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In-context learning, refers to a model's ability to temporarily learn from prompts.For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning.
The worked-example effect is a learning effect predicted by cognitive load theory. [1] [full citation needed] Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving [2] [page needed] and discovery learning.
Limited English proficiency is associated with poorer health outcomes among Latinos, Asian Americans, and other ethnic minorities in the United States. [9] Studies have found that women with LEP disproportionately fail to follow up on abnormal mammogram results, which may lead to increases in delayed diagnosis.
To test this hypothesis, I sat down in front of ChatGPT and gave it a classic freshman-year English prompt: "Please write me an approximately 500-word, five-paragraph essay discussing the role of ...
Some researchers include a metacognitive component in their definition. In this view, the Dunning–Kruger effect is the thesis that those who are incompetent in a given area tend to be ignorant of their incompetence, i.e., they lack the metacognitive ability to become aware of their incompetence.
Aline Alves-Wold, in her article "Assessing Writing Motivation: a Systematic Review of K-5 Students' Self-Reports" states that there is a general lack of research on the motivation of students to write in the first few years of education, which is problematic when one considers how important initial experiences are in motivating students to write.
The expertise reversal effect refers to the reversal of the effectiveness of instructional techniques on learners with differing levels of prior knowledge. [ 1 ] [ 2 ] The primary recommendation that stems from the expertise reversal effect is that instructional design methods need to be adjusted as learners acquire more knowledge in a specific ...
The LLM examines the relationships between tokens, generates probable outputs in response to a prompt, and completes a defined task, such as translating, editing, or writing. The output that is presented is a smoothed collection of words, [42] that is normalized and predictable. Translation, summarization, information retrieval, conversational ...