分析学生对学习分析的期待与信心，发现四类人群。
Exploring Student Expectations and Confidence in Learning Analytics
- 用问卷与聚类分析学生对数据处理的期望
- 识别出热情者、现实者、谨慎者和漠不关心者四类人
- 为教育系统设计更被接受的学习分析方案提供依据
学习分析（Learning Analytics, LA）在现代教育系统中广泛应用，能够收集并分析学生数据，以理解与优化学习过程及环境。然而，数据收集需符合日益严格的隐私法规要求。本文使用《学生对学习分析的期望问卷》（SELAQ），分析不同院系学生对学习分析数据处理的期望与信心。通过聚类算法识别出四类学生群体：热情者、现实者、谨慎者与漠不关心者。该结构化分析揭示了学生对学习分析的接受度与批评点，为教育系统设计更易被接受的学习分析方案提供了重要参考。
原文摘要 · Abstract (English)
Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing of their data for Learning Analytics purposes. This allows us to identify four clusters of students through clustering algorithms: Enthusiasts, Realists, Cautious and Indifferents. This structured analysis provides valuable insights into the acceptance and criticism of Learning Analytics among students.
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