{"title":"为教育工作者设计的基于模板的间隔重复学习解决方案","authors":"Ayman Hajja, Austin J. Hunt","doi":"10.1109/FIE49875.2021.9637051","DOIUrl":null,"url":null,"abstract":"This Innovative Practice Work in Progress paper introduces a freely available API-based platform for spaced repetition education. Spaced repetition, or spaced repetition learning, is a technique used by learners to improve long-term knowledge retention through repeated exposure to information spread out through time, traditionally in the form of flashcard questions. The concept of repeated exposure to information at varying lengths of time, and its effectiveness to improve human memory, has been evolving since first being investigated in the 19th century. Recently, new mobile and web learning applications have been employing spaced repetition algorithms and techniques to improve students' retention; although increasingly popular amongst students, we believe there are certain drawbacks to these systems that we set out to address in this work. First, existing solutions primarily target only students as users without providing instructor-focused functionality for supplementing their teaching; we explore, through the inclusion of instructor-focused interfaces and reporting mechanisms, potential insights to be gained about the effect of spaced repetition learning on student retention. Secondly, to the best of our knowledge, current popular spaced repetition algorithms are purely question-based; that is, re-exposure delays are calculated only per question, and not per topic. To address this, we present a novel question-templating framework that 1) enables knowledge retention to be assessed and analyzed in terms of groups of questions representing topics instead of only specific questions, and 2) provides a tool for instructors to efficiently manage learning material by creating randomizable templates to feed on the fly generation of similar-but-unique question sets for each student. Third, existing solutions widely employ spaced repetition to aid memorization without leveraging it to aid generalization; we aim to highlight a potential for the spaced repetition model to promote retention of generalized skills (transferable among different challenges). Lastly, and most importantly, we seek to provide a free application programming interface (API) that can be integrated with any custom mobile or web user interface to provide the research community with a highly integrable toolkit for exploring new questions about spaced repetition learning.","PeriodicalId":408497,"journal":{"name":"2021 IEEE Frontiers in Education Conference (FIE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Template-Based Spaced Repetition Learning Solution Designed for Educators\",\"authors\":\"Ayman Hajja, Austin J. Hunt\",\"doi\":\"10.1109/FIE49875.2021.9637051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This Innovative Practice Work in Progress paper introduces a freely available API-based platform for spaced repetition education. Spaced repetition, or spaced repetition learning, is a technique used by learners to improve long-term knowledge retention through repeated exposure to information spread out through time, traditionally in the form of flashcard questions. The concept of repeated exposure to information at varying lengths of time, and its effectiveness to improve human memory, has been evolving since first being investigated in the 19th century. Recently, new mobile and web learning applications have been employing spaced repetition algorithms and techniques to improve students' retention; although increasingly popular amongst students, we believe there are certain drawbacks to these systems that we set out to address in this work. First, existing solutions primarily target only students as users without providing instructor-focused functionality for supplementing their teaching; we explore, through the inclusion of instructor-focused interfaces and reporting mechanisms, potential insights to be gained about the effect of spaced repetition learning on student retention. Secondly, to the best of our knowledge, current popular spaced repetition algorithms are purely question-based; that is, re-exposure delays are calculated only per question, and not per topic. To address this, we present a novel question-templating framework that 1) enables knowledge retention to be assessed and analyzed in terms of groups of questions representing topics instead of only specific questions, and 2) provides a tool for instructors to efficiently manage learning material by creating randomizable templates to feed on the fly generation of similar-but-unique question sets for each student. Third, existing solutions widely employ spaced repetition to aid memorization without leveraging it to aid generalization; we aim to highlight a potential for the spaced repetition model to promote retention of generalized skills (transferable among different challenges). Lastly, and most importantly, we seek to provide a free application programming interface (API) that can be integrated with any custom mobile or web user interface to provide the research community with a highly integrable toolkit for exploring new questions about spaced repetition learning.\",\"PeriodicalId\":408497,\"journal\":{\"name\":\"2021 IEEE Frontiers in Education Conference (FIE)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Frontiers in Education Conference (FIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FIE49875.2021.9637051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Frontiers in Education Conference (FIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FIE49875.2021.9637051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Template-Based Spaced Repetition Learning Solution Designed for Educators
This Innovative Practice Work in Progress paper introduces a freely available API-based platform for spaced repetition education. Spaced repetition, or spaced repetition learning, is a technique used by learners to improve long-term knowledge retention through repeated exposure to information spread out through time, traditionally in the form of flashcard questions. The concept of repeated exposure to information at varying lengths of time, and its effectiveness to improve human memory, has been evolving since first being investigated in the 19th century. Recently, new mobile and web learning applications have been employing spaced repetition algorithms and techniques to improve students' retention; although increasingly popular amongst students, we believe there are certain drawbacks to these systems that we set out to address in this work. First, existing solutions primarily target only students as users without providing instructor-focused functionality for supplementing their teaching; we explore, through the inclusion of instructor-focused interfaces and reporting mechanisms, potential insights to be gained about the effect of spaced repetition learning on student retention. Secondly, to the best of our knowledge, current popular spaced repetition algorithms are purely question-based; that is, re-exposure delays are calculated only per question, and not per topic. To address this, we present a novel question-templating framework that 1) enables knowledge retention to be assessed and analyzed in terms of groups of questions representing topics instead of only specific questions, and 2) provides a tool for instructors to efficiently manage learning material by creating randomizable templates to feed on the fly generation of similar-but-unique question sets for each student. Third, existing solutions widely employ spaced repetition to aid memorization without leveraging it to aid generalization; we aim to highlight a potential for the spaced repetition model to promote retention of generalized skills (transferable among different challenges). Lastly, and most importantly, we seek to provide a free application programming interface (API) that can be integrated with any custom mobile or web user interface to provide the research community with a highly integrable toolkit for exploring new questions about spaced repetition learning.