{"title":"基于改善大学生心理健康视角的智能时代大数据mooc现状分析","authors":"Hongfeng Sang, Liyi Ma, Nan Ma","doi":"10.3390/info14090511","DOIUrl":null,"url":null,"abstract":"A three-dimensional MOOC analysis framework was developed, focusing on platform design, organizational mechanisms, and course construction. This framework aims to investigate the current situation of big data MOOCs in the intelligent era, particularly from the perspective of improving the mental health of college students; moreover, the framework summarizes the construction experience and areas for improvement. The construction of 525 big data courses on 16 MOOC platforms is compared and analyzed from three aspects: the platform (including platform construction, resource quantity, and resource quality), organizational mechanism (including the course opening unit, teacher team, and learning norms), and course construction (including course objectives, teaching design, course content, teaching organization, implementation, teaching management, and evaluation). Drawing from the successful practices of international big data MOOCs and excellent Chinese big data MOOCs, and considering the requirements of authoritative government documents, such as the no. 8 document (J.G. [2019]), no. 3 document (J.G. [2015]), no. 1 document (J.G. [2022]), as well as the “Educational Information Technology Standard CELTS-22—Online Course Evaluation Standard”, recommendations about the platform, organizational mechanism, and course construction are provided for the future development of big data MOOCs in China.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students\",\"authors\":\"Hongfeng Sang, Liyi Ma, Nan Ma\",\"doi\":\"10.3390/info14090511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A three-dimensional MOOC analysis framework was developed, focusing on platform design, organizational mechanisms, and course construction. This framework aims to investigate the current situation of big data MOOCs in the intelligent era, particularly from the perspective of improving the mental health of college students; moreover, the framework summarizes the construction experience and areas for improvement. The construction of 525 big data courses on 16 MOOC platforms is compared and analyzed from three aspects: the platform (including platform construction, resource quantity, and resource quality), organizational mechanism (including the course opening unit, teacher team, and learning norms), and course construction (including course objectives, teaching design, course content, teaching organization, implementation, teaching management, and evaluation). Drawing from the successful practices of international big data MOOCs and excellent Chinese big data MOOCs, and considering the requirements of authoritative government documents, such as the no. 8 document (J.G. [2019]), no. 3 document (J.G. [2015]), no. 1 document (J.G. [2022]), as well as the “Educational Information Technology Standard CELTS-22—Online Course Evaluation Standard”, recommendations about the platform, organizational mechanism, and course construction are provided for the future development of big data MOOCs in China.\",\"PeriodicalId\":38479,\"journal\":{\"name\":\"Information (Switzerland)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information (Switzerland)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/info14090511\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information (Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/info14090511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students
A three-dimensional MOOC analysis framework was developed, focusing on platform design, organizational mechanisms, and course construction. This framework aims to investigate the current situation of big data MOOCs in the intelligent era, particularly from the perspective of improving the mental health of college students; moreover, the framework summarizes the construction experience and areas for improvement. The construction of 525 big data courses on 16 MOOC platforms is compared and analyzed from three aspects: the platform (including platform construction, resource quantity, and resource quality), organizational mechanism (including the course opening unit, teacher team, and learning norms), and course construction (including course objectives, teaching design, course content, teaching organization, implementation, teaching management, and evaluation). Drawing from the successful practices of international big data MOOCs and excellent Chinese big data MOOCs, and considering the requirements of authoritative government documents, such as the no. 8 document (J.G. [2019]), no. 3 document (J.G. [2015]), no. 1 document (J.G. [2022]), as well as the “Educational Information Technology Standard CELTS-22—Online Course Evaluation Standard”, recommendations about the platform, organizational mechanism, and course construction are provided for the future development of big data MOOCs in China.