A strategy based on the deep learning approach (DLA) to develop awareness of the effects of climate change and future foresight skills among the second grade preparatory school students.

Document Type : Original Article

Author

Lecturer of curricula and methods of teaching science - Faculty of Education - Damanhour University - Arab Republic of Egypt

Abstract

 The aim of this research is to design a strategy based on the deep learning approach (DLA) and to examine its effectiveness in developing awareness of the effects of climate change and future foresight skills among second grade preparatory school students. The strategy based on the deep learning approach was designed, the teacher's guide and the student's worksheets were prepared, then the research tools were prepared: awareness of the effects of climate change scale and the future foresight test, and were previously applied to the research sample, which numbered (117) students in the second grade preparatory school in the first semester of the academic year 2021/2022. The sample was distributed randomly into two groups: the experimental group, which studied the unit "The Atmosphere and the Protection of the Planet Earth" with the proposed strategy, numbering (59) students, and the control group, which studied the same unit in the usual way, numbering (58) students. Then the search tools were applied post-test to the two groups, and the results revealed a statistically significant difference in each of: awareness of the effects of climate change scale and the future foresight test in favor of the students of the experimental group, In addition, there is a positive correlation between the research sample’s scores on the measure of awareness of the effects of climate change and their scores on the future forecasting skills test.

Keywords


Volume 117, Issue 117 - Serial Number 117
مناهج وطرق التدریس ( اللغة العربیة- الإنجلیزیة – الفرنسیة – الریاضیات – العلوم- الفنون- الاقتصاد المنزلی- التجاری ... )
2024
Pages 249-299
  • Receive Date: 13 October 2023
  • Revise Date: 24 October 2023
  • Accept Date: 29 October 2023