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D293 Glossary
From the printable glossary list
| Term | Definition |
|---|---|
| Ipsative Assessment | Measure self-progress against previous performance |
| Formative assessment | non graded assessment during a course to measure effectiveness of learning material and student understanding. |
| Summative assessment | Final assessment usually with a grade. At the end of a course or section. |
| Diagnostic assessment | why trends occur, investigate data |
| Competency-based assessment | Based on a skills over recall |
| Criterion-referenced assessment | based on a specific standard or outcome. Meet predetermined criteria |
| norm-referenced assessment | Compare scored to peers. See how student compares to other students |
| Psychomotor domain | domain of body movement, coordination, athletic |
| Affective domain | domain of emotional needs of learners, feelings, attitudes, interpersonal skills |
| Cognitive domain | domain of the intellect, thinking, problem solving |
| Descriptive data method | data gathered from the past. What Happened? (Symptoms) |
| Diagnostic data method | data used to uncover trends, find why. Reason, Cause |
| Predictive data method | data used to forecast future trends and outcomes. Patterns and trends, forecast, anticipate what is likely to happen |
| Prescriptive data method | data used to recommend strategies (process) to optimize learning. action to improve outcome. What should be done |
| metrics | specific numerical values derived from raw learning data |
| Quantitative analysis | analysis based on numbers. Used with descriptive data method |
| Qualitative analysis | non-numerical analysis. Used with diagnostic data method |
| Social network analysis | Patterns and trends in relationships. (discussion boards) Used with predictive data method |
| Data for research | raw data, test new theories, WHAT? |
| Data for Accountability | WHERE & WHY Data used to evaluate rank or performance at a given time. compliance, accrediation, evaluations |
| Task-level feedback | feedback addressing specific errors |
| Process-level feedback | feedback advice on methods used with strategy for improvements |
| Regulatory-level feedback | feedback with goals, help learner take control, guides self-monitoring. (time management) |
| Self-level feedback | feedback to boost student confidence and feelings. Influences personal identity and motivation |
| Actionable feedback | Gives a map of what to do next |
| UDL (Multiple means of engagement) | Education to accommodate individual learning differences |
| Perceivable accessibility (WCAG) | All users can see and hear and identify content. |
| Examples of perceivable accessibility | captions, audible text, enlarge font, color contrast, text alternatives, alt text. |
| Operable accessibility (WCAG) | All users navigate using their preferred tools |
| examples of operable accessibility | keyboard alone, time limits, clear headings and labels, highlight what select, no flashing content |
| Understandable accessibility (WCAG) | Consistent and predictable design |
| Understandable accessibility examples | Clear language, consistent layout, clear error identification |
| Robust accessibility (WCAG) | Content works on various technologies, including future |
| examples of Robust accessibility | use on variety of devices, browsers, speech recognition |
| Construct validity bias | Inaccuracy in how an assessment measures.. How the test is built/constructed |
| Construct validity examples | math with extensive reading, complex vocabulary for students learning English, |
| Content validity bias | Failure of assessment to represent subject matter |
| content validity examples | EXAMPLE: not testing on the scope of the subject |
| Predictive validity bias | inaccurate forecasting of future outcomes, not accurate for all broups |
| Item selection bias | a specific item/question that favors or disfavors certain groups. |
| item selection examples | unfamiliar language, idioms, customs |
| descriptive examples | what happened, numbers, charts, graphs, performance levels |
| data for improvement | HOW, see if what we are doing is effective |
| ethics: Data Collection | Protect privacy, secure data |
| ethics: Data analysis | Not public, impact on learners, no assuming |
| ethics: data interpretation | Remove bias, act ethically |
Created by:
JaeleneStarr
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