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首页> 外文会议>IEEE International Conference in MOOCs, Innovation and Technology in Education >Decoding the Indian MOOC learner
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Decoding the Indian MOOC learner

机译：解码印度Mooc学习者

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摘要

Massive Open Online Courses have been looked at both as disruptive innovation as well as the biggest experiment in education in recent times. 15 Million learners have turned to MOOCs as in 2014 and Indians constitute the second largest share of the MOOC user base. This paper attempts to understand some of the essential characteristics that distinguish a MOOC user from a non-user in India. A proprietary survey for understanding MOOC consumers has been used to draw insights on demographics of potential MOOC users including age, occupation, gender, educational backgrounds as well as some salient aspects of their personality, learning styles and life goals. Three logistic regression models have been tested. The first model investigates the impact of background demographic variables and internet skills of respondents on the choice to enroll in at least one MOOC course. The second model includes key personality traits that are hypothesized to influence the user adoption of MOOCs and the full modeladdsvariables pertaining to learning styles, learning environment and life goals. Those with better internet skills and an existing preference for learning through videos were seen to be significantly more likely to adopt MOOCs. Personality traits aligned with an openness to try new things were seen to influence the adoption decision but the learning styles and learning environment did not differentiate users from non-users. The results also indicate a significant influence of gender and age.
机译：大规模开放的在线课程被视为破坏性创新以及最近的教育最大的实验。截至2014年，1500万学习者已转向Moocs，印度人构成了MooC用户群的第二大份额。本文试图了解将MooC用户与印度非用户区分开的一些基本特征。理解Mooc消费者的专有调查已被用来吸引有关潜在的Mooc用户的人口统计数据的洞察，包括年龄，职业，性别，教育背景以及他们个性的一些突出方面，学习款式和生命目标。已经测试了三种逻辑回归模型。第一款模型研究了背景人口变量和受访者的互联网技能对至少一个MooC课程的选择的影响。第二种模型包括假设的关键个性特征，以影响用户采用MOOCS和与学习风格有关的完整模型，学习环境和生命目标。具有更好的互联网技能和通过视频学习的最偏好的人被认为是更有可能采用MOOCS的可能性。人格特征与开放性调整以尝试新的东西被视为影响采用决定，但学习方式和学习环境没有区分来自非用户的用户。结果还表明性别和年龄的重大影响。

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