Welcome to Francis Academic Press

Frontiers in Educational Research, 2021, 4(10); doi: 10.25236/FER.2021.041018.

An exploration of graphical programming learning models based on behavioral sequence analysis

Author(s)

Huang Ruiyan

Corresponding Author:
Huang Ruiyan
Affiliation(s)

Faculty of Education, Beijing Normal University, Beijing, China

Abstract

Compared with learner engagement, the sequence of learning behaviors during the learning process is a better reflection of the trajectory, willingness and cognitive process of learners' learning behaviors. This study empirically explores learners' behavioral sequences and learning patterns through lagged sequence analysis based on log data on the Snap! graphical programming platform. It was found that learners' learning behaviors exhibited the following characteristics: they showed a clear performance orientation; when encountering difficulties, they often took measures of trial and error, consulting information and reviewing previous learning tasks; and when interacting with classmates or encountering difficulties, their attention was more easily distracted. The findings demonstrate that the sequence of learners' learning behaviors can present teachers with a more comprehensive picture of online learning, help them discover learners' learning habits, preferences, and cognitive processes, and assist them in reflecting on the teaching and learning process.

Keywords

graphical programming, learning process, behavioral sequences, lagged sequence analysis

Cite This Paper

Huang Ruiyan. An exploration of graphical programming learning models based on behavioral sequence analysis. Frontiers in Educational Research (2021) Vol. 4, Issue 10: 90-95. https://doi.org/10.25236/FER.2021.041018.

References

[1] Li Shuang,Zhong Yao,Yu Chen,Cheng Gang,Wei Shunping(2017) Exploration of online learning engagement model based on behavioral sequence analysis. Chinese e-learning, 3, 88-95.

[2] Ma C,Wang I-Chi,Yue Yun-Zhu,Du Hong-Yu(2019) Research on online learning behavior model under the threshold of learning analytics. Modern distance education, 6, 35-44.

[3] Peng, Wenhui, Yang, Zongkai, Huang, Kebin (2006) Analysis and modeling of online learning behavior. China e-learning, 10, 31-35.

[4] Yang, J. L., Hong, W. L., Zhang, Y. X. (2008) Research and practice of real-time monitoring of online learning behavior. Open Education Research, 4, 87-92.

[5] Yang Xianmin,Wang Huaibo,Li Jihong(2016) The application of lagged sequence analysis method in learning behavior analysis. China Electrochemical Education, 2, 17-23+32.

[6] Yao, Chunzhen, Mi, Jianrong, Wang, Hongcheng (2009) An overview of learning behavior research at home and abroad. Teaching and Management, 3, 48-50.

[7] Yu, Xiaohua, Gu, Xiaoqing (2013) Learning activity flow:A behavioral model for learning analysis. Journal of Distance Education, 4, 20-28.

[8] Hou, H.-T., Sung, Y.-T., & Chang, K.-E. (2009) Exploring the Behavioral Patterns of an Online Knowledge-Sharing Discussion Activity Among Teachers With Problem-Solving Strategy. Teaching and Teacher Education, 25, 101-108.

[9] KafaiB.Y., LeeE., & SearleK. (2014) A Crafts-oriented Approach to Computing in High School: introducing Computational Concepts, Practices, and Perspectives with Electronic Textiles. ACM Transactions on Computing Education, 14, 1-20.

[10] SackettP.(Ed.).G. (1978) Observing Behavior: Theory and applications in mental retardation (Vol. 1). Baltimore: University Park Press.

[11] YangX., LiJ., GuoX., & LiX.(2015) Group Interactive Network and Behavioral Patterns in Online English-To-Chinese Cooperative Translation Activity. The Internet and Higher Education, 25, 28-36.