zclaw_openfang/pipelines/education/student-analysis.yaml

# ZCLAW Pipeline - 学情分析报告
# 输入班级学生成绩/表现数据，自动生成学情分析报告和分层教学建议

apiVersion: zclaw/v1
kind: Pipeline
metadata:
  name: student-analysis
  displayName: 学情分析报告
  category: education
  industry: education
  description: 输入学生成绩或表现数据，自动分析学情并生成分层教学建议
  tags:
    - 教育
    - 学情分析
    - 分层教学
    - 数据分析
  icon: 📊
  author: ZCLAW
  version: 1.0.0

spec:
  inputs:
    - name: student_data
      type: text
      required: true
      label: 学生数据
      placeholder: |
        粘贴成绩表或表现描述，支持格式：
        姓名,分数 或 姓名,等级,A/B/C 或自由文本描述

    - name: subject
      type: string
      required: false
      label: 科目
      default: 综合
      placeholder: 例如：数学

    - name: analysis_focus
      type: multi-select
      required: false
      label: 分析维度
      default:
        - score_distribution
        - weak_points
        - group_recommendation
      options:
        - score_distribution
        - weak_points
        - group_recommendation
        - improvement_plan
        - parent_communication

    - name: class_name
      type: string
      required: false
      label: 班级名称
      placeholder: 例如：初一(3)班

  steps:
    # Step 1: 数据解析与统计
    - id: parse_data
      description: 解析输入数据并生成基础统计
      action:
        type: llm_generate
        template: |
          请解析以下学生数据并生成基础统计：

          科目: {{subject}}
          班级: {{class_name}}

          学生数据:
          ```
          {{student_data}}
          ```

          请生成 JSON 格式统计：
          {
            "total_students": 0,
            "score_distribution": {
              "excellent": {"count": 0, "range": "90-100", "percentage": 0},
              "good": {"count": 0, "range": "80-89", "percentage": 0},
              "average": {"count": 0, "range": "60-79", "percentage": 0},
              "below_average": {"count": 0, "range": "<60", "percentage": 0}
            },
            "class_average": 0,
            "median_score": 0,
            "std_deviation": "高/中/低"
          }
        input:
          student_data: ${inputs.student_data}
          subject: ${inputs.subject}
          class_name: ${inputs.class_name}
        json_mode: true
        temperature: 0.3
        max_tokens: 2000

    # Step 2: 薄弱环节识别
    - id: identify_weakness
      description: 识别学生群体的薄弱知识点
      action:
        type: llm_generate
        template: |
          基于以下学情统计，识别薄弱环节：

          科目: ${inputs.subject}
          统计数据: ${steps.parse_data.output}

          学生数据:
          ```
          ${inputs.student_data}
          ```

          请生成 JSON 格式分析：
          {
            "weak_areas": [
              {
                "area": "薄弱知识点",
                "affected_group": "受影响群体",
                "severity": "高/中/低",
                "possible_cause": "可能原因"
              }
            ],
            "strong_areas": ["优势领域1", "优势领域2"],
            "polarization": "是否存在两极分化，程度如何"
          }
        input:
          subject: ${inputs.subject}
          stats: ${steps.parse_data.output}
          student_data: ${inputs.student_data}
        json_mode: true
        temperature: 0.4
        max_tokens: 2000

    # Step 3: 分层教学建议
    - id: group_recommendation
      description: 生成分层教学建议
      action:
        type: llm_generate
        template: |
          基于以下学情分析，生成分层教学建议：

          科目: ${inputs.subject}
          统计数据: ${steps.parse_data.output}
          薄弱环节: ${steps.identify_weakness.output}

          请生成 JSON 格式建议：
          {
            "groups": [
              {
                "name": "培优组",
                "criteria": "选拔标准",
                "size": "建议人数",
                "teaching_focus": "教学重点",
                "activities": ["建议活动1", "建议活动2"],
                "resources": ["推荐资源1"]
              },
              {
                "name": "提高组",
                "criteria": "选拔标准",
                "size": "建议人数",
                "teaching_focus": "教学重点",
                "activities": ["建议活动1"],
                "resources": ["推荐资源1"]
              },
              {
                "name": "基础组",
                "criteria": "选拔标准",
                "size": "建议人数",
                "teaching_focus": "教学重点",
                "activities": ["建议活动1"],
                "resources": ["推荐资源1"]
              }
            ],
            "shared_activities": ["全班共同活动1"],
            "timeline": "建议实施周期"
          }
        input:
          subject: ${inputs.subject}
          stats: ${steps.parse_data.output}
          weakness: ${steps.identify_weakness.output}
        json_mode: true
        temperature: 0.6
        max_tokens: 2500

    # Step 4: 生成报告
    - id: generate_report
      description: 生成完整学情分析报告
      action:
        type: llm_generate
        template: |
          基于以上分析，生成一份完整的学情分析报告摘要。

          班级: ${inputs.class_name}
          科目: ${inputs.subject}

          请生成 JSON 格式报告：
          {
            "title": "学情分析报告标题",
            "executive_summary": "200字摘要",
            "data_overview": "数据概览描述",
            "key_findings": ["发现1", "发现2", "发现3"],
            "recommendations": ["建议1", "建议2", "建议3"],
            "preview_text": "300字报告预览"
          }
        input:
          class_name: ${inputs.class_name}
          subject: ${inputs.subject}
          stats: ${steps.parse_data.output}
          weakness: ${steps.identify_weakness.output}
          groups: ${steps.group_recommendation.output}
        json_mode: true
        temperature: 0.5
        max_tokens: 2000

  outputs:
    statistics: ${steps.parse_data.output}
    weakness_analysis: ${steps.identify_weakness.output}
    group_recommendation: ${steps.group_recommendation.output}
    report: ${steps.generate_report.output}

  on_error: stop
  timeout_secs: 240