Pub Date : 2020-10-01DOI: 10.1109/CLEI52000.2020.00066
Cindy Espinoza Aguirre, J. Carretero
Student dropout is a major problem in university studies all around the world. To alleviate this problem, it is important to detect as soon as possible student attrition before he or she becomes a deserter. A student may be considered a deserter when she/he has not completed her academic credits or leave the studies. In this paper we present a study made at a higher education institution, by analyzing the records of 530 higher education students from 52 different careers with application date 2015 to 2018, considering factors such as academic monitoring, financial situation, personal and social information. These are some issues or mix of problems that could affect dropout rates. Analyze student behavior by implementing predictive analytics techniques reduce the gaps between professional demands and applicants' competencies. We applied predictive analytical techniques to identify the relationship of factors characterizing students who leave the university. As a result, we have elaborated a conceptual model to predict the risk of defection and applied machine learning techniques to generate preventive and corrective alerts as a student permanence strategy. This study shows that information is important, but the application of machine learning in the student's prior knowledge and its relationship to a dynamic and pre-established profile of the deserter student is essential to generate early strategies that manage to reduce the gaps between professional demands and applicants' competencies. In addition, a data model has been created to give solution to the issue get generated preventive and corrective alerts.
{"title":"Predictive data analysis techniques applied to dropping out of university studies","authors":"Cindy Espinoza Aguirre, J. Carretero","doi":"10.1109/CLEI52000.2020.00066","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00066","url":null,"abstract":"Student dropout is a major problem in university studies all around the world. To alleviate this problem, it is important to detect as soon as possible student attrition before he or she becomes a deserter. A student may be considered a deserter when she/he has not completed her academic credits or leave the studies. In this paper we present a study made at a higher education institution, by analyzing the records of 530 higher education students from 52 different careers with application date 2015 to 2018, considering factors such as academic monitoring, financial situation, personal and social information. These are some issues or mix of problems that could affect dropout rates. Analyze student behavior by implementing predictive analytics techniques reduce the gaps between professional demands and applicants' competencies. We applied predictive analytical techniques to identify the relationship of factors characterizing students who leave the university. As a result, we have elaborated a conceptual model to predict the risk of defection and applied machine learning techniques to generate preventive and corrective alerts as a student permanence strategy. This study shows that information is important, but the application of machine learning in the student's prior knowledge and its relationship to a dynamic and pre-established profile of the deserter student is essential to generate early strategies that manage to reduce the gaps between professional demands and applicants' competencies. In addition, a data model has been created to give solution to the issue get generated preventive and corrective alerts.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114417787","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00041
Denys A. Flores, Guillermo Morocho
The rapid adoption of Cloud Computing in the last decade has promoted the development and innovation of IT services around the world. This includes the provision of on-demand hardware and software infrastructures, reducing administrative costs, and saving endless deployment efforts. However, public organizations are still reluctant to move towards this computing model due to inherent issues related to the loss of governance and increased IT risks. In this research, we introduce a straightforward 3-phase framework named Cloud-GMR for assisting the decision-making process of determining whether or not moving public services to the Cloud. Our proposal integrates COBIT v.5, ISO 27005 and OCTAVE-S methodologies into a unified qualitative framework for governance and risk management. The novelty of Cloud-GMR is the provision of guidelines for aligning business objectives, identifying migration requirements and assessing risks before adopting any Cloud strategy in the public sector. We also evaluate the applicability of our proposal inside an Ecuadorian public institution.
{"title":"Cloud-GMR: A Qualitative Framework for Governance and Risk Management of Cloud-hosted Public Services","authors":"Denys A. Flores, Guillermo Morocho","doi":"10.1109/CLEI52000.2020.00041","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00041","url":null,"abstract":"The rapid adoption of Cloud Computing in the last decade has promoted the development and innovation of IT services around the world. This includes the provision of on-demand hardware and software infrastructures, reducing administrative costs, and saving endless deployment efforts. However, public organizations are still reluctant to move towards this computing model due to inherent issues related to the loss of governance and increased IT risks. In this research, we introduce a straightforward 3-phase framework named Cloud-GMR for assisting the decision-making process of determining whether or not moving public services to the Cloud. Our proposal integrates COBIT v.5, ISO 27005 and OCTAVE-S methodologies into a unified qualitative framework for governance and risk management. The novelty of Cloud-GMR is the provision of guidelines for aligning business objectives, identifying migration requirements and assessing risks before adopting any Cloud strategy in the public sector. We also evaluate the applicability of our proposal inside an Ecuadorian public institution.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124318575","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 : 2020-10-01DOI: 10.1109/clei52000.2020.00001
{"title":"2020 XLVI Latin American Computing Conference CLEI 2020 [Title page i]","authors":"","doi":"10.1109/clei52000.2020.00001","DOIUrl":"https://doi.org/10.1109/clei52000.2020.00001","url":null,"abstract":"","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122855689","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00059
L. Cernuzzi, Marcelo Alcaraz, Cristhian Parra, Jorge Saldivar
Crowdsourced civic engagement is a novel form of democratic participation that allows citizens to share their ideas and deliberate in a multitude of diverse participatory processes that are emerging all over the world, influencing, often with binding power, urban plans, city budgets, and even legislation, among many other forms of public policy decisions. As a result, hundreds of thousands of civic contributions are produced as ideas, comments, and proposals circulate among citizens and between them and government officials, generating an avalanche of mostly unstructured data, which decision-makers have difficulty to manage. Sentiment analysis techniques have the potential to process and classify these contributions in ways that can make it easier to make sense of them. In this paper, we present the design and implementation of a rule based sentiment analyzer that integrates a lexicon, optimized for the Spanish language and the application domain of civic contributions. We present the results of our first evaluation and discuss the aspects of the proposal that have room for improvement in future work.
{"title":"A Sentiment Analysis Approach to Process Civic Contributions","authors":"L. Cernuzzi, Marcelo Alcaraz, Cristhian Parra, Jorge Saldivar","doi":"10.1109/CLEI52000.2020.00059","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00059","url":null,"abstract":"Crowdsourced civic engagement is a novel form of democratic participation that allows citizens to share their ideas and deliberate in a multitude of diverse participatory processes that are emerging all over the world, influencing, often with binding power, urban plans, city budgets, and even legislation, among many other forms of public policy decisions. As a result, hundreds of thousands of civic contributions are produced as ideas, comments, and proposals circulate among citizens and between them and government officials, generating an avalanche of mostly unstructured data, which decision-makers have difficulty to manage. Sentiment analysis techniques have the potential to process and classify these contributions in ways that can make it easier to make sense of them. In this paper, we present the design and implementation of a rule based sentiment analyzer that integrates a lexicon, optimized for the Spanish language and the application domain of civic contributions. We present the results of our first evaluation and discuss the aspects of the proposal that have room for improvement in future work.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116711735","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00024
G. Ajam, C. Rodríguez, B. Benatallah
Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.
{"title":"API Topics Issues in Stack Overflow Q&As Posts: An Empirical Study","authors":"G. Ajam, C. Rodríguez, B. Benatallah","doi":"10.1109/CLEI52000.2020.00024","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00024","url":null,"abstract":"Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126345813","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00006
Mario Orlando Soto Monárdez, Diego Pizarro
Death by suicide in Chile is not only a local problem, at a global level this phenomenon is suffered, where detecting it and treating it in time is fundamental. The present work was developed and implemented at the Center for Attention and Practical Activities of Psychology at the University of Tarapacá in Iquique, an Expert System based on rules, with the purpose of helping the decision making of psychology students who perform their professional practices at the center, in clinical cases of suicide risk. This allows the early detection of patients who present this behavior and evaluate the risk of suicide. The results of this work demonstrate the response capacity of the expert system developed, imitating the action of a specialist in this case detecting and assessing the risk of suicide in patients in consultations at the psychological center, achieving a good level of effectiveness.
{"title":"Expert System for Assistance in Decision Making of Students in Practice of a Psychological Care Center in Clinical Cases of Suicidal Risk","authors":"Mario Orlando Soto Monárdez, Diego Pizarro","doi":"10.1109/CLEI52000.2020.00006","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00006","url":null,"abstract":"Death by suicide in Chile is not only a local problem, at a global level this phenomenon is suffered, where detecting it and treating it in time is fundamental. The present work was developed and implemented at the Center for Attention and Practical Activities of Psychology at the University of Tarapacá in Iquique, an Expert System based on rules, with the purpose of helping the decision making of psychology students who perform their professional practices at the center, in clinical cases of suicide risk. This allows the early detection of patients who present this behavior and evaluate the risk of suicide. The results of this work demonstrate the response capacity of the expert system developed, imitating the action of a specialist in this case detecting and assessing the risk of suicide in patients in consultations at the psychological center, achieving a good level of effectiveness.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131628748","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00039
Giovanni G. C. Chrysostomo, Marco V. B. A. Vallim, L. A. Silva, A. R. A. V. Filho
This work proposes a framework called Analytical workbench that aims to support the decision making of power generation systems. The framework is structured in three modules. An operational module which receives operating data and prepares it for analysis purposes. The tactical module which allows real time system monitoring. Finally, the strategic module, which allows to make inferences about the future state of the plant's operating data. The results can be seen in a real case study in a Brazilian hydroelectric plant and the main highlights are: identification of faulty sensors, measurement errors, real-time monitoring (every 5 seconds) of all data or just some selected variables and, finally, forecast of the plant's operational status in one more day.
{"title":"Analytical Workbench: A Framework to Support Predictive Maintenance of Industrial Systems","authors":"Giovanni G. C. Chrysostomo, Marco V. B. A. Vallim, L. A. Silva, A. R. A. V. Filho","doi":"10.1109/CLEI52000.2020.00039","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00039","url":null,"abstract":"This work proposes a framework called Analytical workbench that aims to support the decision making of power generation systems. The framework is structured in three modules. An operational module which receives operating data and prepares it for analysis purposes. The tactical module which allows real time system monitoring. Finally, the strategic module, which allows to make inferences about the future state of the plant's operating data. The results can be seen in a real case study in a Brazilian hydroelectric plant and the main highlights are: identification of faulty sensors, measurement errors, real-time monitoring (every 5 seconds) of all data or just some selected variables and, finally, forecast of the plant's operational status in one more day.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122015453","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 : 2020-10-01DOI: 10.1109/clei52000.2020.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/clei52000.2020.00003","DOIUrl":"https://doi.org/10.1109/clei52000.2020.00003","url":null,"abstract":"","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116954351","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00013
Rosymeire Oliveira da Silva, Adriano Fiorese
Learning Object Repositories (LOR), also known as Open Educational Repositories (OER), are proper web platforms to store educational resources such as Learning Objects (LOs). Learning Object Repositories are design to make easy searching and reusing of educational resources. However, LORs great variety of available educational resources makes difficult to a user to find an LOR that suits his needs. Software Quality Indicators such as Functionality, Usability, Reliability, and Compatibility are important characteristics, according to ISO 25010 software quality norm, regarding general software use and they can be used as criteria for choosing the most suitable LOR according to user will. This work aims to present a support decision-making method for choosing the most suitable LOR to a user. This method uses the multicriteria decision-making method AHP for ranking LORs according to a user requested set of Indicators and values. Proposed method evaluation envolving different user requests shows its viability and applicability.
{"title":"Learning Object Repositories Evaluation Based on Quality Indicators using AHP Method","authors":"Rosymeire Oliveira da Silva, Adriano Fiorese","doi":"10.1109/CLEI52000.2020.00013","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00013","url":null,"abstract":"Learning Object Repositories (LOR), also known as Open Educational Repositories (OER), are proper web platforms to store educational resources such as Learning Objects (LOs). Learning Object Repositories are design to make easy searching and reusing of educational resources. However, LORs great variety of available educational resources makes difficult to a user to find an LOR that suits his needs. Software Quality Indicators such as Functionality, Usability, Reliability, and Compatibility are important characteristics, according to ISO 25010 software quality norm, regarding general software use and they can be used as criteria for choosing the most suitable LOR according to user will. This work aims to present a support decision-making method for choosing the most suitable LOR to a user. This method uses the multicriteria decision-making method AHP for ranking LORs according to a user requested set of Indicators and values. Proposed method evaluation envolving different user requests shows its viability and applicability.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121685784","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 : 2020-10-01DOI: 10.1109/CLEI52000.2020.00033
Miguel Ecar, J. P. S. D. Silva, A. Finger, Tiago Gazzoni Soldá
Software Process Improvement (SPI) consists of a set of changes in software development companies, which introduces new and improved methods, techniques, and tools. SPI projects generally are performed based on a reference model, such as CMMI, ISO 9001, ISO 15504, among others. In most cases, there is a need to use more than one reference model or add aspects of other models. Based on this, it is necessary to harmonize the chosen models to reach the expected goals, but it is not a simple task. The goal of this paper is to introduce a domain ontology called Base of Knowledge about Software Engineering Practices (Badge). To evaluate our proposal we performed a focus group session with practitioners and academic subjects. The focus group outcome is that Badge is able to generalize the models that say “what should be done”. We also advocate that using a generalization structure enables the possibility to automate some tasks in SPI initiatives.
{"title":"Badge: Towards a Domain Ontology for SPI Resources","authors":"Miguel Ecar, J. P. S. D. Silva, A. Finger, Tiago Gazzoni Soldá","doi":"10.1109/CLEI52000.2020.00033","DOIUrl":"https://doi.org/10.1109/CLEI52000.2020.00033","url":null,"abstract":"Software Process Improvement (SPI) consists of a set of changes in software development companies, which introduces new and improved methods, techniques, and tools. SPI projects generally are performed based on a reference model, such as CMMI, ISO 9001, ISO 15504, among others. In most cases, there is a need to use more than one reference model or add aspects of other models. Based on this, it is necessary to harmonize the chosen models to reach the expected goals, but it is not a simple task. The goal of this paper is to introduce a domain ontology called Base of Knowledge about Software Engineering Practices (Badge). To evaluate our proposal we performed a focus group session with practitioners and academic subjects. The focus group outcome is that Badge is able to generalize the models that say “what should be done”. We also advocate that using a generalization structure enables the possibility to automate some tasks in SPI initiatives.","PeriodicalId":413655,"journal":{"name":"2020 XLVI Latin American Computing Conference (CLEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134053590","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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