ABSTRACT Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students’ difficulty to master the introductory programming module, often referred to as CS1. Objective: The objective of this article is to describe the evolution of a prediction model named PreSS (Predict Student Success) over a 13-year period (2005–2018). Method: This article ties together, the PreSS prediction model; pilot studies; a longitudinal, multi-institutional re-validation and replication study; improvements to the model since its inception; and interventions to reduce attrition rates. Findings: The outcome of this body of work is an end-to-end real-time web-based tool (PreSS#), which can predict student success early in an introductory programming module (CS1), with an accuracy of 71%. This tool is enhanced with interventions that were developed in conjunction with PreSS#, which improved student performance in CS1. Implications: This work contributes significantly to the computer science education (CSEd) community and the ITiCSE 2015 working group’s call (in particular the second grand challenge), by re-validating and developing further the original PreSS model, 13 years after it was developed, on a modern, disparate, multi-institutional data set.

中文翻译：

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CS1：他们会怎么做？我们能帮你什么吗？十年的研究与实践

摘要 背景和背景：计算机科学的流失率（在西方世界）非常令人担忧，每年都有大量学生未能取得进步。众所周知，这种损耗的一个重要因素是学生难以掌握介绍性编程模块，通常称为 CS1。目标：本文的目标是描述名为 PreSS（预测学生成功）的预测模型在 13 年期间（2005-2018 年）的演变。方法：本文结合，PreSS预测模型；试点研究；纵向、多机构再验证和复制研究；自成立以来对模型的改进；和干预措施以降低流失率。发现：这项工作的成果是一个端到端的基于网络的实时工具 (PreSS#)，它可以在介绍性编程模块 (CS1) 中尽早预测学生的成功，准确率为 71%。该工具通过与 PreSS# 一起开发的干预措施得到增强，提高了学生在 CS1 中的表现。意义：这项工作对计算机科学教育 (CSEd) 社区和 ITiCSE 2015 工作组的号召（特别是第二个大挑战）做出了重大贡献，在其开发 13 年后，通过重新验证和进一步开发原始 PreSS 模型，在现代的、不同的、多机构的数据集上。