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Foundations of Data Literacy and Learning Analytics

Introduction to Student Performance Data

Fundamental Metrics and Terminology

Reading Foundational Learning Analytics Reports

Interpreting Item-Level Feedback

Understanding Assessment Items and Alignment

Interpreting Item-Level Performance Data

Using Feedback to Identify Student Misconceptions

Identifying Learning Gaps Across Cohorts

Comparing Performance Across Cohorts

Spotting Patterns, Trends, and Learning Gaps

Avoiding Common Interpretation Errors

Translating Data into Curriculum Adjustments

From Data Findings to Instructional Decisions

Designing Actionable Curriculum Adjustments

Evaluating the Impact of Instructional Changes

Applied Data Analysis Practice

Guided Analysis of a Student Performance Dashboard

Data-Informed Curriculum Improvement Case Study

Quiz

Quiz Question 1: Foundational Metrics

Quiz Question 2: Item-Level Feedback

Quiz Question 3: Cohort Comparisons

Quiz Question 4: Identifying Learning Gaps

Quiz Question 5: Curriculum Adjustments

Survey and Feedback

Course Feedback Survey

Are you an educator, instructional designer, or academic administrator looking to harness the power of data to improve student outcomes. Welcome to "Data Literacy: Analyzing Student Performance with Learning Analytics.

Are you an educator, instructional designer, or academic administrator looking to harness the power of data to improve student outcomes? Welcome to "Data Literacy: Analyzing Student Performance with Learning Analytics." In today's educational landscape, data is everywhere—from learning management systems to attendance records and assessment scores. However, transforming this raw data into actionable instructional strategies requires a specialized skill set. This comprehensive course is designed to bridge the gap between data collection and curriculum improvement.

Throughout this course, you will explore the foundations of data literacy and learn how to interpret foundational learning analytics reports. We will dive deep into item-level feedback, teaching you how to analyze assessment items, identify student misconceptions, and distinguish between genuine learning gaps and assessment design flaws. You will also discover how to compare performance across different student cohorts, spot meaningful trends, and avoid common data interpretation errors.

Beyond just reading data, this course empowers you to take action. You will learn how to translate your findings into targeted curriculum adjustments, apply principles of constructive alignment, and evaluate the impact of your instructional changes. With hands-on applied data analysis practice, including guided dashboard analysis and real-world case studies, you will leave this course equipped with the confidence and tools to make evidence-based decisions that truly enhance teaching and learning. Join us to unlock the potential of learning analytics and drive student success!

Data Literacy: Analyzing Student Performance with Learning Analytics

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            <p>The modern educational landscape increasingly relies on robust data to inform instructional strategies and improve student outcomes. "Data Literacy: Analyzing Student Performance with Learning Analytics" provides educators and instructional leaders with the essential skills required to navigate, interpret, and apply student performance data effectively. By establishing a strong foundation in learning analytics, this course empowers professionals to move beyond basic metrics and engage in deep, diagnostic analysis of student learning. Participants will explore the ethical use of data, foundational performance metrics, and the critical distinction between descriptive and diagnostic analytics, ensuring a comprehensive understanding of how data can be leveraged to support responsible instructional decision-making.</p>

<p>Throughout this comprehensive program, learners will systematically examine the lifecycle of educational data analysis, progressing from interpreting item-level feedback to implementing targeted curriculum adjustments. The curriculum delves into the alignment of assessment items with Bloom's Taxonomy, enabling educators to identify specific student misconceptions and distinguish between genuine learning gaps and assessment design flaws. Furthermore, the course guides participants through the process of comparing performance across diverse student cohorts, recognizing persistent trends, and avoiding common data interpretation errors. By culminating in applied data analysis practice and real-world case studies, this course ensures that participants can confidently translate complex data findings into actionable, evidence-based curriculum improvements that measurably enhance student success.</p>
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