How Cognitive Styles Affect the Learning Behaviors of Online Problem-Solving Based Discussion Activity

Abstract

Cognitive styles play an important role in influencing the learning process, but to date no relevant study has been conducted using lag sequential analysis to assess knowledge construction learning patterns based on different cognitive styles in computersupported collaborative learning activities in online collaborative discussions. This study applies Pask's cognitive styles (holist-serialist dimension) as the grouping basis for students who performed an online problem-solving based discussion activity to analyze how cognitive styles affect learning behaviors. By using quantitative content analysis and lag sequential analysis, the study attempts to identify the distribution structure of the knowledge construction activities demonstrated by students with different cognitive styles, as well as the sequential pattern of those behaviors. The results suggest that, with respect to the quantitative content analysis, students who are holists demonstrate sharing, comparing, and discovery tendencies while neglecting the negotiation and coconstruction of knowledge. As a result of their preference for sequential discussion behavior patterns, students who are serialists may find they have limited time for subsequent discussions because they often tend to continue the discussion of previous, unresolved topics. With respect to the pattern of knowledge

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners’ cognitive presence and learning achievements

“…In addition, exploring sequential patterns of cognition-related behaviors is critical to uncover the temporal transition of learner cognitive presence, and lag sequential analysis (LSA) is usually used to uncover sequential relationships between each two adjacent learning behaviors in chronological order based on a statistical model (Bakeman & Gottman, 1997). To explore cognitive behavioral sequences in the learning process, Wu and Hou (2015) used LSA to explore the sequential discussion behavior patterns of knowledge construction behaviors and their difference among learners with different cognitive styles. Yang et al, (2015) proposed a two-tier test strategy and explored its effects on learners' behavior patterns.…”

“…GSEQ as a flexible application can process the encoded behavior data, and eventually generated adjust residuals table of cognitive presence with different discussion pacing groups (shown in Table 3). In Table 3, the columns and rows represent the starting behavior and following behaviors respectively (Wu & Hou, 2015). Furthermore, the sequential transition diagrams of the cognitive presence were drawn to better display and compare the differences between sequential patterns of different pacings of discussion groups (shown in Fig.…”

The Effects of Mothers’ Directive Utterances and Language-Modelling Utterances on the Verbal Behavior of Children with Autism Spectrum Disorder

“…The sequence of behavioral codes is then statistically analyzed to determine how often behavioral shifts occur and to calculate whether these shifts are significant and occur with a higher than expected probability. Currently, LSA is mainly applied to educational research, such as exploring the action paths of online learners (Hou, 2012), knowledge construction patterns in online forums (Wu & Hou, 2015), or criminal psychology (Ellis, Clarke, & Keatley, 2017;Longridge et al, 2020) and so on. The integration of LSA with language research is still rare.…”

Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis

“…For example, S.-Y. Wu and Hou (2015) used LSA to explore how cognitive styles affect online discussion behaviours during the problem-solving process. Lamsa et al (2020) combined log data and LSA to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes.…”