Global Certificate in Data-Backed Student Assessment Techniques
-- ViewingNowThe Global Certificate in Data-Backed Student Assessment Techniques is a comprehensive course designed to empower educators with the skills to leverage data in student assessment. In an era where data-driven decision-making is paramount, this course is crucial for industry demand.
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⢠Data Collection Techniques – An introduction to various data collection methods, including surveys, interviews, and observational studies, with a focus on those that can be used in a student assessment context.
⢠Data Analysis Basics – An overview of fundamental data analysis concepts, such as data cleaning, exploration, and visualization, to help learners understand and interpret assessment data.
⢠Psychometrics and Assessment – An exploration of psychometric properties and their role in student assessment, emphasizing concepts like reliability, validity, and fairness.
⢠Assessment Tools and Strategies – A survey of different assessment tools and strategies, such as formative and summative assessments, standardized tests, and performance-based assessments, and their applications.
⢠Data-Driven Decision Making – A discussion on how to use data to inform educational decisions, including setting goals, identifying areas for improvement, and evaluating interventions.
⢠Data Privacy and Ethics – An examination of data privacy and ethical considerations when conducting student assessments, emphasizing the importance of confidentiality and informed consent.
⢠Technology in Assessment – An overview of how technology can enhance student assessment, including the use of online platforms, data dashboards, and adaptive testing.
⢠Assessment for Learning – An exploration of the role of assessment in promoting student learning, emphasizing the importance of providing feedback, setting learning goals, and tracking progress.
⢠Culturally Responsive Assessment – A discussion on how to create culturally responsive student assessments, including the use of culturally relevant assessment items, linguistic accommodations, and bias review.
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