{"title":"MOOCs评级:游戏化的影响","authors":"Tai Wang, Qianqian Liu, Jing Jiang, Tong-gui Li","doi":"10.1109/EITT.2017.42","DOIUrl":null,"url":null,"abstract":"MOOCs actually share many similarities with MMOPGs (Massively Multiplayer Online Games). Since the latter is more successful in appealing participants, it will be beneficial to reflect which ideas from game design are effective to lengthen the engagement of MOOCs students. In this paper, feasible implications from gamification monographs were listed into a questionnaire to analyze students' preferences on MOOCs instructional environment. Three environment factors are extracted: content preparation, interaction and assessment. A structural equation model was proposed to illustrate the relationships between these factors. After verified, the implications work as criteria for volunteers to rate the environment quality of 76 Chinese MOOCs in six different platforms. It turned out that the scores on three factors can be used to predict the engagement endurance level of MOOCs by using K-Nearest Neighbors algorithm. Its error rate is 18.92%. Moreover, correlation analysis found that content preparation (r=0.57, p=","PeriodicalId":412662,"journal":{"name":"2017 International Conference of Educational Innovation through Technology (EITT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rating MOOCs: Implications from Gamification\",\"authors\":\"Tai Wang, Qianqian Liu, Jing Jiang, Tong-gui Li\",\"doi\":\"10.1109/EITT.2017.42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MOOCs actually share many similarities with MMOPGs (Massively Multiplayer Online Games). Since the latter is more successful in appealing participants, it will be beneficial to reflect which ideas from game design are effective to lengthen the engagement of MOOCs students. In this paper, feasible implications from gamification monographs were listed into a questionnaire to analyze students' preferences on MOOCs instructional environment. Three environment factors are extracted: content preparation, interaction and assessment. A structural equation model was proposed to illustrate the relationships between these factors. After verified, the implications work as criteria for volunteers to rate the environment quality of 76 Chinese MOOCs in six different platforms. It turned out that the scores on three factors can be used to predict the engagement endurance level of MOOCs by using K-Nearest Neighbors algorithm. Its error rate is 18.92%. Moreover, correlation analysis found that content preparation (r=0.57, p=\",\"PeriodicalId\":412662,\"journal\":{\"name\":\"2017 International Conference of Educational Innovation through Technology (EITT)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference of Educational Innovation through Technology (EITT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EITT.2017.42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference of Educational Innovation through Technology (EITT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EITT.2017.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MOOCs actually share many similarities with MMOPGs (Massively Multiplayer Online Games). Since the latter is more successful in appealing participants, it will be beneficial to reflect which ideas from game design are effective to lengthen the engagement of MOOCs students. In this paper, feasible implications from gamification monographs were listed into a questionnaire to analyze students' preferences on MOOCs instructional environment. Three environment factors are extracted: content preparation, interaction and assessment. A structural equation model was proposed to illustrate the relationships between these factors. After verified, the implications work as criteria for volunteers to rate the environment quality of 76 Chinese MOOCs in six different platforms. It turned out that the scores on three factors can be used to predict the engagement endurance level of MOOCs by using K-Nearest Neighbors algorithm. Its error rate is 18.92%. Moreover, correlation analysis found that content preparation (r=0.57, p=