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"cells": [
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"source": [
"# AssistMent2017 数据集分析\n",
"\n",
"# 数据集简介\n",
"该数据集来自2017年的ASSISTments平台，是一个匿名化的完整数据集。与2009和2012数据集相比，该数据集包含了更多的特征工程列和学生行为预测指标，包括学生的情绪状态预测、游戏行为检测等。\n",
"\n",
"# 数据集列含义\n",
"ASSISTments2017数据集包含82个特征列，可以分为以下几类：\n",
"\n",
"## 学生基本信息\n",
"- studentId：学生ID\n",
"- MiddleSchoolId：学校ID\n",
"- InferredGender：推断的性别（Male/Female）\n",
"- SY ASSISTments Usage：学年ASSISTments使用情况\n",
"\n",
"## 学生整体表现指标\n",
"- AveKnow：平均知识水平\n",
"- AveCarelessness：平均粗心程度\n",
"- AveCorrect：平均正确率\n",
"- NumActions：操作次数\n",
"- AveResBored、AveResEngcon、AveResConf、AveResFrust、AveResOfftask、AveResGaming：各种情绪状态的平均残差值\n",
"\n",
"## 问题相关信息\n",
"- problemId：问题ID\n",
"- problemType：问题类型\n",
"- skill：技能名称\n",
"- assignmentId：作业ID\n",
"- assistmentId：辅助问题ID\n",
"\n",
"## 答题记录信息\n",
"- action_num：操作序号\n",
"- startTime：开始时间\n",
"- endTime：结束时间\n",
"- timeTaken：花费时间\n",
"- correct：是否正确（1=正确，0=错误）\n",
"- original：是否为主问题（1=主问题，0=支撑问题）\n",
"- attemptCount：尝试次数\n",
"\n",
"## 提示和支撑信息\n",
"- hint：是否使用了提示\n",
"- hintCount：提示次数\n",
"- hintTotal：总提示数\n",
"- scaffold：是否使用了支撑\n",
"- bottomHint：是否使用了最底层提示\n",
"\n",
"## 特征工程列\n",
"包含大量历史行为和统计特征，如：\n",
"- frIsHelpRequest、frPast5HelpRequest、frPast8HelpRequest：帮助请求相关\n",
"- stlHintUsed、past8BottomOut：提示使用相关\n",
"- totalFrPercentPastWrong、totalFrPastWrongCount等：历史错误统计\n",
"- frWorkingInSchool：是否在学校学习\n",
"- 各种时间和机会统计特征\n",
"\n",
"## 情绪和行为预测\n",
"两套预测系统：\n",
"- confidence系列：BORED, CONCENTRATING, CONFUSED, FRUSTRATED, OFF TASK, GAMING\n",
"- RES系列：RES_BORED, RES_CONCENTRATING, RES_CONFUSED, RES_FRUSTRATED, RES_OFFTASK, RES_GAMING\n",
"\n",
"## 学习结果信息\n",
"- Ln-1、Ln：学习相关指标\n",
"- MCAS：马萨诸塞州综合评估系统成绩\n",
"- Enrolled：是否注册\n",
"- Selective：是否选择性\n",
"- isSTEM：是否为STEM相关"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fdeb0a98",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Load the ASSISTments 2017 dataset\n",
"data = pd.read_csv(\n",
" \"data/assistment17/anonymized_full_release_competition_dataset.csv\",\n",
" low_memory=False,\n",
" encoding=\"latin1\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "66a947c8",
"metadata": {},
"outputs": [
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"text/plain": [
" studentId MiddleSchoolId InferredGender SY ASSISTments Usage AveKnow \\\n",
"0 8 2 Male 2004-2005 0.352416 \n",
"1 8 2 Male 2004-2005 0.352416 \n",
"2 8 2 Male 2004-2005 0.352416 \n",
"3 8 2 Male 2004-2005 0.352416 \n",
"4 8 2 Male 2004-2005 0.352416 \n",
"5 8 2 Male 2004-2005 0.352416 \n",
"6 8 2 Male 2004-2005 0.352416 \n",
"7 8 2 Male 2004-2005 0.352416 \n",
"8 8 2 Male 2004-2005 0.352416 \n",
"9 8 2 Male 2004-2005 0.352416 \n",
"\n",
" AveCarelessness AveCorrect NumActions AveResBored AveResEngcon ... \\\n",
"0 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"1 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"2 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"3 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"4 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"5 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"6 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"7 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"8 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"9 0.183276 0.483902 1056 0.208389 0.679126 ... \n",
"\n",
" RES_CONFUSED RES_FRUSTRATED RES_OFFTASK RES_GAMING Ln-1 \\\n",
"0 0.000000 0.0 0.785585 0.000264 0.13 \n",
"1 0.887452 0.0 0.468252 0.001483 0.061190409 \n",
"2 0.887452 0.0 0.468252 0.001483 0.116 \n",
"3 0.000000 0.0 0.108417 0.010665 0.116 \n",
"4 0.000000 0.0 0.108417 0.010665 0.033305768 \n",
"5 0.000000 0.0 0.785585 0.002026 0.033305768 \n",
"6 0.000000 1.0 0.108417 0.005952 0.033305768 \n",
"7 0.060808 0.0 0.785585 0.010665 0.348 \n",
"8 0.060808 0.0 0.916914 0.012562 0.168 \n",
"9 0.060808 0.0 0.916914 0.012562 0.168 \n",
"\n",
" Ln MCAS Enrolled Selective isSTEM \n",
"0 0.061190409 45 0 0 NaN \n",
"1 0.213509945 45 0 0 NaN \n",
"2 0.033305768 45 0 0 NaN \n",
"3 0.033305768 45 0 0 NaN \n",
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"8 0.097910759 45 0 0 NaN \n",
"9 0.097910759 45 0 0 NaN \n",
"\n",
"[10 rows x 82 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 显示数据集的前十行\n",
"data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "39845c19",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据集的列名：\n",
"Index(['studentId', 'MiddleSchoolId', 'InferredGender', 'SY ASSISTments Usage',\n",
" 'AveKnow', 'AveCarelessness', 'AveCorrect', 'NumActions', 'AveResBored',\n",
" 'AveResEngcon', 'AveResConf', 'AveResFrust', 'AveResOfftask',\n",
" 'AveResGaming', 'action_num', 'skill', 'problemId', 'problemType',\n",
" 'assignmentId', 'assistmentId', 'startTime', 'endTime', 'timeTaken',\n",
" 'correct', 'original', 'hint', 'hintCount', 'hintTotal', 'scaffold',\n",
" 'bottomHint', 'attemptCount', 'frIsHelpRequest', 'frPast5HelpRequest',\n",
" 'frPast8HelpRequest', 'stlHintUsed', 'past8BottomOut',\n",
" 'totalFrPercentPastWrong', 'totalFrPastWrongCount', 'frPast5WrongCount',\n",
" 'frPast8WrongCount', 'totalFrTimeOnSkill', 'timeSinceSkill',\n",
" 'frWorkingInSchool', 'totalFrAttempted', 'totalFrSkillOpportunities',\n",
" 'responseIsFillIn', 'responseIsChosen', 'endsWithScaffolding',\n",
" 'endsWithAutoScaffolding', 'frTimeTakenOnScaffolding',\n",
" 'frTotalSkillOpportunitiesScaffolding',\n",
" 'totalFrSkillOpportunitiesByScaffolding', 'frIsHelpRequestScaffolding',\n",
" 'timeGreater5Secprev2wrong', 'sumRight', 'helpAccessUnder2Sec',\n",
" 'timeGreater10SecAndNextActionRight', 'consecutiveErrorsInRow',\n",
" 'sumTime3SDWhen3RowRight', 'sumTimePerSkill',\n",
" 'totalTimeByPercentCorrectForskill', 'Prev5count', 'timeOver80',\n",
" 'manywrong', 'confidence(BORED)', 'confidence(CONCENTRATING)',\n",
" 'confidence(CONFUSED)', 'confidence(FRUSTRATED)',\n",
" 'confidence(OFF TASK)', 'confidence(GAMING)', 'RES_BORED',\n",
" 'RES_CONCENTRATING', 'RES_CONFUSED', 'RES_FRUSTRATED', 'RES_OFFTASK',\n",
" 'RES_GAMING', 'Ln-1', 'Ln', 'MCAS', 'Enrolled', 'Selective', 'isSTEM'],\n",
" dtype='object')\n",
"总记录数：942816\n"
]
}
],
"source": [
"# 查看数据集的列名和基本信息\n",
"print(\"数据集的列名：\")\n",
"print(data.columns)\n",
"print(f\"总记录数：{len(data)}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "78eabc32",
"metadata": {},
"outputs": [
{
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"shape": {
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"rows": 8
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>studentId</th>\n",
" <th>MiddleSchoolId</th>\n",
" <th>AveKnow</th>\n",
" <th>AveCarelessness</th>\n",
" <th>AveCorrect</th>\n",
" <th>NumActions</th>\n",
" <th>AveResBored</th>\n",
" <th>AveResEngcon</th>\n",
" <th>AveResConf</th>\n",
" <th>AveResFrust</th>\n",
" <th>...</th>\n",
" <th>RES_BORED</th>\n",
" <th>RES_CONCENTRATING</th>\n",
" <th>RES_CONFUSED</th>\n",
" <th>RES_FRUSTRATED</th>\n",
" <th>RES_OFFTASK</th>\n",
" <th>RES_GAMING</th>\n",
" <th>MCAS</th>\n",
" <th>Enrolled</th>\n",
" <th>Selective</th>\n",
" <th>isSTEM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
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" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
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" <td>942816.000000</td>\n",
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" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>942816.000000</td>\n",
" <td>316974.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3844.844105</td>\n",
" <td>2.515472</td>\n",
" <td>0.195155</td>\n",
" <td>0.109436</td>\n",
" <td>0.372681</td>\n",
" <td>869.850594</td>\n",
" <td>0.232949</td>\n",
" <td>0.658442</td>\n",
" <td>0.098940</td>\n",
" <td>0.131406</td>\n",
" <td>...</td>\n",
" <td>0.232949</td>\n",
" <td>6.584415e-01</td>\n",
" <td>0.098940</td>\n",
" <td>0.131406</td>\n",
" <td>0.172212</td>\n",
" <td>0.192703</td>\n",
" <td>-95.982302</td>\n",
" <td>0.641147</td>\n",
" <td>0.300434</td>\n",
" <td>0.204178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2250.484065</td>\n",
" <td>1.039785</td>\n",
" <td>0.116451</td>\n",
" <td>0.059952</td>\n",
" <td>0.107367</td>\n",
" <td>530.210725</td>\n",
" <td>0.030637</td>\n",
" <td>0.027440</td>\n",
" <td>0.034788</td>\n",
" <td>0.038875</td>\n",
" <td>...</td>\n",
" <td>0.116371</td>\n",
" <td>1.734275e-01</td>\n",
" <td>0.249505</td>\n",
" <td>0.300351</td>\n",
" <td>0.216997</td>\n",
" <td>0.340232</td>\n",
" <td>332.827628</td>\n",
" <td>0.479664</td>\n",
" <td>0.458447</td>\n",
" <td>0.403100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>8.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.028057</td>\n",
" <td>0.007801</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.170871</td>\n",
" <td>0.403309</td>\n",
" <td>0.005075</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.156027</td>\n",
" <td>8.890000e-07</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000001</td>\n",
" <td>-999.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1952.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.110542</td>\n",
" <td>0.068760</td>\n",
" <td>0.294989</td>\n",
" <td>478.000000</td>\n",
" <td>0.209035</td>\n",
" <td>0.642060</td>\n",
" <td>0.076385</td>\n",
" <td>0.107278</td>\n",
" <td>...</td>\n",
" <td>0.156027</td>\n",
" <td>5.117519e-01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.048295</td>\n",
" <td>0.001483</td>\n",
" <td>14.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3766.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.159285</td>\n",
" <td>0.094513</td>\n",
" <td>0.345575</td>\n",
" <td>754.000000</td>\n",
" <td>0.230394</td>\n",
" <td>0.660669</td>\n",
" <td>0.096357</td>\n",
" <td>0.127504</td>\n",
" <td>...</td>\n",
" <td>0.156027</td>\n",
" <td>7.115475e-01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.122595</td>\n",
" <td>0.005797</td>\n",
" <td>23.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
" <td>5781.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.247704</td>\n",
" <td>0.137316</td>\n",
" <td>0.428822</td>\n",
" <td>1151.000000</td>\n",
" <td>0.252082</td>\n",
" <td>0.676588</td>\n",
" <td>0.119282</td>\n",
" <td>0.150582</td>\n",
" <td>...</td>\n",
" <td>0.376427</td>\n",
" <td>7.726099e-01</td>\n",
" <td>0.000000</td>\n",
" <td>0.009561</td>\n",
" <td>0.122595</td>\n",
" <td>0.259648</td>\n",
" <td>34.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>7783.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.752498</td>\n",
" <td>0.430576</td>\n",
" <td>0.932990</td>\n",
" <td>3057.000000</td>\n",
" <td>0.440870</td>\n",
" <td>0.723990</td>\n",
" <td>0.402483</td>\n",
" <td>0.543463</td>\n",
" <td>...</td>\n",
" <td>0.505313</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.999377</td>\n",
" <td>54.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 76 columns</p>\n",
"</div>"
],
"text/plain": [
" studentId MiddleSchoolId AveKnow AveCarelessness \\\n",
"count 942816.000000 942816.000000 942816.000000 942816.000000 \n",
"mean 3844.844105 2.515472 0.195155 0.109436 \n",
"std 2250.484065 1.039785 0.116451 0.059952 \n",
"min 8.000000 1.000000 0.028057 0.007801 \n",
"25% 1952.000000 2.000000 0.110542 0.068760 \n",
"50% 3766.000000 2.000000 0.159285 0.094513 \n",
"75% 5781.000000 4.000000 0.247704 0.137316 \n",
"max 7783.000000 4.000000 0.752498 0.430576 \n",
"\n",
" AveCorrect NumActions AveResBored AveResEngcon \\\n",
"count 942816.000000 942816.000000 942816.000000 942816.000000 \n",
"mean 0.372681 869.850594 0.232949 0.658442 \n",
"std 0.107367 530.210725 0.030637 0.027440 \n",
"min 0.000000 2.000000 0.170871 0.403309 \n",
"25% 0.294989 478.000000 0.209035 0.642060 \n",
"50% 0.345575 754.000000 0.230394 0.660669 \n",
"75% 0.428822 1151.000000 0.252082 0.676588 \n",
"max 0.932990 3057.000000 0.440870 0.723990 \n",
"\n",
" AveResConf AveResFrust ... RES_BORED RES_CONCENTRATING \\\n",
"count 942816.000000 942816.000000 ... 942816.000000 9.428160e+05 \n",
"mean 0.098940 0.131406 ... 0.232949 6.584415e-01 \n",
"std 0.034788 0.038875 ... 0.116371 1.734275e-01 \n",
"min 0.005075 0.000000 ... 0.156027 8.890000e-07 \n",
"25% 0.076385 0.107278 ... 0.156027 5.117519e-01 \n",
"50% 0.096357 0.127504 ... 0.156027 7.115475e-01 \n",
"75% 0.119282 0.150582 ... 0.376427 7.726099e-01 \n",
"max 0.402483 0.543463 ... 0.505313 1.000000e+00 \n",
"\n",
" RES_CONFUSED RES_FRUSTRATED RES_OFFTASK RES_GAMING \\\n",
"count 942816.000000 942816.000000 942816.000000 942816.000000 \n",
"mean 0.098940 0.131406 0.172212 0.192703 \n",
"std 0.249505 0.300351 0.216997 0.340232 \n",
"min 0.000000 0.000000 0.000000 0.000001 \n",
"25% 0.000000 0.000000 0.048295 0.001483 \n",
"50% 0.000000 0.000000 0.122595 0.005797 \n",
"75% 0.000000 0.009561 0.122595 0.259648 \n",
"max 1.000000 1.000000 1.000000 0.999377 \n",
"\n",
" MCAS Enrolled Selective isSTEM \n",
"count 942816.000000 942816.000000 942816.000000 316974.000000 \n",
"mean -95.982302 0.641147 0.300434 0.204178 \n",
"std 332.827628 0.479664 0.458447 0.403100 \n",
"min -999.000000 0.000000 0.000000 0.000000 \n",
"25% 14.000000 0.000000 0.000000 0.000000 \n",
"50% 23.000000 1.000000 0.000000 0.000000 \n",
"75% 34.000000 1.000000 1.000000 0.000000 \n",
"max 54.000000 1.000000 1.000000 1.000000 \n",
"\n",
"[8 rows x 76 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 显示数据集的基本统计信息\n",
"data.describe()"
]
},
{
"cell_type": "markdown",
"id": "d9c5acde",
"metadata": {},
"source": [
"# 数据缺失情况"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fcce1540",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"存在缺失值的列数：3\n",
"\n",
"缺失值统计（按缺失数量降序排列）：\n",
"isSTEM: 625842 (66.38%)\n",
"InferredGender: 173656 (18.42%)\n",
"sumTime3SDWhen3RowRight: 85 (0.01%)\n"
]
}
],
"source": [
"# 统计原始数据中所有存在缺失值的列\n",
"missing_values = data.isnull().sum()\n",
"missing_cols = missing_values[missing_values > 0].sort_values(ascending=False)\n",
"print(f\"存在缺失值的列数：{len(missing_cols)}\")\n",
"print(\"\\n缺失值统计（按缺失数量降序排列）：\")\n",
"for col, count in missing_cols.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"{col}: {count} ({percentage:.2f}%)\")"
]
},
{
"cell_type": "markdown",
"id": "77148bb6",
"metadata": {},
"source": [
"# 数据集原始数据量\n",
"以下数据描述了原始数据集中包含的数据数量。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "67609cc2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"学生数量: 1709\n",
"总问题数量: 3162\n",
"技能数量: 102\n",
"主问题数量: 1183\n",
"支撑问题数量: 2651\n",
"总答题记录数: 942816\n",
"学校数量: 4\n"
]
}
],
"source": [
"# 统计学生数量\n",
"num_students = data[\"studentId\"].nunique()\n",
"print(f\"学生数量: {num_students}\")\n",
"\n",
"# 统计问题数量\n",
"num_questions = data[\"problemId\"].nunique()\n",
"print(f\"总问题数量: {num_questions}\")\n",
"\n",
"# 统计技能数量\n",
"num_skills = data[\"skill\"].nunique()\n",
"print(f\"技能数量: {num_skills}\")\n",
"\n",
"# 主问题数量\n",
"num_main_questions = data[data[\"original\"] == 1][\"problemId\"].nunique()\n",
"print(f\"主问题数量: {num_main_questions}\")\n",
"\n",
"# 支撑问题数量\n",
"num_scaffolding_questions = data[data[\"original\"] == 0][\"problemId\"].nunique()\n",
"print(f\"支撑问题数量: {num_scaffolding_questions}\")\n",
"\n",
"# 总答题记录数\n",
"total_records = len(data)\n",
"print(f\"总答题记录数: {total_records}\")\n",
"\n",
"# 学校数量\n",
"num_schools = data[\"MiddleSchoolId\"].nunique()\n",
"print(f\"学校数量: {num_schools}\")"
]
},
{
"cell_type": "markdown",
"id": "9d574992",
"metadata": {},
"source": [
"# 统计数据量\n",
"以下数据通过一些统计量来描述数据集的结构。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "48a7acc0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"平均每个学生的答题次数: 551.68\n",
"中位数每个学生的答题次数: 441.00\n",
"\n",
"每个问题关联的技能数量统计:\n",
"count 3162.000000\n",
"mean 1.225174\n",
"std 0.428971\n",
"min 1.000000\n",
"25% 1.000000\n",
"50% 1.000000\n",
"75% 1.000000\n",
"max 3.000000\n",
"Name: skill, dtype: float64\n",
"\n",
"平均每个问题关联的技能数量: 1.23\n",
"中位数每个问题关联的技能数量: 1.00\n",
"\n",
"每个技能关联的问题数量统计:\n",
"count 102.000000\n",
"mean 37.980392\n",
"std 108.896261\n",
"min 1.000000\n",
"25% 4.000000\n",
"50% 18.000000\n",
"75% 44.750000\n",
"max 1071.000000\n",
"Name: problemId, dtype: float64\n",
"\n",
"平均每个技能关联的问题数量: 37.98\n",
"中位数每个技能关联的问题数量: 18.00\n"
]
}
],
"source": [
"# 平均每个学生的答题次数\n",
"attempts_per_student = data.groupby(\"studentId\")[\"problemId\"].count()\n",
"avg_attempts_per_student = attempts_per_student.mean()\n",
"median_attempts_per_student = attempts_per_student.median()\n",
"print(f\"平均每个学生的答题次数: {avg_attempts_per_student:.2f}\")\n",
"print(f\"中位数每个学生的答题次数: {median_attempts_per_student:.2f}\")\n",
"\n",
"# 每个问题关联的技能数量（检查是否一个问题对应一个技能）\n",
"skills_per_question = data.groupby(\"problemId\")[\"skill\"].nunique()\n",
"print(\"\\n每个问题关联的技能数量统计:\")\n",
"print(skills_per_question.describe())\n",
"avg_skills_per_question = skills_per_question.mean()\n",
"median_skills_per_question = skills_per_question.median()\n",
"print(f\"\\n平均每个问题关联的技能数量: {avg_skills_per_question:.2f}\")\n",
"print(f\"中位数每个问题关联的技能数量: {median_skills_per_question:.2f}\")\n",
"\n",
"# 每个技能关联的问题数量\n",
"questions_per_skill = data.groupby(\"skill\")[\"problemId\"].nunique()\n",
"print(\"\\n每个技能关联的问题数量统计:\")\n",
"print(questions_per_skill.describe())\n",
"avg_questions_per_skill = questions_per_skill.mean()\n",
"median_questions_per_skill = questions_per_skill.median()\n",
"print(f\"\\n平均每个技能关联的问题数量: {avg_questions_per_skill:.2f}\")\n",
"print(f\"中位数每个技能关联的问题数量: {median_questions_per_skill:.2f}\")"
]
},
{
"cell_type": "markdown",
"id": "f780d0f1",
"metadata": {},
"source": [
"# 其他列的分析\n",
"\n",
"- 主问题和支撑问题\n",
"- 问题类型\n",
"- 性别分布\n",
"- 提示和支撑使用情况\n",
"- 情绪和行为预测"
]
},
{
"cell_type": "markdown",
"id": "c772cca4",
"metadata": {},
"source": [
"### 主问题和支撑问题 (original)\n",
"- 主问题：1\n",
"- 支撑问题：0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f093ab97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"主问题和支撑问题的答题记录分布:\n",
"主问题 (original=1): 249105 条记录 (26.42%)\n",
"支撑问题 (original=0): 693711 条记录 (73.58%)\n",
"\n",
"主问题唯一数量: 1183\n",
"支撑问题唯一数量: 2651\n"
]
}
],
"source": [
"# 主问题和支撑问题的答题记录分布\n",
"original_counts = data[\"original\"].value_counts()\n",
"print(\"主问题和支撑问题的答题记录分布:\")\n",
"print(f\"主问题 (original=1): {original_counts.get(1, 0)} 条记录 ({original_counts.get(1, 0)/len(data)*100:.2f}%)\")\n",
"print(f\"支撑问题 (original=0): {original_counts.get(0, 0)} 条记录 ({original_counts.get(0, 0)/len(data)*100:.2f}%)\")\n",
"\n",
"# 主问题和支撑问题的唯一问题数量\n",
"main_questions = data[data[\"original\"] == 1][\"problemId\"].unique()\n",
"print(f\"\\n主问题唯一数量: {len(main_questions)}\")\n",
"scaffolding_questions = data[data[\"original\"] == 0][\"problemId\"].unique()\n",
"print(f\"支撑问题唯一数量: {len(scaffolding_questions)}\")"
]
},
{
"cell_type": "markdown",
"id": "3a5bbab0",
"metadata": {},
"source": [
"### 问题类型 (problemType)\n",
"统计不同问题类型的分布。"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6fcfa3da",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"问题类型分布（按答题记录数）:\n",
"textfieldquestion: 264399 条记录 (28.04%)\n",
"radioquestion: 196207 条记录 (20.81%)\n",
"noprobtype: 187501 条记录 (19.89%)\n",
"1: 116765 条记录 (12.38%)\n",
"algebrafieldquestion: 75612 条记录 (8.02%)\n",
"other: 41767 条记录 (4.43%)\n",
"algebra: 21808 条记录 (2.31%)\n",
"popupmenuquestion: 13816 条记录 (1.47%)\n",
"interfaceradioquestion1: 11392 条记录 (1.21%)\n",
"checkboxquestion: 4120 条记录 (0.44%)\n",
"interfacetextfieldquestion1: 3244 条记录 (0.34%)\n",
"algebrafieldquestion1: 2854 条记录 (0.30%)\n",
"interfaceradioquestion: 1531 条记录 (0.16%)\n",
"interfacetextfieldquestion: 1242 条记录 (0.13%)\n",
"interfacepopupmenuquestion1: 486 条记录 (0.05%)\n",
"0: 72 条记录 (0.01%)\n",
"\n",
"每种类型的唯一问题数量:\n",
"textfieldquestion: 678 个问题\n",
"radioquestion: 865 个问题\n",
"noprobtype: 1265 个问题\n",
"1: 489 个问题\n",
"algebrafieldquestion: 334 个问题\n",
"other: 458 个问题\n",
"algebra: 340 个问题\n",
"popupmenuquestion: 70 个问题\n",
"interfaceradioquestion1: 54 个问题\n",
"checkboxquestion: 5 个问题\n",
"interfacetextfieldquestion1: 14 个问题\n",
"algebrafieldquestion1: 12 个问题\n",
"interfaceradioquestion: 9 个问题\n",
"interfacetextfieldquestion: 6 个问题\n",
"interfacepopupmenuquestion1: 3 个问题\n",
"0: 28 个问题\n"
]
}
],
"source": [
"# 问题类型分布\n",
"problem_types = data[\"problemType\"].value_counts()\n",
"print(\"问题类型分布（按答题记录数）:\")\n",
"for ptype, count in problem_types.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"{ptype}: {count} 条记录 ({percentage:.2f}%)\")\n",
"\n",
"# 每种类型的唯一问题数量\n",
"print(\"\\n每种类型的唯一问题数量:\")\n",
"for ptype in problem_types.index:\n",
" unique_count = data[data[\"problemType\"] == ptype][\"problemId\"].nunique()\n",
" print(f\"{ptype}: {unique_count} 个问题\")"
]
},
{
"cell_type": "markdown",
"id": "0e49d54d",
"metadata": {},
"source": [
"### 性别分布 (InferredGender)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3354ffc8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"性别分布（答题记录）:\n",
"Male: 407637 条记录 (43.24%)\n",
"Female: 361523 条记录 (38.35%)\n",
"\n",
"有性别信息的学生数量: 1378\n",
"缺失性别信息的记录数: 173656 (18.42%)\n"
]
}
],
"source": [
"# 性别分布\n",
"gender_counts = data[\"InferredGender\"].value_counts()\n",
"print(\"性别分布（答题记录）:\")\n",
"for gender, count in gender_counts.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"{gender}: {count} 条记录 ({percentage:.2f}%)\")\n",
"\n",
"# 统计有性别信息的学生数量\n",
"students_with_gender = data[data[\"InferredGender\"].notna()][\"studentId\"].nunique()\n",
"print(f\"\\n有性别信息的学生数量: {students_with_gender}\")\n",
"print(f\"缺失性别信息的记录数: {data['InferredGender'].isna().sum()} ({data['InferredGender'].isna().sum()/len(data)*100:.2f}%)\")"
]
},
{
"cell_type": "markdown",
"id": "4a7c6454",
"metadata": {},
"source": [
"### 提示和支撑使用情况"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f63b5d65",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"提示使用情况:\n",
"hint=0: 630720 条记录 (66.90%)\n",
"hint=1: 312096 条记录 (33.10%)\n",
"\n",
"支撑使用情况:\n",
"scaffold=0: 579142 条记录 (61.43%)\n",
"scaffold=1: 363674 条记录 (38.57%)\n",
"\n",
"最底层提示使用情况:\n",
"bottomHint=0: 883613 条记录 (93.72%)\n",
"bottomHint=1: 59203 条记录 (6.28%)\n",
"\n",
"提示次数统计:\n",
"平均提示次数: 1.22\n",
"中位数提示次数: 0.00\n",
"最大提示次数: 56\n"
]
}
],
"source": [
"# 提示使用情况\n",
"hint_usage = data[\"hint\"].value_counts()\n",
"print(\"提示使用情况:\")\n",
"for hint_val, count in hint_usage.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"hint={hint_val}: {count} 条记录 ({percentage:.2f}%)\")\n",
"\n",
"# 支撑使用情况\n",
"scaffold_usage = data[\"scaffold\"].value_counts()\n",
"print(\"\\n支撑使用情况:\")\n",
"for scaffold_val, count in scaffold_usage.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"scaffold={scaffold_val}: {count} 条记录 ({percentage:.2f}%)\")\n",
"\n",
"# 最底层提示使用情况\n",
"bottom_hint_usage = data[\"bottomHint\"].value_counts()\n",
"print(\"\\n最底层提示使用情况:\")\n",
"for bh_val, count in bottom_hint_usage.items():\n",
" percentage = (count / len(data)) * 100\n",
" print(f\"bottomHint={bh_val}: {count} 条记录 ({percentage:.2f}%)\")\n",
"\n",
"# 提示次数统计\n",
"print(\"\\n提示次数统计:\")\n",
"print(f\"平均提示次数: {data['hintCount'].mean():.2f}\")\n",
"print(f\"中位数提示次数: {data['hintCount'].median():.2f}\")\n",
"print(f\"最大提示次数: {data['hintCount'].max()}\")"
]
},
{
"cell_type": "markdown",
"id": "c9a4056d",
"metadata": {},
"source": [
"### 情绪和行为预测\n",
"数据集包含两类情绪预测：\n",
"1. **confidence系列**：BORED, CONCENTRATING, CONFUSED, FRUSTRATED, OFF TASK, GAMING\n",
"2. **RES系列**：RES_BORED, RES_CONCENTRATING, RES_CONFUSED, RES_FRUSTRATED, RES_OFFTASK, RES_GAMING"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e7bdf726",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confidence系列情绪预测统计:\n",
"\n",
"confidence(BORED):\n",
" 均值: 0.4370\n",
" 标准差: 0.1208\n",
" 最小值: 0.3557\n",
" 最大值: 0.6810\n",
"\n",
"confidence(CONCENTRATING):\n",
" 均值: 0.5401\n",
" 标准差: 0.1830\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"confidence(CONFUSED):\n",
" 均值: 0.1344\n",
" 标准差: 0.2929\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"confidence(FRUSTRATED):\n",
" 均值: 0.1641\n",
" 标准差: 0.3261\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"confidence(OFF TASK):\n",
" 均值: 0.2560\n",
" 标准差: 0.2132\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"confidence(GAMING):\n",
" 均值: 0.3379\n",
" 标准差: 0.3353\n",
" 最小值: 0.0000\n",
" 最大值: 0.9997\n",
"\n",
"\n",
"RES系列情绪预测统计:\n",
"\n",
"RES_BORED:\n",
" 均值: 0.2329\n",
" 标准差: 0.1164\n",
" 最小值: 0.1560\n",
" 最大值: 0.5053\n",
"\n",
"RES_CONCENTRATING:\n",
" 均值: 0.6584\n",
" 标准差: 0.1734\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"RES_CONFUSED:\n",
" 均值: 0.0989\n",
" 标准差: 0.2495\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"RES_FRUSTRATED:\n",
" 均值: 0.1314\n",
" 标准差: 0.3004\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"RES_OFFTASK:\n",
" 均值: 0.1722\n",
" 标准差: 0.2170\n",
" 最小值: 0.0000\n",
" 最大值: 1.0000\n",
"\n",
"RES_GAMING:\n",
" 均值: 0.1927\n",
" 标准差: 0.3402\n",
" 最小值: 0.0000\n",
" 最大值: 0.9994\n"
]
}
],
"source": [
"# confidence系列情绪预测统计\n",
"confidence_columns = ['confidence(BORED)', 'confidence(CONCENTRATING)', 'confidence(CONFUSED)', \n",
" 'confidence(FRUSTRATED)', 'confidence(OFF TASK)', 'confidence(GAMING)']\n",
"\n",
"print(\"Confidence系列情绪预测统计:\")\n",
"for col in confidence_columns:\n",
" if col in data.columns:\n",
" print(f\"\\n{col}:\")\n",
" print(f\" 均值: {data[col].mean():.4f}\")\n",
" print(f\" 标准差: {data[col].std():.4f}\")\n",
" print(f\" 最小值: {data[col].min():.4f}\")\n",
" print(f\" 最大值: {data[col].max():.4f}\")\n",
"\n",
"# RES系列情绪预测统计\n",
"res_columns = ['RES_BORED', 'RES_CONCENTRATING', 'RES_CONFUSED', \n",
" 'RES_FRUSTRATED', 'RES_OFFTASK', 'RES_GAMING']\n",
"\n",
"print(\"\\n\\nRES系列情绪预测统计:\")\n",
"for col in res_columns:\n",
" if col in data.columns:\n",
" print(f\"\\n{col}:\")\n",
" print(f\" 均值: {data[col].mean():.4f}\")\n",
" print(f\" 标准差: {data[col].std():.4f}\")\n",
" print(f\" 最小值: {data[col].min():.4f}\")\n",
" print(f\" 最大值: {data[col].max():.4f}\")"
]
},
{
"cell_type": "markdown",
"id": "866f42d0",
"metadata": {},
"source": [
"# 数据结构可视化\n",
"这一板块中包含了对数据集中重要数据的可视化代码和结果。\n",
"\n",
"- 学生的答题次数分布图\n",
"- 问题类型分布图\n",
"- 整体答题正确率分布图\n",
"- 主问题vs支撑问题分布\n",
"- 情绪状态分布图\n",
"- 性别分布图\n",
"- 每一个问题关联的技能数量"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3bdfa11d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 每个学生的答题次数\n",
"student_attempts = data.groupby(\"studentId\")[\"problemId\"].count()\n",
"\n",
"# 绘制学生答题次数的分布图\n",
"plt.figure(figsize=(10, 6))\n",
"plt.hist(student_attempts, bins=50, color='skyblue', edgecolor='black')\n",
"plt.title('Distribution of Student Attempts')\n",
"plt.xlabel('Number of Attempts')\n",
"plt.ylabel('Number of Students')\n",
"plt.yscale('log')\n",
"plt.grid(axis='y', alpha=0.3)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3642b132",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 问题类型分布（只显示前10个最常见的类型）\n",
"problem_type_counts = data[\"problemType\"].value_counts().head(10)\n",
"\n",
"# 绘制问题类型数量的分布图\n",
"plt.figure(figsize=(12, 6))\n",
"problem_type_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
"plt.title('Distribution of Top 10 Problem Types')\n",
"plt.xlabel('Problem Type')\n",
"plt.ylabel('Number of Records')\n",
"plt.xticks(rotation=45, ha='right')\n",
"plt.grid(axis='y', alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "faefaffa",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"正确答题记录数: 351370\n",
"错误答题记录数: 591446\n"
]
}
],
"source": [
"# 答题正确率\n",
"correct_data = data[\"correct\"].value_counts()\n",
"\n",
"# 绘制答题正确率的饼图\n",
"plt.figure(figsize=(8, 8))\n",
"colors = ['lightcoral', 'lightskyblue']\n",
"labels = ['Incorrect', 'Correct']\n",
"plt.pie(correct_data, labels=labels, autopct='%1.1f%%', startangle=90, colors=colors)\n",
"plt.title('Overall Correctness Distribution')\n",
"plt.show()\n",
"\n",
"print(f\"正确答题记录数: {correct_data.get(1, 0)}\")\n",
"print(f\"错误答题记录数: {correct_data.get(0, 0)}\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b53a302d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 主问题vs支撑问题分布\n",
"original_counts = data[\"original\"].value_counts()\n",
"\n",
"plt.figure(figsize=(8, 8))\n",
"colors = ['lightgreen', 'lightsalmon']\n",
"labels = ['Scaffolding Questions', 'Main Questions']\n",
"sizes = [original_counts.get(0, 0), original_counts.get(1, 0)]\n",
"plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=colors)\n",
"plt.title('Main Questions vs Scaffolding Questions Distribution')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2cb7b670",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GG/P8XNy9e3e7M43Dw8MlXfjMffHxyl1emPw1YMAA1a5d23b/0pxy9OhR7dq1S0OGDLHbtq5du6pVq1YFrr84ubtDhw7q0qWLbVmtWrU0atQo/fbbb9q7d69d/4JyYlGsWrVK9erV0z333GNbVqNGDY0aNcqu386dO3Xw4EHdf//9+uuvv2w59OzZs+revbs2bdrk8Lf60EMP2d2/8cYb9ddff9n2z+eff66cnBxNnjxZrq72ZeDc3B0TE6PTp0/rvvvus3u/4ObmpvDwcK1fv77I21wVUURHiUlNTc33xWvAgAHq3LmzRowYocDAQA0cOFAfffRRkZJ1/fr1i3SBhiZNmtjdd3FxUePGjfXbb78Veh3FkZCQoODgYIf90bx5c1v7xfL62XDt2rV16tSpAsdp0qSJwwuhs3GKom7duoqMjLS7XXPNNbb2oh6LS02fPl2nT5/Wtddeq1atWmnChAn65ZdfirSO0NDQPJe/9957at26tTw9PVWnTh35+/tr5cqVOnPmTIHrPHTokIwxevbZZ+3ekPj7+9uump578ZDc/X+ppk2bFjiOxWKRpHzf1F6srJ5TzsZ2dXW1exMpOW5ncnKyTp8+rfnz5zvsu9w31rn7zlmcuW+ycuM8fPiwJKlly5ZO4yvKMQNQPOT4v1XmHJ8bc365pzCF9pJ2/vx5TZ48WQ0bNpSHh4fq1q0rf39/nT592i53F/a9Q1pamnr37q22bdvqo48+KtTzirycd5zkZaByI3//rSrk79TUVLvlnTt3tp3wljslx8VWrFihjh07ytPTU35+frZpTfP6XHzp9vr4+EiSGjZsmOfywuSvgnJK7r5o3Lixw2PzWnap4uTuvD6rF/Y5cGn8RZGQkKDGjRvbXfhUcszduScKDB061CGHLliwQOnp6Q7HrzC529XVNd+L2OaOe8sttziMu3btWvL2/8ec6CgRf/zxh86cOZPvC52Xl5c2bdqk9evXa+XKlVq9erU+/PBD3XLLLVq7dm2h5rAqyhxphXXpi1iu7OzsQsVUEpyNY4wpk/GLo6jHIjs72+7+TTfdpMOHD+uLL77Q2rVrtWDBAs2aNUvz5s3TiBEjih3Df/7zHz3wwAPq06ePJkyYoICAALm5uWnGjBm2D375yX3D+M9//lNRUVF59ilMQi9I48aNVa1aNe3ateuy15WX8nhO5e67f/zjHxo6dGiefS6dI64k4iyrYwZcqcjxl6ci5fjcD4m//PKL2rRpk2ef3KJ0fh+0StojjzyiRYsWady4cYqIiJCPj49cXFw0cOBAu0JOYd87eHh4qFevXvriiy+0evVq3X777QXGQF6+gLwMVB3k78tTkfJ3s2bNJEm7d+/W9ddfb1vu7++vyMhISRc+B1/s22+/1Z133qmbbrpJb731lurVq6fq1atr0aJFWrp0qcMYzrb3cvZDae/D3P2ya9cu9enTp0TWebHyzN2vvPKK0/dql/4ioSRz95IlSxQUFOTQXq0a5WOJIjpKSO6FH529Uc7l6uqq7t27q3v37nrttdf04osvatKkSVq/fr0iIyOdJsviuvTnvsYYHTp0yO4DQ+3atXX69GmHxyYkJNideV2U2EJCQvTNN98oJSXF7pvu/fv329pLQkhIiH755Rfl5OTYfdNd0uMURV77MyMjQ8eOHXPo6+fnp2HDhmnYsGFKTU3VTTfdpKlTp9o+CBfn+fDJJ5/ommuu0fLly+0en3vmUy5n68495tWrV7e9IXEmJCQkz5+UHzhwoMA4a9SooVtuuUXr1q3T77//7vANf15jlcVzytnYOTk5Onz4sN035Zdup7+/v7y9vZWdnV3gvius3LPsdu/e7XSdRTlmAIqOHG+vMuf4nj17ys3NTUuWLHF6cdH3339f1apVU48ePYoXeDF88sknGjp0qF599VXbsrS0tDyPXUHvHaQLx/ODDz7QXXfdpf79++vrr79Wt27d8o2BvFw45GWg8iB/26sK+fuDDz4o1NShkvTpp5/K09NTa9assZvSbNGiRUUev7Tk7otDhw45tOW17FJdunRR7dq19d///ldPP/10gV+whISE5PlZvaxy9+7du2WMsXveXhpPbp61WCwlmrtzcnK0d+9ep4X53HEDAgLI3flgOhdctnXr1um5555TaGhovi/oJ0+edFiW+wecnp4uSapZs6Yk5Zkwi+P999+3+2nPJ598omPHjqlnz562ZY0aNdIPP/ygjIwM27IVK1bo999/t1tXUWLr1auXsrOz9eabb9otnzVrllxcXOzGvxy9evVSYmKiPvzwQ9uyrKwsvfHGG6pVq5a6du1aIuMURaNGjbRp0ya7ZfPnz3c4E/2vv/6yu1+rVi01btzY9lyQivd8yE2cF3/rumXLFsXFxdn1q1GjRp7rDggIULdu3fTvf/87z8J/cnKy7f+9evXSDz/8oK1bt9q1f/DBB4WKdcqUKTLGaPDgwQ4/zZOk7du367333rONVRbPqbzkrvv111+3Wz579my7+25uburXr58+/fRT7d6922E9F++7wmrXrp1CQ0M1e/Zsh2OVe4yLcswAFA053lFlzvENGzbUsGHD9M033+jtt992aJ83b57WrVun4cOHq0GDBpcVf1G4ubk5nC31xhtvFOu9Qy53d3ctX75cN9xwg+644w67XO0Meblg5GWgciB/O6rM+fuqq67Sgw8+qK+//toh/lyX5lE3Nze5uLjY5dLffvtNn3/+eZHHLy3BwcFq2bKl3n//fbu8u3HjxkL9MqxGjRp68skntW/fPj355JN5nnn9n//8x/YeoFevXtq6datdbeDs2bOaP3++rr766lL9FV6vXr109OhRffLJJ7Zl586d0/z58+36hYWFqVGjRvrXv/6V53uR4uTQPn36yNXVVdOnT3eYqil3n0VFRclisejFF19UZmZmiYxbFXEmOork66+/1v79+5WVlaWkpCStW7dOMTExCgkJ0ZdffilPT0+nj50+fbo2bdqk3r17KyQkRMePH9dbb72lBg0a2C7s0KhRI/n6+mrevHny9vZWzZo1FR4e7nTu64L4+fmpS5cuGjZsmJKSkjR79mw1btxYI0eOtPUZMWKEPvnkE/Xo0UP33nuvDh8+rP/85z8O80wWJbY77rhDN998syZNmqTffvtN119/vdauXasvvvhC48aNc1h3cY0aNUr//ve/9cADD2j79u26+uqr9cknn+i7777T7Nmzy3Q+01wjRozQQw89pH79+unWW2/Vzz//rDVr1qhu3bp2/Vq0aKFu3bopLCxMfn5+2rZtmz755BONHTvW1icsLEzShQtnRUVFyc3NTQMHDsx3/Ntvv13Lly/X3Xffrd69eys+Pl7z5s1TixYt7JKQl5eXWrRooQ8//FDXXnut/Pz81LJlS7Vs2VJz585Vly5d1KpVK40cOVLXXHONkpKSFBcXpz/++EM///yzJOmJJ57QkiVL1KNHDz322GOqWbOm5s+fbzv7oCCdOnXS3Llz9fDDD6tZs2YaPHiwmjRpopSUFG3YsEFffvmlnn/+eUll95zKS5s2bXTffffprbfe0pkzZ9SpUyfFxsbmeXbASy+9pPXr1ys8PFwjR45UixYtdPLkSf3000/65ptv8nyjnh9XV1e9/fbbuuOOO9SmTRsNGzZM9erV0/79+7Vnzx6tWbNGkgp9zAA4R46/MnL8rFmztH//fj388MNavXq17YzzNWvW6IsvvlDXrl3tzggvC7fffruWLFkiHx8ftWjRQnFxcfrmm29Up04du36Fee9wMS8vL61YsUK33HKLevbsqY0bN+Y7lzd5uWDkZaDiIX9fGfl79uzZio+P1yOPPKJly5bpjjvuUEBAgE6cOKHvvvtOX331ld2vk3r37q3XXntNPXr00P3336/jx49r7ty5aty4cZGvRVaaXnzxRd11113q3Lmzhg0bplOnTunNN99Uy5Yt8ywiX2rChAnas2ePXn31Va1fv1733HOPgoKClJiYqM8//1xbt27V999/L0l66qmn9N///lc9e/bUo48+Kj8/P7333nuKj4/Xp59+6jCPfUkaOXKk3nzzTQ0ZMkTbt29XvXr1tGTJEtvJfblcXV21YMEC9ezZU9ddd52GDRum+vXr688//9T69etlsVj01VdfFWnsxo0ba9KkSXruued04403qm/fvvLw8NCPP/6o4OBgzZgxQxaLRW+//bYGDx6sdu3aaeDAgfL399eRI0e0cuVKde7c2ekXOFcUAxTCokWLjCTbzd3d3QQFBZlbb73VzJkzx1itVofHTJkyxVz8FIuNjTV33XWXCQ4ONu7u7iY4ONjcd9995n//+5/d47744gvTokULU61aNSPJLFq0yBhjTNeuXc11112XZ3xdu3Y1Xbt2td1fv369kWT++9//mokTJ5qAgADj5eVlevfubRISEhwe/+qrr5r69esbDw8P07lzZ7Nt2zaHdeYX29ChQ01ISIhd35SUFDN+/HgTHBxsqlevbpo0aWJeeeUVk5OTY9dPkhkzZoxDTCEhIWbo0KF5bu/FkpKSzLBhw0zdunWNu7u7adWqlS2uS9fXu3fvAtdXmL75HYvs7Gzz5JNPmrp165oaNWqYqKgoc+jQIYftef75502HDh2Mr6+v8fLyMs2aNTMvvPCCycjIsPXJysoyjzzyiPH39zcuLi6251N8fLyRZF555RWH8XNycsyLL75oQkJCjIeHh2nbtq1ZsWJFnsfo+++/N2FhYcbd3d1IMlOmTLG1HT582AwZMsQEBQWZ6tWrm/r165vbb7/dfPLJJ3br+OWXX0zXrl2Np6enqV+/vnnuuefMu+++aySZ+Ph4p/vwYtu3bzf333+/7blSu3Zt0717d/Pee++Z7OxsW7+Sfk7l/p18/PHHdv0u/ds1xpjz58+bRx991NSpU8fUrFnT3HHHHeb333932G/GXHhOjhkzxjRs2NBUr17dBAUFme7du5v58+cXOHbusb30Obx582Zz6623Gm9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beY4YMUL9+/fXfffdp4ULF8rhcGj27NlKTk42m9cPPfSQli9frpkzZ+qBBx7Q1q1b9c4772jjxo1mHqmpqUpMTNTgwYM1ZMgQLV26VGfOnNGkSZMkqV65AADQkvF7NwAALUeDmugrV65s7DwAAEATaqzaXVBQoIkTJ+rEiRMKCgrSoEGDtHnzZv3kJz+RJC1ZskRWq1UJCQkqKyuT3W7XK6+8Yu7v4+OjDRs2aOrUqYqNjVXnzp2VmJioefPmmTFRUVHauHGjpk+frmXLlqlnz556/fXXZbfbzZhx48apsLBQc+bMkcPhUHR0tDIyMlxuNlpXLgAAtGT83g0AQMvR4O+anT9/Xh988IF+97vfmTdP+fbbb1VSUtJoyQEAgMbTGLX7jTfe0D/+8Q+VlZWpoKBAH3zwgdlAly6sBZuWlqaTJ0/qzJkzevfdd2usQR4ZGalNmzbp7NmzKiws1IsvvqgOHVw/1x8+fLj27t2rsrIyHT16VPfff3+NXFJSUnTs2DGVlZVp586dGjp0qMt4fXIBAKAla47fuysrK/Xkk08qKipKAQEB6t27t+bPny/DMMwYwzA0Z84cde/eXQEBAYqLi9ORI0dcjnPy5ElNmDBBNptNwcHBSkpKqpHnF198oZtvvln+/v6KiIjQwoULa+Szbt069e3bV/7+/ho4cGCNJd4AAPCGBl2JfuzYMY0cOVLHjx9XWVmZfvKTnygwMFDPP/+8ysrKlJ6e3th5AgCAy0DtBgCgdWmu2v3888/r1Vdf1Ztvvqlrr71We/bs0aRJkxQUFKT/+Z//kSQtXLhQL730kt58801FRUXpySeflN1u18GDB82bqU6YMEEnTpxQZmamKioqNGnSJE2ZMkVr1qyRdOFm3SNGjFBcXJzS09O1b98+PfDAAwoODtaUKVMkSTt27NA999yjBQsW6Kc//anWrFmjMWPG6PPPP9eAAQMa5fU2h8LCQjmdTrfjNptN3bp1a8aMAACXq0FN9EceeUSDBw/W3/72N3Xt2tXc/vOf/1yTJ09utOQAAEDjoHYDANC6NFft3rFjh+666y7Fx8dLkq666ir93//9n3bt2iXpwlXoS5cu1ezZs3XXXXdJkt566y2FhYVp/fr1Gj9+vL788ktlZGRo9+7dGjx4sCTp5Zdf1ujRo/Xiiy+qR48eWr16tcrLy7VixQr5+vrq2muvVW5urhYvXmw20ZctW6aRI0dqxowZkqT58+crMzNTy5cvbzUf+BcWFmripAdVVHzWbUxwYCe9tfJ1GukA0Io0qIn+8ccfa8eOHfL19XXZftVVV+lf//pXoyQGAAAaD7UbAIDWpblq9w033KDXXntNX331lX74wx/qb3/7mz755BMtXrxYkpSXlyeHw6G4uDhzn6CgIA0dOlTZ2dkaP368srOzFRwcbDbQJSkuLk5Wq1U7d+7Uz3/+c2VnZ+uWW25xeT12u13PP/+8Tp06pS5duig7O1upqaku+dntdq1fv95t/mVlZSorKzOfV18BXlVVpaqqqgbPi2EYslgsslx45jJmkWSxWGQYRo1znD59WqdLzumHtybI1jVM3+f8Ll9Htr+r06dPu3w40hI1dA5am/byOgHUrr4/2w1qoldVVamysrLG9n/+858KDAxsyCEBAEATonYDANC6NFftfuyxx+R0OtW3b1/5+PiosrJSzz77rCZMmCBJcjgckuRy8+7q59VjDodDoaGhLuMdOnRQSEiIS0xUVFSNY1SPdenSRQ6Hw+N5arNgwQLNnTu3xvbCwkKVlpbW+frdKS4uVu+oSHUNkAKtZS5j1gCpd1SkiouLVVBQUOt+UT3DFBh8Zc3jBkhVbvZtaRo6B61Ne3mdAGpXfc+RujSoiT5ixAgtXbpUr732mqQLn8qVlJToqaee0ujRoxtySAAA0ISo3QAAtC7NVbvfeecdrV69WmvWrDGXWJk2bZp69OihxMTERjtPU5k1a5bL1etOp1MRERHq1q2bbDZbg49bUlKio3nHFBwtVdn8XMZOnZOO5h1TYGBgjQ8PPO1X174tTUPnoLVpL68TQO2q7+1RlwY10RctWiS73a7+/furtLRU9957r44cOaIrr7xS//d//9eQQwIAgCZE7QYAoHVprto9Y8YMPfbYYxo/frwkaeDAgTp27JgWLFigxMREhYeHS5Ly8/PVvXt3c7/8/HxFR0dLksLDw2tcpXv+/HmdPHnS3D88PFz5+fkuMdXP64qpHq+Nn5+f/PxqNqutVqusVmudr9+d6iU8LizuYXEZM/SfJUC+fw5P+9W1b0vT0DlobdrL6wRQu/r+bDeoid6zZ0/97W9/09q1a/XFF1+opKRESUlJmjBhggICAhpySAAA0ISo3QAAtC7NVbvPnj1bo4Hg4+NjrhEbFRWl8PBwZWVlmU1zp9OpnTt3aurUqZKk2NhYFRUVKScnRzExMZKkrVu3qqqqSkOHDjVjnnjiCVVUVKhjx46SpMzMTF1zzTXq0qWLGZOVlaVp06aZuWRmZio2NrbRXi8AAA3RoCa6dGF9s1/96leNmQsAAGhC1G4AAFqX5qjdd955p5599ln16tVL1157rfbu3avFixfrgQcekHThKt1p06bpmWeeUZ8+fRQVFaUnn3xSPXr00JgxYyRJ/fr108iRIzV58mSlp6eroqJCKSkpGj9+vHr06CFJuvfeezV37lwlJSXp0Ucf1f79+7Vs2TItWbLEzOWRRx7RrbfeqkWLFik+Pl5r167Vnj17zCVtAADwlgY10d966y2P4xMnTmxQMgAAoGlQuwEAaF2aq3a//PLLevLJJ/XrX/9aBQUF6tGjh/77v/9bc+bMMWNmzpypM2fOaMqUKSoqKtJNN92kjIwMl3VkV69erZSUFN1xxx2yWq1KSEjQSy+9ZI4HBQVpy5YtSk5OVkxMjK688krNmTNHU6ZMMWNuuOEGrVmzRrNnz9bjjz+uPn36aP369RowYECjvFYAABqqQU30Rx55xOV5RUWFzp49K19fX3Xq1IlfxAEAaGGo3QAAtC7NVbsDAwO1dOlSLV261G2MxWLRvHnzNG/ePLcxISEhWrNmjcdzDRo0SB9//LHHmLFjx2rs2LEeYwAAaG4NuivCqVOnXB4lJSU6fPiwbrrpJm5OBgBAC0TtBgCgdaF2AwDQcjTarYX79Omj5557rsan5Z68+uqrGjRokGw2m2w2m2JjY/XXv/7VHC8tLVVycrK6du2qK664QgkJCTXu1H38+HHFx8erU6dOCg0N1YwZM3T+/HmXmG3btun666+Xn5+frr76aq1atapGLmlpabrqqqvk7++voUOHateuXS7j9ckFAIDWpCG1G61LYWGhjh49WuujsLDQ2+kBAC4RtRsAAO9o8I1Faz1Yhw769ttv6x3fs2dPPffcc+rTp48Mw9Cbb76pu+66S3v37tW1116r6dOna+PGjVq3bp2CgoKUkpKiu+++W59++qkkqbKyUvHx8QoPD9eOHTt04sQJTZw4UR07dtRvf/tbSVJeXp7i4+P10EMPafXq1crKytKDDz6o7t27y263S5LefvttpaamKj09XUOHDtXSpUtlt9t1+PBhhYaGSlKduQAA0Bpdau1G61FYWKiJkx5UUfHZWseDAzvprZWvq1u3bs2cGQDgclC7AQBofg1qor///vsuzw3D0IkTJ7R8+XLdeOON9T7OnXfe6fL82Wef1auvvqrPPvtMPXv21BtvvKE1a9bo9ttvlyStXLlS/fr102effaZhw4Zpy5YtOnjwoD744AOFhYUpOjpa8+fP16OPPqqnn35avr6+Sk9PV1RUlBYtWiTpwl3DP/nkEy1ZssRsoi9evFiTJ0/WpEmTJEnp6enauHGjVqxYoccee0ynT5+uMxcAAFqyxqrdaD2cTqeKis+qz60JsnUNcx37Ll9HPvqTnE4nTXQAaKGo3QAAtBwNaqKPGTPG5bnFYlG3bt10++23m83qS1VZWal169bpzJkzio2NVU5OjioqKhQXF2fG9O3bV7169VJ2draGDRum7OxsDRw4UGFh//nF0G63a+rUqTpw4IB+9KMfKTs72+UY1THTpk2TJJWXlysnJ0ezZs0yx61Wq+Li4pSdnS1J9cqlNmVlZSorKzOfO51OSVJVVZWqqqoaNE/ShX88WSwWWS48cxmz6MKfh2EYl3UOAID3Neb7eFPUbrQOtq5hCgnr6e00AACXiNoNAEDL0aAmemP+Ur9v3z7FxsaqtLRUV1xxhd577z31799fubm58vX1VXBwsEt8WFiYHA6HJMnhcLg00KvHq8c8xTidTp07d06nTp1SZWVlrTGHDh0yj1FXLrVZsGCB5s6dW2N7YWGhSktL3e5Xl+LiYvWOilTXACnQWuYyZg2QekdFqri4WAUFBQ0+BwDA+4qLixvtWHywCgBA60LtBgCg5WjUNdEb4pprrlFubq5Onz6tP/7xj0pMTNRHH33k7bQaxaxZs5Sammo+dzqdioiIULdu3WSz2Rp83JKSEh3NO6bgaKnK5ucyduqcdDTvmAIDA8313AEArZO/v7+3UwAAAAAAoN1rUBP94sZwXRYvXuxx3NfXV1dffbUkKSYmRrt379ayZcs0btw4lZeXq6ioyOUK8Pz8fIWHh0uSwsPDtWvXLpfj5efnm2PV/63ednGMzWZTQECAfHx85OPjU2vMxceoK5fa+Pn5yc/Pr8Z2q9Uqq9XqaVo8ql6u5cJCLhaXMUP/We7lcs4BAPC+xnwfb8zaDQAAmh61GwCAlqNBTfS9e/dq7969qqio0DXXXCNJ+uqrr+Tj46Prr7/ejLNYLO4O4VZVVZXKysoUExOjjh07KisrSwkJCZKkw4cP6/jx44qNjZUkxcbG6tlnn1VBQYF51XVmZqZsNpv69+9vxmzatMnlHJmZmeYxfH19FRMTo6ysLHPNuaqqKmVlZSklJUWS6pULAAAtWVPWbgAA0Pio3QAAtBwNaqLfeeedCgwM1JtvvqkuXbpIkk6dOqV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"text/plain": [
"<Figure size 1500x1000 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 情绪状态分布图（confidence系列）\n",
"confidence_columns = ['confidence(BORED)', 'confidence(CONCENTRATING)', 'confidence(CONFUSED)', \n",
" 'confidence(FRUSTRATED)', 'confidence(OFF TASK)', 'confidence(GAMING)']\n",
"confidence_labels = ['Bored', 'Concentrating', 'Confused', 'Frustrated', 'Off Task', 'Gaming']\n",
"\n",
"fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
"axes = axes.ravel()\n",
"\n",
"for i, (col, label) in enumerate(zip(confidence_columns, confidence_labels)):\n",
" if col in data.columns:\n",
" axes[i].hist(data[col].dropna(), bins=50, color='steelblue', edgecolor='black', alpha=0.7)\n",
" axes[i].set_title(f'Distribution of {label} Confidence')\n",
" axes[i].set_xlabel('Confidence Score')\n",
" axes[i].set_ylabel('Frequency')\n",
" axes[i].grid(axis='y', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2566c909",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 性别分布图\n",
"gender_counts = data[\"InferredGender\"].value_counts()\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"gender_counts.plot(kind='bar', color=['steelblue', 'coral'], edgecolor='black')\n",
"plt.title('Gender Distribution')\n",
"plt.xlabel('Gender')\n",
"plt.ylabel('Number of Records')\n",
"plt.xticks(rotation=0)\n",
"plt.grid(axis='y', alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "88ea3e8c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 每一个问题关联的技能数量\n",
"skill_counts = data[['problemId', 'skill']].drop_duplicates().groupby('problemId').size()\n",
"# 统计每个问题对应技能数量的分布\n",
"skill_count_distribution = skill_counts.value_counts().sort_index()\n",
"# 绘制直方图\n",
"plt.figure(figsize=(10, 6))\n",
"plt.bar(skill_count_distribution.index, skill_count_distribution.values, color='skyblue')\n",
"plt.xlabel('Number of Skills per Problem')\n",
"plt.ylabel('Number of Problems')\n",
"plt.title('Distribution of Number of Skills per Problem')\n",
"plt.xticks(skill_count_distribution.index)\n",
"plt.grid(axis='y')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "74d26e6b",
"metadata": {},
"source": [
"# 数据集总结\n",
"\n",
"## 数据规模\n",
"- **学生数量**：1,709名学生\n",
"- **问题数量**：3,162个问题（主问题1,183个，支撑问题2,651个）\n",
"- **技能数量**：102个技能\n",
"- **总记录数**：942,816条答题记录\n",
"- **学校数量**：4所学校\n",
"\n",
"## 数据特点\n",
"1. **答题活跃度高**：平均每个学生有551.68次答题记录，中位数为441次，远高于2009和2012数据集\n",
"2. **支撑问题占比高**：73.6%的记录是支撑问题，说明学生在解题过程中需要大量辅助\n",
"3. **整体正确率较低**：只有37.3%的答题记录是正确的，错误率达到62.7%\n",
"4. **问题-技能关系**：平均每个问题关联1.23个技能，大部分问题只对应一个技能\n",
"5. **技能-问题关系**：平均每个技能关联37.98个问题，但分布不均（中位数仅18个）\n",
"\n",
"## 问题类型分布\n",
"最常见的问题类型为：\n",
"1. textfieldquestion（文本框问题）：28.04%\n",
"2. radioquestion（单选题）：20.81%\n",
"3. noprobtype（无类型）：19.89%\n",
"\n",
"## 学生使用行为\n",
"1. **提示使用**：33.10%的记录使用了提示，平均提示次数1.22次\n",
"2. **支撑使用**：38.57%的记录使用了支撑结构\n",
"3. **最底层提示**：仅6.28%的记录使用了最底层提示\n",
"\n",
"## 性别分布\n",
"- 男性学生记录：43.24%\n",
"- 女性学生记录：38.35%\n",
"- 缺失性别信息：18.42%\n",
"\n",
"## 情绪状态特征\n",
"- **Concentrating（专注）**：平均置信度最高（0.54），说明学生大部分时间处于专注状态\n",
"- **Bored（无聊）**：平均置信度0.44\n",
"- **Gaming（游戏行为）**：平均置信度0.34\n",
"- **Off Task（离线任务）**：平均置信度0.26\n",
"- **Frustrated（挫败）**：平均置信度0.16\n",
"- **Confused（困惑）**：平均置信度0.13\n",
"\n",
"## 与其他数据集的比较\n",
"\n",
"| 特征 | ASSISTment09 | ASSISTment12 | ASSISTment17 |\n",
"|------|--------------|--------------|--------------|\n",
"| 学生数量 | 4,217 | 29,018 | 1,709 |\n",
"| 问题数量 | 26,688 | 50,993 | 3,162 |\n",
"| 技能数量 | 123 | 265 | 102 |\n",
"| 总记录数 | 401,756 | 2,506,853 | 942,816 |\n",
"| 平均每学生答题数 | 95.27 | 86.36 | 551.68 |\n",
"| 特征列数 | 28 | 47 | 82 |\n",
"| 情绪预测 | 无 | 有（4种） | 有（6种×2套） |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "data-analysis",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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