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

Abstract

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.

Applying Learning Analytics in Online Environments: Measuring Learners’ Engagement Unobtrusively

“…As Worsley et al (2021) mentioned: raw data collected by LA or any other tools can seldom give clear, immediate answers to any research questions. The goal of data analysis is to transform data into actionable insights, which requires an in-depth understanding of the local context by researchers (e.g., setting, participants, available resources and analytic techniques).…”

From Sensor Data to Educational Insights

“…Using sensor data in education offers researchers novel perceived affordances to generate a richer picture of the learning experience by going beyond what can be captured from mouse clicks and keystrokes. Sensor data can enable the automated analysis and support of learning activities that are not necessarily mediated by a computer [ 11 ], such as activity unfolding in a maker space [ 12 ] or in physical simulation-based training rooms [ 13 ]. Similarly, video, audio devices, and other sensing devices have been used in physical learning spaces to model aspects of the classroom that, in the past, could only be studied via direct observations and ethnographic studies such as teacher–student communication [ 14 ], spatial dynamics [ 15 ], and students’ collaborative dialogue [ 16 ].…”

What Makes Learning Analytics Research Matter

“…For example, this year's articles mobilize LA to address several problems, including the lack of learning reflection among adolescents (Cloude et al, 2021), a need for students to read and respond effectively to different collaborative situations (Worsley et al, 2021), and the ways that confusion can impede students' metacognition (Zhang et al, 2021). We also see increased interest in tackling the challenges of more open-ended (Emara et al, 2021) and inquiry-based learning environments (Dickler et al, 2021;Rodríguez-Triana et al, 2021).…”