TY - GEN

T1 - Application of Machine Learning Methods to Assess Player Skills via Business Simulation Logs

AU - Гадасина, Людмила Викторовна

AU - Масалимова, Азалия Азатовна

AU - Вьюненко, Людмила Федоровна

PY - 2025

Y1 - 2025

N2 - The purpose of the study is to develop an approach to constructing multi-modal machine learning models for assessing soft skills of business simulation players using game logs. The study was performed by analyzing the logs of business simulation “Corporate Governance”, which simulates the management of an enterprise in a real market. Within the framework of the study, business simulation is considered not as a learning game that forms competencies, but as a diagnostic one for assessing the players’ soft skills. The approach allows taking into account simultaneously each player’s individual strategy and the overall team scores in the assessment. An approach to the application of machine learning methods for analyzing business simulation logs is proposed, based on constructing a meta-algorithm that takes into account various types of input data. Individual player actions data are considered as action sequences and are treated by text data processing methods. As research implications, this paper presents a new integrated conceptual approach, which can be useful for studies focused on recruitment techniques and employee skills diagnostics. Currently, the player’s competencies are actually measured manually, rather than using tools for automated assessment. It is time-consuming and costly, especially when it is necessary to conduct mass business simulations. This research provides guidance for automating the process of assessing player skills thus delivering benefits of practical importance.

AB - The purpose of the study is to develop an approach to constructing multi-modal machine learning models for assessing soft skills of business simulation players using game logs. The study was performed by analyzing the logs of business simulation “Corporate Governance”, which simulates the management of an enterprise in a real market. Within the framework of the study, business simulation is considered not as a learning game that forms competencies, but as a diagnostic one for assessing the players’ soft skills. The approach allows taking into account simultaneously each player’s individual strategy and the overall team scores in the assessment. An approach to the application of machine learning methods for analyzing business simulation logs is proposed, based on constructing a meta-algorithm that takes into account various types of input data. Individual player actions data are considered as action sequences and are treated by text data processing methods. As research implications, this paper presents a new integrated conceptual approach, which can be useful for studies focused on recruitment techniques and employee skills diagnostics. Currently, the player’s competencies are actually measured manually, rather than using tools for automated assessment. It is time-consuming and costly, especially when it is necessary to conduct mass business simulations. This research provides guidance for automating the process of assessing player skills thus delivering benefits of practical importance.

KW - business simulation

KW - game analytics

KW - game-based assessment

KW - machine-learning methods

KW - meta-classifier

KW - soft skills

UR - https://www.mendeley.com/catalogue/a3b88aaa-84c6-30c3-96cc-557e158d21cf/

U2 - 10.1007/978-3-031-76368-7_1

DO - 10.1007/978-3-031-76368-7_1

M3 - Conference contribution

SN - 978-3-031-76367-0

T3 - Lecture Notes in Business Information Processing

SP - 3

EP - 16

BT - Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2024)

PB - Springer Nature

CY - Cham

Y2 - 9 May 2024 through 11 May 2024

ER -