scholarly journals A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA

2021 ◽  
pp. 1-18
Author(s):  
Marcelo Worsley ◽  
Roberto Martinez-Maldonado ◽  
Cynthia D'Angelo
Keyword(s):  
Qualitative Research ◽  
Learning Analytics ◽  
Data Dissemination ◽  
Summer Institute ◽  
Multimodal Learning ◽  
High Quality ◽  
New Era ◽  
Critical Studies ◽  
The Past ◽  
Multimodal Learning Analytics

Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12 commitments that we believe are critical for creating effective MMLA innovations. Moreover, as MMLA grows in use, it is important to articulate a set of core commitments that can help guide both MMLA researchers and the broader learning analytics community. The commitments that we describe are deeply rooted in the origins of MMLA and also reflect the ways that MMLA has evolved over the past 10 years. We organize the 12 commitments in terms of (i) data collection, (ii) analysis and inference, and (iii) feedback and data dissemination and argue why these commitments are important for conducting ethical, high-quality MMLA research. Furthermore, in using the language of commitments, we emphasize opportunities for MMLA research to align with established qualitative research methodologies and important concerns from critical studies.

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.

scholarly journals Shaping our Taxonomic Legacy through Openly Sharing Primary Biodiversity Data in Taxonomic Revisions

2019 ◽  
Vol 3 ◽  
Author(s):  
Torsten Dikow
Keyword(s):  
Data Dissemination ◽  
Original Description ◽  
Published Data ◽  
Data Standards ◽  
Global Biodiversity Information Facility ◽  
High Quality ◽  
Data Repositories ◽  
The Past ◽  
Text Document ◽  
Digital Format

Taxonomy has a long tradition of describing earth’s biodiversity. For the past 20 years or so, taxonomic revisions have become available in PDF format, which is regarded by most practicing taxonomists to be a good means of digital dissemination. However, a PDF document is nothing more than a text document that can be transferred easily for viewing among researchers and computer platforms. In today’s world, traditional taxonomic techniques need to be met with novel tools to make data dissemination a reality, make species hypotheses more robust, and open the field up to rigorous scientific testing. Here, I argue that high-quality taxonomic output is not just the publication of detailed species descriptions and re-descriptions, precise taxon delimitations, easy-to-use identification keys, and comprehensively undertaken and illustrated revisions. Rather, in addition high-quality taxonomic output embraces digital workflows and data standards to disseminate captured and published data in structured, machine-readable formats to data repositories so as to make all data openly accessible. Imagine that a taxonomist today has every original description and every subsequent re-description of a species at her/his fingertips online, has every specimen photograph produced by a previous reviser digitally available in the original resolution, and can take advantage of existing, openly accessible data and resources produced by peers in digital format in the past. When we as taxonomists provide such findable, accessible, interoperable, and reusable (FAIR) data, the future of biodiversity discovery will accelerate and our own taxonomic legacy will be enhanced. Cybertaxonomic tools provide methods to accomplish this goal and their use and implementation is here summarized in the context of revisionary taxonomy from the standpoint of a publishing taxonomist. While many of the tools have been around for some time now, very few taxonomists embrace and utilize these tools in their publications. This presentation will provide information on what kind of data can and should be openly shared (e.g., specimen occurrence data, digital images, names, descriptions, authors) and outline best practices utilizing globally unique identifiers for specimens and data. Data standards and the best-suited data repositories such as the Global Biodiversity Information Facility (GBIF) and Zenodo, with its Biodiversity Literature Repository, and the Plazi TreatmentBank, an emerging species portal, are discussed to illustrate retrospective and prospective data capture of taxonomic revisions.

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