Term explanation
Asynchronous learning refers to educational experiences that do not require all participants to be present at the same time. Learners access materials, complete exercises, and submit work according to their own schedule within a defined timeframe. Common formats include recorded video lectures, discussion boards, and self-paced e-courses. Asynchronous learning offers flexibility for geographically dispersed or busy learners and forms the backbone of most online learning programs.
Want to add live interaction to your asynchronous learning program? SlideLizard LIVE lets you run interactive PowerPoint sessions with polls, quizzes, and Q&A that bring distributed learners together in real time.
Learn more →Closed questions are questions that can be answered with a limited set of responses — most commonly a simple 'yes' or 'no', or a selection from predefined options. They are used to gather specific, factual information quickly and efficiently. In presentations and training settings, closed questions are useful for gauging audience understanding, confirming agreement, or running quick polls. While efficient, they offer little depth and should be balanced with open-ended questions when richer feedback or discussion is needed.
Learn moreThe outline view in PowerPoint shows a list with the whole text of all slides on the left of the screen. There are no images and graphics displayed in this view. It's useful for editing the presentation and can also be saved as a Word document.
Learn moreNormal View is the default editing mode in Microsoft PowerPoint. It displays the current slide in the center panel, a thumbnail panel on the left for navigating between slides, and a notes panel at the bottom for speaker notes. Normal View is where most presentation editing takes place — adding content, formatting text, inserting images, and adjusting layouts. It provides a clear, work-focused interface for building and refining individual slides.
Learn moreChain of thought is an AI reasoning technique in which a model explicitly works through intermediate steps before arriving at a final answer. By laying out its reasoning step by step, the model produces more accurate and reliable outputs — especially for complex, multi-part problems. In agentic AI systems, chain-of-thought reasoning is used to plan workflows and make decisions at each stage of an agent loop. For enterprise applications, it increases transparency and makes AI behavior easier to audit.
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