**1. The applications in behavior analysis**

As the era of big data and artificial intelligence has come, many research fields are heading toward more precise process analyses. In particular, using innovative methods to analyze different human behaviors as well as to understand specific behavioral patterns help exploring the structures and contexts in all kinds of human behaviors, which can serve as theoretical innovation and strategies to solve human problems. So far, behavior analysis is gradually emphasized in many research fields, including education, human-mechanism interaction, learning science, psychology, sociology, guidance and counseling, marketing and management, etc.

Many research methods have different characteristics in exploring the unknown; for instance, experiment research emphasizes the foundation of positivism. The author believes that behavior analysis focuses on the exploration in latent structures of human behaviors and interactions, which should be based on structuralism. Simon Blackburn suggests that the structuralism is "the belief that phenomena of human life are not intelligible except through their interrelations. These relations constitute a structure, and behind local variations in the surface phenomena there are constant laws of abstract culture" [1]. Based on this philosophy, behavior analysis research, exploring the potential structure in human behaviors, helps connect human behavioral structure and their basic physiological and cognitive structures, which further helps investigate how these behaviors influence social interactions, and even the structure of social organization interactions.

More and more analysis techniques are applied in integrating qualitative and quantitative analysis methods to analyze behavioral process based on structures, including sequential analysis [2], progressive sequential analysis [3], quantitative content analysis [4, 5],

cluster analysis or data mining [6], social network analysis [7], etc. Many other studies also keep developing innovative analysis techniques to integrate multi-dimensional research methods and overcome challenging research difficulties, such as self-report-based sequential analysis (SRSA), exploring behavioral structures in learners' self-reports from their learning behavioral sequential patterns [8]. This technique can further explore the causes of learners' specific learning behaviors and sequences. On the other hand, this innovative analysis method can be applied in all types of research issues across disciplines.

**Author details**

Address all correspondence to: hthou@mail.ntust.edu.tw

2nd ed. UK: Cambridge University Press; 1997

Research and Development. 2004;**52**(1):5-18

Educational Computing Research. 2015;**53**(1):95-123

Online Journal of Educational Technology. 2010;**9**(3):52-60

Science and Technology, Taipei, Taiwan

University Press; 2008

Graduate Institute of Applied Science and Technology, National Taiwan University of

[1] Blackburn S. Oxford Dictionary of Philosophy, Second Edition Revised. Oxford: Oxford

Introductory Chapter: The Research Trend and Applications in Behavior Analysis

http://dx.doi.org/10.5772/intechopen.76106

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[2] Bakeman R, Gottman JM. Observing Interaction: An Introduction to Sequential Analysis.

[3] Hou HT. Exploring the behavioural patterns in project-based learning with online discussion: Quantitative content analysis and progressive sequential analysis. Turkish

[4] Rourke L, Anderson T. Validity in quantitative content analysis. Educational Technology

[5] Hou HT. Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education. 2012;**58**(4):1225-1233

[6] Hou HT, Li MC. Evaluating multiple aspects of a digital educational problem-solving-

[8] Lin YC, Hsieh YH, Hou HT. Developing a self-report-based sequential analysis method for educational technology systems: A process-based usability evaluation. Journal of

[9] Hou HT. Integrating cluster and sequential analysis to explore learners' flow and behavioral patterns in a simulation game with situated-learning context for science courses: A video-based process exploration. Computers in Human Behavior. 2015;**48**:424-435 [10] Wang SM, Hou HT, Wu SY. Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): A preliminary study.

[11] Hou HT, Wu SY. Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant mes-

based adventure game. Computers in Human Behavior. 2014;**30**:29-38

Educational Technology Research and Development. 2017;**65**(2):301-323

saging tool: A case study. Computers & Education. 2011;**57**(2):1459-1469

[7] Scott J. Social Network Analysis: A Handbook. London: SAGE Publications; 2000

Huei-Tse Hou

**References**
