Name
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 revalidation 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-toend 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/08993408.2019.1612679 | COMPUTER SCIENCE EDUCATION |
Keywords | Field | DocType |
Introductory programming, predicting programming performance, interventions, CS1, attrition rates, programming performance, programming self-efficacy, machine learning, growth mindset, artificial neural networks | Psychological intervention,Computer science,Knowledge management,Mathematics education,Self-efficacy,Attrition,Academic achievement,Artificial neural network,Western world | Journal |
Volume | Issue | ISSN |
29 | 2-3 | 0899-3408 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keith Quille | 1 | 0 | 0.68 |
| Susan Bergin | 2 | 162 | 21.31 |