Introduction. Currently, the leadership of the People’s Republic of China pays special attention to the issues of technical vocational education. The learners from the PRC studying in Russia have specific cognitive orientations in mastering technical disciplines. The aim of the article is studying the specifics of academic resilience in mastering technical sciences by PRC students of Ural Federal University. Materials and methods. The academic resilience of PRC students from Ural Federal University in terms of mastering technical sciences was measured using the ‘Technics Resilience Scale for University Students’ (TRSUS). TRSUS aims to build Feedback literacy and Self-assessment with a view to help a student to pursue technical studies productively. The survey involved 659 students who had a technical science learning experience in one of the educational programmes: “Civil Engineering”, “Architecture”, “Geodesy and Remote Sensing”, “Steam Power Industry and Heat Engineering”, “Water Supply and Drainage”. The students assessed their resilience in technical sciences under a 5-score Likert response scale, with the use of Tencent Questionnaire in English. The statistical processing of measurement results was carried out using Structural Equation Modelling (SEM) methodology with the use of SPSS 23.0 and Amos 18.0 software packages. Results. The statistical analysis of results of PRC students from Ural Federal University in terms of measuring resilience in technical sciences under the TRSUS scale showed that the wording of the questions was adequate to the measurement context. The primacy of students’ incremental beliefs/goals towards improving the efficiency of learning activities was revealed. A strong correlation between students’ answers with the TRSUS questions was found. Cronbachs alpha was equal to 0.977, which is evident of high internal consistency of students’ answers. The structural model of confirmatory factor analysis of TRSUS data including four indicators (factors) – “Persistence”, “Perceived Value”, “Cognitive Complexity” and “Incremental Beliefs” – has the best source data fitting criteria. The method of exploratory factor analysis of TRSUS data revealed the specificity of cognitive orientations of PRC students from Ural Federal University: the choice of educational programme influences most prominently the indicator “Perceived Value”, which is explained by the peculiarities of Chinese technological culture. Conclusion. An original methodology for measuring university students’ academic resilience in technical sciences – ‘Technics Resilience Scale for University Students’ (TRSUS) – is presented. The use of TRSUS makes it possible to determine the specifics of students’ cognitive orientations in mastering technical disciplines and to form their competent feedback and self-assessment for efficient continuation of education.