experienced by students, including difficulties in recognizing letters, difficulties in pronouncing, and difficulties in reading words, syllables, sentences, as well as aspects of incorrect pronunciation. solving initial reading difficulties includes the important role of teachers in providing learning that suits students' needs, parental support in providing motivation, as well as creating a social environment that supports the process of learning to read. By overcoming initial reading difficulties, it is hoped that elementary school students can develop their reading skills effectively to support better academic development

Undergraduate Students’ Difficulties in Translating an Editorial of the Jakarta Post

The objective of this research was to find out the students’ dominant difficulties in translating an editorial text of The Jakarta Post. The subjects of this research were 30 seventh semester students of Education Study Program at Universitas Bengkulu in the academic year 2020/2021. This research used a quantitative descriptive method. The data were collected with a questionnaire and were counted in percentage terms. The results of this research showed that there were 5 students’ dominants difficulties in translating the editorial, i.e. a.)The difficulties in understanding unfamiliar words(60%), b.) The difficulties in understanding the texts although they understand the meaning of the words but they could not understand it coherently. Sometimes one sentence looked like disconnected with another sentence so it became odd (57%),c.)The difficulties in understanding the difficult sentences (47%), d.)The difficulties in translating the words that had multiple meanings which made it difficult for them to determine which meaning was correct and in understanding the ambiguous words (43%), and e.)The difficulties in translating phrases (43%). In conclusion, among all the dominant difficulties in translating the editorial the most dominant one was the difficulties in understanding unfamiliar words (60%). It was suggested that the undergraduate students should study lexicology and syntax intensively.

Wordle word difficulty classification based on K-means

Wordle is a popular word-guessing game, and the analysis of Wordle games will play an important role in updating its iterations. In this paper, a word difficulty classification model based on K-means cluster analysis is developed. Clustering the dataset of the number of times required for word guessing, the words can be classified into three categories according to their difficulty: hard, medium and easy, with the corresponding labels of 3, 2 and 1. The attributes of the words in each category were counted and analyzed to arrive at the following: 1) The more common the word, the less difficult it was. 2) The more repeated letters in the word, the more difficult it was. 3) The experiment substitutes the example word EERIE into the model and the difficulty classification result is medium. The profile coefficient index of the model is 0.372. The cluster analysis method applied in this paper has good training results and is suitable as technical support for Wordle game analysis.

Empowering Reading Habit of English Fiction Books to Improve English Vocabulary: Evidence from Students in an Indonesian Higher Education

Low English literacy has a major impact on the education sector. In particular, it has an impact on less qualified human resources. This study aims to explore the students’ perception of reading habits on English fiction books to improve English vocabulary in Indonesian higher education. This study used a qualitative case study method. Data were gathered through semi-structured interviews with four students consisting of two female and two male students from the English Education Department. The results of the study reveal that students believe that reading fiction books can help them memorize English vocabulary, and English fiction books can enrich English vocabulary, especially new words. On the other hand, some challenges appear in reading English fiction books. These include the difficulty of understanding unfamiliar words in English fiction books among male students and the need for sufficient time to read English fiction books completely. Finally, the implication of this study was discussed.

ANALYZING LEXICAL FEATURES AND ACADEMIC VOCABULARY IN ACADEMIC WRITING

Introduction. Academic writing is considered to be an important means of sharing knowledge and academic experience. The article investigates lexical features of academic writing and academic vocabulary, correlation of academic vocabulary and writing proficiency. The main objective of teaching EAP is the development and further improvement of communicative skills of post-graduate students in professional and academic fields. There is a reasonable necessity to study and improve practical aspects of foreign language education of postgraduate students, contributing to the effective mastering of English for academic purposes (EAP), development of academic writing skills, mastering academic vocabulary. The purpose of the research is to analyze such lexical features as lexical diversity, lexical complexity, lexical density, word frequency and academic vocabulary usage of postgraduate students’ academic writing outcomes in the process of learning English for academic purposes. Theoretical and practical methods of research have been used in the article: theoretical analysis of scientific researches, methods of logical generalization, observation, analyses of EAP students’ academic writing outcomes. Results of the research. The article highlights specific peculiarities of academic writing like formal structure, specific rules, citation, traditional English grammar usage, punctuation, spelling etc. In the frame of the article we explored lexical features and academic vocabulary in English academic writing of EAP learners’ in the process of learning English for academic purposes. We studied the relation between academic vocabulary and writing abilities. The author investigated ways of word complexity, paying special attention to affixes and compound words. Different approaches to the analyses of lexical density and diversity are implemented. The most frequent academic words (nouns, verbs and adjectives) are analyzed. Conclusion. Our results showed a strong relation between academic vocabulary and writing proficiency of postgraduate students. Our empirical observation revealed correlation of lexical diversity and academic writing quality of EAP learners. Academic vocabulary demonstrates significant interrelationship with the quality of writing. Word frequency correlates with word difficulty. According to our findings advanced learners predominantly use less frequent words and have larger vocabulary size.

The importance of phonological awareness as a natural approach in children’s reading process from Manuela Cañizares School

The present study examines the significant impact of phonological awareness on the reading acquisition of children aged 10 to 12, with a specific focus on those acquiring English as a second language in Ecuador. The report highlights the pressing need for educational reforms considering the nation’s difficulties in achieving mastery in the English language. The study used a mixed-methods methodology, using questionnaires and observations to examine the complexities of phonological awareness as a naturalistic approach and its influence on reading ability. The results of the study indicate that a considerable percentage of pupils have difficulties in reading English words, highlighting the need to implement efficient teaching approaches. This research paper presents a survey on a phonological awareness intervention, showing its capacity to improve both phonological awareness abilities and reading ability. Furthermore, the study reveals a deficiency in the use of natural approach techniques and pronunciation tactics, underscoring prospects for pedagogical improvement. The study’s larger ramifications beyond the realm of education, prompting scientists in interconnected fields to contemplate the crucial significance of phonological awareness in the process of language learning. Although this study provides interesting insights, it is important to acknowledge several limitations. One drawback is the narrow scope of the examined linguistic aspects, which may restrict the generalizability of the findings. Additionally, there may be limits on the sample size, which might impact the overall validity and representativeness of the results. Subsequent investigations may be conducted to overcome these constraints and delve into the enduring effects of interventions, therefore offering full insights to educators, policymakers, and researchers interested in language acquisition and teaching. In general, this study highlights the importance of phonological awareness in resolving challenges with reading, facilitating successful language training, and contributing to wider conversations on language acquisition approaches.

Subjective cognitive impairment and biomarkers of Alzheimer’s disease

AbstractBackgroundSubjective cognitive impairment (SCI) is common in older adults. It may represent the earliest clinical manifestation of cognitive decline and Alzheimer’s disease (AD).Objectives1) to evaluate associations between SCI and cerebrospinal fluid (CSF) biomarkers of AD; 2) to identify specific single questions or question combinations that are related to the presence of cerebral AD pathology; 3) to evaluate whether using SCI related scores or specific questions combined with other clinical features and biomarkers may improve prediction of cognitive decline.MethodWe conducted a longitudinal study in 164 non‐demented subjects participating on a biomarker study in a memory clinic setting, aged 53 to 88 years, with CDR scores of 0 (N = 84) and 0.5 (N = 80). SCI was assessed using the Cognitive Complaint Interview (CCI, Thomas‐Antérion et al., 2006), a validated 10 items questionnaire. Accordingly, SCI is defined as present when the subject answers “yes” to 3 or more items; and/or to item 5, and/or to items A, 4, 5, 7, 8. All participants underwent comprehensive neuropsychological evaluations and CSF biomarkers analysis. “Amyloid positivity" and “AD pathology” were defined according to the CSF Aβ1‐42 levels and pTau181/Aβ1‐42 ratio, respectively. Logistic regressions were used to address the associations of interest.ResultsParticipants with SCI had significantly lower Aβ1‐42 and higher Tau and pTau181 levels compared to those without SCI. SCI was associated with amyloid positivity while CCI total score and the question A (reporting changes in memory) were associated with both amyloid positivity and AD pathology in the whole cohort. When performing analysis separately in the CDR = 0 and CDR = 0.5 subgroups the associations remained significant in the CDR = 0.5 group. Only question 6 (difficulties in finding words) was associated with AD pathology in the CDR = 0 group. Results on the prediction of cognitive decline at follow‐up visits will be presented at the conference.ConclusionIn dementia‐free subjects from this memory clinic cohort SCI‐related scores and single questions indicate amyloid positivity and the presence of AD pathology depending on the absence/presence of mild cognitive impairment. Further studies are needed to evaluate the diagnostic and prognostic utility of cognitive complains in memory clinic patients.

A Word Difficulty Classification Research Based on K-Means Method

Wordle is a globally popular game and many researchers have conducted research on its game mechanics. However, few studies have explored the influence of attributes on the difficulty of the word. Therefore, this paper uses K-means algorithm to classify word difficulty based on certain word attributes. In the paper, the main attributes character repeat times, the presence of "th" or "er", the initial letter (s/c/a/t), and the final letter (e/y/r/t) that affect word difficulty are selected, and a comparison is made regarding the number of difficulty categories, and the most appropriate number of categories is three. Finally, it has been validated by Spearman that this method possesses strong reliability.