Web挖掘基于信息系统成功模型的MOOCs质量评价影响因素
Web Mining MOOCs Quality Evaluation Influencing Factors
DOI: 10.12677/AE.2019.94077, PDF,  被引量    国家科技经费支持
作者: 吴冰*, 杜 宁：同济大学经济与管理学院，上海
关键词: MOOCs；信息质量；服务质量；质量评价；MOOCs； Information Quality； Service Quality； Quality Evaluation
摘要: MOOCs (Massive Open Online Courses，大规模开放在线课程)引发全球化教育竞争，但目前国内外对MOOCs质量评价研究，仍采用传统在线网络课程评价方式，忽略了MOOCs特点。本文集成MOOCs教学者和学习者视角，结合MOOCs特征，将MOOCs质量评价作为MOOCs系统收益，构建MOOCs系统成功模型，应用Web挖掘MOOCs平台数据，实证研究影响MOOCs质量评价的影响因素。研究结果表明：1) MOOCs平台特征显著正向影响MOOCs学习服务质量、教学服务质量和学习信息质量；2) MOOCs学习服务质量和教学服务质量均显著正向影响MOOCs质量评价；3) MOOCs学习信息质量和教学信息质量分别显著正向影响MOOCs学习行为和教学行为；4) MOOCs教学行为显著正向影响MOOCs学习行为；5) MOOCs学习行为显著正向影响MOOCs质量评价。由此，为MOOCs的规范建设和长效发展提供策略指导。
Abstract: MOOCs (Massive Open Online Courses) trigger the global education competition. However, current studies of MOOCs quality evaluation still adopt the way in the traditional online network courses, ignoring the characteristics of MOOCs. This paper, combining the features of MOOCs and Web mining the data of MOOCs, empirically studied the factors influencing the quality evaluation of MOOCs from the perspective of both educators and learners. Research results show that: 1) the characteristics of the MOOCs platform have significant positive effects on the quality of learning service, the quality of teaching service and the quality of learning information; 2) both the quality of learning service and the quality of teaching services have significantly positive impacts on the MOOCs quality evaluation; 3) the quality of learning information and the quality of teaching information positively influence the learning behavior and teaching behavior respectively; 4) MOOCs teaching behavior significantly has a positive effect on MOOCs learning behavior; 5) MOOCs learning behavior significantly has a positive effect on the MOOCs quality evaluation. Consequently, strategic guidance for MOOCs construction and long-term development are provided.
文章引用：吴冰, 杜宁. Web挖掘基于信息系统成功模型的MOOCs质量评价影响因素[J]. 教育进展, 2019, 9(4): 454-465. https://doi.org/10.12677/AE.2019.94077

参考文献

[1] [1] 刘路, 刘志民, 罗英姿. 欧洲MOOC教育质量评价方法及启示[J]. 开放教育研究, 2015, 21(5): 57-65.
[2] 谢宾, 施秋萍, 刘洋, 刘晟蓝. MOOC教学过程质量监控评价体系的构建与研究[J]. 教育教学论坛, 2016(32): 342-345.
[3] 李丽, 等. 影响MOOC学习质量的关键因素探讨:基于学习者行为视角[J]. 工业和信息化教育, 2016(8): 42-52.
[4] 胡莉芳. MOOC教师个体属性及其教学行为分析[J]. 复旦教育论坛, 2017, 15(6): 63-68.
[5] 王珂珂. 基于库伯体验学习理论的MOOC质量影响因素分析[J]. 成人教育, 2016(9): 1-5.
[6] 邹勇. 基于MOOC的翻转课堂教学模式探究——评《MOOC: 宏观研究与微观探索》[J]. 中国教育学刊, 2018(7): 148.
[7] 李加军. 基于模糊综合评价法的MOOC教学质量评价研究[J]. 当代继续教育, 2016, 34(4): 50-54.
[8] 邱均平, 欧玉芳. 慕课质量评价指标体系构建及应用研究[J]. 高教发展与评估, 2015, 31(5): 72-81.
[9] 童小素, 贾小军. MOOC质量评价体系的构建探究[J]. 中国远程教育, 2017(5): 63-71.
[10] 王晶心, 原帅, 赵国栋. 混合式教学对大学生学习成效的影响——基于国内一流大学MOOC应用效果的实证研究[J]. 现代远距离教育, 2018, 179(5): 39-47.
[11] Shattuck, K., Zimmerman, W.A. and Adair, D. (2014) Continuous Improvement of the QM Rubric and Re-view Processes: Scholarship of Integration and Application. Internet Learning, 3, 25-34. [Google Scholar] [CrossRef] 
[12] Rieber, L.P. (2017) Participation Patterns in a Massive Open Online Course (MOOC) about Statistics. British Journal Educational Technology, 48, 1295-1304. [Google Scholar] [CrossRef] 
[13] Lowenthal, P.R. and Hodges, C.B. (2015) In Search of Quality: Using Quality Matters to Analyze the Quality of Massive, Open, Online Courses (MOOCs). International Review of Research in Open and Distributed Learning, 16, 83-101. [Google Scholar] [CrossRef] 
[14] Joksimovic, S., Poquet, O., Kovanovic, V., et al. (2018) How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs. Review of Educational Research, 88, 43-86. [Google Scholar] [CrossRef] 
[15] Margaryan, A., Bianco, M. and Littlejohn, A. (2015) Instructional Quality of Massive Open Online Courses (MOOCs). Computers & Education, 80, 77-83. [Google Scholar] [CrossRef] 
[16] Watson, S.L., Watson, W.R., Yu, J.H., et al. (2017) Learner Profiles of Attitudinal Learning in a MOOC: An Explanatory Sequential Mixed Methods Study. Computers & Education, 114, 274-285. [Google Scholar] [CrossRef] 
[17] Sunar, A.S., White, S., Abdullah, N.A., et al. (2017) How Learners’ Interactions Sustain Engagement: A MOOC Case Study. IEEE Transactions on Learning Technologies, 10, 475-487. [Google Scholar] [CrossRef] 
[18] Creelman, A., Ehlers, U.D. and Ossiannilsson, E.S. (2014) Perspectives on MOOC Quality: An Account of the EFQUEL MOOC Quality Project. International Journal for Innovation and Quality in Learning, 2, 79-87.
[19] Maloshonok, N. and Terentev, E. (2016) The Impact of Visual De-sign and Response Formats on Data Quality in a Web Survey of MOOC Students. Computers in Human Behavior, 62, 506-515. [Google Scholar] [CrossRef] 
[20] Chen, O., Woolcott, G. and Sweller, J. (2017) Using Cognitive Load Theory to Structure Computer-Based Learning Including MOOCs. Journal of Computer Assisted Learning, 33, 293-305. [Google Scholar] [CrossRef] 
[21] Leris, D., Sein-Echaluce, M.L., Hernandez, M., et al. (2017) Val-idation of Indicators for Implementing an Adaptive Platform for MOOCs. Computers in Human Behavior, 72, 783-795. [Google Scholar] [CrossRef] 
[22] Pursel, B.K., Zhang, L., Jablokow, K.W., et al. (2016) Understand-ing MOOC Students: Motivations and Behaviours Indicative of MOOC completion. Journal of Computer Assisted Learning, 32, 202-217. [Google Scholar] [CrossRef] 
[23] Hew, K.F. (2016) Promoting Engagement in Online Courses: What Strate-gies Can We Learn from Three Highly Rated MOOCs. British Journal of Educational Technology, 47, 320-341. [Google Scholar] [CrossRef] 
[24] Martin, J.L., Amado-Salvatierra, H.R. and Hilera, J.R. (2016) MOOCs for All: Evaluating the Accessibility of Top MOOC Platforms. International Journal of Engineering Education, 32, 2274-2283.
[25] Alraimi, K.M., Zo, H.J. and Ciganek, A.P. (2015) Understanding the MOOCs Continuance: The Role of Openness and Reputation. Computers & Education, 80, 28-38. [Google Scholar] [CrossRef] 
[26] Aparicio, M., Bacao, F. and Oliveira, T. (2016) Cultural Im-pacts on e-Learning Systems’ Success. Internet and Higher Education, 31, 58-70. [Google Scholar] [CrossRef] 
[27] Marjanovic, U., Delic, M. and Lalic, B. (2016) Developing a Model to Assess the Success of e-Learning Systems: Evidence from a Manufacturing Company in Transitional Economy. Information Systems and E-Business Management, 14, 253-272. [Google Scholar] [CrossRef] 
[28] Mohammadi, H. (2015) Investigating Users’ Perspectives on e-Learning: An Integration of TAM and IS Success Model. Computers in Human Behavior, 45, 359-374. [Google Scholar] [CrossRef] 
[29] Alsabawy, A.Y., Cater-Steel, A. and Soar, J. (2013) IT Infrastruc-ture Services as a Requirement for e-Learning System Success. Computers & Educations, 69, 431-451. [Google Scholar] [CrossRef] 
[30] Wu, B. and Chen, X.H. (2017) Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior, 67, 221-232. [Google Scholar] [CrossRef] 
[31] Delgado-Marquez, B.L., Escudero-Torres, M.A. and Hurta-do-Torres, N.E. (2013) Being Highly Internationalized Strengthens Your Reputation: An Empirical Investigation of Top Higher Education Institutions. Higher Education, 66, 619-633. [Google Scholar] [CrossRef] 
[32] Petter, S. and McLean, E.R. (2009) A Meta-Analytic Assessment of the DeLone and McLean IS Success Model: An Examination of IS Success at the Individual Level. Information & Management, 46, 159-166. [Google Scholar] [CrossRef] 
[33] Saba, T. (2013) Implications of e-Learning Systems and Self-Efficacy on Students Outcomes: A Model Approach. Human-Centric Computing and Information Science, 2, 2-11. [Google Scholar] [CrossRef] 
[34] Suh, H., Chung, S. and Choi, J. (2017) An Empirical Analysis of a Maturity Model to Assess Information System Success: A Firm-Level Perspective. Behaviour & Information Technology, 36, 792-808. [Google Scholar] [CrossRef] 
[35] Nagase, M. and Kano, Y. (2017) Identifiability of Nonre-cursive Structural Equation Models. Statistics & Probability Letters, 122, 109-117. [Google Scholar] [CrossRef] 
[36] Alario-Hoyos, C., Estevez-Ayres, I., Perez-Sanagustin, M., et al. (2017) Understanding Learners’ Motivation and Learning Strategies in MOOCs. International Review of Research in Open and Distributed Learning, 18, 119-137. [Google Scholar] [CrossRef] 
[37] Almatrafi, O., Johri, A. and Rangwala, H. (2018) Needle in a Hay-stack: Identifying Learner Posts That Require Urgent Response in MOOC Discussion Forums. Computers & Education, 118, 1-9. [Google Scholar] [CrossRef] 
[38] Zawacki-Richter, O., Bozkurt, A., Alturki, U., et al. (2018) What Research Says about MOOCs: An Explorative Content Analysis. International Review of Research in Open and Distributed Learning, 19, 242-259. [Google Scholar] [CrossRef] 
[39] 张成龙, 李丽娇. 提升学生网络自我调节学习成效的实证研究[J]. 现代远距离教育, 2018(2): 45-52.

为你推荐