TY - GEN
T1 - Agent-based Simulation model for identifying failure on students' project
AU - Khuankrue, Issarapong
AU - Kumeno, Fumihiro
AU - Ohashi, Yutaro
AU - Tsujimura, Yasuhiro
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - In learning and development, students need to be made aware of their slips and mistakes in terms of acquiring the required knowledge, skills and competencies, especially, in active learning approaches. However, assessments may not necessarily lead to an effective understanding of student's errors. Most assessors are required to measure the extent of student's accurate output in terms of exam answers or practical assessments. Areas of failure tend to be overlooked and for this reason, learners at-risk of achieving poor results can become careless when completing assessments. In the present study, we examine the following research questions: (i) How can we simulate the landscape of data on both individual and team behaviour in terms of failure in their projects? (ii) How can we develop ways of identifying at-risk students in each team and support them immediately? Therefore, in order to address this problem, this research proposes an agent-based simulation of student projects, which considers both individual and team errors. This model is created on the basis of particular characteristics of individual learners within the team. We assume that individuals working within a team are subject to recognition-primed decision making when confronted with particular problems to solve. The adjustment proposed by this model includes analysing the problem using prior knowledge and planning as well as judgements on the suitability of particular solutions. This research will be further developed to identify at-risk learners in terms of investigating the areas they want to develop to achieve their learning goals in particular subjects and especially in the software development fields.
AB - In learning and development, students need to be made aware of their slips and mistakes in terms of acquiring the required knowledge, skills and competencies, especially, in active learning approaches. However, assessments may not necessarily lead to an effective understanding of student's errors. Most assessors are required to measure the extent of student's accurate output in terms of exam answers or practical assessments. Areas of failure tend to be overlooked and for this reason, learners at-risk of achieving poor results can become careless when completing assessments. In the present study, we examine the following research questions: (i) How can we simulate the landscape of data on both individual and team behaviour in terms of failure in their projects? (ii) How can we develop ways of identifying at-risk students in each team and support them immediately? Therefore, in order to address this problem, this research proposes an agent-based simulation of student projects, which considers both individual and team errors. This model is created on the basis of particular characteristics of individual learners within the team. We assume that individuals working within a team are subject to recognition-primed decision making when confronted with particular problems to solve. The adjustment proposed by this model includes analysing the problem using prior knowledge and planning as well as judgements on the suitability of particular solutions. This research will be further developed to identify at-risk learners in terms of investigating the areas they want to develop to achieve their learning goals in particular subjects and especially in the software development fields.
KW - Agent-based simulation
KW - Human failure awareness
KW - Recognition-primed decision model
KW - Students' project
UR - http://www.scopus.com/inward/record.url?scp=85044184888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044184888&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8123105
DO - 10.1109/SMC.2017.8123105
M3 - Conference contribution
AN - SCOPUS:85044184888
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 3113
EP - 3118
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -