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学生考试数据分析系统-简介
基于Django的学生考试表现影响因素数据可视化分析系统是一个集成大数据处理与Web展示的综合性分析平台。该系统采用Hadoop+Spark作为大数据处理核心，结合Django后端框架和Vue前端技术，实现对学生考试成绩影响因素的深度挖掘与直观展示。系统通过HDFS分布式文件系统存储海量学生数据，运用Spark SQL进行高效的数据清洗与预处理，并结合Pandas、NumPy等数据科学库完成复杂的统计分析计算。在数据分析层面，系统构建了五大核心分析维度：学生个人学习行为分析、家庭背景因素分析、教育资源环境分析、社会健康因素分析以及综合关联性分析，涵盖学习时长、出勤率、睡眠质量、家长参与度、教师质量、同伴影响等20余项关键指标。前端采用Vue+ElementUI+Echarts技术栈，提供丰富的图表展示形式，包括散点图、热力图、柱状图、雷达图等多种可视化方案，帮助教育工作者直观理解各因素对学生成绩的具体影响程度，为教育决策提供数据支撑。
学生考试数据分析系统-技术
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 数据库：MySQL
学生考试数据分析系统-背景
随着教育信息化程度的不断提升，各类教育机构积累了大量的学生学习数据，这些数据包含了学生的考试成绩、出勤记录、学习时长、家庭背景等多维度信息。传统的教育评价方式往往只关注最终的考试分数，缺乏对影响学生成绩背后深层因素的系统性分析。教育工作者虽然凭借经验能够感知到家庭环境、学习习惯、教师质量等因素对学生表现的影响，但缺少量化的数据支撑来验证这些直觉判断。同时，面对海量的教育数据，传统的Excel表格分析方式已经无法满足深度挖掘的需求，急需借助大数据技术来处理和分析这些复杂的多维数据。在这样的背景下，构建一个能够系统分析学生考试表现影响因素的数据平台，既符合当前教育数字化发展的趋势，也为教育研究提供了新的技术手段。
本课题的研究具有一定的理论价值和实际应用意义。在理论层面，该系统通过对多维度数据的关联分析，能够帮助验证教育学中关于学习效果影响因素的各种理论假设，为教育研究提供数据验证工具。在实际应用方面，系统可以帮助学校管理者更好地理解学生群体的学习状况，识别出影响学生成绩的关键因素，从而制定更有针对性的教育策略。教师可以通过系统了解班级整体的学习特征，调整教学方法和重点关注对象。学生和家长也能通过数据分析结果，认识到学习习惯、家庭环境等因素对学业的影响，促进家校协作。从技术发展角度来看，该项目将大数据处理技术应用到教育领域，为类似的教育数据分析项目提供了参考模板。当然，作为一个毕业设计项目，其主要意义在于通过实际开发过程，加深对大数据技术栈的理解和应用，提升数据分析和系统开发能力。
学生考试数据分析系统-视频展示
学生考试数据分析系统-图片展示
学生考试数据分析系统-代码展示
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import pandas as pd
import numpy as np
import json
import logging
spark = SparkSession.builder.appName("StudentAnalysisSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def analyze_study_time_performance(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
file_path = data.get('file_path', '/hadoop/student_data/exam_performance.csv')
df = spark.read.csv(file_path, header=True, inferSchema=True)
df.createOrReplaceTempView("student_performance")
study_time_analysis = spark.sql("""
SELECT
CASE
WHEN Hours_Studied <= 2 THEN '低强度学习(0-2小时)'
WHEN Hours_Studied <= 5 THEN '中等强度学习(3-5小时)'
WHEN Hours_Studied <= 8 THEN '高强度学习(6-8小时)'
ELSE '超高强度学习(8小时以上)'
END as study_intensity,
COUNT(*) as student_count,
ROUND(AVG(Exam_Score), 2) as avg_score,
ROUND(STDDEV(Exam_Score), 2) as score_std,
MIN(Exam_Score) as min_score,
MAX(Exam_Score) as max_score,
ROUND(AVG(Hours_Studied), 2) as avg_study_hours
FROM student_performance
WHERE Hours_Studied IS NOT NULL AND Exam_Score IS NOT NULL
GROUP BY
CASE
WHEN Hours_Studied <= 2 THEN '低强度学习(0-2小时)'
WHEN Hours_Studied <= 5 THEN '中等强度学习(3-5小时)'
WHEN Hours_Studied <= 8 THEN '高强度学习(6-8小时)'
ELSE '超高强度学习(8小时以上)'
END
ORDER BY avg_score DESC
""")
correlation_analysis = spark.sql("""
SELECT
CORR(Hours_Studied, Exam_Score) as correlation_coefficient,
COUNT(*) as total_samples
FROM student_performance
WHERE Hours_Studied IS NOT NULL AND Exam_Score IS NOT NULL
""")
efficiency_analysis = spark.sql("""
SELECT
Hours_Studied,
Exam_Score,
ROUND(Exam_Score / Hours_Studied, 2) as study_efficiency,
CASE
WHEN Exam_Score / Hours_Studied >= 15 THEN '高效学习者'
WHEN Exam_Score / Hours_Studied >= 10 THEN '普通学习者'
ELSE '低效学习者'
END as efficiency_level
FROM student_performance
WHERE Hours_Studied > 0 AND Exam_Score IS NOT NULL
ORDER BY study_efficiency DESC
LIMIT 100
""")
study_time_result = study_time_analysis.collect()
correlation_result = correlation_analysis.collect()
efficiency_result = efficiency_analysis.collect()
analysis_summary = {
'study_intensity_stats': [row.asDict() for row in study_time_result],
'correlation_coefficient': correlation_result[0]['correlation_coefficient'],
'total_samples': correlation_result[0]['total_samples'],
'efficiency_distribution': [row.asDict() for row in efficiency_result],
'analysis_insights': {
'best_performance_group': max(study_time_result, key=lambda x: x['avg_score'])['study_intensity'],
'optimal_study_range': '根据数据分析，建议学习时长控制在合理范围内',
'efficiency_trend': '学习效率与时长呈现复杂的非线性关系'
}
}
return JsonResponse({
'success': True,
'data': analysis_summary,
'message': '学习时长与考试成绩关系分析完成'
})
except Exception as e:
logging.error(f"学习时长分析错误: {str(e)}")
return JsonResponse({'success': False, 'message': f'分析过程中出现错误: {str(e)}'})
return JsonResponse({'success': False, 'message': '请求方法不正确'})
@csrf_exempt
def analyze_family_background_impact(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
file_path = data.get('file_path', '/hadoop/student_data/exam_performance.csv')
df = spark.read.csv(file_path, header=True, inferSchema=True)
df.createOrReplaceTempView("student_family_data")
family_income_analysis = spark.sql("""
SELECT
Family_Income as income_level,
COUNT(*) as student_count,
ROUND(AVG(Exam_Score), 2) as avg_exam_score,
ROUND(STDDEV(Exam_Score), 2) as score_std,
ROUND(AVG(CAST(Attendance AS DOUBLE)) * 100, 2) as avg_attendance_rate,
ROUND(AVG(Hours_Studied), 2) as avg_study_hours,
ROUND(AVG(Tutoring_Sessions), 2) as avg_tutoring_sessions
FROM student_family_data
WHERE Family_Income IS NOT NULL AND Exam_Score IS NOT NULL
GROUP BY Family_Income
ORDER BY avg_exam_score DESC
""")
parental_involvement_analysis = spark.sql("""
SELECT
Parental_Involvement as involvement_level,
COUNT(*) as student_count,
ROUND(AVG(Exam_Score), 2) as avg_score,
ROUND(MIN(Exam_Score), 2) as min_score,
ROUND(MAX(Exam_Score), 2) as max_score,
ROUND(AVG(Hours_Studied), 2) as avg_study_time,
ROUND(AVG(CAST(Attendance AS DOUBLE)) * 100, 2) as attendance_rate
FROM student_family_data
WHERE Parental_Involvement IS NOT NULL AND Exam_Score IS NOT NULL
GROUP BY Parental_Involvement
ORDER BY avg_score DESC
""")
parental_education_analysis = spark.sql("""
SELECT
Parental_Education_Level as education_level,
COUNT(*) as student_count,
ROUND(AVG(Exam_Score), 2) as avg_score,
ROUND(STDDEV(Exam_Score), 2) as score_variance,
ROUND(AVG(Previous_Scores), 2) as avg_previous_score,
ROUND(AVG(Tutoring_Sessions), 2) as avg_tutoring
FROM student_family_data
WHERE Parental_Education_Level IS NOT NULL AND Exam_Score IS NOT NULL
GROUP BY Parental_Education_Level
ORDER BY avg_score DESC
""")
comprehensive_family_analysis = spark.sql("""
SELECT
Family_Income,
Parental_Involvement,
Parental_Education_Level,
COUNT(*) as group_size,
ROUND(AVG(Exam_Score), 2) as group_avg_score,
ROUND(AVG(Tutoring_Sessions), 2) as avg_tutoring_sessions
FROM student_family_data
WHERE Family_Income IS NOT NULL
AND Parental_Involvement IS NOT NULL
AND Parental_Education_Level IS NOT NULL
AND Exam_Score IS NOT NULL
GROUP BY Family_Income, Parental_Involvement, Parental_Education_Level
HAVING COUNT(*) >= 5
ORDER BY group_avg_score DESC
LIMIT 20
""")
income_result = family_income_analysis.collect()
involvement_result = parental_involvement_analysis.collect()
education_result = parental_education_analysis.collect()
comprehensive_result = comprehensive_family_analysis.collect()
family_analysis_summary = {
'income_impact': [row.asDict() for row in income_result],
'parental_involvement_impact': [row.asDict() for row in involvement_result],
'parental_education_impact': [row.asDict() for row in education_result],
'comprehensive_family_groups': [row.asDict() for row in comprehensive_result],
'key_findings': {
'highest_income_group_performance': income_result[0]['income_level'] if income_result else 'N/A',
'best_involvement_level': involvement_result[0]['involvement_level'] if involvement_result else 'N/A',
'optimal_education_background': education_result[0]['education_level'] if education_result else 'N/A',
'family_factor_correlation': '家庭背景多维度因素对学生成绩存在显著影响'
}
}
return JsonResponse({
'success': True,
'data': family_analysis_summary,
'message': '家庭背景因素影响分析完成'
})
except Exception as e:
logging.error(f"家庭背景分析错误: {str(e)}")
return JsonResponse({'success': False, 'message': f'家庭背景分析失败: {str(e)}'})
return JsonResponse({'success': False, 'message': '请求方法错误'})
@csrf_exempt
def generate_comprehensive_correlation_heatmap(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
file_path = data.get('file_path', '/hadoop/student_data/exam_performance.csv')
df = spark.read.csv(file_path, header=True, inferSchema=True)
numerical_columns = ['Hours_Studied', 'Attendance', 'Sleep_Hours', 'Previous_Scores', 'Tutoring_Sessions', 'Physical_Activity', 'Exam_Score']
df_clean = df.select(*numerical_columns).filter(
df.Hours_Studied.isNotNull() &
df.Attendance.isNotNull() &
df.Sleep_Hours.isNotNull() &
df.Previous_Scores.isNotNull() &
df.Tutoring_Sessions.isNotNull() &
df.Physical_Activity.isNotNull() &
df.Exam_Score.isNotNull()
)
df_clean.createOrReplaceTempView("clean_numerical_data")
correlation_matrix = {}
for col1 in numerical_columns:
correlation_matrix[col1] = {}
for col2 in numerical_columns:
if col1 == col2:
correlation_matrix[col1][col2] = 1.0
else:
corr_query = spark.sql(f"""
SELECT CORR({col1}, {col2}) as correlation_value
FROM clean_numerical_data
""")
corr_result = corr_query.collect()
correlation_value = corr_result[0]['correlation_value'] if corr_result[0]['correlation_value'] is not None else 0.0
correlation_matrix[col1][col2] = round(correlation_value, 3)
statistical_summary = spark.sql("""
SELECT
'Hours_Studied' as feature,
ROUND(AVG(Hours_Studied), 2) as mean_value,
ROUND(STDDEV(Hours_Studied), 2) as std_dev,
MIN(Hours_Studied) as min_value,
MAX(Hours_Studied) as max_value
FROM clean_numerical_data
UNION ALL
SELECT
'Attendance' as feature,
ROUND(AVG(Attendance), 2) as mean_value,
ROUND(STDDEV(Attendance), 2) as std_dev,
MIN(Attendance) as min_value,
MAX(Attendance) as max_value
FROM clean_numerical_data
UNION ALL
SELECT
'Sleep_Hours' as feature,
ROUND(AVG(Sleep_Hours), 2) as mean_value,
ROUND(STDDEV(Sleep_Hours), 2) as std_dev,
MIN(Sleep_Hours) as min_value,
MAX(Sleep_Hours) as max_value
FROM clean_numerical_data
UNION ALL
SELECT
'Previous_Scores' as feature,
ROUND(AVG(Previous_Scores), 2) as mean_value,
ROUND(STDDEV(Previous_Scores), 2) as std_dev,
MIN(Previous_Scores) as min_value,
MAX(Previous_Scores) as max_value
FROM clean_numerical_data
UNION ALL
SELECT
'Exam_Score' as feature,
ROUND(AVG(Exam_Score), 2) as mean_value,
ROUND(STDDEV(Exam_Score), 2) as std_dev,
MIN(Exam_Score) as min_value,
MAX(Exam_Score) as max_value
FROM clean_numerical_data
""")
strongest_correlations = spark.sql("""
SELECT
ROUND(CORR(Hours_Studied, Exam_Score), 3) as study_time_score_corr,
ROUND(CORR(Attendance, Exam_Score), 3) as attendance_score_corr,
ROUND(CORR(Sleep_Hours, Exam_Score), 3) as sleep_score_corr,
ROUND(CORR(Previous_Scores, Exam_Score), 3) as previous_current_corr,
ROUND(CORR(Tutoring_Sessions, Exam_Score), 3) as tutoring_score_corr,
ROUND(CORR(Physical_Activity, Exam_Score), 3) as activity_score_corr
FROM clean_numerical_data
""")
summary_result = statistical_summary.collect()
correlation_result = strongest_correlations.collect()
feature_stats = [row.asDict() for row in summary_result]
key_correlations = correlation_result[0].asDict() if correlation_result else {}
top_positive_correlations = []
top_negative_correlations = []
for col1 in numerical_columns:
if col1 != 'Exam_Score':
corr_value = correlation_matrix[col1]['Exam_Score']
if corr_value > 0.1:
top_positive_correlations.append({'feature': col1, 'correlation': corr_value})
elif corr_value < -0.1:
top_negative_correlations.append({'feature': col1, 'correlation': corr_value})
comprehensive_analysis_result = {
'correlation_matrix': correlation_matrix,
'feature_statistics': feature_stats,
'key_correlations_with_exam_score': key_correlations,
'top_positive_correlations': sorted(top_positive_correlations, key=lambda x: x['correlation'], reverse=True),
'top_negative_correlations': sorted(top_negative_correlations, key=lambda x: x['correlation']),
'analysis_insights': {
'strongest_predictor': max(key_correlations.items(), key=lambda x: abs(x[1]))[0] if key_correlations else 'N/A',
'data_quality': f"分析了{df_clean.count()}条完整记录",
'correlation_pattern': '多因素间存在复杂的相互关系，需要综合分析'
}
}
return JsonResponse({
'success': True,
'data': comprehensive_analysis_result,
'message': '综合相关性热力图分析完成'
})
except Exception as e:
logging.error(f"相关性分析错误: {str(e)}")
return JsonResponse({'success': False, 'message': f'相关性分析失败: {str(e)}'})
return JsonResponse({'success': False, 'message': '请求方法不支持'})
学生考试数据分析系统-结语
计算机毕设选题推荐：基于Hadoop+Django学生考试数据分析系统技术实现指南 毕业设计/选题推荐/深度学习/数据分析/数据挖掘/机器学习/随机森林 如果你觉得内容不错，欢迎一键三连（点赞、收藏、关注）支持一下！也欢迎在评论区或在博客主页上私信联系留下你的想法或提出宝贵意见，期待与大家交流探讨！谢谢！
⚡⚡获取源码主页-->：计算机毕设指导师 ⛽⛽实战项目：有源码或者技术上的问题欢迎在评论区一起讨论交流！ ⚡⚡如果遇到具体的技术问题或其他需求，你也可以问我，我会尽力帮你分析和解决问题所在，支持我记得一键三连，再点个关注，学习不迷路！~~
