Archived CfP: Special section on multimodal learning analytics

Journal of Learning Analytics

SPECIAL ISSUE on Multimodal Learning Analytics
Guest Editors: MarceloWorsley & Xavier Ochoa


The Journal of Learning Analytics Special Issue on Multimodal Learning Analytics is focused on research that discusses the salience of using data from a combination of modalities (or data streams) to enhance the field’s ability to support, understand and improve student learning. Papers can come from a variety of learning contexts, and are free to utilize any number of data sources. Additionally, papers in this special issue should feature both an in-depth presentation of any analytic approaches utilized, as well as theoretical justification for the analytic techniques and the reported findings. Finally, authors are encourage to explore new metrics and analytic techniques, and should consider reporting their findings even if they do not garner the expected results.

Particular modalities of interest include, but are not limited to:
  • Gaze
  • Gesture
  • Speech
  • Arousal
  • Engagement
  • Motivation
  • Persistence
  • Content Knowledge
  • Click Streams
  • Actions (with a virtual or physical interface)
  • Survey Responses

Note that the modalities analyzed do not have to be automatically derived. Manual annotations and data that is derived from student surveys are both acceptable and encouraged.

  • Multimodal pattern/behavior recognition and/or modeling from a given learning environment
  • Predicting student success on a series of activities in an Intelligent Tutoring System based on data from various data streams.
  • Predicting collaboration quality using audio, video, gesture and/or digital pen data.
  • Identifying the patterns and behaviors of students that have a certain characteristic.
  • Modeling the speech and gesture of a virtual teaching agent, and showing how those multimodal behaviors are improving student learning (and perhaps their gaze patterns).
  • Using student lecture viewing practices and discussion forum posts in a MOOC to predict performance on assignments and quizzes.
  • Analyzing student computer programming snapshots in conjunction with other relevant data to model different approaches that students use and how those correlate with different constructs of learning.
  • Using classroom level indicators of engagement (through multimodal sensors) to predict transfer

Deadline for submissions: March 1, 2015
Anticipated publication date: September, 2015

Submissions will take place through JLA’s online submission system at
When submitting a paper, select the section "Special section: Multimodal learning analytics". 
All submissions should follow JLA’s standard manuscript guidelines and will undergo peer review.
For any additional questions, please contact

The Journal of Learning Analytics is peer-reviewed, open-access, and is the official publication of the Society for Learning Analytics Research (SoLAR).