在线继续教育课程中的行为轨迹检测

2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) Pub Date : 2019-07-15 DOI:10.1109/ICALT.2019.00069

B. Penteado, Seiji Isotani, Paula Maria Pereira Paiva, Marina Morettin-Zupelari, D. Ferrari

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引用次数: 3

摘要

在线继续教育在全球范围内稳步增长，其学生的概况往往不同于其他正规模式。因此，了解这些学生在其课程轨迹上的行为是相关的。在这项工作中，我们调查了一个涉及听力保健专业人员(n=96)的专业在线课程，以发现不同的导航策略和可能影响其的因素。为此，我们应用序列分析技术来解释不同的导航模式和选择可能影响这些行为轨迹的人口统计协变量。发现了三个集群，它们之间存在细微差异，三个变量具有更强的预测能力:年龄、专业经验和地区。

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Detecting Behavioral Trajectories in Continued Education Online Courses

Continuing education online has been steadily increasing worldwide and its students' profiles tend to be different from those in other formal modalities. Hence, it is relevant to understand the behavior of these students along their course trajectories. In this work, we investigate a specialization online course involving hearing healthcare professionals (n=96), to discover different navigation strategies and the factors which may influence it. To that end, we applied sequence analysis techniques to interpret different navigation patterns and selected demographic covariables which may influence these behavioral trajectories. Three clusters were found, with subtle differences among them and three variables with greater predictive power: age, professional experience and region.

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2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)

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