Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Adaptive Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.

Place, publisher, year, edition, pages
2017. , p. 65
Keywords [en]
adaptive learning, machine learning, e-learning, biosyncing, biometric sensors, Empatica E4, Intelligent Tutoring Systems, WEKA
National Category
Computer and Information Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:ltu:diva-61648OAI: oai:DiVA.org:ltu-61648DiVA, id: diva2:1068891
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level
Supervisors
Examiners
Available from: 2017-02-16 Created: 2017-01-26 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(2842 kB)609 downloads
File information
File name FULLTEXT01.pdfFile size 2842 kBChecksum SHA-512
99c6e9817f6837f64b411465e65e831f27c7ad11c6b3735698e15ffb15d1d1b59993244e4a26899391281832f0d6755844dd717fb483471729d7c7190d05381a
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science, Electrical and Space Engineering
Computer and Information SciencesEngineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 609 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 2180 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf