{"title":"学习系统模型","authors":"P. Buriak, B. McNurlen, J. Harper","doi":"10.1109/FIE.1995.483022","DOIUrl":null,"url":null,"abstract":"Develops a model of learning that differs greatly from traditional or intuitive models. This hard system is specifically designed for the context of problem-solving/higher-order thinking, rather than automatic learning. Research in educational psychology and cognitive science provides the basis for the model. Learning is the integration of new knowledge/behaviors into a framework, and subsequently recalling what is relevant in the appropriate situation. To understand learning, we must consider how new information is received and the stages through which new information is processed as it progresses from immediate sensory experience to long-term storage. It is also important to understand how novices and experts organize, analyze or encode, and then retrieve necessary information. In this particular case, engineering students are the novices and engineering educators are the experts. Teaching consists of organizing, planning, delivering and evaluating the content of the subject area. Teaching problem-solving in science requires a deep understanding of the subject matter, as well as an appreciation of the characteristics of the students, of presentation skills, and of evaluation techniques. This study presents a soft systems model for the craft of teaching, and develops a hard systems model for the science of learning.","PeriodicalId":137465,"journal":{"name":"Proceedings Frontiers in Education 1995 25th Annual Conference. Engineering Education for the 21st Century","volume":"250 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Systems model for learning\",\"authors\":\"P. Buriak, B. McNurlen, J. Harper\",\"doi\":\"10.1109/FIE.1995.483022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Develops a model of learning that differs greatly from traditional or intuitive models. This hard system is specifically designed for the context of problem-solving/higher-order thinking, rather than automatic learning. Research in educational psychology and cognitive science provides the basis for the model. Learning is the integration of new knowledge/behaviors into a framework, and subsequently recalling what is relevant in the appropriate situation. To understand learning, we must consider how new information is received and the stages through which new information is processed as it progresses from immediate sensory experience to long-term storage. It is also important to understand how novices and experts organize, analyze or encode, and then retrieve necessary information. In this particular case, engineering students are the novices and engineering educators are the experts. Teaching consists of organizing, planning, delivering and evaluating the content of the subject area. Teaching problem-solving in science requires a deep understanding of the subject matter, as well as an appreciation of the characteristics of the students, of presentation skills, and of evaluation techniques. This study presents a soft systems model for the craft of teaching, and develops a hard systems model for the science of learning.\",\"PeriodicalId\":137465,\"journal\":{\"name\":\"Proceedings Frontiers in Education 1995 25th Annual Conference. Engineering Education for the 21st Century\",\"volume\":\"250 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Frontiers in Education 1995 25th Annual Conference. Engineering Education for the 21st Century\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FIE.1995.483022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Frontiers in Education 1995 25th Annual Conference. Engineering Education for the 21st Century","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FIE.1995.483022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Develops a model of learning that differs greatly from traditional or intuitive models. This hard system is specifically designed for the context of problem-solving/higher-order thinking, rather than automatic learning. Research in educational psychology and cognitive science provides the basis for the model. Learning is the integration of new knowledge/behaviors into a framework, and subsequently recalling what is relevant in the appropriate situation. To understand learning, we must consider how new information is received and the stages through which new information is processed as it progresses from immediate sensory experience to long-term storage. It is also important to understand how novices and experts organize, analyze or encode, and then retrieve necessary information. In this particular case, engineering students are the novices and engineering educators are the experts. Teaching consists of organizing, planning, delivering and evaluating the content of the subject area. Teaching problem-solving in science requires a deep understanding of the subject matter, as well as an appreciation of the characteristics of the students, of presentation skills, and of evaluation techniques. This study presents a soft systems model for the craft of teaching, and develops a hard systems model for the science of learning.