{"title":"遥感科学与技术专业学生毕业属性评价","authors":"Y. Zhang, Yan Gong, W. Cui","doi":"10.1109/TALE54877.2022.00081","DOIUrl":null,"url":null,"abstract":"Taking the program of remote sensing science and technology as example, this paper calculated the degree of achievement of the 12 graduate attributes (GAs) adopted by Washington Accord for each student using the direct assessment method based on supporting courses’ grades. To make full use of the elective science courses and elective engineering program courses the course combos were adopted as supporting courses linking the GAs to the learning activities. The achievement scores of 12 GAs for 614 students of 2019-2021 were calculated. The results show that on average the 12 scores are all above 0.8. There is a small and steady improvement of the overall achievement scores except for GA6 Engineer and society of the past 3 years. The attributes with high scores are attribute 7 Environment and sustainability, 5 Modern tool usage. However, the low scores are from some soft competency requirement such as 10 Communication, 12 Life-long learning, and 11 Project management, which are the relatively deficient parts in the program of remote sensing science and technology. Comparison of the distribution in different achievement score ranges indicates that the students numbers of a score above 0.89 has increased, from 20% to 22% in 2020 and to 32% in 2021.","PeriodicalId":369501,"journal":{"name":"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graduate Attribute Assessment for Each Student of Remote Sensing Science and Technology Program\",\"authors\":\"Y. Zhang, Yan Gong, W. Cui\",\"doi\":\"10.1109/TALE54877.2022.00081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Taking the program of remote sensing science and technology as example, this paper calculated the degree of achievement of the 12 graduate attributes (GAs) adopted by Washington Accord for each student using the direct assessment method based on supporting courses’ grades. To make full use of the elective science courses and elective engineering program courses the course combos were adopted as supporting courses linking the GAs to the learning activities. The achievement scores of 12 GAs for 614 students of 2019-2021 were calculated. The results show that on average the 12 scores are all above 0.8. There is a small and steady improvement of the overall achievement scores except for GA6 Engineer and society of the past 3 years. The attributes with high scores are attribute 7 Environment and sustainability, 5 Modern tool usage. However, the low scores are from some soft competency requirement such as 10 Communication, 12 Life-long learning, and 11 Project management, which are the relatively deficient parts in the program of remote sensing science and technology. Comparison of the distribution in different achievement score ranges indicates that the students numbers of a score above 0.89 has increased, from 20% to 22% in 2020 and to 32% in 2021.\",\"PeriodicalId\":369501,\"journal\":{\"name\":\"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TALE54877.2022.00081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TALE54877.2022.00081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graduate Attribute Assessment for Each Student of Remote Sensing Science and Technology Program
Taking the program of remote sensing science and technology as example, this paper calculated the degree of achievement of the 12 graduate attributes (GAs) adopted by Washington Accord for each student using the direct assessment method based on supporting courses’ grades. To make full use of the elective science courses and elective engineering program courses the course combos were adopted as supporting courses linking the GAs to the learning activities. The achievement scores of 12 GAs for 614 students of 2019-2021 were calculated. The results show that on average the 12 scores are all above 0.8. There is a small and steady improvement of the overall achievement scores except for GA6 Engineer and society of the past 3 years. The attributes with high scores are attribute 7 Environment and sustainability, 5 Modern tool usage. However, the low scores are from some soft competency requirement such as 10 Communication, 12 Life-long learning, and 11 Project management, which are the relatively deficient parts in the program of remote sensing science and technology. Comparison of the distribution in different achievement score ranges indicates that the students numbers of a score above 0.89 has increased, from 20% to 22% in 2020 and to 32% in 2021.