A Framework for Interactive Exploratory Learning Analytics
A Framework for Interactive Exploratory Learning Analytics
复制标题
DOI：
10.1007/978-3-319-91152-6_25
复制
发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
M. Mahzoon;M. Maher;Omar Eltayeby;Wenwen Dou;Kazjon Grace
中科院分区：
文献类型：
--
作者：
M. Mahzoon;M. Maher;Omar Eltayeby;Wenwen Dou;Kazjon Grace
已经开发了许多分析工具来从学生数据中发现知识。然而，知识发现过程需要先进的分析建模技能，这使其成为数据科学家的领域。这阻碍了教育领导者、教授和顾问直接参与知识发现过程的能力。因此，分析利用领域专业知识是具有挑战性的，使其结果通常既不有趣也没有用。通常，这些分析工具产生的结果是静态的，阻止领域专家通过改变工具内的数据模型或预测模型来探索不同的假设。我们已经开发了一个交互式和探索性学习分析的框架，开始解决这些挑战。我们通过组织两个焦点小组，与大学领域的专家一起进行数据探索和假设生成。我们使用这些焦点小组的发现来验证我们的框架，认为它使领域专家能够探索数据，分析和解释学生数据，以发现有用和有趣的知识。
Many analytic tools have been developed to discover knowledge from student data. However, the knowledge discovery process requires advanced analytical modelling skills, making it the province of data scientists. This impedes the ability of educational leaders, professors, and advisors to engage with the knowledge discovery process directly. As a result, it is challenging for analysis to take advantage of domain expertise, making its outcome often neither interesting nor useful. Usually the outcome produced from such analytic tools is static, preventing domain experts from exploring different hypotheses by changing data models or predictive models inside the tool. We have developed a framework for interactive and exploratory learning analytics which begins to address these challenges. We engaged in data exploration and hypotheses generation with our university domain experts by conducting two focus groups. We used the findings of these focus groups to validate our framework, arguing that it enables domain experts to explore the data, analysis and interpretation of student data to discover useful and interesting knowledge.