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学生辍学风险因素数据分析与可视化系统-简介
基于Spark+Django的学生辍学风险因素数据分析与可视化系统是一套针对高等教育机构设计的大数据分析平台，通过整合Hadoop分布式存储和Spark大数据处理框架，对学生的人口统计学特征、学业背景、在校表现以及社会经济状况等多维度数据进行深度挖掘和分析。系统采用Django作为后端开发框架，结合MySQL数据库进行数据管理，前端使用Vue+ElementUI构建交互界面，通过Echarts实现数据的多样化可视化展示。系统核心功能包括学生人口统计学特征与辍学风险关联分析、学业背景与专业选择的辍学预测、在校学业表现的动态监测预警、财务与社会经济状况影响评估，以及基于机器学习算法的综合性关键辍学风险因素挖掘。通过Spark SQL进行大规模数据查询和统计分析，利用Pandas和NumPy进行数据预处理和特征工程，系统能够为教育管理者提供科学的决策支持，帮助及早识别潜在辍学风险学生，制定针对性的干预措施。
学生辍学风险因素数据分析与可视化系统-技术
开发语言：Python或Java 大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis) 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
学生辍学风险因素数据分析与可视化系统-背景
随着高等教育规模的不断扩大和教育信息化程度的深入发展，各大高校积累了海量的学生数据资源，这些数据蕴含着丰富的教育规律和学生行为模式。然而，学生辍学现象作为困扰全球高等教育的普遍性问题，不仅影响着学生个人的发展轨迹，也对高校的教学质量、资源配置和社会声誉产生深远影响。传统的学生管理模式往往依赖经验判断和单一指标评估，缺乏对多维度数据的综合分析能力，难以准确识别辍学风险因素的复杂关联性。在大数据技术日趋成熟的今天，利用Hadoop+Spark等分布式计算框架处理和分析教育大数据已成为可能。通过对学生的人口统计信息、学业表现、家庭背景、经济状况等多元化数据进行深度挖掘，可以发现隐藏在数据背后的辍学风险模式，为教育管理决策提供科学依据。
本系统的开发具有重要的实际应用价值和技术创新意义。从教育管理角度来看，系统能够帮助高校建立起科学的学生辍学预警机制，通过数据驱动的方式识别高风险学生群体，使教育工作者能够提前介入并制定个性化的帮扶策略，有效降低辍学率并提升教育资源的利用效率。从技术实践层面，项目将大数据技术与教育场景深度结合，为教育数据挖掘领域提供了一个完整的解决方案范例，验证了Spark+Django技术栈在教育数据分析中的可行性和有效性。从社会价值维度考虑，系统的应用能够减少人才流失，提高高等教育的完成率，为社会培养更多合格的专业人才，间接促进社会经济发展。同时，通过对辍学风险因素的深入分析，可以为教育政策制定者提供数据支撑，推动教育公平性和质量的持续改善，为构建更加完善的高等教育体系贡献力量。
学生辍学风险因素数据分析与可视化系统-视频展示
学生辍学风险因素数据分析与可视化系统-图片展示
学生辍学风险因素数据分析与可视化系统-代码展示
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("StudentDropoutAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def analyze_demographic_dropout_risk(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_data").option("user", "root").option("password", "password").load()
gender_analysis = df.groupBy("Gender", "Target").count().orderBy("Gender", "Target")
gender_pivot = gender_analysis.groupBy("Gender").pivot("Target").agg(first("count"))
gender_result = gender_pivot.withColumn("dropout_rate", col("Dropout") / (col("Dropout") + col("Graduate") + col("Enrolled")))
marital_analysis = df.groupBy("Marital_status", "Target").count().orderBy("Marital_status", "Target")
marital_pivot = marital_analysis.groupBy("Marital_status").pivot("Target").agg(first("count"))
marital_result = marital_pivot.withColumn("dropout_rate", col("Dropout") / (col("Dropout") + col("Graduate") + col("Enrolled")))
age_df = df.withColumn("age_group", when(col("Age_at_enrollment") <= 20, "18-20").when(col("Age_at_enrollment") <= 25, "21-25").otherwise("26+"))
age_analysis = age_df.groupBy("age_group", "Target").count().orderBy("age_group", "Target")
age_pivot = age_analysis.groupBy("age_group").pivot("Target").agg(first("count"))
age_result = age_pivot.withColumn("dropout_rate", col("Dropout") / (col("Dropout") + col("Graduate") + col("Enrolled")))
scholarship_analysis = df.groupBy("Scholarship_holder", "Target").count().orderBy("Scholarship_holder", "Target")
scholarship_pivot = scholarship_analysis.groupBy("Scholarship_holder").pivot("Target").agg(first("count"))
scholarship_result = scholarship_pivot.withColumn("dropout_rate", col("Dropout") / (col("Dropout") + col("Graduate") + col("Enrolled")))
attendance_analysis = df.groupBy("Daytime_evening_attendance", "Target").count().orderBy("Daytime_evening_attendance", "Target")
attendance_pivot = attendance_analysis.groupBy("Daytime_evening_attendance").pivot("Target").agg(first("count"))
attendance_result = attendance_pivot.withColumn("dropout_rate", col("Dropout") / (col("Dropout") + col("Graduate") + col("Enrolled")))
gender_data = [row.asDict() for row in gender_result.collect()]
marital_data = [row.asDict() for row in marital_result.collect()]
age_data = [row.asDict() for row in age_result.collect()]
scholarship_data = [row.asDict() for row in scholarship_result.collect()]
attendance_data = [row.asDict() for row in attendance_result.collect()]
result_data = {"gender_analysis": gender_data, "marital_analysis": marital_data, "age_analysis": age_data, "scholarship_analysis": scholarship_data, "attendance_analysis": attendance_data}
return JsonResponse(result_data, safe=False)
def analyze_academic_performance_risk(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_data").option("user", "root").option("password", "password").load()
first_sem_grade = df.groupBy("Target").agg(avg("Curricular_units_1st_sem_grade").alias("avg_first_sem_grade"))
second_sem_grade = df.groupBy("Target").agg(avg("Curricular_units_2nd_sem_grade").alias("avg_second_sem_grade"))
grade_comparison = first_sem_grade.join(second_sem_grade, "Target")
grade_comparison = grade_comparison.withColumn("grade_improvement", col("avg_second_sem_grade") - col("avg_first_sem_grade"))
credit_df = df.withColumn("first_sem_pass_rate", col("Curricular_units_1st_sem_approved") / col("Curricular_units_1st_sem_enrolled"))
credit_df = credit_df.withColumn("second_sem_pass_rate", col("Curricular_units_2nd_sem_approved") / col("Curricular_units_2nd_sem_enrolled"))
pass_rate_analysis = credit_df.groupBy("Target").agg(avg("first_sem_pass_rate").alias("avg_first_pass_rate"), avg("second_sem_pass_rate").alias("avg_second_pass_rate"))
pass_rate_analysis = pass_rate_analysis.withColumn("pass_rate_change", col("avg_second_pass_rate") - col("avg_first_pass_rate"))
evaluation_analysis = df.groupBy("Target").agg(avg("Curricular_units_1st_sem_without_evaluations").alias("avg_no_eval_first"), avg("Curricular_units_2nd_sem_without_evaluations").alias("avg_no_eval_second"))
grade_trend_df = df.withColumn("grade_change", col("Curricular_units_2nd_sem_grade") - col("Curricular_units_1st_sem_grade"))
grade_trend_analysis = grade_trend_df.groupBy("Target").agg(avg("grade_change").alias("avg_grade_change"), count("*").alias("student_count"))
declining_students = grade_trend_df.filter(col("grade_change") < -2).groupBy("Target").count().withColumnRenamed("count", "declining_count")
performance_risk_df = df.filter((col("Curricular_units_1st_sem_grade") < 10) | (col("first_sem_pass_rate") < 0.5) | (col("Curricular_units_1st_sem_without_evaluations") > 2))
high_risk_count = performance_risk_df.groupBy("Target").count().withColumnRenamed("count", "high_risk_count")
grade_data = [row.asDict() for row in grade_comparison.collect()]
pass_rate_data = [row.asDict() for row in pass_rate_analysis.collect()]
evaluation_data = [row.asDict() for row in evaluation_analysis.collect()]
trend_data = [row.asDict() for row in grade_trend_analysis.collect()]
declining_data = [row.asDict() for row in declining_students.collect()]
risk_data = [row.asDict() for row in high_risk_count.collect()]
result_data = {"grade_analysis": grade_data, "pass_rate_analysis": pass_rate_data, "evaluation_analysis": evaluation_data, "trend_analysis": trend_data, "declining_analysis": declining_data, "risk_analysis": risk_data}
return JsonResponse(result_data, safe=False)
def analyze_key_dropout_factors(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_data").option("user", "root").option("password", "password").load()
df_encoded = df.withColumn("target_numeric", when(col("Target") == "Dropout", 1).otherwise(0))
df_encoded = df_encoded.withColumn("gender_numeric", when(col("Gender") == "Male", 1).otherwise(0))
df_encoded = df_encoded.withColumn("scholarship_numeric", when(col("Scholarship_holder") == 1, 1).otherwise(0))
df_encoded = df_encoded.withColumn("debtor_numeric", when(col("Debtor") == 1, 1).otherwise(0))
df_encoded = df_encoded.withColumn("tuition_updated_numeric", when(col("Tuition_fees_up_to_date") == 1, 1).otherwise(0))
feature_columns = ["Age_at_enrollment", "Curricular_units_1st_sem_grade", "Curricular_units_1st_sem_approved", "Curricular_units_1st_sem_enrolled", "Curricular_units_2nd_sem_grade", "gender_numeric", "scholarship_numeric", "debtor_numeric", "tuition_updated_numeric", "Unemployment_rate", "Inflation_rate", "GDP"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
df_vectorized = assembler.transform(df_encoded)
train_data, test_data = df_vectorized.randomSplit([0.8, 0.2], seed=42)
dt = DecisionTreeClassifier(labelCol="target_numeric", featuresCol="features", maxDepth=10, minInstancesPerNode=20)
dt_model = dt.fit(train_data)
feature_importance = dt_model.featureImportances.toArray()
importance_dict = {}
for i, col_name in enumerate(feature_columns):
importance_dict[col_name] = float(feature_importance[i])
sorted_importance = sorted(importance_dict.items(), key=lambda x: x[1], reverse=True)
predictions = dt_model.transform(test_data)
evaluator = MulticlassClassificationEvaluator(labelCol="target_numeric", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
correlation_features = ["Curricular_units_1st_sem_grade", "Curricular_units_1st_sem_approved", "Curricular_units_2nd_sem_grade", "Age_at_enrollment"]
correlation_df = df_encoded.select(correlation_features)
correlation_pandas = correlation_df.toPandas()
correlation_matrix = correlation_pandas.corr()
correlation_dict = correlation_matrix.to_dict()
dropout_students = df_encoded.filter(col("target_numeric") == 1)
cluster_features = ["Age_at_enrollment", "Curricular_units_1st_sem_grade", "Curricular_units_1st_sem_approved"]
cluster_assembler = VectorAssembler(inputCols=cluster_features, outputCol="cluster_features")
dropout_vectorized = cluster_assembler.transform(dropout_students)
from pyspark.ml.clustering import KMeans
kmeans = KMeans(k=3, seed=42, featuresCol="cluster_features")
kmeans_model = kmeans.fit(dropout_vectorized)
clustered_data = kmeans_model.transform(dropout_vectorized)
cluster_summary = clustered_data.groupBy("prediction").agg(avg("Age_at_enrollment").alias("avg_age"), avg("Curricular_units_1st_sem_grade").alias("avg_grade"), count("*").alias("cluster_size"))
cluster_data = [row.asDict() for row in cluster_summary.collect()]
result_data = {"feature_importance": sorted_importance, "model_accuracy": accuracy, "correlation_matrix": correlation_dict, "dropout_clusters": cluster_data}
return JsonResponse(result_data, safe=False)
学生辍学风险因素数据分析与可视化系统-结语
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