Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 5 |
| Since 2017 (last 10 years) | 6 |
| Since 2007 (last 20 years) | 7 |
Descriptor
| Assignments | 7 |
| Programming | 7 |
| Computer Science Education | 5 |
| Automation | 3 |
| Classification | 3 |
| Evaluation Methods | 3 |
| Introductory Courses | 3 |
| Models | 3 |
| Teaching Methods | 3 |
| Undergraduate Students | 3 |
| Artificial Intelligence | 2 |
| More ▼ | |
Source
| International Educational… | 7 |
Author
| Barnes, Tiffany | 2 |
| Chi, Min | 2 |
| Shi, Yang | 2 |
| Barnes, Tiffany, Ed. | 1 |
| Carvalho, Leandro S. G. | 1 |
| Chi, Min, Ed. | 1 |
| Chris Piech | 1 |
| Christopher M. Warren | 1 |
| Cohen, Anat | 1 |
| Cristea, Alexandra I. | 1 |
| Feng, Mingyu, Ed. | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 6 |
| Speeches/Meeting Papers | 6 |
| Collected Works - Proceedings | 1 |
Education Level
| Higher Education | 5 |
| Postsecondary Education | 5 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
| Afghanistan | 1 |
| Brazil | 1 |
| Illinois (Chicago) | 1 |
| Virginia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Yunsung Kim; Jadon Geathers; Chris Piech – International Educational Data Mining Society, 2024
"Stochastic programs," which are programs that produce probabilistic output, are a pivotal paradigm in various areas of CS education from introductory programming to machine learning and data science. Despite their importance, the problem of automatically grading such programs remains surprisingly unexplored. In this paper, we formalize…
Descriptors: Grading, Automation, Accuracy, Programming
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding
Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
Kaden Hart; Christopher M. Warren; Seth Poulsen; John Edwards – International Educational Data Mining Society, 2024
We report on a study in which we examined the work habits of six students who agreed to use do not disturb on their phone while working on programming assignments. Two students tried do not disturb, and quickly quit using it. Three out of four remaining student participants were more productive while using do not disturb when working on their…
Descriptors: Telecommunications, Handheld Devices, Computer Use, Student Behavior
Gabbay, Hagit; Cohen, Anat – International Educational Data Mining Society, 2023
In MOOCs for programming, Automated Testing and Feedback (ATF) systems are frequently integrated, providing learners with immediate feedback on code assignments. The analysis of the large amounts of trace data collected by these systems may provide insights into learners' patterns of utilizing the automated feedback, which is crucial for the…
Descriptors: MOOCs, Feedback (Response), Teaching Methods, Learning Strategies
Fonseca, Samuel C.; Pereira, Filipe Dwan; Oliveira, Elaine H. T.; Oliveira, David B. F.; Carvalho, Leandro S. G.; Cristea, Alexandra I. – International Educational Data Mining Society, 2020
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for…
Descriptors: Programming, Introductory Courses, Computer Science Education, Automation
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction

Peer reviewed
