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Exploring Student Expectations and Confidence in Learning Analytics

Hayk Asatryan, Basile Tousside, Janis Mohr, Malte Neugebauer, Hildo Bijl, Paul Spiegelberg, Claudia Frohn-Schauf, Jörg Frochte

TL;DR

This paper addresses how university students expect and feel confident about Learning Analytics (LA) while balancing Data Protection (DP) requirements. It uses an adapted SELAQ survey across multiple faculties and applies K-means clustering to reveal four student profiles—Enthusiasts, Realists, Cautious, and Indifferents—and uses a CART decision tree to explain those clusters. The study also analyzes how these profiles vary by discipline and examines lecturer-related trust issues to guide transparent LA implementation. The findings offer actionable guidance for tailoring LA deployments to student needs and emphasize the importance of DP compliance and lecturer readiness in higher education.

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.

Exploring Student Expectations and Confidence in Learning Analytics

TL;DR

This paper addresses how university students expect and feel confident about Learning Analytics (LA) while balancing Data Protection (DP) requirements. It uses an adapted SELAQ survey across multiple faculties and applies K-means clustering to reveal four student profiles—Enthusiasts, Realists, Cautious, and Indifferents—and uses a CART decision tree to explain those clusters. The study also analyzes how these profiles vary by discipline and examines lecturer-related trust issues to guide transparent LA implementation. The findings offer actionable guidance for tailoring LA deployments to student needs and emphasize the importance of DP compliance and lecturer readiness in higher education.

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.
Paper Structure (7 sections, 5 figures, 2 tables)

This paper contains 7 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Study Design
  4. Analysing the Questionnaire Results
  5. Understanding the Student Clusters
  6. Discussion
  7. Conclusion and Future Prospects

Figures (5)

  • Figure 1: Number of students participating in the survey per field of study. Civil engineering, Business Studies and Computer Science are the primary groups.
  • Figure 2: Desire (blue) and expectation (red) average grade per question group. For each question group, the expectation is always lower than the desire.
  • Figure 3: Overall average grades by question (bars) and in what degree course (annotations) the lowest / highest average (lines) is measured: Architecture (AR), Civil Engineering (CE), Electro-Mechanical Engineering (EM), Computer Science (CS), Sustainability (SU), Surveying (SV), Business Studies (BS), Other Engineering Courses (OE).
  • Figure 4: Average rating of all respondents and divided according to the clusters formed (with cluster size). Each for both sub-groups (LA, DP) and sub-questions (desire, expectation).
  • Figure 5: Analyzing Student Clusters and Attitudes: Decision Tree Analysis and Distribution Across Study Fields.