Adaptive Reasoning: An Affect Related Feedback Approach for Enhanced E-Learning

Christine Asaju, Hima Vadapalli

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


Recognition of affective states to enhance e-learning platforms has been a topic of machine learning research. Compared to other input modalities, facial expressions have the potential to reveal nonverbal cues about a learner’s learning affect. However, most studies were limited in their analysis of learning affects exhibited by a learner with the possibility of providing appropriate feedback to teachers and learners. This work proposes an adaptive reasoning mechanism that considers the estimated affective states and learning affect in generating feedback with reasoning incorporated. This work utilizes a Convolutional Neural Network- Bidirectional Long-Short Term Memory (CNN-BiLSTM) cascade framework for affective states analysis through processing a live/stored observation of a learner in the form of a temporal signal. Using the proposed ensemble, four affective states were estimated, namely boredom, confusion, frustration, and engagement. Dataset for Affective States in E-Environment (DAiSEE) was used to train, validate, and test the baseline model, which reported an accuracy of 86% on 4305 test samples. In the next stage, mappings between estimated affective states and learning affects (i.e. positive, negative and neutral) were established based on an adaptive mapping mechanism, to consolidate the mapping between affective states and learning affects. Live testing and survey feedback were then used to further validate, adapt and amend the feedback process. Incorporating and interpreting the estimated affective states and learning affect is imperative in providing information to both teachers and learners, and hence potentially improve the existing e-learning platforms.

Original languageEnglish
Title of host publicationArtificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Proceedings
EditorsAnban Pillay, Edgar Jembere, Aurona Gerber
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031223204
Publication statusPublished - 2022
Event3rd Southern African Conference on Artificial Intelligence Research, SACAIR 2022 - Stellenbosch, South Africa
Duration: 5 Dec 20229 Dec 2022

Publication series

NameCommunications in Computer and Information Science
Volume1734 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference3rd Southern African Conference on Artificial Intelligence Research, SACAIR 2022
Country/TerritorySouth Africa


  • Adaptive reasoning
  • Affective states recognition
  • E-learning
  • Learning affect

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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