Research trends in multimodal learning analytics: A systematic mapping study

Ouhaichi, Hamza; Vogel, Bahtijar

doi:10.1016/j.caeai.2023.100136

Cited by 10 publications

(13 citation statements)

References 19 publications

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“…We acknowledge that there are different ways to approach and position a systematic review into MMLA, see for example, Mu et al (2020) and Ouhaichi et al (2023). In this systematic review, we positioned MMLA as located in in-between spaces-found between (1) laboratory or experimental settings, on the one hand, and classroom settings, on the other hand;…”

Section: Discussion Of Findings and Emerging Themesmentioning

confidence: 99%

“…MMLA has existed under various guises for many years, for example within educational data mining (EDM), the wider field of LA (eg, Ouhaichi et al, 2023;Shankar et al, 2018), and/ or in the field of Human Computer Interaction (HCI) (Mu et al, 2020) and Human Centered Design (HCD). Blikstein (2013) provides an early definition of MMLA as "a set of techniques that can be used to collect multiple sources of data in high frequency (video, logs, audio, gestures, biosensors), synchronise and code the data, and examine learning in realistic, ecologically valid, social, mixed-media learning environments."…”

Section: Multimodal Learning Analytics-a Brief Overview and Related L...mentioning

confidence: 99%

“…It is important to recognise a number of existing systematic reviews on MMLA. Ouhaichi et al (2023), for example, examine seven systematic literature reviews and highlight various shortcomings, such as a lack of information about the review process, the absence of inclusion and exclusion criteria and missing details about the data-extraction process. Their analysis presents 14 topics clustered into four themes, namely learning context, learning process, systems and modality, and technologies.…”

Section: Type Of Datamentioning

confidence: 99%

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Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

Prinsloo,

Slade,

Khalil

2023

Brit J Educational Tech

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Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML), and the provision of automated, mostly real‐time feedback to students and instructors. Much of MMLA takes place in experimental and laboratory settings, researching students' learning in in‐between spaces—between research and classroom application, in‐between students' learning in private and public spaces as researchers track students' learning both in their use of social media and connected devices, and through the use of context‐aware and adaptive devices; and lastly, in‐between respecting students' privacy while increasingly using intrusive technologies. This study seeks to establish what is known about MMLA in terms of rationale for applications, the nature and scope of data collected, the study contexts, evidence of commercial interests and/or downstream uses of students' data, and consideration of ethics, privacy, and the protection of student data. This systematic review analysed 192 articles using a search string consisting of various combinations of multimodal (data) and learning analytics. The main findings include, inter alia, that though MMLA provide insights into learning and teaching, there is little evidence of MMLA findings successfully being applied to classroom settings, at scale. Given that the nature of MMLA research often necessitates the use of a range of (intrusive) sensors and recording technologies and can include children in its samples; the encroachment of students' right to privacy is a huge concern that is not addressed. There is also a need to reconsider the rationale for collecting multimodal data, the conditions under which such data collection will be ethical and in service of students' wellness, and the boundaries that should protect their (multimodal) data. Practitioner notesWhat is already known about this topic Experimental educational research predates both multimodal learning analytics (MMLA) and educational data mining (EDM). MMLA has been an integral part of learning analytics as research focus and practice since its inception. The increased digitisation and datafication, advances in technology (eg, sensor technology, geo‐tracking, etc.) and a growing normalisation of wearable technologies provides greater scope for collecting multimodal data. There is a vast body of published MMLA research providing a range of insight into students' and educators' behaviours aiming to increase the effectiveness of teaching. What this paper adds While there are several studies providing insights into the state of MMLA research, this study provides insight to a selected range of factors in MMLA research such as the type of research (empirical/conceptual); the nature and scope of data collected; the sample populations (pre‐higher education, higher education, etc.); evidence of commercial interests or consideration of downstream uses; issues pertaining to consent, privacy, data protection and ethics; and evidence of how findings were used to improve teaching and learning. The vast majority of MMLA research targets higher education, is empirical in nature and is based on relatively small samples of participation in experimental settings. Confirms previous research that found the predominance of small samples, and a lack of replicability and, as a result, lacking scalability. That there is very little explicit discussion of the ethical and privacy implications and data protection, either at design stage or for future implementation. Similarly, that there is little consideration of potential commercial interests or downstream uses of data. Implications for practice and/or policy MMLA in its essence requires interdisciplinary approaches and teams. For MMLA to move beyond small‐scale, experimental settings to application in real (classroom) settings, larger, replicable studies should be conducted together with ways to make study findings actionable for teachers and students. Ethical issues, commercial interests and downstream uses of collected data must be considered within the design and approval of MMLA research.

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Section: Discussion Of Findings and Emerging Themesmentioning

confidence: 99%

Section: Multimodal Learning Analytics-a Brief Overview and Related L...mentioning

confidence: 99%

Section: Type Of Datamentioning

confidence: 99%

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Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

Prinsloo,

Slade,

Khalil

2023

Brit J Educational Tech

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“…Most participants in the research analyzed in the reviews consisted of undergraduate students [2,[11][12][13][14][15][16][17][18][19][20][21] and graduate students [2,[11][12][13]19,20], or, if not specified, simply defined by 'higher education' students or "course demographics" [12,[22][23][24][25][26][27], and teacher/lecturer group [16,21,27,28]. While virtually all reviews included studies conducted in higher education [2,12,13,[16][17][18][19]26], studies with K12 students were fewer, and only two reviews focused solely on K12 [29,30].…”

Section: Target Usersmentioning

confidence: 99%

“…Common visualization techniques were various graphs [2,12,13,19,21,23,25,26,28], such as line charts, bar charts, progress bars, pie charts, and timelines. In some of the reviews, there was no mention of what visualizations are represented in the dashboards [15][16][17][18]20,27,29,31].…”

Section: Visualization Elements Of Dashboardsmentioning

confidence: 99%

A Current Overview of the Use of Learning Analytics Dashboards

Masiello,

Mohseni,

Palma

et al. 2024

Education Sciences

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The promise of Learning Analytics Dashboards in education is to collect, analyze, and visualize data with the ultimate ambition of improving students’ learning. Our overview of the latest systematic reviews on the topic shows a number of research trends: learning analytics research is growing rapidly; it brings to the front inequality and inclusiveness measures; it reveals an unclear path to data ownership and privacy; it provides predictions which are not clearly translated into pedagogical actions; and the possibility of self-regulated learning and game-based learning are not capitalized upon. However, as learning analytics research progresses, greater opportunities lie ahead, and a better integration between information science and learning sciences can bring added value of learning analytics dashboards in education.

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