💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于大数据的学生习惯数据可视化分析系统介绍
《基于大数据的学生习惯数据可视化分析系统》是一个集大数据处理、数据挖掘与可视化展示于一体的综合性分析平台，系统基于Hadoop分布式存储框架和Spark大数据计算引擎构建核心数据处理架构，通过HDFS实现海量学生行为数据的分布式存储，利用Spark SQL进行高效的数据查询与分析处理，结合Pandas和NumPy等Python科学计算库对学生习惯数据进行深度挖掘和统计分析。系统采用前后端分离的技术架构，后端基于Django框架或Spring Boot框架提供RESTful API接口服务，前端采用Vue.js框架结合ElementUI组件库构建用户交互界面，通过Echarts图表库实现丰富的数据可视化效果，系统功能涵盖学生多维分析、背景因素分析、学业习惯分析、身心健康分析、外部环境分析以及综合画像分析等核心模块，提供大屏可视化展示功能，能够从多个维度对学生的学习行为、生活习惯、心理状态等进行全方位的数据分析和可视化呈现，为教育管理者和研究人员提供科学的数据支撑和决策依据，系统还具备完善的用户权限管理和系统管理功能，确保数据安全和系统稳定运行，整个系统充分体现了大数据技术在教育信息化领域的应用价值和实践意义。
基于大数据的学生习惯数据可视化分析系统演示视频
基于大数据的学生习惯数据可视化分析系统演示图片
基于大数据的学生习惯数据可视化分析系统代码展示
spark = SparkSession.builder.appName("StudentHabitAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def student_multi_dimension_analysis(student_id):
student_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_info").option("user", "root").option("password", "password").load()
behavior_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_behavior").option("user", "root").option("password", "password").load()
score_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_scores").option("user", "root").option("password", "password").load()
joined_df = student_df.join(behavior_df, "student_id", "left").join(score_df, "student_id", "left")
filtered_df = joined_df.filter(col("student_id") == student_id)
study_time_stats = filtered_df.agg(avg("daily_study_time").alias("avg_study_time"), max("daily_study_time").alias("max_study_time"), min("daily_study_time").alias("min_study_time"))
library_visit_stats = filtered_df.agg(sum("library_visits").alias("total_visits"), avg("library_duration").alias("avg_duration"))
course_performance = filtered_df.select("course_name", "score", "attendance_rate").orderBy(desc("score"))
social_activity_level = filtered_df.agg(count("activity_participation").alias("activity_count"), avg("social_interaction_score").alias("social_score"))
sleep_pattern_analysis = filtered_df.agg(avg("sleep_duration").alias("avg_sleep"), stddev("sleep_duration").alias("sleep_variance"))
exercise_habits = filtered_df.agg(sum("exercise_frequency").alias("total_exercise"), avg("exercise_duration").alias("avg_exercise_time"))
comprehensive_score = filtered_df.select((col("academic_score") * 0.4 + col("behavior_score") * 0.3 + col("social_score") * 0.3).alias("comprehensive_rating"))
result_data = {"study_stats": study_time_stats.collect()[0].asDict(), "library_stats": library_visit_stats.collect()[0].asDict(), "course_performance": [row.asDict() for row in course_performance.collect()], "social_stats": social_activity_level.collect()[0].asDict(), "sleep_analysis": sleep_pattern_analysis.collect()[0].asDict(), "exercise_data": exercise_habits.collect()[0].asDict(), "overall_rating": comprehensive_score.collect()[0].asDict()}
student_profile = {"student_id": student_id, "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "multi_dimension_data": result_data}
return JsonResponse(student_profile)
def academic_habit_analysis(class_id=None, time_range=30):
base_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "academic_records").option("user", "root").option("password", "password").load()
habit_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "study_habits").option("user", "root").option("password", "password").load()
attendance_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "attendance_records").option("user", "root").option("password", "password").load()
combined_df = base_df.join(habit_df, "student_id", "inner").join(attendance_df, "student_id", "inner")
if class_id:
combined_df = combined_df.filter(col("class_id") == class_id)
time_filtered_df = combined_df.filter(col("record_date") >= date_sub(current_date(), time_range))
study_pattern_analysis = time_filtered_df.groupBy("student_id").agg(avg("daily_study_hours").alias("avg_daily_hours"), sum("weekly_study_hours").alias("total_weekly_hours"), max("consecutive_study_days").alias("max_consecutive_days"))
homework_completion_rate = time_filtered_df.groupBy("student_id").agg((sum("completed_homework") / sum("assigned_homework") * 100).alias("completion_percentage"))
class_participation_metrics = time_filtered_df.groupBy("student_id").agg(avg("class_participation_score").alias("avg_participation"), count("active_discussion").alias("discussion_count"))
study_efficiency_index = time_filtered_df.groupBy("student_id").agg((avg("test_scores") / avg("study_hours_per_subject")).alias("efficiency_ratio"))
learning_consistency = time_filtered_df.groupBy("student_id").agg(stddev("daily_study_hours").alias("study_time_variance"), count(when(col("missed_classes") == 0, 1)).alias("perfect_attendance_days"))
procrastination_tendency = time_filtered_df.groupBy("student_id").agg(avg("assignment_delay_days").alias("avg_delay"), count(when(col("late_submission") == True, 1)).alias("late_count"))
peak_performance_time = time_filtered_df.groupBy("student_id", "study_time_slot").agg(avg("concentration_level").alias("avg_concentration")).orderBy(desc("avg_concentration"))
habit_correlation = time_filtered_df.select(corr("study_hours", "test_scores").alias("study_score_correlation"), corr("sleep_hours", "concentration_level").alias("sleep_concentration_correlation"))
academic_habits_summary = {"study_patterns": [row.asDict() for row in study_pattern_analysis.collect()], "homework_rates": [row.asDict() for row in homework_completion_rate.collect()], "participation_data": [row.asDict() for row in class_participation_metrics.collect()], "efficiency_metrics": [row.asDict() for row in study_efficiency_index.collect()], "consistency_analysis": [row.asDict() for row in learning_consistency.collect()], "procrastination_data": [row.asDict() for row in procrastination_tendency.collect()], "optimal_time_slots": [row.asDict() for row in peak_performance_time.collect()], "correlation_insights": habit_correlation.collect()[0].asDict()}
return JsonResponse({"analysis_type": "academic_habits", "time_range_days": time_range, "class_id": class_id, "analysis_results": academic_habits_summary})
def dashboard_visualization_data():
student_summary_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_summary").option("user", "root").option("password", "password").load()
performance_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "performance_metrics").option("user", "root").option("password", "password").load()
trend_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "trend_analysis").option("user", "root").option("password", "password").load()
total_students = student_summary_df.count()
grade_distribution = student_summary_df.groupBy("grade_level").agg(count("student_id").alias("student_count"), avg("overall_score").alias("avg_score")).orderBy("grade_level")
performance_categories = performance_df.groupBy("performance_category").agg(count("student_id").alias("count"), (count("student_id") * 100.0 / total_students).alias("percentage"))
monthly_trends = trend_df.groupBy("month", "year").agg(avg("academic_performance").alias("avg_performance"), avg("behavior_score").alias("avg_behavior"), avg("attendance_rate").alias("avg_attendance")).orderBy("year", "month")
top_performers = performance_df.select("student_id", "student_name", "overall_ranking", "comprehensive_score").orderBy("overall_ranking").limit(10)
subject_performance_radar = performance_df.groupBy("major_subject").agg(avg("average_score").alias("subject_avg"), count("student_id").alias("student_count")).orderBy(desc("subject_avg"))
activity_participation_stats = student_summary_df.agg(avg("extracurricular_activities").alias("avg_activities"), sum("volunteer_hours").alias("total_volunteer_hours"), avg("social_interaction_score").alias("avg_social_score"))
geographic_distribution = student_summary_df.groupBy("home_province").agg(count("student_id").alias("student_count")).orderBy(desc("student_count"))
health_wellness_metrics = student_summary_df.agg(avg("physical_health_score").alias("avg_physical_health"), avg("mental_health_score").alias("avg_mental_health"), avg("sleep_quality_index").alias("avg_sleep_quality"))
learning_style_analysis = student_summary_df.groupBy("learning_style_preference").agg(count("student_id").alias("count"), avg("academic_satisfaction").alias("avg_satisfaction"))
risk_warning_students = performance_df.filter((col("attendance_rate") < 0.8) | (col("academic_performance") < 60) | (col("mental_health_score") < 50)).select("student_id", "student_name", "risk_factors").limit(20)
dashboard_data = {"overview_stats": {"total_students": total_students, "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}, "grade_analysis": [row.asDict() for row in grade_distribution.collect()], "performance_distribution": [row.asDict() for row in performance_categories.collect()], "trend_analysis": [row.asDict() for row in monthly_trends.collect()], "top_students": [row.asDict() for row in top_performers.collect()], "subject_radar_data": [row.asDict() for row in subject_performance_radar.collect()], "activity_overview": activity_participation_stats.collect()[0].asDict(), "regional_data": [row.asDict() for row in geographic_distribution.collect()], "wellness_summary": health_wellness_metrics.collect()[0].asDict(), "learning_preferences": [row.asDict() for row in learning_style_analysis.collect()], "risk_alerts": [row.asDict() for row in risk_warning_students.collect()]}
return JsonResponse(dashboard_data)
基于大数据的学生习惯数据可视化分析系统文档展示
💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
