Research Article
Minnie H.-M. Hsieh, Alex Maritz, Chich-Jen Shieh
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep689
ABSTRACT
This study examined whether generative AI-driven environmental education changes university students’ social norms and environmental awareness differently from conventional instruction when course content is held constant. Environmental awareness was operationalized as nature relatedness. A pre-test post-test control group quasi-experimental design was used with 257 undergraduate students enrolled in a green building environment course, 129 in the experimental group and 128 in the control group. The experimental group reached the course material through a custom ChatGPT module restricted to that material, while the control group received conventional instruction. The intervention lasted 14 weeks. Social norms were measured with a purpose-developed 10-item instrument distinguishing descriptive from injunctive norms, and nature relatedness with the 21-item nature relatedness scale. Data were analyzed with Bayesian paired samples t-tests and Bayesian ANCOVA. Both conditions produced large pre-test to post-test gains on all five dimensions, with Bayes factors above 10 to the power of 42 and effect sizes between d = 0.90 and d = 1.41. After pre-test scores were controlled, the evidence favored the absence of a group difference on every dimension: Bayes factors for adding group to the pre-test model ranged from 0.21 to 0.48, and the strongest evidence against a group effect was obtained for nature experience, BF10 = 0.24, the dimension on which an advantage for continuous access was most plausible. Men scored slightly higher than women on the nature-self dimension. The findings indicate that the quality of course content, rather than the medium through which it is delivered, accounts for the change observed here.
Keywords: generative AI, ChatGPT, environmental education, social norms, nature relatedness, Bayesian analysis
Research Article
Fatima K. Urakova, Izida I. Ishmuradova, Nataliia A. Kondakchian, Roza Sh. Akhmadieva, Julia V. Torkunova, Irina N. Meshkova, Nikolay A. Mashkin
CONT ED TECHNOLOGY, Volume 15, Issue 1, Article No: ep398
ABSTRACT
Learning in the digital age is a pervasive idea that encompasses all aspects of a person's life, including work and leisure. As a result of the development of new teaching and learning tools, an increasing number of students are acquiring knowledge on the Internet- connected to the Internet. Therefore, all citizens must develop digital literacy as a lifelong learning skill. Studies have been conducted on students' digital skills in higher education institutions. In this context, this study aimed to investigate the skills of college students. The participants were students from a university in the Kazan region of Russia who volunteered to participate. Three hundred and eighty students completed the questionnaire online. The scale consists of a total of 25 questions and six dimensions. Since our independent variables are binary values, we applied the Bayesian t-test. We obtained the values of the Bayes factor (BF10) for each dimension and the total scale. In general, students' digital skills are well-developed. However, it was found that creating and using digital information requires fewer skills than in other areas. The hypothesis that there is no difference based on student gender was supported to a higher degree but not to a very high degree. The hypothesis that there is no difference based on students' fields of study was supported to a greater extent, but only to a moderate extent.
Keywords: digital skills, higher education, Bayesian analysis, Russia