Predicting the Assessment Course Performance of Criminology Students Using Data Mining (original) (raw)
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Prediction model of effective studies at the Academy of criminalistics and police studies
Nauka, bezbednost, policija, 2015
The paper deals with the connection between the input characteristics of students of the Academy of Criminalistic and Police Studies in Belgrade related to the acquired and inherited values and effective studying. Data collection was performed using the questionnaire technique on a sample of 120 students and the logic regression method. The questionnaire comprised 11 closed questions, 10 relating to predictor variables (gender, high school, success in high school, place in which it was finished, going in for sport, respondent's family and financial situation, whether a member of the family is employed by the Ministry of Interior and the course of studies), and one relating to the criterion variable (studying without repeating years). The results of logic regression showed that the overall model explains between 40.4% and 55.7% of variance in the status of effective studying, and it correctly classified 83.6% of cases. Only five predictor variables provided a unique statistically significant contribution to the model. The paper proved that if a student had a specific set of inherited and acquired characteristics, probability that he/she would study effectively is significantly 1 This paper is the result of the research on project: "Management of police organization in preventing and mitigating threats to security in the Republic of Serbia", which is financed and carried out by the Academy of Criminalistic and Police Studies, Belgrade-the cycle of scientific projects 2015-2019.
PREDICTION MODEL OF EFFECTIVE STUDIES AT THE ACADEMY OF CRIMINALISTIC AND POLICE STUDIES
The paper deals with the connection between the input characteristics of students of the Academy of Criminalistic and Police Studies in Belgrade related to the acquired and inherited values and effective studying. Data collection was performed using the questionnaire technique on a sample of 120 students and the logic regression method. The questionnaire comprised 11 closed questions, 10 relating to predictor variables (gender, high school, success in high school, place in which it was finished, going in for sport, respondent’s family and financial situation, whether a member of the family is employed by the Ministry of Interior and the course of studies), and one relating to the criterion variable (studying without repeating years). The results of logic regression showed that the overall model explains between 40.4% and 55.7% of variance in the status of effective studying, and it correctly classified 83.6% of cases. Only five predictor variables provided a unique statistically significant contribution to the model. The paper proved that if a student had a specific set of inherited and acquired characteristics, probability that he/she would study effectively is significantly increased. The paper proved that higher police education institutions should pay attention to the inherited and acquired characteristics at the entrance examination. Unlike any previous research, the paper deals with desired characteristics modelling implying that the candidate has increased chances to study effectively.
A Data Mining Based Approach to Evaluate Assessment Performances of Graduating Students of Schools
In the current trends of advance computing methodologies, data of students' performances in different grades can be used to improve the quality of managerial decisions. Student's academic performance is based upon various factors like personal characteristics and psychological factors. Educational database contains useful information for predicting a students' performance, rank factor and details. By applying different data mining techniques to educational data to analyse them as well as to develop good methods to knowledge gain and management. Finding better correlation between different data variables can allow us to make better and beneficial decision which can facilitate better resource utilization in terms of educational service delivery. This paper aims to analyses and predict the correlation between English, Mathematics and science subjects in terms of student academic result in 10th and 12th grade by using Aprior data mining techniques which mines required information. National level examination results of 10th and 12th grade students' have been used for this research. The results show strong relationships between subjects as well as subject relationships with gender of the student in a specific grade. The results of this research help educationist to develop proper education model to improve results and to get better achievements in the areas where lacking.
Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute
American Journal of Educational Research, 2014
Education-oriented data mining allows to predict determined type of factor or characteristic of a case, phenomenon or situation. In this article the mining models used are described and the main results are discussed. Mining models of clustering, classification and association are considered especially. In all cases seeks to determine patterns of academic success and failure for students, thus predicting the likelihood of dropping them or having poor academic performance, with the advantage of being able to do it early, allowing addressing action to reverse this situation. This work was done in 2013 with information on the years 2009 to 2013, students of the subject Operating Systems tertiary career Superior Technical Analyst (TSAP) Higher Institute of Curuzú Cuatiá (ISCC), Corrientes, Argentina.
Appling Data Mining Technique for Crime Prevention: The Case of Hossaena Town Police Office
The Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information and hence data mining can greatly improve crime analysis and aid in reducing and preventing crime. The purpose of this study is to construct predictive models that could help in the effort of crime pattern analysis with the aim of supporting the crime prevention activities at the Hossaena town police office. For this study, a six-step hybrid knowledge discovery process model is followed, due to the nature of the problem and attributes in the dataset. The classification technique such as J48 decision tree and Naive Bayes used to build the models. Performance of the models is compared using accuracy, True Positive Rate, False Positives Rate, and the area under the Relative Optical character curve. J48 decision tree registers better performance with 96.34% accuracy. Lastly for extracting the knowledge the researcher develop the prototype for the user for support the decision which crime is assigned under the serious, medium or low for this purpose the researcher generate the prototypes.
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The relatively low% of students promoted and regularized in Operating Systems Course of the LSI (Bachelor's Degree in Information Systems) of FaCENA (Faculty of Sciences and Natural Surveying -Facultad de Ciencias Exactas, Naturales y Agrimensura) of UNNE (academic success), prompted this work, whose objective is to determine the variables that affect the academic performance, whereas the final status of the student according to the Res. 185/03 CD (scheme for evaluation and promotion): promoted, regular or free1.
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International Research Journal of Innovations in Engineering and Technology, 2020
The study intends to devise an earlier and more accurate analytical means of predicting the class of degree a student will graduate with. This will help in decision making and the design of academic programs and curriculum development; also it will help in student guidance and counseling. Because the earlier we can predict the class of degree a student is likely to graduate with, the better it will be for the student to keep it up or improve on his performance and this will consequently assist in mitigating and minimizing the student dropout, attrition, or dismissal from school after wasting reasonable amount of time without acquiring the certificate. Academic data of some Communication & Information Technology students; such as year of admission, year of completion, individual grades obtained from the courses he/she offered at a 1 year diploma program, 1 year advance diploma, and the class of degree he obtains from a 1year top-up degree program was introduced to a Classification Data Mining algorithms to extract a pattern and a model for students' final grade prediction. The study's result shows that timely completion of the first two programs, a high score in computer architecture course, programming, network, and discrete mathematics courses are determining factors that can be used to predict students' final grades at graduation.
Abstract The aim of this research to extract meaningful crime trends regarding offences against children from the data in existing police records with the help of data mining techniques. We know children are exposed to different offences but we do not know which children are exposed to what type of offence (crime category). The output from this research helps to identify which children are exposed to which crime categories. Currently the police officers try to understand the relation between any two attributes but they do not know the relation among more than two attributes and the relationship between other variables and a class variable. This is why this can be achieved by using data mining techniques in an efficient and accurate manner than those achieved by trained personnel and traditional simple statistics to analyze crime data. The researcher used the six-phased CRISP-DM for data mining process and each of the steps in this model starting from business understanding up to evaluation and deployment phases are performed step wise and iteratively when needed. Even though all the phases are equally important the data pre-processing part has got due emphasis since police records are inconsistent and frequently incomplete making task of formal analysis inaccurate and time consuming. These analytical processes would benefit from using data mining techniques in a structured approach. Both unsupervised and supervised learning are used within the structured methodology to mine the police data. This research will serve as a reference material for researchers, crime investigators, planners and NGOs that work on prevention and control of offences against children. Based on this, it can also help to implement different crime preventive programs like through awareness creation programs. The research demonstrates that data mining techniques can be successfully used in proactive policing to prevent crimes. This is more applicable for high volume crimes such as theft, violence and sexual assaults that have been committed most commonly. These crimes can often be segmented and classified and the generated models can be used to predict potential victims of a specified crime category through predictive models as well as to attribute the profile of victims with the help of descriptive techniques. Some of the rules in association rule are not interesting due to few values that are unable to generate patterns. From all the crime categories in the crime records sexual assault has the highest number and best rules are generated related with sexual assault. Almost all interesting rules generated in association rule are included in the rules generated by the classification model. Generally, promising results are registered with encourage further researches in the area.
UHD Journal of Science and Technology, 2019
In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.