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.

Learning Analytics and Education Data Mining in Higher Education

2021 ◽  
pp. 108-126
Author(s):  
Samira ElAtia ◽  
Donald Ipperciel
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Large Scale ◽  
Educational Data Mining ◽  
Current Situation ◽  
Research Perspective ◽  
Education Data ◽  
The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

The Role of Learning Analytic in Education Reform

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruihong Dai ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Education Reform ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Research Community ◽  
Educational Setting ◽  
Future Performance ◽  
Education Data

In year 2009, the nascent research community of Educational Data Mining (EDM) has been found to continually and increasingly grow. Now the education data mining has become popular and deeply studied in all universities. Specially, in United Kingdom, United State, Canada, they held several conferences annually on learning analytic discussion, which is related with Educational Data Mining. Learning analytics refers to the collection of large volume of data about students in an educational setting and to analyze the data to predict the students' future performance, identify risk and provide recommendations for improvement. LA is an increasingly emerging field, it is necessary for higher education stakeholders to become more familiar with the issues related to LA's use in education. Such a paper provides a brief introduction, methods and benefits, and challenges of LA.

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 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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