research-article

Children’s Play and Problem Solving in Motion-Based Educational Games: Synergies between Human Annotations and Multi-Modal Data

Authors:

Serena Lee-Cultura,

Kshitij Sharma,

Giulia Cosentino,

Sofia Papavlasopoulou,

Michail GiannakosAuthors Info & Claims

IDC '21: Proceedings of the 20th Annual ACM Interaction Design and Children Conference

Pages 408 - 420

https://doi.org/10.1145/3459990.3460702

Published: 24 June 2021 Publication History

Abstract

Identifying and supporting children’s play and problem solving behaviour is important for designing educational technologies. This can inform feedback mechanisms to scaffold learning (provide hints or progress information), and assist facilitators (teachers, parents) in supporting children. Traditionally, researchers manually code video to dissect children’s nuanced play and problem solving behaviour. Advancements in sensing technologies and their respective Multi-Modal Data (MMD), afford observation of invisible states (cognitive, affective, physiological), and provide opportunities to inspect internal processes experienced during learning and play. However, limited research combines traditional video annotations and MMD to understand children’s behaviour as they interact with educational technology. To address this concern, we collected data from webcam, wristband, eye-trackers, and Kinect, as 26 children, aged 10-12, played a Motion-Based Educational Games (MBEG). Results showed significant differences in children’s experience during play and problem solving episodes, and motivate design considerations aimed to facilitate children’s interactions with MBEG.

References

[1]

Efthimios Alepis. 2011. AFOL: Towards a new intelligent interactive programming language for children. In Intelligent Interactive Multimedia Systems and Services. Springer, 199–208.

[2]

Ioannis Altanis and Symeon Retalis. [n.d.]. A multifaceted students’ performance assessment framework for motion-based game-making projects with Scratch. Educational Media International([n. d.]).

[3]

Alejandro Andrade. 2017. Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. 70–79.

Digital Library

[4]

Alejandro Andrade, Ginette Delandshere, and Joshua A Danish. 2016. Using Multimodal Learning Analytics to Model Student Behaviour: A Systematic Analysis of Behavioural Framing.Journal of Learning Analytics 3, 2 (2016), 282–306.

[5]

Alissa N Antle. 2013. Exploring how children use their hands to think: An embodied interactional analysis. Behaviour & Information Technology 32, 9 (2013), 938–954.

Digital Library

[6]

Saskia Bakker, Panos Markopoulos, and Yvonne De Kort. 2008. OPOS: an observation scheme for evaluating head-up play. In Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges. 33–42.

Digital Library

[7]

Laura Bartoli, Clara Corradi, Franca Garzotto, and Matteo Valoriani. 2013. Exploring motion-based touchless games for autistic children’s learning. In Proceedings of the 12th international conference on interaction design and children. ACM, 102–111.

Digital Library

[8]

Carole R Beal and Paul R Cohen. 2008. Temporal data mining for educational applications. In Pacific Rim International Conference on Artificial Intelligence. Springer, 66–77.

Digital Library

[9]

Gary G Berntson and John T Cacioppo. 2004. Heart rate variability: Stress and psychiatric conditions. Dynamic electrocardiography 41, 2 (2004), 57–64.

[10]

Matthew P Black, Daniel Bone, Marian E Williams, Phillip Gorrindo, Pat Levitt, and Shrikanth Narayanan. 2011. The usc care corpus: Child-psychologist interactions of children with autism spectrum disorders. In Twelfth Annual Conference of the International Speech Communication Association.

[11]

Paulo Blikstein and Marcelo Worsley. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3, 2 (2016), 220–238.

[12]

Trevor S Breusch and Adrian R Pagan. 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society (1979), 1287–1294.

[13]

Christine Bruce. 2008. Informed learning. Assoc of Cllge & Rsrch Libr.

[14]

Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z Arshad, Ahmad Khawaji, and Dan Conway. 2016. Robust multimodal cognitive load measurement. Springer.

[15]

David Cohen. 2007. The development of play. Routledge.

[16]

Lucrezia Crescenzi-Lanna. 2020. Multimodal Learning Analytics research with young children: A systematic review. British Journal of Educational Technology 51, 5 (2020), 1485–1504.

[17]

Ciera Crowell. 2018. Analysis of Interaction Design and Evaluation Methods in Full-Body Interaction for Special Needs. In 23rd International Conference on Intelligent User Interfaces. ACM, 673–674.

[18]

Mihaly Csikszentmihalyi. 2020. Finding flow: The psychology of engagement with everyday life. Hachette UK.

[19]

Mutlu Cukurova, Michail Giannakos, and Roberto Martinez-Maldonado. 2020. The promise and challenges of multimodal learning analytics.

[20]

Bronwyn J Cumbo and Ole Sejer Iversen. 2020. CCI in the wild: designing for environmental stewardship through children’s nature-play. In Proceedings of the Interaction Design and Children Conference. 335–348.

Digital Library

[21]

Sabrine DHAOUADI and Mohamed Moncef BEN KHELIFA. 2020. A multimodal Physiological-Based Stress Recognition: Deep Learning Models’ Evaluation in Gamers’ Monitoring Application. In 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 1–6.

[22]

Leandro L Di Stasi, Andrés Catena, José J Canas, Stephen L Macknik, and Susana Martinez-Conde. 2013. Saccadic velocity as an arousal index in naturalistic tasks. Neuroscience & Biobehavioral Reviews 37, 5 (2013), 968–975.

[23]

Sidney D’Mello. 2013. A selective meta-analysis on the relative incidence of discrete affective states during learning with technology.Journal of Educational Psychology 105, 4 (2013), 1082.

[24]

Afke Donker and Panos Markopoulos. 2002. A comparison of think-aloud, questionnaires and interviews for testing usability with children. In People and computers XVI-Memorable yet invisible. Springer, 305–316.

[25]

Paul Dourish. 2004. Where the action is: the foundations of embodied interaction. MIT press.

[26]

Andrew T Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos. 2018. The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13. https://doi.org/10.1145/3173574.3173856

Digital Library

[27]

Adelmo Eloy, Danielle Cruz, Kavindya Thennakoon, and Wayne Grant. 2020. Buildagram: a constructionist environment for spatial reasoning. In Proceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts. 280–283.

Digital Library

[28]

Julia Fink, Séverin Lemaignan, Pierre Dillenbourg, Philippe Rétornaz, Florian Vaussard, Alain Berthoud, Francesco Mondada, Florian Wille, and Karmen Franinović. 2014. Which robot behavior can motivate children to tidy up their toys? Design and Evaluation of” Ranger”. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction. 439–446.

Digital Library

[29]

Panagiotis Fotaris, Nikolaos Pellas, Ioannis Kazanidis, and Paul Smith. 2017. A systematic review of Augmented Reality game-based applications in primary education. In Memorias del XI Congreso Europeo en Aprendizaje Basado en el Juego Graz. 181–191.

[30]

Robert S Friedman and Fadi P Deek. 2002. The integration of problem-based learning and problem-solving tools to support distributed education environments. In 32nd Annual Frontiers in Education, Vol. 2. IEEE, F3E–F3E.

[31]

Joachim Funke and Peter Frensch. 1995. Complex problem solving research in North America and Europe: An integrative review. Foreign Psychology 5(1995), 42–47.

[32]

Emily R Fyfe and Bethany Rittle-Johnson. 2016. Feedback both helps and hinders learning: The causal role of prior knowledge.Journal of Educational Psychology 108, 1 (2016), 82.

[33]

M. N. Giannakos, S. Papavlasopoulou, and K. Sharma. 2020. Monitoring Children’s Learning Through Wearable Eye-Tracking: The Case of a Making-Based Coding Activity. IEEE Pervasive Computing(2020), 1–12. https://doi.org/10.1109/MPRV.2019.2941929

[34]

Michail N Giannakos, Kshitij Sharma, Sofia Papavlasopoulou, Ilias O Pappas, and Vassilis Kostakos. 2020. Fitbit for learning: Towards capturing the learning experience using wearable sensing. International Journal of Human-Computer Studies 136 (2020), 102384.

Digital Library

[35]

Robyn M Gillies, Annemaree Carroll, Ross Cunnington, Mary Rafter, Kelsey Palghat, Jeff Bednark, and Amanda Bourgeois. 2016. Multimodal representations during an inquiry problem-solving activity in a Year 6 science class: A case study investigating cooperation, physiological arousal and belief states. Australian Journal of Education 60, 2 (2016), 111–127.

[36]

Sukeshini A Grandhi, Gina Joue, and Irene Mittelberg. 2011. Understanding naturalness and intuitiveness in gesture production: insights for touchless gestural interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 821–824.

Digital Library

[37]

Emmanuel Guigon, Pierre Baraduc, and Michel Desmurget. 2007. Computational motor control: redundancy and invariance. Journal of neurophysiology 97, 1 (2007), 331–347.

[38]

Gary Hackbarth, Varun Grover, and Y Yi Mun. 2003. Computer playfulness and anxiety: positive and negative mediators of the system experience effect on perceived ease of use. Information & management 40, 3 (2003), 221–232.

[39]

Uri Hasson, Orit Furman, Dav Clark, Yadin Dudai, and Lila Davachi. 2008. Enhanced intersubject correlations during movie viewing correlate with successful episodic encoding. Neuron 57, 3 (2008), 452–462.

[40]

Curtis R Henrie, Lisa R Halverson, and Charles R Graham. 2015. Measuring student engagement in technology-mediated learning: A review. Computers & Education 90 (2015), 36–53.

Digital Library

[41]

Juan Pablo Hourcade, Alissa Antle, Lisa Anthony, Jerry Fails, O Iversen, Elisa Rubegni, Mikael Skov, Petr Slovak, Greg Walsh, Anja Zeising, 2018. Child-computer interaction, ubiquitous technologies, and big data. interactions 25, 6 (2018), 78–81.

[42]

Juan Pablo Hourcade, Anja Zeising, Ole Sejer Iversen, Mikael B Skov, Alissa N Antle, Lisa Anthony, Jerry Alan Fails, and Greg Walsh. 2018. Child-Computer Interaction SIG: Ubiquity and Big Data–A Changing Technology Landscape for Children. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, SIG07.

Digital Library

[43]

Hui-mei Justina Hsu. 2011. The potential of kinect in education. International Journal of Information and Education Technology 1, 5(2011), 365.

[44]

Katherine Isbister, Mike Karlesky, Jonathan Frye, and Rahul Rao. 2012. Scoop!: a movement-based math game designed to reduce math anxiety. In CHI’12 extended abstracts on human factors in computing systems. ACM, 1075–1078.

[45]

Keri Johnson, Jebediah Pavleas, and Jack Chang. 2013. Kinecting to mathematics through embodied interactions. Computer 46, 10 (2013), 101–104.

Digital Library

[46]

David H Jonassen. 2000. Toward a design theory of problem solving. Educational technology research and development 48, 4(2000), 63–85.

[47]

E Joseph. 2005. Engagement tracing: using response times to model student disengagement. Artificial intelligence in education: Supporting learning through intelligent and socially informed technology 125 (2005), 88.

[48]

Panagiotis Kosmas, Andri Ioannou, and Symeon Retalis. 2018. Moving bodies to moving minds: a study of the use of motion-based games in special education. TechTrends 62, 6 (2018), 594–601.

[49]

J Richard Landis and Gary G Koch. 1977. The measurement of observer agreement for categorical data. biometrics (1977), 159–174.

[50]

Charlotte Larmuseau, Jan Cornelis, Luigi Lancieri, Piet Desmet, and Fien Depaepe. 2020. Multimodal learning analytics to investigate cognitive load during online problem solving. British Journal of Educational Technology 51, 5 (2020), 1548–1562.

[51]

Serena Lee-Cultura, Kshitij Sharma, Sofia Papavlasopoulou, and Michail Giannakos. 2020. Motion-Based Educational Games: Using Multi-Modal Data to Predict Player’s Performance. In 2020 IEEE Conference on Games (CoG). IEEE, 17–24.

[52]

Serena Lee-Cultura, Kshitij Sharma, Sofia Papavlasopoulou, Symeon Retalis, and Michail Giannakos. 2020. Using sensing technologies to explain children’s self-representation in motion-based educational games. In Proceedings of the Interaction Design and Children Conference. 541–555.

Digital Library

[53]

Dominik Leiner, Andreas Fahr, and Hannah Früh. 2012. EDA positive change: A simple algorithm for electrodermal activity to measure general audience arousal during media exposure. Communication Methods and Measures 6, 4 (2012), 237–250.

[54]

Christian S Loh. 2012. Information trails: In-process assessment of game-based learning. In Assessment in game-based learning. Springer, 123–144.

[55]

Jing Lu, Chun Wang, Jiwei Zhang, and Jian Tao. 2020. A mixture model for responses and response times with a higher-order ability structure to detect rapid guessing behaviour. Brit. J. Math. Statist. Psych. 73, 2 (2020), 261–288.

[56]

Panos Markopoulos, Janet Read, and Michail Giannakos. 2020. (in press) Design of Digital Technologies for Children. Handbook of Human Factors and Ergonomics 5th ed (2020).

[57]

Richard E Mayer and Merlin C Wittrock. 2006. Problem solving. Handbook of educational psychology 2 (2006), 287–303.

[58]

Philipp Mayring. 2000. Qualitative Content Analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], http://qualitative-research.net/fqs/fqs-e/2-00inhalt-e.htm 1 (06 2000).

[59]

Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino Audia, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K Dey, 2019. Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 37.

Digital Library

[60]

Ranjita Misra, Michelle McKean, Sarah West, and Tony Russo. 2000. Academic stress of college students: Comparison of student and faculty perceptions.College Student Journal 34, 2 (2000).

[61]

Behnaz Nojavanasghari, Charles E Hughes, and Louis-Philippe Morency. 2017. Exceptionally social: Design of an avatar-mediated interactive system for promoting social skills in children with autism. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1932–1939.

Digital Library

[62]

Omid Noroozi, Iman Alikhani, Sanna Järvelä, Paul A Kirschner, Ilkka Juuso, and Tapio Seppänen. 2019. Multimodal data to design visual learning analytics for understanding regulation of learning. Computers in Human Behavior 100 (2019), 298–304.

Digital Library

[63]

Netta Ofer, Hadas Erel, Idan David, Tom Hitron, and Oren Zuckerman. 2018. A little bit of coding goes a long way: effects of coding on outdoor play. In Proceedings of the 17th ACM Conference on Interaction Design and Children. 599–604.

Digital Library

[64]

Ayumi Ohnishi, Kaoru Saito, Tsutomu Terada, and Masahiko Tsukamoto. 2017. Toward Interest Estimation from Head Motion Using Wearable Sensors: A Case Study in Story Time for Children. In International Conference on Human-Computer Interaction. Springer, 353–363.

[65]

A Olsen. 2012. The tobii i-vt fixation filter: Algorithm description [white paper]. Retrieved from Tobii Technology from http://www. tobiipro. com/siteassets/tobiipro/learn-and-support/analyze/how-do-we-classify-eyemovements/tobii-pro-i-vtfixation-filter. pdf (2012).

[66]

Temitayo Olugbade, Joseph Newbold, Rose Johnson, Erica Volta, Paolo Alborno, Radoslaw Niewiadomski, Max Dillon, Gualtiero Volpe, and Nadia Bianchi-Berthouze. 2020. Automatic Detection of Reflective Thinking in Mathematical Problem Solving based on Unconstrained Bodily Exploration. IEEE Transactions on Affective Computing(2020).

[67]

Héctor Manuel Ocampo Orona, Guillermo Silva Maldonado, and Norma Patricia Salinas Martínez. 2015. Kinect team: Kinesthetic learning applied to mathematics using kinect. Procedia Computer Science 75 (2015), 169–172.

[68]

Fred GWC Paas and Jeroen JG Van Merriënboer. 1994. Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach.Journal of educational psychology 86, 1 (1994), 122.

[69]

Zacharoula Papamitsiou and Anastasios A Economides. 2016. Process mining of interactions during computer-based testing for detecting and modelling guessing behavior. In International Conference on Learning and Collaboration Technologies. Springer, 437–449.

Digital Library

[70]

Zacharoula K Papamitsiou and Anastasios A Economides. 2013. Towards the alignment of computer-based assessment outcome with learning goals: the LAERS architecture. In 2013 IEEE Conference on e-Learning, e-Management and e-Services. IEEE, 13–17.

[71]

Sofia Papavlasopoulou, Kshitij Sharma, Michail Giannakos, and Letizia Jaccheri. 2017. Using eye-tracking to unveil differences between kids and teens in coding activities. In Proceedings of the 2017 Conference on Interaction Design and Children. ACM, 171–181.

Digital Library

[72]

Héctor J Pijeira-Díaz, Hendrik Drachsler, Paul A Kirschner, and Sanna Järvelä. 2018. Profiling sympathetic arousal in a physics course: How active are students?Journal of Computer Assisted Learning 34, 4 (2018), 397–408.

[73]

Ingrid Pramling Samuelsson and Eva Johansson. 2006. Play and learning—inseparable dimensions in preschool practice. Early child development and care 176, 1 (2006), 47–65.

[74]

Mary Reilly. 1974. Play as exploratory learning: Studies of curiosity behavior. Sage Publications, Inc.

[75]

Lloyd P Rieber. 1996. Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games. Educational technology research and development 44, 2(1996), 43–58.

[76]

JP Royston. 1982. Algorithm AS 181: the W test for normality. Applied Statistics (1982), 176–180.

[77]

Calvin Rubens, Sean Braley, Julie Torpegaard, Nicklas Lind, Roel Vertegaal, and Timothy Merritt. 2020. Flying LEGO Bricks: Observations of Children Constructing and Playing with Programmable Matter. In Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction. 193–205.

Digital Library

[78]

Kshitij Sharma and Michail Giannakos. 2021. (forthcoming) Sensing Technologies and Child-Computer Interaction: Opportunities, Challenges and Ethical Considerations. International Journal of Child Computer Interaction (2021).

[79]

Kshitij Sharma, Zacharoula Papamitsiou, and Michail Giannakos. 2019. Building pipelines for educational data using AI and multimodal analytics: A “grey-box” approach. British Journal of Educational Technology(2019).

[80]

Kshitij Sharma, Zacharoula Papamitsiou, and Michail N Giannakos. 2019. Modelling Learners’ Behaviour: A Novel Approach Using GARCH with Multimodal Data. In European Conference on Technology Enhanced Learning. Springer, 450–465.

[81]

Hyunjin Shin, Bugeun Kim, and Gahgene Gweon. 2020. Guessing or Solving? Exploring the Use of Motion Features from Educational Game Logs. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1–8.

Digital Library

[82]

Miguel Sicart. 2014. Play matters. mit Press.

[83]

Carmen Petrick Smith, Barbara King, and Jennifer Hoyte. 2014. Learning angles through movement: Critical actions for developing understanding in an embodied activity. The Journal of Mathematical Behavior 36 (2014), 95–108.

[84]

James Soland, Nate Jensen, Tran D Keys, Sharon Z Bi, and Emily Wolk. 2019. Are test and academic disengagement related? Implications for measurement and practice. Educational Assessment 24, 2 (2019), 119–134.

[85]

Iris Soute, Saskia Bakker, Remco Magielse, and Panos Markopoulos. 2013. Evaluating player experience for children’s outdoor pervasive games. Entertainment Computing 4, 1 (2013), 25–38.

[86]

Priyashri K Sridhar, Samantha WT Chan, and Suranga Nanayakkara. 2018. Going beyond performance scores: understanding cognitive-affective states in kindergarteners. In Proceedings of the 17th ACM Conference on Interaction Design and Children. ACM, 253–265.

Digital Library

[87]

Marianne B Staempfli. 2007. Adolescent playfulness, stress perception, coping and well being. Journal of Leisure Research 39, 3 (2007), 393–412.

[88]

John Sweller. 2011. Cognitive load theory. In Psychology of learning and motivation. Vol. 55. Elsevier, 37–76.

[89]

Sietske Tacoma, Paul Drijvers, and Johan Jeuring. 2020. Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education. Journal of Computer Assisted Learning(2020).

[90]

Joachim Taelman, Steven Vandeput, Arthur Spaepen, and Sabine Van Huffel. 2009. Influence of mental stress on heart rate and heart rate variability. In 4th European conference of the international federation for medical and biological engineering. Springer, 1366–1369.

[91]

Varun Thakkar, Adeet Shah, Mohini Thakkar, Abhijit Joshi, and Neha Mendjoge. 2012. Learning math using gesture. In International Conference on Education and e-Learning Innovations. IEEE, 1–3.

[92]

Katherine R Thorson, Tessa V West, and Wendy Berry Mendes. 2018. Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies.Psychological methods 23, 4 (2018), 595.

[93]

Chih-Hsiao Tsai, Yin-Hao Kuo, Kuo-Chung Chu, and Jung-Chuan Yen. 2015. Development and evaluation of game-based learning system using the Microsoft Kinect sensor. International Journal of Distributed Sensor Networks 11, 7 (2015), 498560.

Digital Library

[94]

Damyanka Tsvyatkova and Cristiano Storni. 2019. A review of selected methods, techniques and tools in Child–Computer Interaction (CCI) developed/adapted to support children’s involvement in technology development. International Journal of Child-Computer Interaction 22 (2019), 100148.

Digital Library

[95]

Lev S Vygotsky. 1978. Mind in society: The development of higher mental processes (E. Rice, Ed. & Trans.).

[96]

Justin W Weeks and Ashley N Howell. 2012. The bivalent fear of evaluation model of social anxiety: Further integrating findings on fears of positive and negative evaluation. Cognitive Behaviour Therapy 41, 2 (2012), 83–95.

[97]

DeWayne P Williams, Claudia Cash, Cameron Rankin, Anthony Bernardi, Julian Koenig, and Julian F Thayer. 2015. Resting heart rate variability predicts self-reported difficulties in emotion regulation: a focus on different facets of emotion regulation. Frontiers in psychology 6 (2015), 261.

[98]

Cara Wilson, Margot Brereton, Bernd Ploderer, and Laurianne Sitbon. 2018. MyWord: Enhancing engagement, interaction and self-expression with minimally-verbal children on the autism spectrum through a personal audio-visual dictionary. In Proceedings of the 17th ACM Conference on Interaction Design and Children. 106–118.

Digital Library

[99]

Margaret Wilson. 2002. Psychonomic bulletin & review. Six views of embodied cognition 9 (2002), 625–636.

[100]

Steven L Wise and Xiaojing Kong. 2005. Response time effort: A new measure of examinee motivation in computer-based tests. Applied Measurement in Education 18, 2 (2005), 163–183.

[101]

Marcelo Worsley and Paulo Blikstein. 2015. Leveraging multimodal learning analytics to differentiate student learning strategies. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. ACM, 360–367.

Digital Library

[102]

Marcelo Worsley and Paulo Blikstein. 2018. A multimodal analysis of making. International Journal of Artificial Intelligence in Education 28, 3(2018), 385–419.

[103]

Chi Yang, Chun-Hui Jen, Chun-Yen Chang, and Ting-Kuang Yeh. 2018. Comparison of animation and static-picture based instruction: Effects on performance and cognitive load for learning genetics. Journal of Educational Technology & Society 21, 4 (2018), 1–11.

[104]

Kelly Yap, Clement Zheng, Angela Tay, Ching-Chiuan Yen, and Ellen Yi-Luen Do. 2015. Word out!: learning the alphabet through full body interactions. In Proceedings of the 6th Augmented Human International Conference. ACM, 101–108.

Digital Library

[105]

Serdar Yildirim and Shrikanth Narayanan. 2009. Recognizing child’s emotional state in problem-solving child-machine interactions. In Proceedings of the 2nd Workshop on Child, Computer and Interaction. ACM, 14.

Digital Library

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IDC '21: Proceedings of the 20th Annual ACM Interaction Design and Children Conference

June 2021

697 pages

ISBN:9781450384520

DOI:10.1145/3459990

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MacKenzie JKlarkowski MHorton ETheobald MDanby SKervin LBarrie LAmery PAndradi MSmith SMandryk RJohnson D(2024)Using Psychophysiological Data to Facilitate Reflective Conversations with Children about their Player ExperiencesProceedings of the ACM on Human-Computer Interaction10.1145/36771128:CHI PLAY(1-33)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1145/3677112
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https://dl.acm.org/doi/10.1145/3628516.3655805
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Lee-Cultura SSharma KGiannakos M(2024)Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case StudyIEEE Transactions on Learning Technologies10.1109/TLT.2023.327684817(181-201)Online publication date: 1-Jan-2024
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Gadad JAsundi MM VBaligar P(2024)Understanding Student Behavior and Learning Patterns through Video Analysis: A Comprehensive Literature Review2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)10.1109/ICETCI62771.2024.10704168(28-33)Online publication date: 22-Aug-2024
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Zhang FPapavlasopoulou SMotland JGiannakos M(2024)A Review of Empirical Studies on Gamification in K-12 Environmental Education: Is This Chocolate-Covered Broccoli?2024 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON60312.2024.10578636(01-10)Online publication date: 8-May-2024
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Castañeda ARios ODíaz E(2024)Recommendations for the Development of Augmented Reality Video Games for Children with ADHDInformation Management and Big Data10.1007/978-3-031-63616-5_24(316-330)Online publication date: 29-Jun-2024
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Brown MWiedbusch MPatel MNaderi ECapello SLlinas AAzevedo RMargondai A(2024)Designing for Self-Regulated Learning: A Dual-View Intelligent Visualization Dashboard to Support Instructors and Students Using Multimodal Trace Data in ClassroomsHCI International 2024 Posters10.1007/978-3-031-61953-3_2(9-19)Online publication date: 1-Jun-2024
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