Sensor-Based Analytics in Education: Lessons Learned from Research in Multimodal Learning Analytics

Giannakos, Michail N.; Cukurova, Mutlu; Papavlasopoulou, Sofia

doi:10.1007/978-3-031-08076-0_13

Cited by 8 publications

(4 citation statements)

References 56 publications

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“…However, most studies were limited to surveys, system logs, tests, online discussions, interviews, written text and observations. With the current advancements in multimodal learning analytics (Sharma & Giannakos, 2020) and sensing technologies (Giannakos et al., 2022), it has become easier to use more than system logs and text‐based data to analyse learning processes. We argue that by not using sensing technologies and multimodal data, the researchers are limited to self‐reported data and system logs that do not present factual‐real‐time information about the learning processes (Ginnakos et al., 2019; Lee‐Cultura et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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Adaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state.

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Section: Discussionmentioning

confidence: 99%

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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“…At the same time, future MMLA research should also consider the great potential of leveraging on and developing knowledge that is more generative than an instantiation (an experiment or an artefact) and yet not at the scope of generalized theory (eg, ontological innovations, strong concepts). Such intermediate-level knowledge may inform the practice of researchers and practitioners (eg, see Abrahamson et al (2011) for an example ontological innovation and Höök and Löwgren (2012) for examples of strong concepts) and greatly advance MMLA research and practice (see Giannakos, 2023).…”

Section: Learning Theories and Multimodal Learning Analyticsmentioning

confidence: 99%

The role of learning theory in multimodal learning analytics

Giannakos

Cukurova

2023

Brit J Educational Tech

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This study presents the outcomes of a semi‐systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory‐driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory‐driven MMLA research and how this acceleration can extend or even create new theoretical knowledge. Practitioner notesWhat is already known about this topic Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena. MMLA can help us monitor learning activity at the micro‐level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces. MMLA provide new opportunities to support students' learning. What this paper adds Some MMLA works use theory, but, overall, the role of theory is currently limited. The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory. Most of the theory‐driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it. Implications for practice and/or policy If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories. MMLA research is mature enough to contribute to learning theory, and more research should aim to do so. MMLA researchers and practitioners, including technology designers, developers, educators and policy‐makers, can use this review as an overview of the current state of theory‐driven MMLA.

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“…Sensor data have the capacity to pursue three (sometimes complementary) objectives [43]; therefore, we classified the objectives of the sensor data following these three categories:…”

Section: Data Codingmentioning

confidence: 99%

Multisensory Interaction and Analytics to Enhance Smart Learning Environments: A Systematic Literature Review

Cosentino

Giannakos

2023

IEEE Trans. Learning Technol.

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Smart learning environments (SLEs) leverage technological developments to enable effective, engaging and personalized learning. SLEs rely on sensor and advanced interconnectivity capabilities to infer and reason. A natural set of affordances for SLEs are those of Multisensory Environments (MSEs). MSEs enable humans to make full use of their senses and equip learning systems with new information, methods of information exchange, and intelligent capabilities. In addition to novel forms of interaction, MSEs also offer novel forms of "learner traces" through Multimodal Learning Analytics (MMLA). This paper presents the results of a systematic literature review on how multisensory interactions and the respective analytics can support the use and design of SLEs. The findings from the analysis of 33 papers synthesize and clarify the latest advancements in the intersection of interactions and analytics in MSEs, discuss how those advancements can support SLEs' affordances and uses, and pave the way for improving our understanding of how various interaction modalities can support learning and under what conditions.

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Sensor-Based Analytics in Education: Lessons Learned from Research in Multimodal Learning Analytics

Cited by 8 publications

References 56 publications

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

The role of learning theory in multimodal learning analytics

Multisensory Interaction and Analytics to Enhance Smart Learning Environments: A Systematic Literature Review

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