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All Journal International Journal of Evaluation and Research in Education (IJERE)
Sofiya Nikolaeva
Kyiv National Linguistic University
Education

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Formative assessment in the translation classroom: Closing a feedback loop Sofiya Nikolaeva; Tetiana Korol
International Journal of Evaluation and Research in Education (IJERE) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v10i2.21274

Abstract

Feedback as the key component of formative assessment was studied extensively in the context of the acquisition of the foreign language productive skills by different categories of learners. Similarly, this paper aimed at the investigation of the optimal design of the feedback loop in teaching translation to undergraduate students. A mixed-methods research involved 40 sophomores of Poltava University of Economics and Trade and was conducted during 2018–2019 academic year. It examined the efficacy of four feedback response types from both quantitative and qualitative perspectives. Sample Group 1 had to study the received teacher feedback arbitrarily and consider it, while working on their further translations. Sample Group 2 was required to prepare a written free-form self-reflection report on the teacher feedback. Sample Group 3 was supposed to resubmit their corrected translations to the teacher. Finally, Sample Group 4 had to accompany their self-reflection reports with the corrected translations. The obtained data showed statistically significant difference between the acquired levels of the translation skills by the students of the four sample groups. The highest results were achieved due to the combination of self-reflection reports and translation correction. The questionnaire on the students’ attitude to these practices proved the received statistical data.
Assessing students’ global competence using artificial intelligence: experimental research Sofiya Nikolaeva; Yana Diachkova; Anastasiia Syzenko
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i1.34038

Abstract

This paper addresses the challenge of assessing global competencies in higher education, an essential skill set for students in today’s interconnected world. Despite its importance, current assessment methods struggle to accurately capture the multidimensional nature of global competence, often due to subjective grading systems, cultural biases, and limited resources. This paper proposes using artificial intelligence (AI)-driven assessment tools to streamline and enhance the evaluation of global competencies. Through a literature review and experimental study conducted at T. Shevchenko National University of Kyiv (Ukraine), we examine how AI technologies, such as natural language processing, machine learning, and sentiment analysis, can be applied to assess cultural awareness, communication skills, and critical thinking. The results suggest that AI-driven assessments are more efficient, objective, and capable of providing personalized feedback than traditional methods. This paper concludes that AI has the potential to revolutionize the assessment of global competencies, offering a more inclusive and effective approach to preparing students for global citizenship.
Co-Authors Anastasiia Syzenko Tetiana Korol Yana Diachkova