Fine-Grained Detection of Programming Students’ Frustration Using Keystrokes, Mouse Clicks and Interaction Logs

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ISSN Print: 2327-5952
ISSN Online: 2327-5960
www.scirp.org/journal/jss
E-mail: jss@scirp.org

"Fine-Grained Detection of Programming Students’ Frustration Using Keystrokes, Mouse Clicks and Interaction Logs"
written by Fwa Hua Leong,
published by Open Journal of Social Sciences, Vol.4 No.9, 2016

has been cited by the following article(s):

[1]	An Unobtrusive Approach to Emotion Detection in E-Learning Systems
	The Computer Journal, 2022

[2]	Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions
	ACM Transactions on …, 2022

[3]	ProLog2vec: Detecting Novices' Difficulty in Programming Using Deep Learning
	2021

[4]	Analyzing and fostering students' self-regulated learning through the use of peripheral data in online learning environments
	2020

[5]	Emotion Detection Using Unobtrusive methods: An Integrated Approach
	2019

[6]	TESS: A tool for optically measuring digital reading interactions from screen recordings
	2019

[1]	Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions ACM Transactions on Multimedia Computing, Communications, and Applications, 2022 DOI:10.1145/3480968

[2]	Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions ACM Transactions on Multimedia Computing, Communications, and Applications, 2022 DOI:10.1145/3480968

[3]	ProLog2vec: Detecting Novices’ Difficulty in Programming Using Deep Learning IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3067505

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