디지털융복합연구 (Journal of Digital Convergence)

  • 제17권1호
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  • Pages.99-109
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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행위자 기반의 의견 표출 행동 모형 : 대학 수업을 중심으로

An Agent-based Model for an Act of Expression or Silence : Focused on College Students' Behavior in Class

  • 이성룡 (한국외국어대학교 산업경영공학과)
  • Yee, Soung Ryong (Dept. of Industrial and Management Engineering, Hankuk University of Foreign Studies)
  • 투고 : 2018.12.04
  • 심사 : 2019.01.20
  • 발행 : 2019.01.28
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초록

대학 수업에서 학생들의 침묵은 학습 분위기에 부정적 영향을 미친다. 본 연구에서는 학생들의 의견 표출과 관련한 동태적 행동 모형을 탐구함으로써 그 해결책에 관한 시사점을 얻고자 한다. 모형에 필요한 기본적인 변수들은 관련 문헌을 통해 추출하고 변수들 간의 동태적인 관련성 및 기타 중요 요소들은 설문조사를 통해 파악하였다. 작성된 모형은 행위자 기반의 컴퓨터 모형으로 구축하여 모의실험을 진행하였다. 실험 결과 개인의 행동은 주변 인원의 반응으로부터 영향을 받으며, 주변에 의견을 표출하는 인원이 많을수록 자신의 의견 표출에 대한 동기로 작용함을 발견하였다. 또한 호의적인 분위기는 비평적인 분위기에 비해 침묵의 비율을 현저히 감소시킬 수 있음을 확인하였고, 비평적인 분위기에서는 침묵의 다수 현상 또한 발생할 수 있음을 발견하였다. 제안된 모형은 의견 표출이나 침묵에 대한 행동 분석이 필요한 다양한 연구 분야에 기초적인 모형으로 제공 될 수 있으리라 기대한다.

This paper explores a way to solve students' silence problem in the class by studying dynamic behaviors in the class. In order to build a behavioral model, several factors affecting the students' behavior were extracted by literature review and by performing a survey to the students. The paper also implemented the model into an agent-based computer model for simulation. The simulation result showed that the behavior of an individual depends on the neighboring students. The more students are expressing their opinion, and then the better motivation one can get to express an opinion. Other observations are that supportive atmosphere considerably reduces the silent ratio, and even the majority of silence phenomenon appears at critical atmosphere.

키워드

DJTJBT_2019_v17n1_99_f0001.png 이미지

Fig. 1. Act of expression or silence depending on the values of variables and atmosphere

DJTJBT_2019_v17n1_99_f0002.png 이미지

Fig. 2. Population distribution after stabilization

DJTJBT_2019_v17n1_99_f0003.png 이미지

Fig. 3. Silent ratio by different control variables

DJTJBT_2019_v17n1_99_f0004.png 이미지

Fig. 4. Time to stabilization by different control variables

DJTJBT_2019_v17n1_99_f0005.png 이미지

Fig. 5. Effect of injecting active students

Table 1. Motivation and reward dynamics

DJTJBT_2019_v17n1_99_t0001.png 이미지

Table 2. Representative control and response variables

DJTJBT_2019_v17n1_99_t0002.png 이미지

Table 3. Values of control variables for simulation

DJTJBT_2019_v17n1_99_t0003.png 이미지

Table 4. Percentage of occurring extreme cases

DJTJBT_2019_v17n1_99_t0004.png 이미지

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