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A Case Study in Technology-Enhanced, Data-Informed Teaching: Advancing Equitable and Reflective Learning in Higher Education (103891)
Session: On Demand
Room: Virtual Video Presentation
Presentation Type: Virtual Presentation
Large, multi-section communication courses face persistent challenges in sustaining consistent feedback, equitable learning experiences, and assessment integrity in multilingual, digitally complex higher-education environments. The rapid adoption of generative-AI tools has further transformed how students draft and revise academic writing, heightening the need for pedagogical models that promote ethical AI use, learner autonomy, and coherent instructional practices. Guided by Schön’s Reflective Practice, feedback literacy theory, and research on cognitive load and authentic assessment, this design-based case study examines a technology-enhanced learning model implemented across 18 sections of a first-year technical communication course at a public applied university in the Gulf. The model integrates three components: (1) AI-supported feedback as a reflective learning partner; (2) scaffolded, multimodal microlearning modules within the LMS to strengthen foundational writing skills; and (3) an integrity-focused assessment architecture, grounded in principles of authentic assessment and higher-order performance, that restructured task sequencing, recalibrated weightings, and integrated scaffolded, performance-based components to promote fairness, cognitive engagement, and AI-resilient learning outcomes. These elements were supported by a shared digital ecosystem incorporating calibrated rubrics, exemplars, microlearning artefacts, faculty reflection logs, an instructional Error Bank, and aggregated LMS analytics to align instruction and inform pedagogical decisions. Analysis of de-identified artefacts, aggregated LMS data, norming-session records, and weekly instructor reflections reveals emerging impacts: improved learner readiness for authentic writing tasks, greater cross-section consistency, earlier identification of common challenges, and reduced variation in rubric interpretation. The findings illustrate how ethically grounded, technology-enhanced, data-informed design strengthens equity, integrity, and sustainable reflective practice.
Authors:
Irum Naz, University of Doha for Science and Technology, Qatar
Ridha Ben Rejeb, University of Doha for Science and Technology, Qatar
Mariami Akopian, University of Doha for Science and Technology, Qatar
About the Presenter(s)
Dr. Irum Naz is a highly accomplished educator who obtained her Ph.D. in applied linguistics.
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Virtual Presentation
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