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9 a.m. - 6 p.m.
Phone: (843) 805-6930
West Ashley Library
9 a.m. - 4 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
McClellanville Library
9 a.m. - 1 p.m.
Phone: (843) 887-3699
Folly Beach Library
9 a.m. - 1 p.m.
Phone: (843) 588-2001
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. – 3 p.m.
Phone: (843) 869-2355
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 6 p.m.
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Dorchester Road Library
9 a.m. - 6 p.m.
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Edgar Allan Poe/Sullivan's Island Library
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John's Island Library
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Otranto Road Library
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Hurd/St. Andrews Library
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Baxter-Patrick James Island
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Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
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![loading](/sites/all/modules/hf_eds/images/loading.gif)
Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels
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![loading](/sites/all/modules/hf_eds/images/loading.gif)
- Author(s): Gomathy Ramaswami (ORCID
Gomathy Ramaswami (ORCID 0000-0002-5400-3843 ); Teo Susnjak (ORCIDTeo Susnjak (ORCID 0000-0001-9416-1435 ); Anuradha Mathrani (ORCIDAnuradha Mathrani (ORCID 0000-0002-9124-2536 )- Language:
English- Source:
Journal of Learning Analytics. 2023 10(3):115-134.- Publication Date:
2023- Document Type:
Journal Articles
Reports - Research - Language:
- Additional Information
- Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: [email protected]; Web site: https://learning-analytics.info/index.php/JLA/index
- Peer Reviewed: Y
- Source: 20
- Education Level: Postsecondary Education
Higher Education - Subject Terms:
- Subject Terms:
- ISSN: 1929-7750
- Abstract: Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students' online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive components, while none possess the ability to explain how the predictive models work and how they have arrived at specific conclusions for a given student. A further gap exists within existing LADs with respect to prescriptive analytics that generate data-driven feedback to students on how to adjust their learning behaviour. The LAD in this study attempts to address this gap and integrates a full spectrum of current analytics technologies for sense-making while anchoring them within theoretical educational frameworks. This study's LAD (SensEnablr) was evaluated for its effectiveness in impacting learning in a student cohort at a tertiary institution. Our findings demonstrate that student engagement with learning technologies and course resources increased significantly immediately following interactions with the dashboard. Meanwhile, results showed that the dashboard boosted the respondents' learning motivation levels and that the novel analytics insights drawn from predictive and prescriptive analytics were beneficial to their learning. This study, therefore, has implications for future research when investigating student outcomes and optimizing student learning using LAD technologies.
- Abstract: As Provided
- Publication Date: 2024
- Accession Number: EJ1411453
- Availability:
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