💖💖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Web的学生学业质量分析系统介绍
基于Web的学生学业质量分析系统是一个采用现代化技术栈构建的教育数据管理与分析平台，该系统支持Java和Python两种开发语言实现，后端分别采用Spring Boot框架（整合Spring+SpringMVC+MyBatis）和Django框架，前端使用Vue.js结合ElementUI组件库构建用户界面，数据存储采用MySQL数据库，整体采用B/S架构模式进行设计。系统核心功能涵盖系统首页展示、数据看板可视化分析、学生信息管理、课程分类与课程信息管理、学业质量数据的收集分析处理、系统管理功能、公告资讯发布与管理、轮播图管理、智能学习AI辅助功能、公告资讯分类管理，以及个人信息维护和密码修改等功能模块。该系统通过对学生的学习数据进行科学化采集和智能化分析，能够为教育管理者提供直观的数据展示和深度的质量评估报告，帮助学校和教师更好地了解学生的学习状况和academic表现趋势，从而制定更加精准的教学策略和个性化的学习指导方案，同时为学生提供智能化的学习建议和资源推荐，实现教育数据的有效利用和教学质量的持续提升。
基于Web的学生学业质量分析系统演示视频
基于Web的学生学业质量分析系统演示图片
基于Web的学生学业质量分析系统代码展示
from pyspark.sql import SparkSession
import pandas as pd
import numpy as np
from datetime import datetime
import json
spark = SparkSession.builder.appName("StudentQualityAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def analyzeStudentPerformance(student_id, course_list, score_data):
student_scores = []
total_score = 0
subject_count = 0
performance_trend = []
for course in course_list:
if course['student_id'] == student_id:
current_score = float(course['score'])
student_scores.append({
'course_name': course['course_name'],
'score': current_score,
'credit': course['credit'],
'semester': course['semester']
})
total_score += current_score * float(course['credit'])
subject_count += float(course['credit'])
if current_score >= 90:
performance_level = "优秀"
elif current_score >= 80:
performance_level = "良好"
elif current_score >= 70:
performance_level = "中等"
elif current_score >= 60:
performance_level = "及格"
else:
performance_level = "不及格"
performance_trend.append({
'semester': course['semester'],
'avg_score': current_score,
'level': performance_level
})
weighted_average = total_score / subject_count if subject_count > 0 else 0
rank_position = calculateStudentRank(student_id, weighted_average, score_data)
improvement_suggestions = generateImprovementSuggestions(student_scores, weighted_average)
analysis_result = {
'student_id': student_id,
'weighted_gpa': round(weighted_average, 2),
'total_credits': subject_count,
'rank': rank_position,
'performance_trend': performance_trend,
'suggestions': improvement_suggestions,
'analysis_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return analysis_result
def generateQualityReport(class_id, semester, all_students_data):
class_students = [student for student in all_students_data if student['class_id'] == class_id]
total_students = len(class_students)
passing_students = 0
excellent_students = 0
score_distribution = {'优秀': 0, '良好': 0, '中等': 0, '及格': 0, '不及格': 0}
subject_performance = {}
total_class_score = 0
for student in class_students:
student_avg = calculateStudentAverage(student['scores'])
total_class_score += student_avg
if student_avg >= 60:
passing_students += 1
if student_avg >= 90:
excellent_students += 1
score_distribution['优秀'] += 1
elif student_avg >= 80:
score_distribution['良好'] += 1
elif student_avg >= 70:
score_distribution['中等'] += 1
elif student_avg >= 60:
score_distribution['及格'] += 1
else:
score_distribution['不及格'] += 1
for subject, score in student['scores'].items():
if subject not in subject_performance:
subject_performance[subject] = []
subject_performance[subject].append(float(score))
class_average = total_class_score / total_students if total_students > 0 else 0
passing_rate = (passing_students / total_students) * 100 if total_students > 0 else 0
excellent_rate = (excellent_students / total_students) * 100 if total_students > 0 else 0
subject_stats = {}
for subject, scores in subject_performance.items():
subject_stats[subject] = {
'average': round(np.mean(scores), 2),
'max_score': max(scores),
'min_score': min(scores),
'std_deviation': round(np.std(scores), 2)
}
quality_report = {
'class_id': class_id,
'semester': semester,
'total_students': total_students,
'class_average': round(class_average, 2),
'passing_rate': round(passing_rate, 2),
'excellent_rate': round(excellent_rate, 2),
'score_distribution': score_distribution,
'subject_statistics': subject_stats,
'report_generated': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return quality_report
def processLearningDataAnalysis(student_data_list, time_period, analysis_type):
spark_df = spark.createDataFrame(student_data_list)
spark_df.createOrReplaceTempView("student_learning_data")
filtered_data = spark.sql(f"SELECT * FROM student_learning_data WHERE semester = '{time_period}'")
analysis_results = []
learning_patterns = {}
performance_correlations = {}
if analysis_type == "trend_analysis":
trend_query = "SELECT student_id, course_name, AVG(score) as avg_score, COUNT(*) as attempt_count FROM student_learning_data GROUP BY student_id, course_name"
trend_results = spark.sql(trend_query).collect()
for row in trend_results:
student_trend = {
'student_id': row['student_id'],
'course': row['course_name'],
'average_performance': float(row['avg_score']),
'learning_frequency': int(row['attempt_count'])
}
analysis_results.append(student_trend)
elif analysis_type == "difficulty_analysis":
difficulty_query = "SELECT course_name, AVG(score) as course_avg, MIN(score) as min_score, MAX(score) as max_score, COUNT(DISTINCT student_id) as student_count FROM student_learning_data GROUP BY course_name"
difficulty_results = spark.sql(difficulty_query).collect()
for row in difficulty_results:
course_difficulty = {
'course_name': row['course_name'],
'difficulty_level': "困难" if float(row['course_avg']) < 70 else "适中" if float(row['course_avg']) < 85 else "简单",
'average_score': round(float(row['course_avg']), 2),
'score_range': f"{row['min_score']}-{row['max_score']}",
'student_participation': int(row['student_count'])
}
analysis_results.append(course_difficulty)
for student_data in student_data_list:
student_id = student_data['student_id']
study_hours = student_data.get('study_hours', 0)
assignment_completion = student_data.get('assignment_rate', 0)
final_score = student_data.get('final_score', 0)
if student_id not in learning_patterns:
learning_patterns[student_id] = {
'study_efficiency': (final_score / study_hours) if study_hours > 0 else 0,
'completion_impact': assignment_completion * 0.3 + final_score * 0.7,
'learning_style': "勤奋型" if study_hours > 100 else "效率型" if (final_score / study_hours if study_hours > 0 else 0) > 0.8 else "普通型"
}
performance_correlations[student_id] = calculatePerformanceCorrelation(study_hours, assignment_completion, final_score)
comprehensive_analysis = {
'analysis_type': analysis_type,
'time_period': time_period,
'detailed_results': analysis_results,
'learning_patterns': learning_patterns,
'performance_correlations': performance_correlations,
'total_records_analyzed': len(student_data_list),
'analysis_timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return comprehensive_analysis
def calculateStudentRank(student_id, gpa, all_scores):
student_scores = [(s['student_id'], s['gpa']) for s in all_scores]
sorted_scores = sorted(student_scores, key=lambda x: x[1], reverse=True)
for i, (sid, score) in enumerate(sorted_scores):
if sid == student_id:
return i + 1
return len(sorted_scores)
def generateImprovementSuggestions(scores, avg_score):
suggestions = []
weak_subjects = [s for s in scores if s['score'] < 70]
if len(weak_subjects) > 0:
suggestions.append(f"重点关注{weak_subjects[0]['course_name']}等薄弱科目")
if avg_score < 75:
suggestions.append("建议增加学习时间，提高整体成绩")
return suggestions
def calculateStudentAverage(scores_dict):
if not scores_dict:
return 0
return sum(float(score) for score in scores_dict.values()) / len(scores_dict)
def calculatePerformanceCorrelation(study_hours, completion_rate, final_score):
if study_hours == 0:
return 0
correlation_factor = (completion_rate * 0.4 + (final_score / 100) * 0.6) * (study_hours / 100)
return round(correlation_factor, 3)
基于Web的学生学业质量分析系统文档展示
💖💖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
