More Web Proxy on the site http://driver.im/

research-article

Understanding Affective Dynamics of Learning Toward a Ubiquitous Learning System

Authors:

Varun Mandalapu,

Jiaqi GongAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 23, Issue 2

Pages 9 - 15

https://doi.org/10.1145/3372300.3372303

Published: 14 November 2019 Publication History

Abstract

Understanding student learning behaviors is of prime importance for educational research. Many complex factors influence learning processes, but one collective impact of all these factors is how they affect learning and the degree of motivation. In this study, we discuss the current state of human affect detection in education, our proposed affect change model and its implications. This study adopts dataset from ASSISTments online learning platform, which consists of student interaction data, and ground truth labels for affect states coded by Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) certified coders to develop and validate the affect change model. We show that the proposed affect change model in combination with the adoption of machine learning algorithms will support the development of a ubiquitous learning system that tracks the student learning process within the context of contributing factors and provide interventions when needed.

References

[1]

Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017, June). Improving sensor-free affect detection using deep learning. International Conference on Artificial Intelligence in Education, 40--51. Springer, Cham.

[2]

Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., ... & Biswas, G. (2018, June). Expert feature-engineering vs. deep neural networks: Which is better for sensor-free affect detection? International Conference on Artificial Intelligence in Education, 198--211. Springer, Cham.

[3]

Baker, R., & Ocumpaugh, J. (2015). Interaction- Based Affect Detection in Educational Software, 233--245. New York: Oxford University Press.

[4]

D'mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys (CSUR), 47(3), 43.

Digital Library

[5]

Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., & Zhao, W. (2015, March). Automatic detection of learning-centered affective states in the wild. In Proceedings of the 20th international Conference on Intelligent User Interfaces, 379--388). ACM.

Digital Library

[6]

D'Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145--157.

[7]

Botelho, A. F., Baker, R. S., Ocumpaugh, J., & Heffernan, N. T. (2018). Studying Affect Dynamics and Chronometry Using Sensor-Free Detectors. International Educational Data Mining Society.

[8]

D'Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25(7), 1299--1308.

[9]

Ocumpaugh, J. (2015). Baker Rodrigo Ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. New York, NY, and Manila, Philippines: Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences, 60.

[10]

Mandalapu, V., & Gong, J. (2018, June). Towards better affect detectors: Detecting changes rather than states. International Conference on Artificial Intelligence in Education, 199--203). Springer, Cham.

[11]

Wang, Y., Heffernan, N. T., & Heffernan, C. (2015, March). Towards better affect detectors: Effect of missing skills, class features and common wrong answers. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 31--35. ACM.

[12]

Hemenover, Scott H. (2003). Individual differences in rate of affect change: Studies in affective chronometry, Journal of Personality and Social Psychology 85, no. 1, 121.

[13]

Feidakis, M. (2011). Emotional scaffolding with respect to time factors in networking collaborative learning environments. eLearn Center Research Paper Series.

[14]

Shute, V. J., D'Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., ... & Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86, 224--235.

Digital Library

[15]

Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: an evidence-based review (No. w23744). National Bureau of Economic Research.

[16]

Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2(4), 42--47.

[17]

Guyon, I., & Elisseeff, A. (2006). An introduction to feature extraction. In Feature Extraction, 1--25. Springer, Berlin, Heidelberg.

[18]

Marchand, G. C., & Gutierrez, A. P. (2012). The role of emotion in the learning process: Comparisons between online and face-toface learning settings. The Internet and Higher Education, 15(3), 150--160.

[19]

Mierswa, I., & Wurst, M. (2006, July). Information preserving multi-objective feature selection for unsupervised learning. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, 1545--1552). ACM.

[20]

Mierswa, I., & Klinkenberg, R. (2019). RapidMiner Studio (9.2) [Data science, machine learning, predictive analytics]. Retrieved from https://rapidminer.com/

[21]

Sak, H., Senior, A., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Fifteenth Annual Conference of the International Speech Communication Association.

[22]

Kuppens, P. (2015). It's about time: A special section on affect dynamics. Emotion Review, 7(4), 297--300.

[23]

Caruso, C., & Quarta, F. (1998). Interpolation methods comparison. Computers & Mathematics with Applications, 35(12), 109--126.

[24]

Thévenaz, P., Blu, T., & Unser, M. (2000). Interpolation revisited [medical images application]. IEEE Transactions on Medical Imaging, 19(7), 739--758.

[25]

Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004, April). Off-task behavior in the cognitive tutor classroom: when students game the system. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 383--390). ACM.

[26]

Rodrigo, M. M. T., Baker, R. S., Lagud, M. C., Lim, S. A. L., Macapanpan, A. F., Pascua, S. A. M. S., ... & Viehland, N. J. (2007). Affect and usage choices in simulation problem solving environments. Frontiers in Artificial Intelligence and Applications, 158, 145.

[27]

Huang, Y., Gong, J., Rucker, M., Chow, P., Fua, K., Gerber, M. S., ... & Barnes, L. E. (2017, June). Discovery of behavioral markers of social anxiety from smartphone sensor data. In Proceedings of the 1st Workshop on Digital Biomarkers, 9--14). ACM.

Digital Library

[28]

Aminikhanghahi, S., & Cook, D. J. (2017, March). Using change point detection to automate daily activity segmentation. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 262--267. IEEE.

Cited By

Rahman MSalem HYeom SOllington NOllington RRahman M(2023)A Preliminary Study on Learners’ Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE57739.2023.10165219(287-292)Online publication date: 20-May-2023
https://doi.org/10.1109/ISCAIE57739.2023.10165219
Yang ZRen Y(2023)Design of Community Education E-learning Platform Based on Data Network Technology2023 2nd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)10.1109/ICDACAI59742.2023.00055(257-262)Online publication date: 17-Oct-2023
https://doi.org/10.1109/ICDACAI59742.2023.00055

Understanding Affective Dynamics of Learning Toward a Ubiquitous Learning System
1. Computing methodologies

Recommendations

A machine learning enabled affective E-learning system model
Abstract
The purpose of this study is to propose an e-learning system model for learning content personalisation based on students’ emotions. The proposed system collects learners’ brainwaves using a portable Electroencephalogram and processes them via a ...
Toward Efficient Agnostic Learning
Special issue on computational learning theory, COLT'92

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed ...
The relationship of learning traits, motivation and performance-learning response dynamics

This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 23, Issue 2

June 2019

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3372300

Editor:
Landon Cox
Microsoft Research

Issue’s Table of Contents

Copyright © 2019 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2019

Published in SIGMOBILE-GETMOBILE Volume 23, Issue 2

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
129
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rahman MSalem HYeom SOllington NOllington RRahman M(2023)A Preliminary Study on Learners’ Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE57739.2023.10165219(287-292)Online publication date: 20-May-2023
https://doi.org/10.1109/ISCAIE57739.2023.10165219
Yang ZRen Y(2023)Design of Community Education E-learning Platform Based on Data Network Technology2023 2nd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)10.1109/ICDACAI59742.2023.00055(257-262)Online publication date: 17-Oct-2023
https://doi.org/10.1109/ICDACAI59742.2023.00055

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents