In:
ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference, Rezekne Academy of Technologies, Vol. 2 ( 2024-06-22), p. 336-339
Abstract:
The increased number of learning hours for students’ self-study in university ESP courses, on the one hand, and the high requirements for future engineers in the world labour market, on the other hand, confirm the necessity to foster students’ ESP skills in a self-study mode. Thanks to the rapid development of educational technologies, studying any discipline can be made interesting and effective. Our study suggests that utilizing AI tools in engineering podcast activities can improve engineering students’ listening comprehension skills in self-study mode. The post-study test designed with the help of the YT Class platform which generates assessment tests for video content, is compared across the control and experimental groups (52 third-year engineering students). The participants of both groups had experience using Ted podcasts in ESP learning and previous research proved the efficacy of implementing such activities in ESP classrooms with the first-year students of the Engineering and Technology Department. The final test based on the results of the processed podcasts was evaluated at 100 points, where the high score level was set at 60 points. In the experimental group, 15 students scored more than 60 points, in the control group, 8 students received more than 60 points. To confirm or refute the hypothesis that the proportion of students who received more than 60 points in the experimental group is higher than in the control group, the Fisher criterion was used. The progress of the students in the experimental group was statistically proven. AI-based podcast listening activities of engineering students outside the classroom prove to be a significant tool for improving students’ listening comprehension skills and inspiring curiosity and motivation to further development.
Type of Medium:
Online Resource
ISSN:
2256-070X
,
1691-5402
DOI:
10.17770/etr2024vol2.8085
Language:
English
Publisher:
Rezekne Academy of Technologies
Publication Date:
2024
