{"title":"基于问题的生活情境问题探究--以人工智能在自然科学中的学习效果为例","authors":"King-Dow Su","doi":"10.29333/ijese/14420","DOIUrl":null,"url":null,"abstract":"This research focuses on problem-based learning (PBL) teaching methods and designs artificial intelligence (AI) in facial recognition systems, smart streetlights, and drone as teaching materials. To integrate teaching materials of life situation issues into the natural general curriculum and develop a learning perception questionnaire (LPQ) with validity and reliability to evaluate students’ perception of the curriculum. Based on a valid assessment tool, 56 college students were assessed on their learning of emerging technology contextual issues to evaluate their satisfaction, learning situation, and learning effectiveness. The results of the study are, as follows:\n(1) construct teaching materials for AI application in face recognition systems, street lights, and drone situations; (2) develop an LPQ with reliability and validity; (3) most students are satisfied with the integration of AI into PBL teaching; (4) most students believe that the integration of cross-domain learning in different subjects can help improve self-learning effectiveness and ensure continuous learning interest; and (5) many students agree that this course can improve learning outcomes. In the future, the focus will be on teaching practice, incorporating easy-to-use AI textbook content, and enhancing the opportunities for interactive learning; in addition, increasing the number of effective samples in the research to improve the depth of the experiment and the breadth of research.","PeriodicalId":495938,"journal":{"name":"Interdisciplinary journal of environmental and science education","volume":"27 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Problem-based life situational issues exploration–Taking the learning effectiveness of artificial intelligence in natural sciences\",\"authors\":\"King-Dow Su\",\"doi\":\"10.29333/ijese/14420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research focuses on problem-based learning (PBL) teaching methods and designs artificial intelligence (AI) in facial recognition systems, smart streetlights, and drone as teaching materials. To integrate teaching materials of life situation issues into the natural general curriculum and develop a learning perception questionnaire (LPQ) with validity and reliability to evaluate students’ perception of the curriculum. Based on a valid assessment tool, 56 college students were assessed on their learning of emerging technology contextual issues to evaluate their satisfaction, learning situation, and learning effectiveness. The results of the study are, as follows:\\n(1) construct teaching materials for AI application in face recognition systems, street lights, and drone situations; (2) develop an LPQ with reliability and validity; (3) most students are satisfied with the integration of AI into PBL teaching; (4) most students believe that the integration of cross-domain learning in different subjects can help improve self-learning effectiveness and ensure continuous learning interest; and (5) many students agree that this course can improve learning outcomes. In the future, the focus will be on teaching practice, incorporating easy-to-use AI textbook content, and enhancing the opportunities for interactive learning; in addition, increasing the number of effective samples in the research to improve the depth of the experiment and the breadth of research.\",\"PeriodicalId\":495938,\"journal\":{\"name\":\"Interdisciplinary journal of environmental and science education\",\"volume\":\"27 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interdisciplinary journal of environmental and science education\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.29333/ijese/14420\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interdisciplinary journal of environmental and science education","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.29333/ijese/14420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Problem-based life situational issues exploration–Taking the learning effectiveness of artificial intelligence in natural sciences
This research focuses on problem-based learning (PBL) teaching methods and designs artificial intelligence (AI) in facial recognition systems, smart streetlights, and drone as teaching materials. To integrate teaching materials of life situation issues into the natural general curriculum and develop a learning perception questionnaire (LPQ) with validity and reliability to evaluate students’ perception of the curriculum. Based on a valid assessment tool, 56 college students were assessed on their learning of emerging technology contextual issues to evaluate their satisfaction, learning situation, and learning effectiveness. The results of the study are, as follows:
(1) construct teaching materials for AI application in face recognition systems, street lights, and drone situations; (2) develop an LPQ with reliability and validity; (3) most students are satisfied with the integration of AI into PBL teaching; (4) most students believe that the integration of cross-domain learning in different subjects can help improve self-learning effectiveness and ensure continuous learning interest; and (5) many students agree that this course can improve learning outcomes. In the future, the focus will be on teaching practice, incorporating easy-to-use AI textbook content, and enhancing the opportunities for interactive learning; in addition, increasing the number of effective samples in the research to improve the depth of the experiment and the breadth of research.