Exploring Student Expectations and Confidence in Learning Analytics

TL;DR

This paper investigates student expectations and confidence in Learning Analytics using a questionnaire, identifying four distinct student clusters to understand acceptance and concerns about data privacy and usage.

Contribution

It introduces a structured clustering approach to categorize student attitudes towards Learning Analytics, providing insights into acceptance and privacy concerns.

Findings

Identified four student clusters: Enthusiasts, Realists, Cautious, Indifferents.

Provided insights into student acceptance and criticism of Learning Analytics.

Highlighted the importance of understanding student expectations for ethical LA deployment.

Abstract

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.