What does Data Visualization Reveal about Children’s Activities in RoboTutor?

-- Based on Descriptive Data Analysis

Authors

  • Lei Xu

Keywords:

Children’s Activities; Robotutor; Descriptive Data Analysis; Data Visualization.

Abstract

We are in the era of promising mobile education. However, data analysis based on children's activities is still limited. This article used rural Tanzanian children’s activity log data in site 131 from November 2018 to January 2019 provided by the RoboTutor team and applied the method of Descriptive Data Analysis to roughly analyze children’s activity through data visualization. First, children may be more interested in story and literacy than math activities; second, children generally spend more time on tasks they cannot fully complete; finally, all children spend more than 60% of their time completing activities, usually in the morning or afternoon. Using this result can provide a more intuitive understanding of children’s activity patterns, which can be helpful for researchers to measure children's activity engagement.

References

Lenarz, J., & Pelatt, K. (2020). A Transition to Mastery-Based Testing with the Hope of Increasing Student Persistence. PRIMUS, 1-10.

Uchidiuno, J. O. (2020). Facilitating Help-Seeking for K-2 Students in Rural Schools in Tanzania (Doctoral dissertation, Cornell University).

Zhao, R. (2021). RoboTutor: Predictions of Students’ Answer Type. In: Cao, W., Ozcan, A., Xie, H., Guan, B. (eds) Computing and Data Science. CONF-CDS 2021. Communications in Computer and Information Science, vol 1513. Springer, Singapore. https://doi.org/10.1007/978-981-16-8885-0_18.

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Published

07-11-2022

How to Cite

Xu, L. (2022). What does Data Visualization Reveal about Children’s Activities in RoboTutor? -- Based on Descriptive Data Analysis. BCP Education & Psychology, 7, 366–371. Retrieved from http://bcpublication.org/index.php/EP/article/view/2689