Pub Date : 2023-08-10DOI: 10.1108/ijilt-12-2022-0231
Douglas Legramante, A. Azevedo, J. Azevedo
PurposeThis paper aims to analyse the factors that influence the satisfaction and intention of continuity of use, of teachers and students, regarding using Moodle in undergraduate courses in one Campus at the Federal Institute of Rondônia in Brazil. The starting point was an integration of DeLone and McLean's Information Systems Success Model (ISSM) with Davis' Technology Acceptance Model (TAM).Design/methodology/approachA quantitative research approach was adopted. After the definition of the hypotheses, data were collected through self-administered questionnaires. The questionnaires were designed to measure the five constructs: Quality of Information (QI), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), User Satisfaction (US) and Behavioural Intention to use (BI) that make up the conceptual model of the study. The data were analysed based on 144 valid questionnaires. The technique of maximum likelihood estimation was adopted in the data analysis through structural equation modelling (SEM).FindingsThe results confirmed six of the nine hypothesised relationships. QI positively impacts PEOU and US. PEOU positively impacts PU, which in turn positively impacts US and BI. Similarly, US positively impacts Moodle's BI. It was also evidenced that PU is the strongest predictor of US.Practical implicationsThese results can help educational institutions, managers, administrators and designers of e-learning systems to develop strategies to increase Moodle's user satisfaction.Originality/valueThis study provides insights into the perception of students and teachers regarding the use of Moodle. A model that integrates constructs from two models widely used in research related to e-learning (TAM and ISSM) was used in a developing country context. This is important, given cultural differences and social idiosyncrasies in different contexts, particularly in an educational institution in the Amazonia region in northern Brazil.
{"title":"Integration of the technology acceptance model and the information systems success model in the analysis of Moodle's satisfaction and continuity of use","authors":"Douglas Legramante, A. Azevedo, J. Azevedo","doi":"10.1108/ijilt-12-2022-0231","DOIUrl":"https://doi.org/10.1108/ijilt-12-2022-0231","url":null,"abstract":"PurposeThis paper aims to analyse the factors that influence the satisfaction and intention of continuity of use, of teachers and students, regarding using Moodle in undergraduate courses in one Campus at the Federal Institute of Rondônia in Brazil. The starting point was an integration of DeLone and McLean's Information Systems Success Model (ISSM) with Davis' Technology Acceptance Model (TAM).Design/methodology/approachA quantitative research approach was adopted. After the definition of the hypotheses, data were collected through self-administered questionnaires. The questionnaires were designed to measure the five constructs: Quality of Information (QI), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), User Satisfaction (US) and Behavioural Intention to use (BI) that make up the conceptual model of the study. The data were analysed based on 144 valid questionnaires. The technique of maximum likelihood estimation was adopted in the data analysis through structural equation modelling (SEM).FindingsThe results confirmed six of the nine hypothesised relationships. QI positively impacts PEOU and US. PEOU positively impacts PU, which in turn positively impacts US and BI. Similarly, US positively impacts Moodle's BI. It was also evidenced that PU is the strongest predictor of US.Practical implicationsThese results can help educational institutions, managers, administrators and designers of e-learning systems to develop strategies to increase Moodle's user satisfaction.Originality/valueThis study provides insights into the perception of students and teachers regarding the use of Moodle. A model that integrates constructs from two models widely used in research related to e-learning (TAM and ISSM) was used in a developing country context. This is important, given cultural differences and social idiosyncrasies in different contexts, particularly in an educational institution in the Amazonia region in northern Brazil.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48620495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.1108/ijilt-06-2022-0131
Joseph Njiku, Védaste Mutarutinya, Jean François Maniraho
PurposeThis study aims to investigate the development of Mathematics teachers' attitudes towards technology integration through collaborative lesson design activities as part of professional development.Design/methodology/approachThe pre-and post-test for non-equivalent comparison groups quasi-experiment was adopted as the study design where 125 participants were distributed into three groups in Dar es Salaam – Tanzania. Data analysis was done using gain in scores, t-test, split-plot analysis of variance, and eta-squared.FindingsComparison across groups and between pre-intervention and post-intervention showed that collaborative lesson design activities have more potential to develop Mathematics teachers' attitudes than the isolated implementation of such activities. Relevant recommendations are provided.Practical implicationsThe study offers valuable insights for teacher education especially in-service training focussing on effective ways of developing teachers' competencies especially attitudes towards technology integration.Originality/valueAlthough lesson design studies are prevalent, majority have investigated the development of teachers' knowledge rather than attitude for integrating technology. Additionally, the study sheds light on attitude as a multidimensional construct thereby providing more insight into the subject.
{"title":"Developing Mathematics teachers' attitude towards technology integration through school-based lesson design teams","authors":"Joseph Njiku, Védaste Mutarutinya, Jean François Maniraho","doi":"10.1108/ijilt-06-2022-0131","DOIUrl":"https://doi.org/10.1108/ijilt-06-2022-0131","url":null,"abstract":"PurposeThis study aims to investigate the development of Mathematics teachers' attitudes towards technology integration through collaborative lesson design activities as part of professional development.Design/methodology/approachThe pre-and post-test for non-equivalent comparison groups quasi-experiment was adopted as the study design where 125 participants were distributed into three groups in Dar es Salaam – Tanzania. Data analysis was done using gain in scores, t-test, split-plot analysis of variance, and eta-squared.FindingsComparison across groups and between pre-intervention and post-intervention showed that collaborative lesson design activities have more potential to develop Mathematics teachers' attitudes than the isolated implementation of such activities. Relevant recommendations are provided.Practical implicationsThe study offers valuable insights for teacher education especially in-service training focussing on effective ways of developing teachers' competencies especially attitudes towards technology integration.Originality/valueAlthough lesson design studies are prevalent, majority have investigated the development of teachers' knowledge rather than attitude for integrating technology. Additionally, the study sheds light on attitude as a multidimensional construct thereby providing more insight into the subject.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46364781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PurposeThis work aims to assess the effects of information and communication technology (ICT) on inequalities in access to professional training (PT) in Cameroon.Design/methodology/approachThis study used data from the fourth Cameroonian Household Survey (ECAM 4), the concentration index (CI) calculations and the Wagstaff et al. (2003) decomposition.FindingsThe preliminary results show that the CI calculations by groups of individuals reveal the existence of significant inequalities in favour of the poor. This is the case for all groups of individuals who use ICT tools, namely radio, internet, telephone and television. The results of the Wagstaff et al. (2003) decomposition reveal that an equitable distribution of income between those who use and those who do not use the telephone, radio and internet reduces inequalities in access to FP in favour of the poor.Originality/valueDespite the wealth of literature devoted to the study of inequalities in access to education, the consideration of PT is still very marginal. In Cameroon, the literature devoted to the study of inequalities in access to PT is still almost non-existent, probably because of a low level of interest in the scientific community. However, as just seen, PT is a tool for combating unemployment, particularly in economies where the informal sector is important, insofar as the proportion of unemployed and inactive people is very low amongst the ones that have taken a PT course. Moreover, studies on the effects of ICT on inequalities in access to PT are still rare in the literature.
本研究旨在评估喀麦隆信息通信技术(ICT)对专业培训(PT)不平等的影响。本研究使用的数据来自第四次喀麦隆住户调查(ECAM 4)、浓度指数(CI)计算和Wagstaff et al.(2003)分解。初步结果表明，个人群体的CI计算揭示了有利于穷人的显著不平等的存在。使用信通技术工具，即无线电、互联网、电话和电视的所有个人群体都是如此。Wagstaff等人(2003)的分解结果显示，使用电话、广播和互联网的人和不使用电话、广播和互联网的人之间的收入公平分配减少了穷人在获得计划生育方面的不平等。原创性/价值尽管有大量的文献致力于研究教育机会的不平等，但对教育机会的考虑仍然非常边缘化。在喀麦隆，专门研究PT不平等现象的文献仍然几乎不存在，这可能是因为科学界对PT的兴趣不高。然而，正如刚才所看到的，培训是对付失业的一种工具，特别是在非正规部门很重要的经济体中，因为在参加培训课程的人中，失业和不从事活动的人所占的比例非常低。此外，在文献中，关于信息通信技术对获得PT不平等的影响的研究仍然很少。
{"title":"Do ICTs reduce inequalities in access to professional training in Cameroon?","authors":"Fabrice Nzepang, Siméon Serge Atangana, Saturnin Bertrand Nguenda Anya","doi":"10.1108/ijilt-08-2022-0167","DOIUrl":"https://doi.org/10.1108/ijilt-08-2022-0167","url":null,"abstract":"PurposeThis work aims to assess the effects of information and communication technology (ICT) on inequalities in access to professional training (PT) in Cameroon.Design/methodology/approachThis study used data from the fourth Cameroonian Household Survey (ECAM 4), the concentration index (CI) calculations and the Wagstaff et al. (2003) decomposition.FindingsThe preliminary results show that the CI calculations by groups of individuals reveal the existence of significant inequalities in favour of the poor. This is the case for all groups of individuals who use ICT tools, namely radio, internet, telephone and television. The results of the Wagstaff et al. (2003) decomposition reveal that an equitable distribution of income between those who use and those who do not use the telephone, radio and internet reduces inequalities in access to FP in favour of the poor.Originality/valueDespite the wealth of literature devoted to the study of inequalities in access to education, the consideration of PT is still very marginal. In Cameroon, the literature devoted to the study of inequalities in access to PT is still almost non-existent, probably because of a low level of interest in the scientific community. However, as just seen, PT is a tool for combating unemployment, particularly in economies where the informal sector is important, insofar as the proportion of unemployed and inactive people is very low amongst the ones that have taken a PT course. Moreover, studies on the effects of ICT on inequalities in access to PT are still rare in the literature.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47170242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-24DOI: 10.1108/ijilt-02-2023-0017
Ayushi Jain, Poonam Sharma, Jamini Ranjan Meher
PurposeThis research aims to examine the impact of virtual learning platforms and instructor presence (IP) on learner satisfaction (LS). Further, this study examines the role of learner engagement (LE) in order to improve the LS.Design/methodology/approachThis research uses both primary and secondary data sources to compile the research's findings. The primary source of data includes 610 responses from various higher education institutes in India. The collected data were analysed using the partial least square structural equation modelling (PLS-SEM) technique.FindingsThis research provides evidence that the theoretical model is accurate with the gathered data sample. In the model, online platform (OP) is an independent variable, whereas LS is a dependent variable, and IP and LE are the mediating variables. The outcomes demonstrated that OP has a positive impact on IP and LE. Also, the relationships between IP and LE, IP and LS and LE and LS are significantly positive. The mediation analysis validates the importance of the IP and LE for relationships.Originality/valueThis investigation presents a comprehensive model, which demonstrates the relationship between OP, IP, LE and LS. The study makes a unique reference to several theories in order to boost interaction and IP in virtual learning, the learner's learning experience can be enhanced. The model helps teachers and educational institutions formalise strategies to boost interaction and examine the institutions' pedagogy to enhance satisfaction.
{"title":"Effects of online platforms on learner's satisfaction: a serial mediation analysis with instructor presence and student engagement","authors":"Ayushi Jain, Poonam Sharma, Jamini Ranjan Meher","doi":"10.1108/ijilt-02-2023-0017","DOIUrl":"https://doi.org/10.1108/ijilt-02-2023-0017","url":null,"abstract":"PurposeThis research aims to examine the impact of virtual learning platforms and instructor presence (IP) on learner satisfaction (LS). Further, this study examines the role of learner engagement (LE) in order to improve the LS.Design/methodology/approachThis research uses both primary and secondary data sources to compile the research's findings. The primary source of data includes 610 responses from various higher education institutes in India. The collected data were analysed using the partial least square structural equation modelling (PLS-SEM) technique.FindingsThis research provides evidence that the theoretical model is accurate with the gathered data sample. In the model, online platform (OP) is an independent variable, whereas LS is a dependent variable, and IP and LE are the mediating variables. The outcomes demonstrated that OP has a positive impact on IP and LE. Also, the relationships between IP and LE, IP and LS and LE and LS are significantly positive. The mediation analysis validates the importance of the IP and LE for relationships.Originality/valueThis investigation presents a comprehensive model, which demonstrates the relationship between OP, IP, LE and LS. The study makes a unique reference to several theories in order to boost interaction and IP in virtual learning, the learner's learning experience can be enhanced. The model helps teachers and educational institutions formalise strategies to boost interaction and examine the institutions' pedagogy to enhance satisfaction.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41375487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1108/ijilt-01-2023-0003
Kacey Thorne, Sarah DeMark, Tyson Heath, Kristian Young
PurposeThe global labor market has been upended and a new landscape has emerged. New models for ensuring the value and relevance of post-secondary education are needed. Learners need better understanding of the value and relevancy which the education provides and more immediate return on the educational investment. Education providers must ensure the relevance of the credentials. Employers require transparency into skills an individual possesses based on the credentials they hold. New models are needed to guide an understanding of credentials so that all have equitable pathways to opportunity. This paper aims to discuss the aforementioned objectives.Design/methodology/approachThe authors in this paper discuss how Western Governors University implemented a Unified Credential Framework (UCF) for ensuring credentials are relevant, verified, transparent and portable. The UCF is predicated on the use of skills as an underlying foundation.FindingsUsing a structured theory for understanding skills and micro-credentials creates more transparency into what post-secondary credentials represent, and the value they hold for individuals, employers and education providers.Research limitations/implicationsThis paper represents a use case where the proposed solution is still emergent. Additional research is warranted as longitudinal data become available on student outcomes and impacts.Originality/valueThis paper presents a model that any organization can implement for clearer line of sight into the value and relevance of post-secondary credentials.
{"title":"Ensuring student-centered value with skills-denominated credentials","authors":"Kacey Thorne, Sarah DeMark, Tyson Heath, Kristian Young","doi":"10.1108/ijilt-01-2023-0003","DOIUrl":"https://doi.org/10.1108/ijilt-01-2023-0003","url":null,"abstract":"PurposeThe global labor market has been upended and a new landscape has emerged. New models for ensuring the value and relevance of post-secondary education are needed. Learners need better understanding of the value and relevancy which the education provides and more immediate return on the educational investment. Education providers must ensure the relevance of the credentials. Employers require transparency into skills an individual possesses based on the credentials they hold. New models are needed to guide an understanding of credentials so that all have equitable pathways to opportunity. This paper aims to discuss the aforementioned objectives.Design/methodology/approachThe authors in this paper discuss how Western Governors University implemented a Unified Credential Framework (UCF) for ensuring credentials are relevant, verified, transparent and portable. The UCF is predicated on the use of skills as an underlying foundation.FindingsUsing a structured theory for understanding skills and micro-credentials creates more transparency into what post-secondary credentials represent, and the value they hold for individuals, employers and education providers.Research limitations/implicationsThis paper represents a use case where the proposed solution is still emergent. Additional research is warranted as longitudinal data become available on student outcomes and impacts.Originality/valueThis paper presents a model that any organization can implement for clearer line of sight into the value and relevance of post-secondary credentials.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47583682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.1108/ijilt-05-2022-0108
Terence Ma, O. ten Cate
PurposeJob competency frameworks are based on the listing skills required for a job. The assumption is that if a candidate is presumed to have the skills, then the candidate should be able to do the job. Thus, employers hope to identify prospective employees having the required skills. However, this may differ from knowing whether the employee is ready to be trusted to do the job activities with minimal or no supervision. The authors pose the question how employers might know about the capability of prospective employees to perform the job activities for which the employees are being hired.Design/methodology/approachIn health professions education, a job activity-based framework has been developed called “entrustable professional activities” (EPAs, activities to be entrusted). This paper reviews the job activity framework and EPAs used in medical education, considering how this might support preparation for work in other sectors of the labor market.FindingsThe authors describe the EPA framework, some implementation issues and how EPAs lead to a type of microcredential being awarded to individuals as the individuals demonstrate that the individuals can be entrusted with specific job activities.Originality/valueThe focus of this paper is to demonstrate that a medical education model could potentially be adopted by other industries to provide employers with information regarding the ability of a prospective employee in performing the job activities required. Such an approach would address employer's concerns about the job readiness of potential employees.
{"title":"Entrustable professional activities: a model for job activity competency framework with microcredentials","authors":"Terence Ma, O. ten Cate","doi":"10.1108/ijilt-05-2022-0108","DOIUrl":"https://doi.org/10.1108/ijilt-05-2022-0108","url":null,"abstract":"PurposeJob competency frameworks are based on the listing skills required for a job. The assumption is that if a candidate is presumed to have the skills, then the candidate should be able to do the job. Thus, employers hope to identify prospective employees having the required skills. However, this may differ from knowing whether the employee is ready to be trusted to do the job activities with minimal or no supervision. The authors pose the question how employers might know about the capability of prospective employees to perform the job activities for which the employees are being hired.Design/methodology/approachIn health professions education, a job activity-based framework has been developed called “entrustable professional activities” (EPAs, activities to be entrusted). This paper reviews the job activity framework and EPAs used in medical education, considering how this might support preparation for work in other sectors of the labor market.FindingsThe authors describe the EPA framework, some implementation issues and how EPAs lead to a type of microcredential being awarded to individuals as the individuals demonstrate that the individuals can be entrusted with specific job activities.Originality/valueThe focus of this paper is to demonstrate that a medical education model could potentially be adopted by other industries to provide employers with information regarding the ability of a prospective employee in performing the job activities required. Such an approach would address employer's concerns about the job readiness of potential employees.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47138761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1108/ijilt-05-2022-0106
C. Mason, Haohui Chen, David Evans, Gavin Walker
PurposeThis paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.Design/methodology/approachUsing the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.FindingsThis study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.Originality/valueThis study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.
{"title":"Illustrating the application of a skills taxonomy, machine learning and online data to inform career and training decisions","authors":"C. Mason, Haohui Chen, David Evans, Gavin Walker","doi":"10.1108/ijilt-05-2022-0106","DOIUrl":"https://doi.org/10.1108/ijilt-05-2022-0106","url":null,"abstract":"PurposeThis paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.Design/methodology/approachUsing the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.FindingsThis study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.Originality/valueThis study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47628328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comprehensive Explainable Framework for Designing Enhanced Deep Learning Models","authors":"","doi":"10.53819/81018102t3086","DOIUrl":"https://doi.org/10.53819/81018102t3086","url":null,"abstract":"","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":"18 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87925480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding crime patterns in the USA can significantly contribute to effective policymaking and proactive law enforcement strategies. This study aims to utilize a novel method in the field of criminology - the Markov Chain model - to assess state-dependent crime patterns in the USA. The Markov Chain model, a mathematical system that undergoes transitions between different states based on certain probabilistic rules, provides an innovative approach to visualize and predict crime patterns. The application of this model enables us to make informed predictions about future crime rates based on current and historical data, thereby offering valuable insights into crime progression and recurrence. Data sourced from national and state-level crime databases forms the basis of this research. It is categorized into 'states' as per Markov Chain terminologies to represent different crime levels. The transitions between these states simulate the shifts in crime rates. The Markov Chain model is then implemented to map these transitions, yielding state-dependent crime patterns. Initial findings demonstrate a noteworthy degree of predictability in crime patterns, with variations in patterns across different states. Results also indicate that certain states have higher probabilities of experiencing increased crime rates, given their current state. Moreover, the model's ability to provide probabilistic predictions about future states may serve as a valuable tool for strategic planning in law enforcement. This research contributes significantly to the field by introducing a mathematical, probabilistic model to a largely sociological study area. It also has practical implications, as understanding these state-dependent crime patterns can enhance law enforcement efficiency and inform the development of targeted crime prevention strategies. Future studies may focus on refining the model, incorporating other socio-economic variables, and analyzing their impacts on crime transitions. This study thus opens up new avenues for employing mathematical models in criminology, demonstrating the vast potential of such interdisciplinary approaches. Keywords: Markov Chain Model, Crime Patterns, State-Dependent Crime Rates, Predictive Policing, Probabilistic Crime Analysis
{"title":"Assessing State-Dependent Crime Patterns in the USA: A Markov Chain Approach","authors":"Samuel T. Holloway","doi":"10.53819/81018102t4151","DOIUrl":"https://doi.org/10.53819/81018102t4151","url":null,"abstract":"Understanding crime patterns in the USA can significantly contribute to effective policymaking and proactive law enforcement strategies. This study aims to utilize a novel method in the field of criminology - the Markov Chain model - to assess state-dependent crime patterns in the USA. The Markov Chain model, a mathematical system that undergoes transitions between different states based on certain probabilistic rules, provides an innovative approach to visualize and predict crime patterns. The application of this model enables us to make informed predictions about future crime rates based on current and historical data, thereby offering valuable insights into crime progression and recurrence. Data sourced from national and state-level crime databases forms the basis of this research. It is categorized into 'states' as per Markov Chain terminologies to represent different crime levels. The transitions between these states simulate the shifts in crime rates. The Markov Chain model is then implemented to map these transitions, yielding state-dependent crime patterns. Initial findings demonstrate a noteworthy degree of predictability in crime patterns, with variations in patterns across different states. Results also indicate that certain states have higher probabilities of experiencing increased crime rates, given their current state. Moreover, the model's ability to provide probabilistic predictions about future states may serve as a valuable tool for strategic planning in law enforcement. This research contributes significantly to the field by introducing a mathematical, probabilistic model to a largely sociological study area. It also has practical implications, as understanding these state-dependent crime patterns can enhance law enforcement efficiency and inform the development of targeted crime prevention strategies. Future studies may focus on refining the model, incorporating other socio-economic variables, and analyzing their impacts on crime transitions. This study thus opens up new avenues for employing mathematical models in criminology, demonstrating the vast potential of such interdisciplinary approaches. Keywords: Markov Chain Model, Crime Patterns, State-Dependent Crime Rates, Predictive Policing, Probabilistic Crime Analysis","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":"53 6 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91135553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management
{"title":"Fraud Detection in Malaysian Financial Institutions using Data Mining and Machine Learning","authors":"Shih T. Cho","doi":"10.53819/81018102t4152","DOIUrl":"https://doi.org/10.53819/81018102t4152","url":null,"abstract":"The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":"10 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88849821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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