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

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-09-18 DOI:10.3390/info14090511
Hongfeng Sang, Liyi Ma, Nan Ma
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引用次数: 0

摘要

构建了以平台设计、组织机制、课程建设为重点的MOOC三维分析框架。本框架旨在探究智能时代大数据mooc的现状,特别是从改善大学生心理健康的角度;并总结了施工经验和需要改进的地方。从平台(包括平台建设、资源量、资源质量)、组织机制(包括开课单位、师资队伍、学习规范)、课程建设(包括课程目标、教学设计、课程内容、教学组织、实施、教学管理、评价)三个方面对16个MOOC平台上525门大数据课程的建设进行对比分析。借鉴国际大数据mooc的成功实践和国内优秀的大数据mooc,并考虑到政府权威文件的要求,如:文献[J.G.[2019]],第8号。3文献(J.G.[2015]),第1期。参考文献1 (J.G.[2022])和《教育信息技术标准celts -22在线课程评价标准》,为中国大数据mooc的未来发展提供了平台、组织机制、课程建设等方面的建议。
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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.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
期刊最新文献
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