Author: Ziyang Chen

Since my last post about our study, the Cognitive Development and Touchscreen Interaction project has gone through several rounds of study recruiting and running. We have recruited our participants from many interested local families with children aging between 4 to 7 years old. In the meantime, I have been working on some high-level analysis of the data that we have collected.

In my last post, I mentioned that we often receive questions regarding the relationship between children’s cognitive development and their touchscreen interaction. Therefore, as a way to unfold and discover if such a relationship exists, we decided to calibrate some tasks through NIH Toolbox to provide another assessment metric beyond age. We employed two tasks: (1) the Dimensional Change Card Sort to evaluate the cognitive workload of our participants when completing repetitive tasks, and (2) the 9-Hole Pegboard Test to assess our participants’ fine motor skills. Completion accuracy and time are taken into consideration and four different types of scores are returned based on the participant’s demographic information. These scores are called the raw score, uncorrected score, age-corrected score, and fully corrected score. Raw score only looks at participant’s performance in terms of the completion time or the accuracy, the uncorrected score converts raw score into a comparable, normally distributed number score. The age and fully corrected score evaluate participant’s performance considering the factor of age, and all the basic demographic information respectively (i.e., education level, ethnicity, race, etc). We will link participants’ cognitive development scores back to their touchscreen interactions once we are able to come to a conclusion with more confidence.

To study the participants’ touchscreen interactions, we used apps like the ones in our lab’s published papers [1,2] to measure the participant’s gesture and target interactions. We also wanted to calculate some of the simple features mentioned in our lab’s previous publication [3]. Simple features of a gesture include the number of strokes of the gesture, the total path length of the gesture, the line similarity, total angle, sharpness of the gesture, and other geometric and temporal measurements. Employing these features and looking at how each gesture is structured may help us understand how the participant’s cognitive development level links to their gesture behaviors.

It has been a little over a year since I joined the lab and started working on this project. At first, I went through some background studies and ramped up at the beginning stage of my research. Now, I am more comfortable and confident in performing user studies than before and can better guide myself through challenges. At this point, we are continuing to receive interest from faculty with children at the University of Florida to participate in our study. Once we have reached our target number of participants, we will perform a more detailed data analysis. Our team is excited to see the outcome of this study.

Reference

[1] Julia Woodward, Alex Shaw, Annie Luc, Brittany Craig, Juthika Das, Phillip Hall, Akshay Hollay, Germaine Irwin, Danielle Sikich, Quincy Brown, and Lisa Anthony. 2016. Characterizing How Interface Complexity Affects Children’s Touchscreen Interactions. Proceedings of the ACM International Conference on Human Factors in Computing Systems (CHI ’16), ACM Press, 1921–1933.

[2] Lisa Anthony, Quincy Brown, Jaye Nias, Berthel Tate, and Shreya Mohan. 2012. Interaction and Recognition Challenges in Interpreting Children’s Touch and Gesture Input on Mobile Devices. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ACM Press, 225–234. http://doi.org/10.1145/2396636.2396671.

[3] Alex Shaw and Lisa Anthony. 2016. Analyzing the articulation features of children’s touchscreen gestures. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI ’16). Association for Computing Machinery, New York, NY, USA, 333–340. DOI:https://doi.org/10.1145/2993148.2993179.

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Over the course of the last few weeks, I had my first experience of running a user study with younger children, particularly children age 6 to 7 from PK Yonge Blue Wave After School program. My PhD mentor, Alex Shaw, and I went through a week of recruitment and a week of study running at the PK Yonge facility. To me, the recruiting process was quite interesting. At first, I wasn’t exactly sure how to approach potential participants’ parents and give a concise introduction about our study. Knowing that I had to spark parents’ interest in our study without being overly aggressive, I observed how my mentor, Alex, carried out the recruiting process and adapted his techniques. I felt that highlighting the potential benefits, emphasizing the low-risk nature of the research study, and pointing out the timeliness of our study are the three factors that most effectively encouraged parents to allow their children to participate. Another thing I learned is that recruiting is a lengthy process. I was little disappointed at first to only receive few responses back, but being patient and keeping a positive attitude during recruitment eventually gave parents enough time to return the consent forms.

On the other hand, running the study was quite challenging for me at first. Prior to the actual user study, Alex and I ran two pilot studies with other members of the INIT lab, but the real deal was little different. I was nervous during the first study, mumbled my words through and made a mistake on the data entrance: I stopped the timer before the participant finished and submitted the wrong time. Luckily, we have the study recorded and I was able to go through the timestamps and save the data manually. I became much more comfortable and the study process was much smoother in the later studies.

A few things I learned from this study include: since we are running the study with younger children, it is important to clearly present the instructions and make sure the participants fully understand them. I realized that during the fine motor skills assessment from the NIH Toolbox®, the participants tended to use both hands or the wrong hand while the instructions said not to. So, I made sure to emphasize those parts of the instruction to keep the data as accurate as possible. Also, I learned that younger children get bored easily: since the study lasts around 30 minutes and involves many repetitive tasks, there are certainly times when fatigue comes into play and children want to quit the study. In order to avoid those situations as much as possible, I found that spending a few minutes when taking the children to our study room asking how their day is going, asking them a few questions to keep them engaged, and showing them the small prizes we will give out can get them more engaged and keep them excited to participate. Encouraging breaks in between study tasks and keeping a friendly atmosphere during the study also helps.

Overall, I felt the recruiting and the study running process was challenging at first, but it became much easier after the first few times and I actually enjoyed the process. Looking at the data we’ve collected also gives me a sense of accomplishment. Our next step is to analyze the data to answer our study’s research questions, which we will be able to talk about soon. We are also planning to conduct study sessions with younger children at the Baby Gator daycare facility. I’m excited and looking forward for the process.

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Since I joined the INIT lab, I have been working on preparing a study related to the Understanding Gestures project. The goal of the project is to examine the relationship between previous findings about children’s touch and gesture interactions and their cognitive development. Our lab’s previous work has shown that children’s gestures are not recognized as accurately as adults’ gestures and that there are significant differences in articulation features related to gesture production time and geometry. We have received inquiries from readers of our prior publications regarding the cognitive development of the children we collected data from, which led us to pursue this project on understanding how children’s cognitive development is related to the way they interact with touchscreen devices. We believe having this new information will help us gain a more comprehensive understanding of children’s touchscreen interactions.

Cognitive development is a field of study in neuroscience and psychology focusing on a children’s development in terms of information processing, problem solving, and decision making [1]. In our Understanding Gestures project, we are mainly concerned with children’s fine motor skills and children’s executive function, both of which exhibit variance across early ages of childhood and between genders. Fine motor skill measures the coordination of small muscles such as those in the finger and hand [2]. Executive function measures the ability to focus attention and execute tasks [3]. We plan on measuring these two aspects using NIH Toolbox®, a “comprehensive set of neuro-behavioral measurements that quickly assesses cognitive, emotional, sensory, and motor functions” [4]. The creators of the app, the National Institutes of Health, maintain a representative database for comparing children’s performance on the tasks based on their demographic information (e.g., age, gender, etc.). We are excited to be collaborating on this project with Dr. Pavlo Antonenko from the College of Education. We are looking forward to drawing connections between children’s touchscreen interactions and their cognitive development from this study.

I am a third-year undergraduate student majoring in Computer Science, and this is my first full semester in the INIT Lab. The process of preparing a study has been challenging but very interesting. I have always wanted to learn how to run a study and been curious about the work that goes into a research paper. As we prepare for the study, I have performed in-depth independent research on potential topics of exploration regarding children’s cognitive development. I have gained a great sense of accomplishment by playing a role in building the study from scratch, and I am looking forward to continuing my work on the study.

 

REFERENCES

1. Ali, Ajmol & Pigou,Schacter, Daniel L (2009). PSYCHOLOGY. Catherine Woods. p. 429. ISBN 978-1-4292-3719-2.

2. Deborah & Clarke, Linda & Mclachlan, Claire. (2017). Review on Motor Skill and Physical Activity in Preschool Children in New Zealand. Advances in Physical Education. 7. 10-26. 10.4236/ape.2017.71002.

3. Team, Understood. “Understanding Executive Functioning Issues.” Understood.org, www.understood.org/en/learning-attention-issues/child-learning-disabilities/executive-functioning- issues/understanding-executive-functioning-issues.

4. Weintraub, Sandra et al. “Cognition assessment using the NIH Toolbox.” Neurology vol. 80,11 Suppl 3 (2013): S54-64. doi:10.1212/WNL.0b013e3182872ded

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