Modeling nonlinear dynamics of fluency development in an embodied-design mathematics learning environment with Recurrence Quantification Analysis

Tancredi, Sofia; Abdu, Rotem; Abrahamson, Dor; Balasubramaniam, Ramesh

doi:10.1016/j.ijcci.2021.100297

Cited by 21 publications

(14 citation statements)

References 55 publications

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“…Oviatt et al (2021) elaborate on EC theory and limited resource theory, discuss their limitations in MMLA (eg, why the hands, in particular, provide a sensitive window for learners' mental state and why iconic gestures constitute an exception of reducing gestural activity as expertise increases) and outline how MMLA research qualities can complement these theories (eg, by leveraging insights that are not observable by the human eye). One last example is Tancredi et al (2021), who combined EC with dynamic systems theory and coordination dynamics to extend its qualities and investigate the bimanual coordination patterns in different phases of mathematics embodied‐design learning. Moreover, they discussed the benefits of going beyond the theoretical lenses of Piaget and Vygotsky and of embracing qualities from other theories, such as dynamic systems theory.…”

Section: Resultsmentioning

confidence: 99%

“…When it comes to using theory for synthesis, we did not find any papers that juxtapose multiple theories and integrate their concepts into a unified framework in relation to MMLA. Although most of the studies in the analysis category juxtapose multiple theories, such as EC with dynamic systems theory (Tancredi et al, 2021), EC with limited resource theory (Oviatt et al, 2021) and EC with CLT (Lee‐Cultura et al, 2022), the integration of these theories was limited in terms of synthesis. Therefore, most of the MMLA studies use theory ‘as is’ to inform their work (descriptive and application roles), whereas some studies take on a more generative perspective.…”

Section: Resultsmentioning

confidence: 99%

“…EC is used in 11 papers (see Figure 2), and, in four of these, it is combined with other theories to account for other cognitive (Schönborn et al, 2011) and affective (Worsley et al, 2015) aspects of learning as well as the temporality of learning (Tancredi et al, 2021) and limited human resources (Oviatt et al, 2021). For studies that use EC, most of the intended goals are to test a concrete hypothesis that relates either to the use of certain parts of learners' bodies (eg, gestures; Andrade et al, 2017; Junokas et al, 2018), pose (Worsley et al, 2015), with a challenging learning domain and the benefits of embodiment—for example, math education (Oviatt et al, 2021; Tancredi et al, 2021), ecosystem education (Andrade, 2017) and biology (Schönborn et al, 2011)—or with a population with special abilities and the benefits of embodiment (eg, special educational needs; Kosmas et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…This is because MMLA can monitor the learning activity at the micro‐level and help us model humans' cognitive, affective and social factors associated with learning processes. Although traditional educational research and analytics have long examined learners' cognitive, affective and social factors via various methods (eg, knowledge tests, standardized surveys, think‐aloud methods, observations, interviews and logged interactions), they are limited when it comes to investigating the complex and dynamic constructs of learning (Abrahamson et al, 2022; Richardson & Chemero, 2014; Tancredi et al, 2021). Alternatively, MMLA can provide information in a temporal and unobtrusive manner (Giannakos et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The role of learning theory in multimodal learning analytics

Giannakos

Cukurova

2023

Brit J Educational Tech

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“…The Tobii Pro X2-30 has a lower sampling frequency than the more professional Tobii Pro eye tracker (Tobii Pro Fusion, , accessed on 8 July 2021) or EyeLink 1000 (EyeLink 1000 Plus, , accessed on 8 July 2021). However, Tobii Pro X2-30 is being used in various studies for analyzing human gaze behavior [ 58 , 59 , 60 , 61 , 62 , 63 ]. Additionally, Tobii Pro SDK (Tobii Pro SDK, , accessed on 8 July 2021) and eye tracker manager software (Tobii Pro Eye Tracker Manager, , accessed on 8 July 2021) for professional eye-tracking research are provided.…”

Section: Methodsmentioning

confidence: 99%

Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Yoo

Jeong

Jang

2021

Sensors

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Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

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