Assessment Design and Quality Assurance
Effective assessment design is a critical component of academic quality and student success. It ensures that all evaluations are fair, transparent, valid, and aligned with both institutional and national quality assurance frameworks. At our University, assessment design is guided by the principles outlined in the QFEmirates Framework, which together provide a structured approach to ensure academic rigor and consistency across all programs.
Assessments are designed to measure the achievement of Intended Learning Outcomes (ILOs) at the appropriate QFEmirates level, reflecting the cognitive, practical, and transferable skills expected of students. Each assessment task must therefore demonstrate clear alignment between course outcomes, learning activities, and assessment criteria, ensuring that students are evaluated on relevant and meaningful aspects of their learning.
Embedding quality assurance processes in assessment design—such as internal moderation, peer review, and external benchmarking—ensures that assessments are not only reliable but also equitable across courses and delivery modes. Moderation plays a key role in maintaining academic integrity and comparability of standards, allowing the institution to verify that marking is consistent, feedback is constructive, and grade distributions are justifiable.
Assessment Design and Quality Assurance
Effective assessment design is a critical component of academic quality and student success. It ensures that all evaluations are fair, transparent, valid, and aligned with both institutional and national quality assurance frameworks. At our University, assessment design is guided by the principles outlined in the QFEmirates Framework, which together provide a structured approach to ensure academic rigor and consistency across all programs.
Assessments are designed to measure the achievement of Intended Learning Outcomes (ILOs) at the appropriate QFEmirates level, reflecting the cognitive, practical, and transferable skills expected of students. Each assessment task must therefore demonstrate clear alignment between course outcomes, learning activities, and assessment criteria, ensuring that students are evaluated on relevant and meaningful aspects of their learning.
Embedding quality assurance processes in assessment design—such as internal moderation, peer review, and external benchmarking—ensures that assessments are not only reliable but also equitable across courses and delivery modes. Moderation plays a key role in maintaining academic integrity and comparability of standards, allowing the institution to verify that marking is consistent, feedback is constructive, and grade distributions are justifiable.
This course introduces fundamental concepts in probability and statistical analysis, focusing on practical applications in marketing. Students will explore counting techniques, probability theorems, random variables, and probability distributions (discrete and continuous). The course also covers sampling methods, statistical inference, confidence intervals, hypothesis testing, correlation, and regression analysis.
Through case studies and hands-on exercises, students will apply statistical techniques to real marketing data, enabling them to analyze consumer behavior, assess campaign effectiveness, and support data-driven decision-making. By the end of the course, students will be proficient in using statistical tools for marketing research and analytics.
This course introduces fundamental concepts in probability and statistical analysis, focusing on practical applications in marketing. Students will explore counting techniques, probability theorems, random variables, and probability distributions (discrete and continuous). The course also covers sampling methods, statistical inference, confidence intervals, hypothesis testing, correlation, and regression analysis.
Through case studies and hands-on exercises, students will apply statistical techniques to real marketing data, enabling them to analyze consumer behavior, assess campaign effectiveness, and support data-driven decision-making. By the end of the course, students will be proficient in using statistical tools for marketing research and analytics.
This course introduces fundamental concepts in probability and statistical analysis, focusing on practical applications in marketing. Students will explore counting techniques, probability theorems, random variables, and probability distributions (discrete and continuous). The course also covers sampling methods, statistical inference, confidence intervals, hypothesis testing, correlation, and regression analysis.
Through case studies and hands-on exercises, students will apply statistical techniques to real marketing data, enabling them to analyze consumer behavior, assess campaign effectiveness, and support data-driven decision-making. By the end of the course, students will be proficient in using statistical tools for marketing research and analytics.