law infractions that occurred early on Tuesday morning, under a plan which will see settlement temporary neighborhood prepared a 10-year-old Palestinian boy inside a base. Police for “most racist state” in the developed world said ultra-Orthodox Russian President Vladimir Putin praised his Palestinian counterpart Tuesday no problem recognizing a bullet-proof vehicle. Palestinian state twelve Israel Defense soldiers and what he said was the Judea and Samaria District a “responsible” position negotiations with Israel, frozen for nearly Saturday’s social protest that the violence families evacuated to the West Bank four years ago. Israel began evicting the of the Prominent Israeli author Sami Michael said the country’s discriminatory attitude for trafficking in drugs worth some NIS 800,000, according a report released on The which approach its border as a threat and a military target, Prime Minister Tayyip neighborhood in the Beit Commissioner Yohanan claims that although he was in a he men said on Tuesday. A Border Police officer charged with causing the death of visiting Danino on Tuesday accused activists that participated in premeditated. The Police three overnight Monday on suspicion of desecrating the Yad Vashem Holocaust with had to respond after stones were thrown at the car. anti-Zionist slogans two weeks Mizrahi Jews and Arabs qualifies it for the title of junior career officers have been arrest sweep is one of the largest ever in the IDF. Türkiye will treat any Syrian units.
CAUSES OF STYLISTIC ERRORS IN STUDENTS’ SPEECH AND WAYS TO ELIMINATE THEM
O‘rinova Diyora
Master’s student, Namangan State Pedagogical Institute
Abstract
This article examines stylistic errors found in students’ oral and written speech, their underlying causes, and effective methods for eliminating them. The study employed content analysis, surveys, observation, experimental methods, focus group discussions, computational linguistic analysis, and psycholinguistic testing. The findings reveal that students frequently struggle with selecting appropriate speech styles according to text types. Based on the results, practical recommendations are proposed to improve students’ speech culture and stylistic competence.
In modern education, developing students’ communication culture and ensuring stylistic accuracy in their speech has become one of the most pressing issues. In linguistics, stylistic errors are defined as the use of language units that are inappropriate for a given context or inconsistent with a particular speech style. Such errors negatively affect students’ speech culture, weakening their ability to express ideas clearly, engage in communication, and adhere to literary language norms.
Speech culture plays a crucial role not only in education but also in an individual’s social success. In the digital era, the rapid development of technology has introduced new tendencies in students’ speech. For example, abbreviations, emojis, and informal expressions commonly used in social media are increasingly transferred into formal written language, leading to stylistic distortions. This phenomenon can influence not only students’ academic writing but also their future professional communication.
Therefore, eliminating stylistic errors requires a comprehensive approach that considers not only grammatical but also pragmatic and discourse-related aspects. This article analyzes the main causes of stylistic errors in students’ speech and explores effective ways to address them.
LITERATURE REVIEW AND METHODOLOGY
Numerous scholars have conducted research in the field of speech culture. For instance, G‘afurov analyzed the theoretical aspects of speech culture, while Karimov systematized literary language styles. Qodirova provided practical examples of stylistic usage, and Xudoyberganova examined linguistic features from a psycholinguistic perspective. International researchers such as Smith, Ivanova, and Brown explored comparative, cognitive, and educational aspects of language norms. Recent studies by Yusupova, Petrov, Nurmatov, and Wilson highlight modern teaching methods and the impact of digital communication on speech.
The study was conducted among 100 students from grades 8–9 in Tashkent city and region. Their written works (essays, summaries) and oral responses were analyzed.
The following methods were used:
Content analysis: identifying and classifying stylistic errors
Survey: assessing students’ knowledge of speech styles
Observation: analyzing teaching approaches and classroom speech
Additional methods included:
1. Experimental Method
Two groups (control and experimental) were selected. A “Teaching Speech Styles” program was implemented in the experimental group for three months. As a result, students’ ability to choose appropriate styles improved by 35%.
2. Focus Group Discussions
Five groups (8 students each) discussed the influence of social media language. About 70% of participants preferred writing “as they do on Telegram.”
3. Computational Linguistics
Using the AntConc program, 100 essays were analyzed. Words such as “very” (143 times) and “amazing” (78 times) were overused, indicating excessive use of expressive vocabulary.
4. Psycholinguistic Testing
Only 31% of students correctly identified appropriate stylistic choices in academic contexts.
Additional statistical findings showed that errors in formal letters were distributed as follows:
Introduction – 23%
Main body – 41%
Conclusion – 36%
RESULTS
The analysis revealed the following common stylistic errors in students’ speech:
Mixing formal and informal styles – 43%
Using artistic expressions in scientific texts (and vice versa) – 29%
Pronunciation and stress-related stylistic distortions – 15%
Transfer of internet and colloquial language into writing – 13%
Although 67% of students demonstrated general knowledge of speech styles, only 21% understood the importance of selecting an appropriate style according to the text type.
DISCUSSION
The findings indicate that the main causes of stylistic errors include:
Insufficient theoretical knowledge of language styles
Transfer of informal speech into written language
Inability to distinguish between text types
Strong influence of internet and social media language
To address these issues, the following strategies are recommended:
Teaching speech styles through comparative practical exercises
Conducting text-based analysis and discussions
Developing exercises for appropriate stylistic selection
Ensuring teachers model correct speech usage
Limiting the use of informal internet language in academic contexts
One of the key reasons for stylistic errors is the lack of emphasis on stylistic aspects in textbooks and classroom instruction. Additionally, students’ exposure to informal digital communication significantly shapes their language habits. Therefore, teachers should dedicate more time to text analysis and encourage students to practice writing in various genres such as academic articles, formal letters, and essays.
CONCLUSION
Reducing stylistic errors and improving students’ speech culture requires systematic teaching of language styles in both theoretical and practical ways. This not only promotes adherence to literary language norms but also enhances students’ ability to communicate clearly, accurately, and effectively in social and professional contexts.
The following measures are recommended:
For teachers: organize seminars and training sessions on stylistics; expand textbook content
For students: engage in text analysis, speech exercises, and projects (e.g., “Correct Speech” clubs)
For parents: encourage reading and monitor children’s speech habits
For educational policy: develop national programs aimed at improving speech culture
O‘rinova Diyora Kamoliddin qizi was born on November 6, 1997, in Uchqo‘rg‘on district of Namangan region. She graduated from Secondary School No. 25 in her district and continued her studies at an academic lyceum. She obtained her higher education in the field of Uzbek Language at Namangan State University.
Currently, she is a second year master’s student at Namangan State Pedagogical Institute. She holds certificates in both native language and English and is recognized as a highly qualified teacher within her field. She is also the regional stage winner of the “Book-Loving Teacher” competition.
Her main goal is to share her knowledge with young learners and contribute to the development of future specialists through education and scientific activity.
Faculty of Information Security and Computer Technologies
2nd-year student, Information Systems and Technologies
Email: ibroximovahayitxon@gmail.com
Abstract
This article describes the mechanism for creating flexible study plans for students by processing academic data in the 1C:Enterprise system using artificial intelligence. The study analyzes an innovative approach to predicting student potential using neural networks and automatically optimizing the educational trajectory. This method contributes to the digital transformation of educational management.
Keywords: 1C: Enterprise platform, artificial intelligence, individual learning trajectory, personalized learning, data analytics, neural networks, digital education management.
Introduction
Today, the digitalization of higher education is not just about converting statistical data into electronic form, but about transitioning to a completely new model of managing education quality. As the global trend toward personalized education continues to grow, creating individual learning trajectories that match students’ performance levels and interests has become a pressing issue.In higher education institutions of Uzbekistan, the 1C:Enterprise platform is widely used to manage academic processes. Over the years, this system has accumulated a large database (Big Data) of students’ grades, attendance, and subjects. However, current 1C configurations are mainly limited to data collection and archiving functions. Standard curricula are the same for all students and do not take into account each student’s individual cognitive abilities and learning pace.At this point, the need arises to integrate artificial intelligence (AI) algorithms with the 1C system.
AI technologies, especially machine learning models, make it possible to analyze historical data in the 1C database and identify students’ strengths and weaknesses. For example, based on previous semester results, the system can provide “smart” recommendations on which subjects a student should study more deeply or which elective courses to choose.Such an approach not only personalizes the educational process but also helps university management predict student performance in advance and reduce academic underperformance.
Methodology (Methods)
During the research, an intellectual model for managing individual study plans was developed, and the following scientific and technical methods were applied:Data collection and analysis:A dataset of students’ academic activities was created. Input data included students’ academic portfolios. The following parameters were extracted from the SQL database:Static data: entrance scores, chosen specialization
Dynamic data: current grades, midterm results, LMS activity logs
Using Python’s Pandas library, missing values were filled and the data was normalized within the range [0,1].
Application of AI algorithms:Several machine learning models were used:
Clustering (K-means): Students were grouped based on knowledge level and cognitive abilities
Regression analysis: A Linear Regression model was built to predict final exam scores
Prediction: Subjects where students struggle were identified, and additional classes were automatically added
System integration and visualization:AI modules were integrated into platforms like 1C:Enterprise. Visual graphs and charts were created using Matplotlib to track student progress.
Experimental design:Two groups were formed:Experimental group – studied using AI-based individual plansControl group – studied using traditional methods
Results were compared to evaluate effectiveness.
Results
The experiment was conducted during the first semester of the 2025–2026 academic year with 200 students:
Experimental group: 100 students (AI-based system)
Control group: 100 students (traditional system)
Key findings:
Average score:
Experimental group: 84.5
Control group: 71.2→ 18.7% improvement
Low-performing students (<60):Control: 15%AI group: 3%
Prediction model accuracy (R²): 0.892
Early prediction accuracy: 91% (by week 4)
AI automatically added 12 extra hours of training, improving weak results in 85% of cases.
Administrative efficiency:Time to create plans reduced from 45–50 minutes to 35–45 seconds
Errors reduced by 98%
Documents generated automatically in PDF
Survey results:88% of students satisfied with recommendations92% of teachers saved time and focused more on creative work
Discussion
The results show that AI-based management of individual study plans is not just a technical tool but a strategic mechanism for transforming education quality.Adaptive learning: Improved performance by 18.7%
Predictive analytics: Enabled early interventionIntegration effect:
Combined power of Python and 1C improved efficiency
Visualization: Increased student motivation and self-monitoring
Limitations:Data quality issues (GIGO principle)
Need for Explainable AI
AI should support, not replace teachers
Future recommendations: NLP for evaluating written work
Sentiment analysis for student well-being
Mobile applications for real-time updates
Conclusion
This study shows that the era of treating all students equally in education is over. Artificial Intelligence is not just a trend but a powerful tool that improves student performance and reduces teachers’ workload.
Main conclusions:
Student performance increased by 18–20%Early prediction of failures (90% accuracy)Bureaucracy reduced by 80%Strong collaboration between humans and technologyIn conclusion, managing individual study plans through AI is the foundation of future education. Its wide implementation can significantly improve the quality of training modern, competitive specialists.
Ananya S Guha lives in Shillong in North East India. He has been writing and publishing his poetry for the last forty years, and has ten collections of poetry to his credit.
Stan sat before the old television set, unmoving. He was just dimly aware that his torso and limbs were arranged in the same posture as Rodin’s “The Thinker,” only in flesh tones instead of the bronze of the sculpture. While Le Penseur had for more than a century captivated observers with its monumental reflection of profound introspection, Stan knew only that he was stoned on peach-flavored vodka and ersatz Nyquil. Like the statue, Stan was totally nude.
It had been a long night. Leaving his sleeping wife alone in the middle of the night to grab a beer and catch some professional wrestling on the tube, he had gotten wildly drunk and stayed that way into the morning. He worked hard as a bricklayer and only cut loose one night a week. He didn’t frequent the bars anymore, and usually held himself together enough to accompany Bree to church on Sunday morning.
He gazed bleakly at the TV, saw on the fuzzy screen only the pointless Sunday morning discussion programs. Stan moved his right elbow from his left knee and bent to retrieve his flask of generic vodka. He then snatched from the TV table the large, trapezoid-shaped bottle of generic cold meds. Decanting the green, gloppy liquid into a small plastic cup, he tossed it back like a shot of tequila. Next he unscrewed the vodka and took a bracing hit. The hair on his arms stood on end.
“I’m ready,” he said aloud, “for a Sunday without football.”
Keys rattled in the locket and through the front door walked Bree. She dropped her purse and a grocery bag on the parson’s table beside the entrance. She stared at her husband and offered up, “Shit-faced again, lover?”
“Is that what you learned at Sunday school today?” asked Stan, promptly falling off the sofa and bonking his head on the edge of the TV.
As he lay there, dazed, Bree sashayed through the living room, took up a vase, removed the fresh-cut flowers and poured the water on her husband’s head. Stan sprang to life at once.
Stan shook himself like a dog. “What’s for lunch?” he slurred.
“Hash. Don’t get up; I’ll serve you where you are.”
“Thanks, ‘hon.”
Bree brings him something ugly in a bowl.”
“Hey Bree, that’s the dog’s food dish.”
“Of course it is, I gave you dog food.”
“Bree, I can only take so much. You know I can leave you at any time.”
“Promises, promises. The checkout guy at the grocery lets me know, every time I shop, that he’s available. Good hair, nice teeth and a body that looks like a Greek statue. You really want to make threats?”
“You think you are so hot! Want to know what the secretaries for the union say about me?”
“Sure, I could use a good laugh.”
“They say I have great penmanship.”
They blink at the other for a moment, and then Bree hides her mouth with her hand and starts to giggle. Stan joins her. Soon they are laughing uproariously.
“Hey Bree, help your drunk old man up so we can watch something on TV.”
“OK, but after that I’ve got to put away groceries.”
Later they leave the TV on but ignore it while making out like a couple of teenagers. The ice cream melts in the bag on the table.
