Learning analytics of embodied design: Enhancing synergy

Abrahamson, Dor; Worsley, Marcelo; Pardos, Zachary A.; Ou, Lu

doi:10.1016/j.ijcci.2021.100409

Cited by 11 publications

(12 citation statements)

References 26 publications

(18 reference statements)

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“…With the embodiment turn has emerged methods for collecting and analyzing multimodal data to model embodied interactions (Worsley and Blikstein, 2018;Abrahamson et al, 2021). These include data for analyzing gestures (Closser et al, 2021), eye gaze (Schneider and Pea, 2013;Shvarts and Abrahamson, 2019), facial expression (Monkaresi et al, 2016;Sinha, 2021), grip intensity (Laukkonen et al, 2021), and so on, coupled with traditional statistical methods, qualitative methods, and deep learning algorithms that model human behavior based on massive amounts of mouse click and text-based data (e.g., Facebook's DeepText, Google's RankBrain).…”

Section: Growth Of Multimodal Learning Analyticsmentioning

confidence: 99%

Disembodied AI and the limits to machine understanding of students' embodied interactions

Nathan

2023

Front. Artif. Intell.

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The embodiment turn in the Learning Sciences has fueled growth of multimodal learning analytics to understand embodied interactions and make consequential educational decisions about students more rapidly, more accurately, and more personalized than ever before. Managing demands of complexity and speed is leading to growing reliance by education systems on disembodied artificial intelligence (dAI) programs, which, ironically, are inherently incapable of interpreting students' embodied interactions. This is fueling a potential crisis of complexity. Augmented intelligence systems offer promising avenues for managing this crisis by integrating the strengths of omnipresent dAI to detect complex patterns of student behavior from multimodal datastreams, with the strengths of humans to meaningfully interpret embodied interactions in service of consequential decision making to achieve a balance between complexity, interpretability, and accountability for allocating education resources to children.

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Section: Growth Of Multimodal Learning Analyticsmentioning

confidence: 99%

Disembodied AI and the limits to machine understanding of students' embodied interactions

Nathan

2023

Front. Artif. Intell.

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“…One way that the intersection between LA and MSEs has begun to interface is in the form of MMLA [32]. Inspired by micro-ethnographic and interaction-analysis methodologies, MMLA aims to harness the power of sensor data and computational analysis to better understand and support student learning [8]. MMLA research focuses primarily on microlevel data and acts as a virtual observer and analyst of microlevel learning activities [21].…”

Section: B Multisensory Environments and Multimodal Learning Analyticsmentioning

confidence: 99%

“…This is due to the potential of MSEs to activate and scaffold different communication and interaction modalities, as well as the potential of the produced MMLAs to provide granular and timely insights [33]. Moreover, such multimodal and rich data can provide novel affordances that enhance learning (e.g., affective learning [110] and embodied learning [8]).…”

Section: Implication For Design and Practicementioning

confidence: 99%

“…A variety of systems that use multisensory experiences beyond vision and audition have already been investigated and developed to facilitate relaxation, minimize agitation and anxiety [4], enable communication [5], and support learning [6]. The identification of new ways to enhance and facilitate learning is a central goal in both human-computer interaction (HCI) and learning technology research [7], with the synergy between learning analytics (LA) and embodied learning offering a promising approach [8]. Many studies have shown that allowing learners to engage in a variety of ways increases implicit awareness and enhances learning.…”

Section: Introductionmentioning

confidence: 99%

“…Touch-less games use motion sensors and devices such as depth cameras to detect body movements and gestures [13], [14], [15]. This limited use of interaction modalities is particularly relevant for learning with technology, as learning with technology is a complex process associated with many aspects of interaction (e.g., difficult mental operations and cognitive friction) and has the potential to benefit the most from the plurality and complementary use of different interaction modalities [8], [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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