Digital Learning Projection. Learning performance estimation from multimodal learning experiences

Daniele Di Mitri*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Multiple modalities of the learning process can now be captured on real-time through wearable and contextual sensors. By annotating these multimodal data (the input space) by expert assessments or self-reports (the output space), machine learning models can be trained to predict the learning performance. This can lead to continuous formative assessment and feedback generation, which can be used to personalise and contextualise content, improve awareness and support informed decisions about learning.
Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings
EditorsE. André, R. Baker, X. Hu, M.M.T. Rodrigo, B. du Boulay
PublisherSpringer
Pages609–612
ISBN (Electronic)ISBN 978-3-319-61425-0
ISBN (Print)ISBN 978-3-319-61424-3
DOIs
Publication statusPublished - 1 Jul 2017
EventArtificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017 - Wuhan, China
Duration: 28 Jun 20171 Jul 2017
http://119.97.166.163/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10331
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceArtificial Intelligence in Education
Abbreviated titleAIED 2017
CountryChina
CityWuhan
Period28/06/171/07/17
Internet address

Fingerprint

Learning systems
Feedback
Sensors

Keywords

  • multimodal data
  • learning analytics
  • phd project
  • doctoral consortium
  • sensors

Cite this

Di Mitri, D. (2017). Digital Learning Projection. Learning performance estimation from multimodal learning experiences. In E. André, R. Baker, X. Hu, M. M. T. Rodrigo, & B. du Boulay (Eds.), Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings (pp. 609–612). (Lecture Notes in Computer Science; Vol. 10331). Springer. https://doi.org/10.1007/978-3-319-61425-0_75
Di Mitri, Daniele. / Digital Learning Projection. Learning performance estimation from multimodal learning experiences. Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. editor / E. André ; R. Baker ; X. Hu ; M.M.T. Rodrigo ; B. du Boulay. Springer, 2017. pp. 609–612 (Lecture Notes in Computer Science).
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title = "Digital Learning Projection. Learning performance estimation from multimodal learning experiences",
abstract = "Multiple modalities of the learning process can now be captured on real-time through wearable and contextual sensors. By annotating these multimodal data (the input space) by expert assessments or self-reports (the output space), machine learning models can be trained to predict the learning performance. This can lead to continuous formative assessment and feedback generation, which can be used to personalise and contextualise content, improve awareness and support informed decisions about learning.",
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Di Mitri, D 2017, Digital Learning Projection. Learning performance estimation from multimodal learning experiences. in E André, R Baker, X Hu, MMT Rodrigo & B du Boulay (eds), Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10331, Springer, pp. 609–612, Artificial Intelligence in Education, Wuhan, China, 28/06/17. https://doi.org/10.1007/978-3-319-61425-0_75

Digital Learning Projection. Learning performance estimation from multimodal learning experiences. / Di Mitri, Daniele.

Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. ed. / E. André; R. Baker; X. Hu; M.M.T. Rodrigo; B. du Boulay. Springer, 2017. p. 609–612 (Lecture Notes in Computer Science; Vol. 10331).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AB - Multiple modalities of the learning process can now be captured on real-time through wearable and contextual sensors. By annotating these multimodal data (the input space) by expert assessments or self-reports (the output space), machine learning models can be trained to predict the learning performance. This can lead to continuous formative assessment and feedback generation, which can be used to personalise and contextualise content, improve awareness and support informed decisions about learning.

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Di Mitri D. Digital Learning Projection. Learning performance estimation from multimodal learning experiences. In André E, Baker R, Hu X, Rodrigo MMT, du Boulay B, editors, Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. Springer. 2017. p. 609–612. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-61425-0_75