Exploring Cross-Country Prediction Model Generalizability in MOOCs

被引:1
|
作者
Andres-Bray, Juan-Miguel [1 ]
Hutt, Stephen [2 ]
Baker, Ryan S. [1 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
[2] Univ Denver, Denver, CO USA
来源
PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023 | 2023年
基金
美国国家科学基金会;
关键词
MOOCs; Generalizability; Cross-country; Cross-culture; Completion; Predictive Modeling; MORF; CULTURE;
D O I
10.1145/3573051.3593380
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion.
引用
收藏
页码:183 / 193
页数:11
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