Name
Title | ||
|---|---|---|
Taxonomizing features and methods for identifying at-risk students in computing courses. |
Abstract | ||
|---|---|---|
Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3197091.3205845 | ITiCSE |
Keywords | Field | DocType |
educational data mining, analytics | Observational study,Systematic review,Computer science,Knowledge management,At-risk students,Coding (social sciences),Analytics,Educational data mining | Conference |
ISBN | Citations | PageRank |
978-1-4503-5707-4 | 3 | 0.41 |
References | Authors | |
5 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arto Hellas | 1 | 21 | 5.94 |
| Petri Ihantola | 2 | 474 | 35.07 |
| Andrew Petersen | 3 | 226 | 28.21 |
| Vangel V. Ajanovski | 4 | 11 | 4.61 |
| Mirela Gutica | 5 | 4 | 2.45 |
| Timo Hynninen | 6 | 9 | 2.66 |
| Antti Knutas | 7 | 56 | 21.53 |
| Juho Leinonen | 8 | 18 | 6.92 |
| C. H. Messom | 9 | 35 | 6.04 |
| Soohyun Nam Liao | 10 | 21 | 5.16 |