Game-based Education
S.A.H. Hosseini Anari; M. Neshati
Abstract
Background and Objective:In recent years, we have witnessed a dramatic growth of digital gaming media in entertainment and popular culture. Games are firmly entrenched in human culture and have consistently impacted their social life and leisure on an unprecedented scale. One of the achievements of digital ...
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Background and Objective:In recent years, we have witnessed a dramatic growth of digital gaming media in entertainment and popular culture. Games are firmly entrenched in human culture and have consistently impacted their social life and leisure on an unprecedented scale. One of the achievements of digital gaming media is that it stimulates issues beyond entertainment. In general, the game can be described as a voluntary activity in which the player has freedom of action and can enter or leave the game at any time. An emerging strategy in the field of play is gamification, but gamification is slightly different from normal play. Gamification can be defined as the use of gamified thinking in areas that do not have the nature of play, which is created to create interaction and motivation in people to achieve the desired goals. Nowadays, gamification has been turned into a strong motive tool to engage and increase users’ participation in the educational systems. Previous research indicates generally the importance and efficiency of gamification methods to improve educational processes, but in these researches, the characteristics of each audience and its effect on their behavior were not investigated. In this research, we attempt to study individual characteristics such as their gender and education background on their effectiveness rate from gamification. Methods: For this study, we designed an educational system based on gamification approach named as “Elenow” and gave it to the students (N=41) who were studying “Technical writing and presentation method” course. Elenow is a web-based system which is accessible on users’ mobile. In addition to the data collected by Elenow system, a questionnaire was given to the audiences and asked them about the effectiveness of gamification process implemented by the Elenow system. Also, the information about the students’ educational background was extracted from the university educational systems. Findings:The most important findings of this research revealed that the individual characteristics of audiences are significant factors on their effectiveness from gamification. Therefore, we can’t consider a single approach for all individuals participating in a gamified activity. Conclusion: The most important results of this research are: as the audience of educational systems has different individual characteristics, such as gender, the effectiveness of each element of the designed gamification is different for them. Also, their level of interest and satisfaction with the elements of gamification, such as; signs, points, etc. are not the same; therefore, in designing gamified mechanisms for educational purposes, a single version cannot be considered for all audiences. For this reason, it is important to consider these features in gamified designs. In particular, some symptoms motivate female students and others motivate male students. While women get better feedback than homework-related symptoms, men are more interested in receiving skill-related symptoms.
Data Mining
H. Koosha; S. Dangkoub; A. Barzanooni
Abstract
Background and Objective: Student mental health data has been recorded in the information systems of the universities across the country for several years, and due to its high volume, conventional statistical and psychoanalytic methods to predict patterns and factors affecting students' mental health ...
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Background and Objective: Student mental health data has been recorded in the information systems of the universities across the country for several years, and due to its high volume, conventional statistical and psychoanalytic methods to predict patterns and factors affecting students' mental health are not effective. This is where data mining technology comes in handy and helps to predict and identify those at high risk based on the recorded data set of students 'physical and especially mental health status, and to make appropriate and timely decisions to improve students' condition. One of the main objectives of every managers of educational centers is making improvements in students’ educational performance. Besides the educational factors, physical and mental health is considerable which has a significant effect on students’ behavior. Therefore, some rules and patterns are required to make the best decisions, based on the prediction of students’ mental health state. This paper proposes a data mining approach for analyzing and extracting patterns in terms of new students’ mental health, which means whether they need to visit a psychologist. Our effort was on extracting hidden rules in new students’ mental health examination by employing classification approach. Methods:Techniques used in this study are decision tree, rule based classifier, neural network, logistic regression and support vector machine. Moreover, a parameter tuning process is done for all the techniques mentioned and the results presents the list of symptoms of individuals who need detailed examination. Findings:The results of the research represent that one can predict the status of students’ mental helath based on propsed model. One of the outcomes of decision tree is that if a person severely feels disappointed or seems to be obsessive by others, or feels that life is worthless, definitely a consultaion is needed. Conclusion: Considering that most of the existing research in the field of health data mining have focused on physical health, it is suggested that for future studies, all levels of health, i.e dimensions of students' health, including physical, social and spiritual health, as well as a combination of these dimensions be considered. In addition, a review of the various approaches and techniques appropriate to the psychological data set should be conducted with the aim of creating an appropriate classification for the existing techniques in this field. It is also suggested that the present data set or similar data sets (student health monitoring information) be examined with other classification techniques and the results be compared with the results of the present study. In general, it is suggested that data mining technology be used to extract hidden patterns in the mental health data set of school students at different levels of education, office workers and organizations. Finally, it is recommended that future research in this field first implement the clustering approach on the psychological data set and then use the classification and forecasting approaches.
