Predictive chatbot for student success: source code and database schema

This dataset contains the full source code, cron job scripts, and database schema used in the study “Early detection and personalized academic support using a predictive chatbot for student success” (submitted to PeerJ Computer Science). The system integrates a high-performance LightGBM predictive model with an AI-powered WhatsApp® chatbot based on the GPT-4o-mini model to identify at-risk students and deliver early, personalized academic support.

The repository includes:
• Node.js backend code for the chatbot, implemented using the Baileys WhatsApp Web API, Prisma ORM for PostgreSQL, and the OpenAI API (GPT-4o-mini) for conversational responses and semantic search over course syllabi.
• Automation scripts (cron jobs) that periodically trigger the prediction process, read the academic management database, and update the table of at-risk students used by the chatbot for proactive outreach.
• The complete SQL schema of the institutional database tables involved in the predictive pipeline and chatbot integration, including the structure of the enrollment, grades, and chatbot status tables.
• Configuration and documentation files (README) describing the required software versions, environment variables, and step-by-step instructions to deploy the system in a new institutional context.

For ethical and legal reasons, no real student records are included in this dataset. Instead, we provide the full database schema and example configuration files so that other researchers can adapt the code to their own institutional data while preserving student privacy. This dataset is intended to support transparency, reproducibility, and reuse of the proposed predictive-chatbot architecture in higher education settings.

本数据集包含提交至《PeerJ Computer Science》期刊的研究论文《利用预测型聊天机器人实现学生学业成功的早期识别与个性化学业支持》（Early detection and personalized academic support using a predictive chatbot for student success）中所使用的完整源代码、定时任务脚本与数据库架构。该系统将高性能轻量级梯度提升机（LightGBM）预测模型与基于GPT-4o-mini（GPT-4o-mini）模型的AI驱动WhatsApp®聊天机器人相集成，用于识别学业风险学生并提供早期个性化学业支持。

本仓库包含以下内容：
• 聊天机器人的Node.js（Node.js）后端代码：基于Baileys WhatsApp Web API实现，使用Prisma ORM（Prisma ORM）对接PostgreSQL（PostgreSQL）数据库，并通过OpenAI API（GPT-4o-mini）生成对话回复、实现课程大纲的语义检索。
• 自动化脚本（定时任务cron jobs）：可定期触发预测流程、读取学业管理数据库，并更新聊天机器人用于主动触达的学业风险学生表。
• 预测流程与聊天机器人集成所涉及的院校数据库表的完整SQL架构，涵盖选课表、成绩表与聊天机器人状态表的结构。
• 配置文件与说明文档（README）：详细列出所需软件版本、环境变量，以及在新院校环境中部署该系统的分步操作指南。

出于伦理与法律层面的考量，本数据集未包含任何真实学生记录。我们仅提供完整的数据库架构与示例配置文件，以便其他研究人员可在保护学生隐私的前提下，将代码适配至自身院校的数据集。本数据集旨在推动高等教育领域所提出的预测型聊天机器人架构的透明化、可复现性与复用性。