{"title":"使用专业知识而不是数据:为几个小时的代码练习生成提示","authors":"M. Buwalda, J. Jeuring, N. Naus","doi":"10.1145/3231644.3231690","DOIUrl":null,"url":null,"abstract":"Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"41 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Use expert knowledge instead of data: generating hints for hour of code exercises\",\"authors\":\"M. Buwalda, J. Jeuring, N. Naus\",\"doi\":\"10.1145/3231644.3231690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.\",\"PeriodicalId\":20634,\"journal\":{\"name\":\"Proceedings of the Fifth Annual ACM Conference on Learning at Scale\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth Annual ACM Conference on Learning at Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3231644.3231690\",\"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 of the Fifth Annual ACM Conference on Learning at Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3231644.3231690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
在学习编程的在线辅导系统领域,比如Code.org的“编程一小时”(Hour of code),有一种趋势是使用以前学生的数据来提供提示。本文表明,在Code.org的代码一小时(Hour of code)等环境中,最好使用专家知识来提供提示。我们提出了一种基于启发式的方法来生成下一步提示。我们使用模式匹配算法来识别启发式,并将每个识别的启发式应用于输入程序。我们通过使用评分函数选择得分最高的启发式来生成下一步提示。通过将我们的结果与之前在Hour of code上的实验结果进行比较,我们发现基于启发式的方法提供提示的结果是不可能进一步改进的。这些基本的启发式方法足以有效地模仿专家的下一步提示。
Use expert knowledge instead of data: generating hints for hour of code exercises
Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.