Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools

Zhang, Jingjing; Gao, Ming; Holmes, W.; Mavrikis, Manolis; Ma, Ning

doi:10.1080/10494820.2019.1620290

Cited by 13 publications

(8 citation statements)

References 57 publications

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“…However, revealing all the behaviors and analyzing them in detail is essential in designing the teaching environment and choosing the appropriate pedagogical approaches. In this research, a comprehensive investigation of event chains was made by conducting an in-depth examination of behavior patterns with lag sequential analysis (Zhang et al, 2019). In addition, preservice teachers’ driver–navigator behaviors were discussed for the first time (Cetin, 2016; Mentz et al, 2008; Vasconcelos & Kim, 2020; Zha et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Collaborative Behavior Patterns of Students in Programming Instruction

Hopcan

Polat

Albayrak

2022

Journal of Educational Computing Research

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The pair programming approach is used to overcome the difficulties of the programming process in education environments. In this study, the interaction sequences during the paired programming of preservice teachers was investigated. Lag sequential analysis were used to explore students’ behavioral patterns in pair programming. The participants of the study consist of 14 students, seven pairs enrolled in a Programming Languages course. The findings indicate that there are significant behavioral learning sequences. During the program development process, students hesitated to create an algorithm and to improve an existing one while proposing the next step. In addition, they constantly waited for approval. Collaborative behaviors such as giving and receiving feedback and helping other partners were less observed in females. In addition, significant sequential driver and navigator behaviors were presented. The findings of the study have important implications for instructors and designers when using a pair programming approach in teaching programming. In the future, programming instruction environments can be designed by considering the learner behaviors that are presented in this study.

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Section: Discussionmentioning

confidence: 99%

Collaborative Behavior Patterns of Students in Programming Instruction

Hopcan

Polat

Albayrak

2022

Journal of Educational Computing Research

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“…They found that global students demonstrated iterative behaviour, while sequential students showed sequential behaviour. Similarly, LSA was applied by Zhang et al (2021) to demonstrate how students interact with three‐level feedback and the various external representations in an exploratory mathematical learning environment. Recently, Tlili et al (2021) applied LSA to investigate how cultural diversity affects students' learning behavioural patterns in open and online courses.…”

Section: Literature Reviewmentioning

confidence: 99%

Grit and learning: Understanding the sequential learning behaviours in snap!

Gao

Zhang

et al. 2022

Computer Assisted Learning

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Background As a non‐cognitive trait, grit plays an important role in human learning. Although students higher in grit are more likely to perform well on tests, how they learn in the process has been underexamined. Objectives This study attempted to explore how students with different levels of grit behave and learn in an exploratory learning environment. Methods In this study, 66 students participated in seven exploratory tasks in Snap! for approximately 60 min after a 30‐min lecture. Students were categorized into a high grit group and low grit group using a grit scale. The Mann–Whitney U test, epistemic network analysis and lag sequential analysis were used to explore the differences between groups in learning performance, technology acceptance and behavioural patterns. Results Students with different levels of grit engaged in explorative tasks in a short period of time might not present significantly different learning performance, perceptions of usefulness and ease of use, but students higher in grit, actively engaging more different types of activities, tended to put greater sustained effort to solve the challenging task. Take Away Although grit was not significantly correlated with learning performance when students engage in classroom‐based explorative activities, grit did predict whether students are more likely to explore and put greater sustained effort into solving the challenging task.

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“…One approach is lag sequential analysis (LSA), which highlights significant sequential behaviours in relation to the temporal dimension Knight et al, 2017). LSA is commonly used for analyzing the learning trajectories of those participating in learning activities in guided or well-designed explorative and collaborative environments (e.g., Li et al, 2021;Sun et al, 2021;Zhang, Gao et al, 2019;Zheng et al, 2021). The identified patterns of sequential behaviours could help researchers and instructors provide personalized feedback (Hou, 2012;Hwang & Chen, 2017;Yin et al, 2017), promote knowledge construction (Lan et al, 2012;Lin et al, 2013;Yang et al, 2015), etc.…”

Section: Using Network Analytics To Understand Online Learning Behavi...mentioning

confidence: 99%

Video Features, Engagement, and Patterns of Collective Attention Allocation

2022

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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.

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Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools

Cited by 13 publications

References 57 publications

Collaborative Behavior Patterns of Students in Programming Instruction

Collaborative Behavior Patterns of Students in Programming Instruction

Grit and learning: Understanding the sequential learning behaviours in snap!

Video Features, Engagement, and Patterns of Collective Attention Allocation

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