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Learning Analytics in Higher Education -- Exploring Students and Teachers Expectations in Germany

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arxiv 2401.11981 v1 pith:PYNZPGQ6 submitted 2024-01-22 cs.CY

classification cs.CY
keywords educationhigheranalyticslearningstudentsteacherstechnologytowards
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Technology enhanced learning analytics has the potential to play a significant role in higher education in the future. Opinions and expectations towards technology and learning analytics, thus, are vital to consider for institutional developments in higher education institutions. The Sheila framework offers instruments to yield exploratory knowledge about stakeholder aspirations towards technology, such as learning analytics in higher education. The sample of the study consists of students (N = 1169) and teachers (N = 497) at a higher education institution in Germany. Using self-report questionnaires, we assessed students and teachers attitudes towards learning analytics in higher education teaching, comparing ideal and expected circumstances. We report results on the attitudes of students, teachers, as well as comparisons of the two groups and different disciplines. We discuss the results with regard to practical implications for the implementation and further developments of learning analytics in higher education.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics

    cs.CY 2025-07 reject novelty 2.0 of 10

    The paper presents an LLM-powered personalized curriculum framework whose claimed improvements are unsupported by the unrelated datasets and missing evidence.

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