ABSTRACT
As the field of child-computer interaction (CCI) develops and forms an increasingly distinct identity, there is a need for reflection upon the state of the field, and its development thus far. This paper provides an overview of the thematic structure of the CCI field in order to support such reflection, expanding upon previous reviews through implementation of a correlated topic model, an automated, inductive content analysis method, in analysing 4,771 CCI research papers published between 2003 and 2021. Prominence of research topics, and their evolution, are explored. Results portray CCI as a vibrant and varied research landscape which has evolved dynamically over time, exhibiting increasing specialisation and emergence of distinct subfields, and progressing from a technology- to needs-driven agenda. This analysis contributes an extensive empirical mapping of the CCI research landscape, facilitating reflection upon the field and its development, and revealing gaps in extant literature and opportunities for future research.
- Alissa N. Antle and Juan Pablo Hourcade. 2021. Research in Child–Computer Interaction: Provocations and envisioning future directions. International Journal of Child-Computer Interaction (August 2021), 100374. DOI:https://doi.org/10.1016/j.ijcci.2021.100374Google ScholarDigital Library
- Claus Boye Asmussen and Charles Møller. 2019. Smart literature review: a practical topic modelling approach to exploratory literature review. Journal of Big Data 6, 1 (October 2019), 93. DOI:https://doi.org/10.1186/s40537-019-0255-7Google ScholarCross Ref
- Wolmet Barendregt, Olof Torgersson, Eva Eriksson, and Peter Börjesson. 2017. Intermediate-Level Knowledge in Child-Computer Interaction: A Call for Action. In Proceedings of the 2017 Conference on Interaction Design and Children (IDC ’17), Association for Computing Machinery, New York, NY, USA, 7–16. DOI:https://doi.org/10.1145/3078072.3079719Google ScholarDigital Library
- Mathilde Bekker and Panos Markopoulos. 2003. Interaction design and children: (IDC 2002). SIGCHI Bull.: suppl. interactions 2003, (January 2003), 6. DOI:https://doi.org/10.1145/601798.601806Google ScholarDigital Library
- David M. Blei and John D. Lafferty. 2007. A correlated topic model of Science. Ann. Appl. Stat. 1, 1 (June 2007), 17–35. DOI:https://doi.org/10.1214/07-AOAS114Google ScholarCross Ref
- David M. Blei and John D. Lafferty. 2009. Topic Models. In Text Mining: Classification, Clustering and Applications, Ashok N. Srivastava and Mehran Sahami (eds.). Chapman and Hall/CRC, New York, 71–94. DOI:https://doi.org/10.1201/9781420059458-12Google Scholar
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, null (March 2003), 993–1022.Google Scholar
- Ricardo J. G. B. Campello, Davoud Moulavi, Arthur Zimek, and Jörg Sander. 2015. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Trans. Knowl. Discov. Data 10, 1 (July 2015), 5:1-5:51. DOI:https://doi.org/10.1145/2733381Google ScholarDigital Library
- Juan Cao, Tian Xia, Jintao Li, Yongdong Zhang, and Sheng Tang. 2009. A density-based method for adaptive LDA model selection. Neurocomputing 72, 7 (March 2009), 1775–1781. DOI:https://doi.org/10.1016/j.neucom.2008.06.011Google ScholarDigital Library
- Xieling Chen, Di Zou, Gary Cheng, and Haoran Xie. 2020. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education 151, (July 2020), 103855. DOI:https://doi.org/10.1016/j.compedu.2020.103855Google Scholar
- Stijn Daenekindt and Jeroen Huisman. 2020. Mapping the scattered field of research on higher education. A correlated topic model of 17,000 articles, 1991–2018. High Educ 80, 3 (September 2020), 571–587. DOI:https://doi.org/10.1007/s10734-020-00500-xGoogle ScholarCross Ref
- Matthew J. Denny and Arthur Spirling. 2018. Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It. Political Analysis 26, 2 (April 2018), 168–189. DOI:https://doi.org/10.1017/pan.2017.44Google ScholarCross Ref
- Romain Deveaud, Eric SanJuan, and Patrice Bellot. 2014. Accurate and effective latent concept modeling for ad hoc information retrieval. Document numerique 17, 1 (June 2014), 61–84.Google Scholar
- Paul DiMaggio, Manish Nag, and David Blei. 2013. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding. Poetics 41, 6 (December 2013), 570–606. DOI:https://doi.org/10.1016/j.poetic.2013.08.004Google ScholarCross Ref
- Michail Giannakos, Zacharoula Papamitsiou, Panos Markopoulos, Janet Read, and Juan Pablo Hourcade. 2020. Mapping child–computer interaction research through co-word analysis. International Journal of Child-Computer Interaction 23–24, (June 2020), 100165. DOI:https://doi.org/10.1016/j.ijcci.2020.100165Google ScholarDigital Library
- Thomas L. Griffiths, Mark Steyvers, and Joshua B. Tenenbaum. 2007. Topics in semantic representation. Psychol Rev 114, 2 (April 2007), 211–244. DOI:https://doi.org/10.1037/0033-295X.114.2.211Google ScholarCross Ref
- Justin Grimmer and Brandon M. Stewart. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21, 3 (2013), 267–297. DOI:https://doi.org/10.1093/pan/mps028Google ScholarCross Ref
- Fatih Gurcan, Nergiz Ercil Cagiltay, and Kursat Cagiltay. 2021. Mapping Human–Computer Interaction Research Themes and Trends from Its Existence to Today: A Topic Modeling-Based Review of past 60 Years. International Journal of Human–Computer Interaction 37, 3 (February 2021), 267–280. DOI:https://doi.org/10.1080/10447318.2020.1819668Google Scholar
- Fatih Gurcan, Ozcan Ozyurt, and Nergiz Ercil Cagitay. 2021. Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation. The International Review of Research in Open and Distributed Learning 22, 2 (January 2021), 1–18. DOI:https://doi.org/10.19173/irrodl.v22i2.5358Google ScholarCross Ref
- Kristina Höök and Jonas Löwgren. 2012. Strong concepts: Intermediate-level knowledge in interaction design research. ACM Trans. Comput.-Hum. Interact. 19, 3 (October 2012), 23:1-23:18. DOI:https://doi.org/10.1145/2362364.2362371Google ScholarDigital Library
- Juan Pablo Hourcade. 2015. Child-Computer Interaction (First Edition ed.). CreateSpace Independent Publishing Platform. Retrieved from http://homepage.divms.uiowa.edu/∼hourcade/book/child-computer-interaction-first-edition.pdfGoogle Scholar
- Netta Iivari, Sumita Sharma, Leena Ventä-Olkkonen, Tonja Molin-Juustila, Kari Kuutti, Jenni Holappa, and Essi Kinnunen. 2021. Critical agenda driving child–computer interaction research—Taking a stock of the past and envisioning the future. International Journal of Child-Computer Interaction (September 2021), 100408. DOI:https://doi.org/10.1016/j.ijcci.2021.100408Google ScholarDigital Library
- Matthew Inglis and Colin Foster. 2018. Five Decades of Mathematics Education Research. JRME 49, 4 (July 2018), 462–500. DOI:https://doi.org/10.5951/jresematheduc.49.4.0462Google ScholarCross Ref
- Janne J. Jensen and Mikael B. Skov. 2005. A review of research methods in children's technology design. In Proceedings of the 2005 conference on Interaction design and children (IDC ’05), Association for Computing Machinery, New York, NY, USA, 80–87. DOI:https://doi.org/10.1145/1109540.1109551Google ScholarDigital Library
- Mehmed Kantardzic. 2019. Data Mining: Concepts, Models, Methods, and Algorithms (3rd ed.). Wiley-IEEE Press, New Jersey. Retrieved January 7, 2022 from https://www.wiley.com/en-sg/Data+Mining%3A+Concepts%2C+Models%2C+Methods%2C+and+Algorithms%2C+3rd+Edition-p-9781119516040Google ScholarCross Ref
- Saba Kawas, Ye Yuan, Akeiylah DeWitt, Qiao Jin, Susanne Kirchner, Abigail Bilger, Ethan Grantham, Julie A Kientz, Andrea Tartaro, and Svetlana Yarosh. 2020. Another decade of IDC research: examining and reflecting on values and ethics. In Proceedings of the Interaction Design and Children Conference (IDC ’20), Association for Computing Machinery, New York, NY, USA, 205–215. DOI:https://doi.org/10.1145/3392063.3394436Google ScholarDigital Library
- Maurice G Kendall. 1975. Rank Correlation Methods (4th Edition ed.). Charles Griffin, London, UK.Google Scholar
- Jessica Korte. 2020. Patterns and Themes in Designing with Children. HCI 13, 2 (March 2020), 70–164. DOI:https://doi.org/10.1561/1100000079Google ScholarDigital Library
- Effie Lai-Chong Law and Matthias Heintz. 2021. Augmented reality applications for K-12 education: A systematic review from the usability and user experience perspective. International Journal of Child-Computer Interaction 30, (December 2021), 100321. DOI:https://doi.org/10.1016/j.ijcci.2021.100321Google ScholarDigital Library
- Florence Kristin Lehnert, Jasmin Niess, Carine Lallemand, Panos Markopoulos, Antoine Fischbach, and Vincent Koenig. 2021. Child-Computer Interaction: From a systematic review towards an integrated understanding of interaction design methods for children. International Journal of Child-Computer Interaction (September 2021), 100398. DOI:https://doi.org/10.1016/j.ijcci.2021.100398Google ScholarDigital Library
- Shengbo Liu and Chaomei Chen. 2013. The differences between latent topics in abstracts and citation contexts of citing papers. Journal of the American Society for Information Science and Technology 64, 3 (March 2013), 627–639. DOI:https://doi.org/10.1002/asi.22771Google ScholarCross Ref
- Yong Liu, Jorge Goncalves, Denzil Ferreira, Bei Xiao, Simo Hosio, and Vassilis Kostakos. 2014. CHI 1994-2013: mapping two decades of intellectual progress through co-word analysis. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), Association for Computing Machinery, New York, NY, USA, 3553–3562. DOI:https://doi.org/10.1145/2556288.2556969Google ScholarDigital Library
- Henry B. Mann. 1945. Nonparametric Tests Against Trend. Econometrica 13, 3 (1945), 245–259. DOI:https://doi.org/10.2307/1907187Google ScholarCross Ref
- Panos Markopoulos, Janet Read, Johanna Hoÿsniemi, and Stuart MacFarlane. 2008. Child computer interaction: advances in methodological research. Cogn Tech Work 10, 2 (April 2008), 79–81. DOI:https://doi.org/10.1007/s10111-007-0065-0Google ScholarDigital Library
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of Machine Learning. MIT Press, Cambridge, MA, USA.Google ScholarDigital Library
- Leacky Muchene and Wende Safari. 2021. Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. PLOS ONE 16, 1 (January 2021), e0243208. DOI:https://doi.org/10.1371/journal.pone.0243208Google ScholarCross Ref
- Sebastian Munoz-Najar Galvez, Raphael Heiberger, and Daniel McFarland. 2020. Paradigm Wars Revisited: A Cartography of Graduate Research in the Field of Education (1980–2010). American Educational Research Journal 57, 2 (April 2020), 612–652. DOI:https://doi.org/10.3102/0002831219860511Google ScholarCross Ref
- John Muschelli. 2019. rscopus: Scopus Database “API” Interface. Comprehensive R Archive Network (CRAN). Retrieved January 16, 2022 from https://CRAN.R-project.org/package=rscopusGoogle Scholar
- M.F. Porter. 1980. An algorithm for suffix stripping. Program 14, 3 (1980), 130–137. DOI:https://doi.org/10.1108/00330330610681286Google ScholarCross Ref
- Kevin M. Quinn, Burt L. Monroe, Michael Colaresi, Michael H. Crespin, and Dragomir R. Radev. 2010. How to Analyze Political Attention with Minimal Assumptions and Costs. American Journal of Political Science 54, 1 (2010), 209–228. DOI:https://doi.org/10.1111/j.1540-5907.2009.00427.xGoogle ScholarCross Ref
- Janet C. Read and Mathilde M. Bekker. 2011. The nature of child computer interaction. In Proceedings of the 25th BCS Conference on Human-Computer Interaction (BCS-HCI ’11), BCS Learning & Development Ltd., Swindon, GBR, 163–170. DOI:https://doi.org/10.14236/ewic/HCI2011.43Google Scholar
- Janet C. Read and Juan Pablo Hourcade. 2013. Enhancing the research infrastructure for child-computer interaction. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’13), Association for Computing Machinery, New York, NY, USA, 2481–2484. DOI:https://doi.org/10.1145/2468356.2468810Google ScholarDigital Library
- Janet C. Read and Panos Markopoulos. 2013. Child–computer interaction. International Journal of Child-Computer Interaction 1, 1 (January 2013), 2–6. DOI:https://doi.org/10.1016/j.ijcci.2012.09.001Google ScholarCross Ref
- Margaret E. Roberts, Brandon M. Stewart, and Dustin Tingley. 2016. Navigating the Local Modes of Big Data: The Case of Topic Models. In Computational Social Science: Discovery and Prediction, R. Michael Alvarez (ed.). Cambridge University Press, Cambridge, 51–97. DOI:https://doi.org/10.1017/CBO9781316257340.004Google Scholar
- Margaret E Roberts, Brandon M Stewart, and Dustin Tingley. 2019. stm: R Package for Structural Topic Models. Journal of Statistical Software 91, 2 (2019), 1–40. DOI:https://doi.org/10.18637/jss.v091.i02Google ScholarCross Ref
- Deepak Sharma, B. Kumar, and S. Chand. 2019. A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test. International Journal of Intelligent Systems and Applications (2019). DOI:https://doi.org/10.5815/IJISA.2019.02.08Google Scholar
- Rachel C. Smith, Ole S. Iversen, Thomas Hjermitslev, and Aviaja B. Lynggaard. 2013. Towards an ecological inquiry in child-computer interaction. In Proceedings of the 12th International Conference on Interaction Design and Children (IDC ’13), Association for Computing Machinery, New York, NY, USA, 183–192. DOI:https://doi.org/10.1145/2485760.2485780Google ScholarDigital Library
- Shaheen Syed and Marco Spruit. 2017. Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Tokyo, Japan, 165–174. DOI:https://doi.org/10.1109/DSAA.2017.61Google Scholar
- 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, (December 2019), 100148. DOI:https://doi.org/10.1016/j.ijcci.2019.100148Google ScholarDigital Library
- Maarten Van Mechelen, Gökçe Elif Baykal, Christian Dindler, Eva Eriksson, and Ole Sejer Iversen. 2020. 18 Years of ethics in child-computer interaction research: a systematic literature review. In Proceedings of the Interaction Design and Children Conference (IDC ’20), Association for Computing Machinery, New York, NY, USA, 161–183. DOI:https://doi.org/10.1145/3392063.3394407Google ScholarDigital Library
- Maarten Van Mechelen, Line Have Musaeus, Ole Sejer Iversen, Christian Dindler, and Arthur Hjorth. 2021. A Systematic Review of Empowerment in Child-Computer Interaction Research. In Interaction Design and Children (IDC ’21), Association for Computing Machinery, New York, NY, USA, 119–130. DOI:https://doi.org/10.1145/3459990.3460701Google ScholarDigital Library
- Fan Wang, Wei Shao, Haijun Yu, Guangyuan Kan, Xiaoyan He, Dawei Zhang, Minglei Ren, and Gang Wang. 2020. Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series. Frontiers in Earth Science 8, 14 (2020). DOI:https://doi.org/10.3389/feart.2020.00014Google Scholar
- Svetlana Yarosh, Iulian Radu, Seth Hunter, and Eric Rosenbaum. 2011. Examining values: an analysis of nine years of IDC research. In Proceedings of the 10th International Conference on Interaction Design and Children (IDC ’11), Association for Computing Machinery, New York, NY, USA, 136–144. DOI:https://doi.org/10.1145/1999030.1999046Google ScholarDigital Library
Recommendations
Child–Computer Interaction: From a systematic review towards an integrated understanding of interaction design methods for children
AbstractChild–Computer Interaction (CCI) is a steadily growing field that focuses on children as a prominent and emergent user group. For more than twenty years, the Interaction Design for Children (IDC) community has developed, extended, and ...
Parent and child problematic media use: The role of maternal postpartum depression and dysfunctional parent-child interactions in young children
AbstractProblematic media use, or media use that interferes with daily functioning, is most often studied in adolescent or young adult age groups. Less research has examined problematic media use within the family system, among parents and ...
Highlights- Problematic media use is seen in children as young as 3–4 years old and in their parents.
Tangible interaction in parent-child collaboration: encouraging awareness and reflection
IDC '18: Proceedings of the 17th ACM Conference on Interaction Design and ChildrenParent-child interaction during a collaborative activity can empower children if parents are able to envision their child's mental state and regulate their behavior. However, this ability is a great challenge for many parents. We designed a simple ...
Comments