Children’s play and problem-solving in motion-based learning technologies using a multi-modal mixed methods approach

Lee-Cultura, Serena; Sharma, Kshitij; Giannakos, Michail N.

doi:10.1016/j.ijcci.2021.100355

Cited by 29 publications

(19 citation statements)

References 73 publications

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“…Lee-Cultura et al [ 27 ] introduced a motion-based learning technology (MBLT) for children's play recognition. A multimodal mixed method is used in MBLT to find out the behaviors and activities of children.…”

Section: Related Workmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.

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Section: Related Workmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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“…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%

“…As described in Section 3.1.2, another frequently used theory in MMLA research is CLT, which is discussed in six papers (see Figure 3). In four of these works, CLT is combined with other theories to account for other cognitive (Mangaroska et al, 2018) aspects of learning as well as the embodiment (Lee‐Cultura et al, 2022) and Papert's notion of constructionism (Papavlasopoulou et al, 2018). All papers focusing on CLT use MMLA with the goal of accessing learners' cognitive capacities (eg, how they process information; Mangaroska et al, 2018) and identifying differences and commonalities between the observed or self‐reported cognition of learners and cognitive‐related MMLA measurements that are unobservable by naked eye, such as CL based on galvanic skin responses (Larmuseau et al, 2020) and CL and arousal (Lee‐Cultura et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…In light of these findings, it is crucial for the MMLA community to recognize the importance of engaging with learning theory in a mutually beneficial manner, to facilitate a more comprehensive understanding of the learning process and use (or even develop) a scientifically acceptable frame to rationalize observations coming from MMLA research and explain learning phenomena. Currently, most MMLA studies, indeed, offer rich, useful and well‐written discussion and implications (eg, to practice or to design) sections; nevertheless, very few (eg, Abrahamson et al, 2015; Lee‐Cultura et al, 2022; Oviatt et al, 2021; Tancredi et al, 2021) provide any implications for general‐level knowledge and theory.…”

Section: Discussionmentioning

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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“…Other studies have shown that GBL with high levels of engagement leads to positive student attitudes, behaviour, and retention in an enjoyable learning environment (Janakiraman et al, 2021;Xu et al, 2021). Notably, GBL allows students to participate in problem-solving actively, explore alternatives, and make mistakes in a more secure environment (Lee-Cultura et al, 2022;Moon & Ke, 2020;Carpenter et al, 2020). The objective of this study to:…”

Section: Game-based Learningmentioning

confidence: 96%

Enhancing Flipped Classroom with BeeMSee Board Game

Muhammad¹,

Ghani²,

Roslan³

et al. 2022

IJARBSS

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Business Model Canvas (BMC) is an essential topic in Fundamentals of Entrepreneurship. Most students face difficulties in learning BMC because of their lack of conceptual understanding of nine boxes in BMC: customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. To minimise the difficulties, this innovation has introduced BeeMsee as an educational board game for teaching and learning BMC topics in flipped classroom approach. The main objective of this innovation is to provide students with learning materials that increase their understanding and motivation of BMC through a game-based learning approach. This BeeMSee board game is built based on activity theory to provide both fun and learning by facilitating the learning of BMC. It consists of a board, tokens, money, and two types of cards. This innovative BeeMSee board game enables effective collaboration among students, allowing them to contextualise learning within their learning experience and increase their enjoyment simultaneously. It also cultivates students' creativity, critical thinking, communication, and personal and social competencies. They can also prepare questions for the lecturers before coming to the class. Hence, students had more time for discussion during the synchronous learning with the lecturer. This product has the potential to be patented and commercialised. It is aimed at university students and improving the general public's understanding of business decision-making.

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Children’s play and problem-solving in motion-based learning technologies using a multi-modal mixed methods approach

Cited by 29 publications

References 73 publications

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Health Information Prediction System of Infant Sports Based on Deep Learning Network

The role of learning theory in multimodal learning analytics

Enhancing Flipped Classroom with BeeMSee Board Game

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