@inproceedings{376f94b42a5141538bc7a88b6c2726e7,
title = "In-depth feedback on programming assignments using pattern recognition and real-time hints",
abstract = "Automatic grading of students{\textquoteright} programs is a challenge in computer science (CS) education. In the last two years we have been using a machine learning tool called {\textquoteleft}Sense Education{\textquoteright} in our {\textquoteleft}Intro to CS{\textquoteright} course. The tool provides real-time pedagogical hints while students work on solutions, and in-depth feedback on submissions. It also provides a “bird{\textquoteright}s eye” view of the class{\textquoteright}s current capabilities, misconceptions, and biases in approaches to solutions. We describe this effort and several test cases. Our goal is to provide teachers with tips on how to effectively use such tools.",
keywords = "Automated Grading, Clustering, Feedback, Pattern Recognition",
author = "Amir Rubinstein and Noam Parzanchevski and Yossi Tamarov",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s).; 2019 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
day = "2",
doi = "10.1145/3304221.3325552",
language = "אנגלית",
series = "Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE",
publisher = "Association for Computing Machinery",
pages = "243--244",
booktitle = "ITiCSE 2019 - Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education",
}