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Learning Loss Recovery Dashboard: A Proposed Design to Mitigate Learning Loss Post Schools Closure

Author

Listed:
  • Tahani I. Aldosemani

    (Department of Educational Studies, College of Education, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Ahmed Al Khateeb

    (Department of English, College of Arts, King Faisal University, Al Ahsa 31982, Saudi Arabia)

Abstract

Research has shown the effectiveness of designing a Learning Analytics Dashboard (LAD) for learners and instructors, including everyone’s levels of progress and performance. An intertwined relationship exists between learning analytics (LA) and the learning process. Understanding information or data about learners and their learning journey can contribute to a deeper understanding of learners and the learning process. The design of an effective learning dashboard relies heavily on LA, including assessment of the learning process, i.e., gains and losses. A Learning Loss Recovery Dashboard (LLRD) can be designed as an instructional tool, to support the learning process as well as learners’ performance and their academic achievement. The current project proposes a LLRD prototype model to deal with potential learning loss; increase the achievement of learning outcomes; and provide a single, comprehensive learning process, where schools can evaluate and remedy any potential learning loss resulting from the distance-learning period that was caused by the COVID-19 pandemic. This systematic dashboard prototype functions to determine learning gains by K–12 learners. It is expected that the implementation of the proposed dashboard would provide students, teachers, and educational administrators with an integrated portal, for a holistic and unified remedial experience for addressing learning loss.

Suggested Citation

  • Tahani I. Aldosemani & Ahmed Al Khateeb, 2022. "Learning Loss Recovery Dashboard: A Proposed Design to Mitigate Learning Loss Post Schools Closure," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5944-:d:815147
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