Development Trajectory of Student Cognitive Behaviors in a SPOC Forum: An Integrated Approach Combining Epistemic Network Analysis and Lag Sequential Analysis

2021 ◽  
Author(s):  
Zhi Liu ◽  
Ning Zhang ◽  
Shiqi Liu ◽  
Sannyuya Liu
Keyword(s):  
Network Analysis ◽  
Sequential Analysis ◽  
Integrated Approach ◽  
Cognitive Behaviors ◽  
Lag Sequential Analysis ◽  
Development Trajectory

The Development Trajectory of Shared Epistemic Agency in Online Collaborative Learning: A Study Combing Network Analysis and Sequential Analysis

2021 ◽  
pp. 073563312110015
Author(s):  
Seng Chee Tan ◽  
Xinghua Wang ◽  
Lu Li
Keyword(s):  
Network Analysis ◽  
Collaborative Learning ◽  
Sequential Analysis ◽  
Collective Knowledge ◽  
Epistemic Agency ◽  
Online Collaborative Learning ◽  
Lag Sequential Analysis ◽  
Development Trajectory ◽  
Academic Scores ◽  
Insight Into

This study explored the development trajectory of shared epistemic agency in online collaborative learning through epistemic network analysis and lag sequential analysis. It was carried out in a postgraduate course with 14 in-service teachers. Drawing on the online discussion data from six sessions and the participants’ academic scores, this study found a nonlinear development trajectory of learners’ shared epistemic agency across the six sessions. The managerial dimension (e.g., regulative and relational actions) mediated the development of learners’ shared epistemic agency. The analysis of different groups’ mean networks and academic performance revealed a tentative relationship between them. Finally, the transition of shared epistemic agency actions in higher-achieving sessions and groups largely followed an upward sequential pattern. This study provides a graphical insight into how students learn in an online collaborative setting and can inform future pedagogical and technological designs of facilitating students’ shared epistemic agency for the creation of collective knowledge.

scholarly journals Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paola Paci ◽  
Giulia Fiscon ◽  
Federica Conte ◽  
Rui-Sheng Wang ◽  
Lorenzo Farina ◽  
...  
Keyword(s):  
Network Analysis ◽  
Interaction Network ◽  
Integrated Approach ◽  
State Transitions ◽  
Disease Genes ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Interactome Network ◽  
Module Discovery ◽  
Specific Disorders

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Competitive and Cooperative Decision-Making Groups

1980 ◽  
Vol 7 (4) ◽  
pp. 457-478 ◽  
Author(s):  
Dorothy Lenk Krueger
Keyword(s):  
Decision Making ◽  
Sequential Analysis ◽  
Group Interaction ◽  
Communication Patterns ◽  
Sample Group ◽  
Lag Sequential Analysis ◽  
Patterns Of Communication ◽  
Two Measures ◽  
Cooperative Decision Making

This study investigates differences among four decision-making groups and describes the patterns of communication unique to two groups. In the first part of the investigation, four decision-making groups are given either competitive or cooperative inducements and are compared on two measures: competition and satisfaction. The two groups given the competitive inducement (Groups I and III) were found to have significantly higher competition and lower satisfaction than the groups given cooperative inducements (Groups II and IV). In the second part of the study a lag sequential analysis is conducted on the coded communicative sequences in the highest and lowest competition groups (I and II, respectively). This analysis yields patterns to decision-making unique to each sample group. Group I's communication is characterized by highly probable (above-chance) sequences of disagreement messages and few probable agreement messages. Group II's communication patterns consist of highly probable sequences of decision development and probable agreement/support messages throughout the group interaction.

Collaborative Behavior Patterns of Students in Programming Instruction

2022 ◽  
pp. 073563312110622
Author(s):  
Sinan Hopcan ◽  
Elif Polat ◽  
Ebru Albayrak
Keyword(s):  
Preservice Teachers ◽  
Programming Languages ◽  
Sequential Analysis ◽  
Pair Programming ◽  
Programming Approach ◽  
Behavioral Learning ◽  
Programming Instruction ◽  
Collaborative Behavior ◽  
Lag Sequential Analysis ◽  
Programming Process

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.

scholarly journals Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel

2020 ◽  
Vol 17 (8) ◽  
pp. 2618 ◽  
Author(s):  
Yoonju Lee ◽  
Heejin Kim ◽  
Hyesun Jeong ◽  
Yunhwan Noh
Keyword(s):  
Network Analysis ◽  
Chronic Conditions ◽  
Integrated Approach ◽  
Classification Of Diseases ◽  
Standard Classification ◽  
And Gender ◽  
Gender Groups ◽  
Association Rules Analysis ◽  
Different Characteristics ◽  
Public Health Practitioners

This study aimed to identify the prevalence and patterns of multimorbidity among Korean adults. A descriptive study design was used. Of 11,232 adults aged 18 and older extracted from the 2014 Korean Health Panel Survey, 7118 had one or more chronic conditions. The chronic conditions code uses the Korean Standard Classification of Diseases. Association rule analysis and network analysis were conducted to identify patterns of multimorbidity among 4922 participants with multimorbidity. The prevalence of multimorbidity in the overall population was 34.8%, with a higher prevalence among women (40.8%) than men (28.6%). Hypertension had the highest prevalence in both men and women. In men, diabetes mellitus and hypertension yielded the highest probability of comorbidity (10.04%). In women, polyarthrosis and hypertension yielded the highest probability of comorbidity (12.51%). The results of the network analysis in four groups divided according to gender and age showed different characteristics for each group. Public health practitioners should adopt an integrated approach to manage multimorbidity rather than an individual disease-specific approach, along with different strategies according to age and gender groups.

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