WGU-D293

Diagnostic

understand starting knowledge/gaps

Formative

inform ongoing teaching and feedback

Summative

judge mastery at the end of a unit/course

   Strategy

the overall design and approach to assessment.

Method

the concrete tools or tasks used to gather evidence within that strategy.

Purpose/Type

why you assess (diagnostic, formative, summative)

Diagnostic Assessment

An assessment conducted before instruction to identify prior knowledge, learner needs, and skill gaps.

Formative Assessment

An assessment conducted during instruction to monitor learning progress and inform instructional adjustments.

Summative Assessment

An assessment administered at the end of instruction to evaluate achievement and overall mastery.

Criterion-Referenced Assessment

An assessment strategy that compares a learner's performance against predefined criteria or standards rather than against peers.

Competency-Based Assessment

An assessment strategy that evaluates demonstrable competencies through authentic tasks, requiring mastery before progressing.

Ipsative Assessment

An assessment strategy that compares a learner's current performance with their own prior baseline or past performance.

Norm-Referenced Assessment

An assessment strategy that compares a learner's performance with peers or an established norm group.

Traditional Assessment

Conventional tests or knowledge checks, such as multiple-choice and matching questions.

Direct Assessment

An assessment method that produces observable demonstrations or tangible products of learning, such as performances, projects, role-plays, or simulations.

Indirect Assessment

An assessment method based on self-reported perceptions, confidence surveys, self-assessments, or reflections.

Comprehensive Assessment

A broad culminating assessment task that integrates multiple skills and learning objectives.

Rubric

An evaluation tool that makes assessment criteria and performance standards explicit.

Bloom: Remember

The cognitive level of Bloom's Revised Taxonomy focused on recalling facts, terms, and definitions.

Bloom: Understand

The cognitive level of Bloom's Revised Taxonomy focused on explaining ideas or concepts in one's own words.

Bloom: Apply

The cognitive level of Bloom's Revised Taxonomy focused on using knowledge or skills in a specific situation, scenario, or task.

Bloom: Analyze

The cognitive level of Bloom's Revised Taxonomy focused on breaking down information, comparing parts, or identifying causes and relationships.

Bloom: Evaluate

The cognitive level of Bloom's Revised Taxonomy focused on judging, critiquing, and justifying a decision.

Bloom: Create

The highest cognitive level of Bloom's Revised Taxonomy, focused on designing or producing a new plan, idea, or product.

Cognitive Domain

The domain of learning related to knowledge acquisition, thinking, and intellectual processes.

Affective Domain

The domain of learning focused on attitudes, values, emotions, empathy, respect, and emotional regulation.

Psychomotor Domain

The domain of learning focused on physical action skills, body language, eye contact, and practiced behaviors.

Descriptive Analytics

An analytics type that answers 'What happened?' by summarizing facts such as participation, completion, scores, and time on task.

Diagnostic Analytics

An analytics type that answers 'Why did it happen?' by investigating causes such as misconceptions, unclear examples, and skill gaps.

Predictive Analytics

An analytics type that answers 'What is likely to happen?' by forecasting performance, outcomes, or risks using early and historical data.

Prescriptive Analytics

An analytics type that answers 'What should we do?' by recommending targeted actions, coaching, resources, microlearning, or course redesign.

Quantitative Data

Numerical measures such as scores, rates, and time used to compare baselines and outcomes.

Qualitative Data

Narrative data expressed in words, comments, and reflections used to analyze themes, attitudes, and perceptions.

Social Network Analysis (SNA)

A data analysis approach that maps interaction patterns and connections to identify active contributors, isolation, and peer support.

Research Purpose of Data

The use of data to build knowledge and evidence to inform practice.

Accountability Purpose of Data

The use of data to demonstrate impact, compliance, return on investment (ROI), and justify resources.

Improvement Purpose of Data

The use of data to refine instructional content, delivery, retention, and learner outcomes.

Activity Measures

Data measures reflecting learner engagement, such as time-on-task, participation, and module completion.

Performance Measures

Data measures reflecting direct skill demonstration and application, such as role-plays and scenarios.

Task-Level Feedback

Feedback addressing the accuracy, correctness, or quality of a specific completed task.

Process-Level Feedback

Feedback focused on the strategies, methods, or procedures used to perform and complete a task.

Self-Regulatory Feedback

Feedback that encourages learners to monitor, reflect on, and manage their own performance and learning goals.

Self-Level Feedback

Feedback addressing personal qualities or effort; should be used sparingly because it is often not actionable.

Specific Feedback

Feedback that references observable behavior and concrete evidence rather than vague praise or criticism.

Actionable Feedback

Feedback that provides clear, practical, and achievable next steps for improvement.

Timely Feedback

Feedback delivered soon after performance while the learning experience is fresh so misconceptions can be corrected quickly.

Aligned Feedback

Feedback directly based on assessment results, learner needs, learning objectives, and explicit success criteria.

Audience-Aware Feedback

Feedback designed to be clear, respectful, free of jargon, and relevant to the learner's experience and context.

Universal Design for Learning (UDL)

An instructional framework offering multiple means of Representation, Action & Expression, and Engagement to minimize learning barriers.

POUR: Perceivable

The accessibility principle ensuring information can be perceived through varied senses via features like captions, alt text, and color contrast.

POUR: Operable

The accessibility principle ensuring interface components and navigation can be controlled by all users, such as via keyboard-only access.

POUR: Understandable

The accessibility principle ensuring that content and interface operations are clear, using plain language, predictable structures, and plain instructions.

POUR: Robust

The accessibility principle ensuring content remains compatible with current and future user technologies, including assistive tools and WCAG standards.

Construct Validity Bias

A form of assessment bias occurring when an instrument measures an unintended skill or construct rather than the target learning outcome.

Content Validity Bias

A form of assessment bias occurring when an assessment fails to represent all intended objectives or disproportionately overweights specific topics.

Predictive Validity Bias

A form of assessment bias where assessment scores do not accurately forecast a learner's future real-world performance.

Item-Selection Bias

A form of assessment bias in which test items favor particular demographic or cultural backgrounds over others.

Construct-Irrelevant Knowledge

Unrelated knowledge, skills, or cultural references (such as jargon or complex writing requirements) that distort assessment scores.

Assessment Accommodations

Adjustments such as alternative response formats, extended time, plain instructions, and assistive technology that remove barriers without lowering learning goals.

Cognitive Load Management

Strategies to reduce extraneous mental effort, such as using plain language, chunking tasks, logical organization, and scaffolding.

Ethical Data Collection

The practice of gathering only necessary minimal data, securing informed consent, explaining usage, and maintaining privacy and restricted access.

Ethical Data Analysis

Analyzing data with consideration of context and potential bias, using appropriate methods, and reporting limitations cautiously.

Ethical Reporting

Sharing analytics findings transparently and honestly with evidence support, protecting learner identities, and never presenting predictions as guarantees.