{"title":"量化新手在代码谜题路径中的行为、经验和心理努力","authors":"John Allen, Caitlin L. Kelleher","doi":"10.1145/3411763.3451752","DOIUrl":null,"url":null,"abstract":"Code puzzles are an increasingly popular approach to introducing programming to young learners. Today, code puzzles are predominantly introduced through static puzzle sequences with increasing difficulty. However, adaptive systems in other domains have improved learning efficiency. This paper takes a step towards developing adaptive code puzzle systems based on controlling learners’ cognitive load. We conducted a study comparing static code puzzle pathways and adaptive pathways that predict cognitive load on future puzzles. While the trialled adaptive recommendation policy did not result in better learning, our findings point us towards a different policy which may have a greater effect on learner experience. In addition, we identify predictors of student dropout, and use our experimental data to quantify learners’ puzzle-solving experiences into 7 principal component properties and use these factors to suggest approaches for future adaptive systems.","PeriodicalId":265192,"journal":{"name":"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Quantifying Novice Behavior, Experience, and Mental Effort in Code Puzzle Pathways\",\"authors\":\"John Allen, Caitlin L. Kelleher\",\"doi\":\"10.1145/3411763.3451752\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Code puzzles are an increasingly popular approach to introducing programming to young learners. Today, code puzzles are predominantly introduced through static puzzle sequences with increasing difficulty. However, adaptive systems in other domains have improved learning efficiency. This paper takes a step towards developing adaptive code puzzle systems based on controlling learners’ cognitive load. We conducted a study comparing static code puzzle pathways and adaptive pathways that predict cognitive load on future puzzles. While the trialled adaptive recommendation policy did not result in better learning, our findings point us towards a different policy which may have a greater effect on learner experience. In addition, we identify predictors of student dropout, and use our experimental data to quantify learners’ puzzle-solving experiences into 7 principal component properties and use these factors to suggest approaches for future adaptive systems.\",\"PeriodicalId\":265192,\"journal\":{\"name\":\"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3411763.3451752\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3411763.3451752","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantifying Novice Behavior, Experience, and Mental Effort in Code Puzzle Pathways
Code puzzles are an increasingly popular approach to introducing programming to young learners. Today, code puzzles are predominantly introduced through static puzzle sequences with increasing difficulty. However, adaptive systems in other domains have improved learning efficiency. This paper takes a step towards developing adaptive code puzzle systems based on controlling learners’ cognitive load. We conducted a study comparing static code puzzle pathways and adaptive pathways that predict cognitive load on future puzzles. While the trialled adaptive recommendation policy did not result in better learning, our findings point us towards a different policy which may have a greater effect on learner experience. In addition, we identify predictors of student dropout, and use our experimental data to quantify learners’ puzzle-solving experiences into 7 principal component properties and use these factors to suggest approaches for future adaptive systems.