scholarly journals Editorial: Augmenting Learning Analytics with Multimodal Sensory Data​

2016 ◽  
Vol 3 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Xavier Ochoa ◽  
Marcelo Worsley
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Technological System ◽  
Multimodal Learning ◽  
Special Issue ◽  
Sensory Data ◽  
Video And Audio ◽  
Multimodal Learning Analytics

The goal of Learning Analytics is to understand and improve learning.  However, learning does not always occur through or mediated by a technological system that can collect digital traces.  To be able to study learning in non-technology centered environments, several signals, such as video and audio, should be captured, processed and analyzed to produce traces of the actions and interactions of the actors of the learning process. The use and integration of the different modalities present in those signals is known as Multimodal Learning Analytics.  This editorial presents a brief introduction to this new variation of Learning Analytics and summarizes the four representative articles included in this special issue.  The editorial closes with a small discussion about the current opportunities and challenges in multimodal learning analytics.

scholarly journals Detecting Drowsy Learners at the Wheel of e-Learning Platforms with Multimodal Learning Analytics

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ryosuke Kawamura ◽  
Shizuka Shirai ◽  
Noriko Takemura ◽  
Mehrasa Alizadeh ◽  
Mutlu Cukurova ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Multimodal Learning ◽  
E Learning ◽  
Learning Platforms ◽  
Multimodal Learning Analytics

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

scholarly journals Designing Analytics for Collaboration Literacy and Student Empowerment

2021 ◽  
Vol 8 (1) ◽  
pp. 30-48
Author(s):  
Marcelo Worsley ◽  
Khalil Anderson ◽  
Natalie Melo ◽  
JooYoung Jang
Keyword(s):  
Student Perceptions ◽  
21St Century ◽  
Learning Analytics ◽  
Student Empowerment ◽  
Multimodal Learning ◽  
Theoretical Understanding ◽  
Student Collaboration ◽  
Communication Conflict ◽  
Multimodal Learning Analytics ◽  
The Moment

Collaboration has garnered global attention as an important skill for the 21st century. While researchers have been doing work on collaboration for nearly a century, many of the questions that the field is investigating overlook the need for students to learn how to read and respond to different collaborative settings. Existing research focuses on chronicling the various factors that predict the effectiveness of a collaborative experience, or on changing user behaviour in the moment. These are worthwhile research endeavours for developing our theoretical understanding of collaboration. However, there is also a need to centre student perceptions and experiences with collaboration as an important area of inquiry. Based on a survey of 131 university students, we find that student collaboration-related concerns can be represented across seven different categories or dimensions: Climate, Compatibility, Communication, Conflict, Context, Contribution, and Constructive. These categories extend prior research on collaboration and can help the field ensure that future collaboration analytics tools are designed to support the ways that students think about and utilize collaboration. Finally, we describe our instantiation of many of these dimensions in our collaborative analytics tool, BLINC, and suggest that these seven dimensions can be instructive for re-orienting the Multimodal Learning Analytics (MMLA) and collaboration analytics communities.

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