A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

Andrade, Alejandro; Danish, Joshua A.; Maltese, Adam V.

doi:10.18608/jla.2017.43.3

Cited by 26 publications

(18 citation statements)

References 28 publications

(39 reference statements)

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“…Therefore, the studies report that MMD can be successfully used to explain students' trajectories while the students engage in the learning task. From the aforementioned studies, we can see that interaction logs, gestures and posture were related to memory (Junokas et al ., 2018 ;Kosmas et al ., 2018 ), conceptual understanding (Andrade et al ., 2017 ), the artifact quality (Spikol et al ., 2018 ) and cognitive workload and motivation (Mock et al ., 2016 ).…”

Section: For Learning Behavior and Performancementioning

confidence: 99%

“…In collaborative conditions, MMD have been used to identify key moments of collaboration (Noel et al ., 2018 ;Noroozi et al ., 2019 ;Pijeira-Díaz, Drachsler, Järvelä, & Kirschner, 2019 ). MMD have also been used to distinguish between help-seeking and help-giving behavior (Cukurova, Kent, & Luckin, 2019 ), tentative and casual problem-solving behavior (Andrade et al ., 2017 ), nonverbal behaviors (Cukurova, Luckin, Millán, & Mavrikis, 2018 ), solving versus guessing (Sharma, Papamitsiou, et al ., 2019 ) and the reasoning behavior of students (Worsley & Blikstein, 2015, 2018. Therefore, the studies identified in this category demonstrate the rich insights researchers can extract from MMD.…”

Section: For Learning Behavior and Performancementioning

confidence: 99%

“…Third, in the case of formal instruction to individuals, audio, system logs, gesture and video data were used (Blikstein, 2013 ;Mock et al ., 2016 ). Finally, for informal and individual cases, audio, gestures and log data were utilized by most contributions (Andrade et al ., 2017 ;Ochoa et al ., 2018 ). These choices reflect peculiarities of instructional settings.…”

Section: Opportunitiesmentioning

confidence: 99%

See 2 more Smart Citations

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

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Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

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Section: For Learning Behavior and Performancementioning

confidence: 99%

Section: For Learning Behavior and Performancementioning

confidence: 99%

Section: Opportunitiesmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

View full text Add to dashboard Cite

show abstract

“…• Social behaviors (e.g., positive social interactions, collaboration) Cognitive learning outcomes were the dominant outcomes across the majority of the reviewed studies (35 studies, 85.4%), especially those contextualized in STEM education. These studies reported an increase in students' knowledge acquisition on a variety of topics related to mathematics (e.g., Smith et al, 2014), biology (e.g., Andrade et al, 2017), chemistry (e.g., Tolentino et al, 2009) or physics (e.g., Enyedy, Danish, Delacruz & Melissa, 2012). Some of the reviewed studies also reported that students were engaged with effective inquiry learning processes in the embodied learning environments employed (Tolentino et al, 2009) or that the embodied learning technologies were adopted for augmenting the inquiry-based learning process (Anderson & Wall, 2016).…”

Section: Learning Gains (Rq4)mentioning

confidence: 99%

Embodied Learning in a Digital World: A Systematic Review of Empirical Research in K-12 Education

Georgiou

Ioannou

2019

Smart Computing and Intelligence

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There is a widespread assumption that technology enhanced learning environments, which are grounded on the notion of embodied cognition, can promote learning. Τhe current study reviews the empirical basis of this assumption by examining literature published from 2008 to 2017 which employs embodied learning environments in K-12 education. As part of this study, we review a total of 41 empirical studies and we focus on the type of embodied learning environments utilized, the research methods adopted for their evaluation and the educational contexts in which they are implemented. At the core of this review study, we investigate students' learning gains whilst examining the learning effectiveness of embodied learning environments, as compared to other interfaces and instructional approaches. In general, the review revealed positive outcomes from the use of embodied learning environments in K-12. Most of the reviewed studies were contextualized in K-12 STEM education, adopted gesture-based technologies and evaluated students' learning using retrospective measures grounded on pre-post questionnaires. Cognitive learning outcomes were dominant in the reviewed studies, while the evaluation of affective and psychomotor outcomes received less attention. The majority of the reviewed comparative studies reported that students in the embodied learning condition had increased learning gains, when compared to their counterparts in the control group. However, these findings should be treated with caution due to a set of methodological concerns that this review identified. We conclude this chapter with a synthesis of our findings in the form of emerged implications and we provide a set of guidelines for future research and practice in the field of embodied learning environments.

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“…More recently, sequence-based methods have been shown to provide meaningful insights. For example, one study used optimal sequence matching to analyse the sequence of hand movements in a multi-modal learning environment 27 . They found that incorporating temporality into their analysis was crucial to find a correlation between sensorimotor coordination and learning gains.…”

Section: Introductionmentioning

confidence: 99%

Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation

Peach

Greenbury

Johnston

et al. 2021

Sci Rep

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The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners’ behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.

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A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

Cited by 26 publications

References 28 publications

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Embodied Learning in a Digital World: A Systematic Review of Empirical Research in K-12 Education

Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation

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