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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Is cognitive school engagement a better predictor of school performance? Findings from Russian students and teachers

P. 305–306.
Arsalidou M., Faber A., Charkhabi M., Khalezov E., Liashenko A.

School engagement influences multiple educational outcomes, including academic performance of students. Research shows that engaged students are more likely to demonstrate improved knowledge in various subjects and higher academic scores. Critically, school engagement assessments are usually performed in adolescence using self-ratings and fewer using teacher-ratings. Our project examines the relation of school engagement using self- and teacher-ratings and academic performance among primary school students in Russia. Specifically, we investigate which components of school engagement (i.e., behavioral, emotional or cognitive) is a stronger predictor of academic performance from both student’s and teacher’s perspective. Our findings can inform educational practice in understanding the mechanisms underlying academic performance and designing interventions to improve it.

Language: English
Full text
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Keywords: Educational psychologydevelopmental psychologyschool engagement
Publication based on the results of:
Neurocognitive foundations of mathematical cognition in children (2020)

In book

PSY-ВЫШКА Сборник материалов Международной научной конференции
Российский новый университет, 2020.
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