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News
August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
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Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Analysis of the Impact of the Transition to Online Education During the COVID-19 Pandemic on the Future Academic Performance of University Students

Sage Open. 2025. No. 15(1).
Rishko Y., Boboshko D., Eliseeva E., Malkin A., Treistar D.

Discussion of the effectiveness of distance learning as a means of delivering higher education programs at classical universities has been ongoing for the past decade. The article presents the findings of a study of changes in academic performance of university students, covering the period from fall 2018 to fall 2023. This period included a rapid switch to online learning at Russian universities. Our study is based on a large dataset generated through the erp system of the National University of Science and Technology misis (NUST MISIS). The dataset contains over 600,000 students' academic performance entries covering every subject and practical class, including the final assessments. In addition to the abovementioned dataset, the study also used a database of students' unified state examination (use) results. The results of use exams (taken during the final year of high school) determine the applicant's eligibility for admission to university programs. We evaluate the impact of an individual student's use results on their future academic performance at the undergraduate level. The study also analyzes the overall readiness of a university for distance learning at the time of the Covid-19 pandemic outbreak. Excel pivot tables were used as a statistical analysis tool; shewhart charts were employed to analyze the academic performance trends. Our analysis confirmed the initial hypothesis-that there exists a potential negative impact of online learning on the academic performance of university students. The use of big data in learning analytics enhances the reliability of the results to the greatest extent possible.

Research target: Social Sciences
Language: English
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Keywords: academic performancedistance learninghigher educationdigital learning toolsCOVID-19measurement and evaluationteaching and learning management
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