Analysis of Co-regulation Behavioral Patterns by Cluster and Sequential Analysis in CSCL

2016 ◽  
pp. 49-63
Author(s):  
Lanqin Zheng
Keyword(s):  
Sequential Analysis ◽  
Behavioral Patterns

scholarly journals Understanding Arab Students’ Behavioral Patterns in an Online Course: An Explanatory Study Based on Hofstede’s National Cultural Dimensions

2021 ◽  
Vol 13 (22) ◽  
pp. 12426
Author(s):  
Ahmed Tlili ◽  
Mouna Denden ◽  
Saida Affouneh ◽  
Soheil Hussein Salha ◽  
Zhenyu Cai ◽  
...  
Keyword(s):  
Online Learning ◽  
Sequential Analysis ◽  
Learning Experiences ◽  
Cultural Dimensions ◽  
Learning Performance ◽  
Online Course ◽  
Behavioral Patterns ◽  
Cultural Backgrounds ◽  
Arab Region ◽  
Inequality In Education

The provision of online learning experiences has been implemented by many universities worldwide to overcome several challenges, including inequality in education. However, this experience is still not a common approach in public universities in the Arab region. Furthermore, several research studies have pointed out that a country’s culture should be considered in order to enhance online learning, as students may behave differently based on their cultural backgrounds. Nevertheless, little is known about how a given culture may affect the learning behavioral patterns of students. Therefore, to better understand the cultural phenomenon and to enhance the adoption of online learning in the Arab region, this study aims to understand how an Arab culture may affect the online learning behaviors of students. Specifically, this study applies a lag sequential analysis (LSA) approach to understand the behavioral patterns of 116 students from Tunisia in a six-week online course. The study then further discusses the different learning behavior patterns based on the theoretical framework of Hofstede’s national cultural dimensions. The findings highlight that culture can affect how students engage in online learning discussions and how they maintain their learning performance online. The findings further indicate that online learning experiences may be beneficial for female students who experience social pressures in Arab cultures.

Mining Learning Behavioral Patterns of Students by Sequence Analysis in Cloud Classroom

2017 ◽  
Vol 15 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Sanya Liu ◽  
Zhenfan Hu ◽  
Xian Peng ◽  
Zhi Liu ◽  
H. N. H. Cheng ◽  
...  
Keyword(s):  
Learning Style ◽  
Learning Analytics ◽  
Sequential Analysis ◽  
Behavioral Patterns ◽  
Grade Group ◽  
Course Content ◽  
Grade Levels ◽  
Making Sense ◽  
Control Learning ◽  
Learner Activity

In a MOOC environment, each student's interaction with the course content is a crucial clue for learning analytics, which offers an opportunity to record learner activity of unprecedented scale. In online learning, the educators and the administrators need to get informed with students' learning states since the performance of unsupervised learning style is difficult to control. Learning analytics considered as a key process is to provide students and educators with evidence-based, analytical and contextual outcomes in a way of making sense of their learning engagements. In this conceptual framework, this manuscript per the authors intends to adopt sequential analysis method to exploit students' learning behavior patterns in Cloud classroom (an online course platform based on MOOC). Moreover, this research also compares the behavioral patterns of four grade levels in a university, with the purpose of finding the most key behavioral patterns of each grade group.

Discovering the Two-Step Lag Behavioral Patterns of Learners in the College SPOC Platform

2017 ◽  
Vol 13 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Zhi Liu ◽  
Hercy N.H. Cheng ◽  
Sanya Liu ◽  
Jianwen Sun
Keyword(s):  
Frequency Analysis ◽  
Sequential Analysis ◽  
Learning Processes ◽  
Retention Rates ◽  
Online Course ◽  
Behavioral Patterns ◽  
Behavioral Sequence ◽  
Learning Platform ◽  
Similarities And Differences ◽  
Future Work

Due to high retention rates, small private online course (SPOC) has become increasingly popular among universities. However, existing analyses of learning behavioral patterns in SPOC remain extremely lacking. This present study conducts an empirical analysis on the behavioral patterns of 12,517 undergraduates engaging in a college's SPOC platform, called StarC. In this study, the authors collected and summarized the learning behaviors generated from these learners during 348 days of observation. They further coded the behaviors and extracted the two-step lag sequences in learning processes of individuals. The frequency analysis and sequential analysis were subsequently adopted to discover the distributions and frequency transition patterns of the two-step behavioral sequence in StarC. Besides, grade similarities and differences were computed and analyzed in terms of behavioral patterns. With these results, the potential and inadequacies of the learning platform are discussed, and some suggestions are offered for future work on the study and development of SPOCs.

scholarly journals Applying Learning Analytics to Explore the Effects of Motivation on Online Students' Reading Behavioral Patterns

2018 ◽  
Vol 19 (2) ◽  
Author(s):  
Jerry Chih-Yuan Sun ◽  
Che-Tsun Lin ◽  
Chien Chou
Keyword(s):  
Cluster Analysis ◽  
Graduate Students ◽  
Learning Environment ◽  
Learning Analytics ◽  
Sequential Analysis ◽  
Learning Motivation ◽  
Online Course ◽  
Behavioral Patterns ◽  
Online Reading ◽  
High Motivation

This study aims to apply a sequential analysis to explore the effect of learning motivation on online reading behavioral patterns. The study’s participants consisted of 160 graduate students who were classified into three group types:  low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study reveals that highly motivated students exhibited a relatively serious reading pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of motivation in taking an online course. Finally, recommendations were provided to instructors and researchers based on the results of the study.

Food deprivation-induced vs. drug-induced feeding: a behavioral evaluation

1991 ◽  
Vol 260 (3) ◽  
pp. R546-R552 ◽  
Author(s):  
A. S. Levine ◽  
M. A. Kuskowski ◽  
M. Grace ◽  
C. J. Billington
Keyword(s):  
Neuropeptide Y ◽  
Food Deprivation ◽  
Sequential Analysis ◽  
Behavioral State ◽  
Behavioral Patterns ◽  
Drug Induced ◽  
Plastic Tube ◽  
Lag Sequential Analysis ◽  
Almost All ◽  
Time Eating

Several neuroactive substances including neuropeptide Y (NPY), muscimol, and norepinephrine (NE) stimulate feeding in satiated rats. In the present study, we observed the behavioral patterns of rats stimulated to eat by food deprivation or by intracerebroventricular (icv) injection of orexigenic agents to explore the hypothesis that such agents produce a behavioral state resembling hunger. Animals that were food deprived for 24 h spent the majority of their time eating (35%), drinking (5%), resting (44%), and moving (13%) when food was available. If food was removed and substituted with a chewable substrate (plastic tube), they chewed on tubes for a brief period (5%) but spent most of their time moving (14%) or resting (77%). In the absence of food or tubes, they briefly moved about the cage (4%) and spent almost all of their time resting (94%). The patterns observed with the orexigenic drugs were different, particularly in the absence of food. NPY-injected rats were more active than deprived rats, spending 22% of their time moving in the presence of food, 47% in the presence of tubes, and 37% in the absence of food or tubes. Rats injected with muscimol demonstrated a marked increase in the time spent chewing and eating. These rats spent 67% of their time eating in the presence of food and chewed 25% of the time in the absence of either food or tubes. NE-injected rats also chewed when tubes were present (17%) or when no food or tubes were present (10%). Lag sequential analysis further documented differences in behavioral patterns amongst the various treatments.(ABSTRACT TRUNCATED AT 250 WORDS)

A Dual-Scaffolding Framework Integrating Peer-Scaffolding and Cognitive-Scaffolding for an Augmented Reality-Based Educational Board Game: An Analysis of Learners’ Collective Flow State and Collaborative Learning Behavioral Patterns

2020 ◽  
pp. 073563312096940
Author(s):  
Huei-Tse Hou ◽  
Su-Han Keng
Keyword(s):  
Augmented Reality ◽  
Collaborative Learning ◽  
Sequential Analysis ◽  
Collective Flow ◽  
Behavioral Patterns ◽  
Board Game ◽  
Game Based Learning ◽  
Board Games ◽  
Cognitive Scaffolding ◽  
Peer Scaffolding

The design and application of educational board games have been emphasized in game-based learning. The integration of educational board games and augmented reality (AR) can help provide extensive cognitive-scaffolding for learners. This study proposed a dual-scaffolding framework that integrated peer-scaffolding and cognitive-scaffolding for an AR educational board game. This study also conducted an empirical analysis to evaluate this framework. Forty-four college students participated in this study. The researchers investigated these learners’ flow, acceptance, and their collaborative learning behavioral patterns with the sequential analysis. Moreover, this study explored the correlation of flow and acceptance and investigated learners’ behavioral pattern differences between high collective flow groups and low collective flow groups (collective flow referred to the mean of flow from group members). The results showed that there was a positive correlation between learners’ flow and acceptance. These learners’ behavioral patterns also showed that both peer-scaffolding and cognitive-scaffolding facilitated their problem-solving process. Moreover, the study found that high collective flow groups had more reflection and analysis behaviors than low collective flow groups in game-based learning.

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