` or `
` subdivisions, but let's see if there are logical sections. Actually, it reads like a single abstract. I can just put the whole text in a `
` tag, or if needed, split it logically. Let's try grouping into paragraphs. Paragraph 1: Sentences 1-3 (Introduction and Methods). Paragraph 2: Sentences 4-7 (Results). Paragraph 3: Sentences 8-9 (Distribution and Conclusion). Alternatively, just one paragraph. Let's look at the flow. Sentence 1-3: Introduction, dataset, and methodology. Sentence 4-7: Detailed results. Sentence 8-9: Observations and conclusions. Let's use two or three paragraphs to make it look nice. Or just one big `
` tag, as it's an abstract. But the instructions say "每个自然段落用
包裹 (wrap each natural paragraph in
)". Since it's a continuous block of text in the prompt, I should probably either keep it as one `
` or divide it logically. Let's just divide it into logical paragraphs to make it easier to read. Paragraph 1 (Intro/Setup): We evaluate... 203 student-semester records. Six fitted estimators... cross-validation. Paragraph 2 (Results): After averaging... 3.138 for the median baseline. The paired difference... [-0.245,0.013]. Representation changes... small gains. In the lowest... 3.331. Paragraph 3 (Distribution/Conclusion): Scores concentrate... 62.1% of boosting's total absolute error. This retrospective... learning effect. Let's refine the paragraph divisions to be more natural.
We evaluate whether semester-level interaction features from a large language model tutor improve assignment-score prediction beyond constant baselines. StudyChat provides 16,851 student-assistant exchanges from two offerings of an undergraduate artificial intelligence course, with outcomes matched to 175 of 203 student-semester records. Six fitted estimators and training-fold mean and median baselines share repeated nested cross-validation.
After averaging each student's held-out predictions across ten repeats, Gradient Boosting achieves mean absolute error of 3.023 grade points, versus 3.138 for the median baseline. The paired difference is -0.114, with a conditional 95% bootstrap interval of [-0.245,0.013]. Representation changes and a semester-specific median yield similarly small gains. In the lowest observed-score quartile, however, boosting's error is 7.30 points, versus 8.07 for the median and 6.67 for the mean; Ridge has the lowest fitted-model error at 6.37 despite its worse overall error of 3.331.
Scores concentrate near the ceiling: 160 students (91.4%) score above 90, and the lowest quartile contributes 62.1% of boosting's total absolute error. This retrospective comparison shows how baseline choice and error distribution affect predictive value, informing evaluation of student-support tools without establishing a learning effect.
3. Check for specific tags: - Lists (`- `)? None. - Quotes (`
`)? None. - Code (`
`)? None. - Important sentences (``)? Yes, the main result "Gradient Boosting achieves mean absolute error of 3.023 grade points, versus 3.138 for the median baseline." is important. Or the conclusion. 4. Check constraints: - Only output the HTML fragment itself, no Markdown fences, no explanations. - No document-level tags (``, ``, ``). - Preserve all original information, no additions, deletions, or changes. 5. Finalizing the HTML: Let's carefully format the output.We evaluate whether semester-level interaction features from a large language model tutor improve assignment-score prediction beyond constant baselines. StudyChat provides 16,851 student-assistant exchanges from two offerings of an undergraduate artificial intelligence course, with outcomes matched to 175 of 203 student-semester records. Six fitted estimators and training-fold mean and median baselines share repeated nested cross-validation.
After averaging each student's held-out predictions across ten repeats, Gradient Boosting achieves mean absolute error of 3.023 grade points, versus 3.138 for the median baseline. The paired difference is -0.114, with a conditional 95% bootstrap interval of [-0.245,0.013]. Representation changes and a semester-specific median yield similarly small gains. In the lowest observed-score quartile, however, boosting's error is 7.30 points, versus 8.07 for the median and 6.67 for the mean; Ridge has the lowest fitted-model error at 6.37 despite its worse overall error of 3.331.
Scores concentrate near the ceiling: 160 students (91.4%) score above 90, and the lowest quartile contributes 62.1% of boosting's total absolute error. This retrospective comparison shows how baseline choice and error distribution affect predictive value, informing evaluation of student-support tools without establishing a learning effect.
Wait, is there any reason to use `` or `
`? "长文用
/
划分小节 (use
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