Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining

Nowadays, researchers analyse student data to predict the graduation rate by looking at the characteristics of students enrolled and to take corrective actions at an early stage or improve the admission process. Educational data mining (EDM) is an emerging field that can support the implementation of changes in the management of higher education institutions. EDM analyses educational data using the development and the application of data mining (DM) methods and algorithms to information stored in academic data repositories. The purpose of this paper is to review which methods and algorithms of DM can be used in the analysis of educational data to improve decision-making. Furthermore, it evaluates these algorithms using a dataset composed of student data in the computer science school of a private university. The core of the analysis is to discover trends and patterns of study in the graduation rate indicator. Finally, it compares these methods and algorithms and suggests which has the best precision in certain scenarios. Our analyses suggest that random trees had better precision but had limitations due to the difficulty of interpretation while the J48 algorithm had better possibilities of interpretation of results in the visualisation of the classification of data and only had slightly inferior performance

Enterprise Architecture, an enabler of change and knowledge management

Organizations around the world require a sound process of change management to innovate and remain competitive over time. Change and knowledge management needs to be supported with the right tools to overcome the challenges of transformations and transitions in the business models and processes of diverse organizations. Steering boards can use enterprise architecture (EA) to implement new knowledge management initiatives in their strategic planning. EA allows companies to model the current situation (as-is models) of the organization and the desired future scenarios (to-be models) and to establish road maps to enable adequate transformations. Different frameworks exist in the market that support the management of organizations, for example: Control Objectives for Information and Related Technologies (COBIT), Information Technology Infrastructure Library (ITIL), quality models such as the one proposed by the European Foundation for Quality Management (EFQM) and systems such as the Balanced Scorecard (BSC) are widely used for the management of business and information technologies (IT).  However, EA is not widely used with the other mentioned tools. This paper analyzes EA as a tool for change and knowledge management and compares its functionality with other frameworks in the market. The analysis performed in this paper checks if EA can be used and is compatible with other frameworks. To answer this question, an analysis of the most important processes, good practices, perspectives and tools provided by each framework was performed

Suggested Methodologies for Evaluation and Selection of Enterprise Architecture Software for Knowledge Digitization

Knowledge Management (KM) is a practice that allows the creation, use, distribution and transfer of knowledge in organizations. Different KM frameworks exist that include business intelligence or enterprise architecture (EA) components for the implementation of KM in organizations. EA tools are used to digitize, relate and visualize the following dimensions of knowledge: organizational structure, business processes, applications and technology. The objective of this paper is to assess the role of EA as a key component in KM and to suggest software evaluation methodologies that can be adapted for the field of EA. For this, an investigation was realized to identify the existing software evaluation methodologies in the market and to filter those that can be adapted for the field of EA. The methodology used for the research was qualitative and exploratory using a case study performed in an international logistic service provider. The case study describes the process done for the selection of the evaluation methodology. Furthermore, it describes the steps for knowledge digitization

A Holistic View of Data Warehousing in Education

Data warehousing (DW) is a widespread and essential practice in business organizations that support the data analytic and decision-making process. Despite the importance of DW in complex organizations, the adoption of a data warehouse (DWH) in education is apparently lower compared with other industries. To clarify this situation, this paper presents a systematic mapping that includes the study of empirical research papers from 2008 to 2018 on the topic of DW in education. For this paper, we applied a qualitative and quantitative approach based on a four-stage research method with the objective to have a holistic view of DWHs in education. After filtering and applying the proposed method, 34 relevant papers were identified and studied in detail. The study revealed interesting facts; for example, Kimball’s approach is the most applied methodology for DWH design in education. In addition, a mapping between this comprehensive collection of research papers covering educational DW and six dimensions of analysis (schema proposal, analysis of the user requirements, analysis of the business requirements, effectiveness, implementation, and data analysis) was performed. From this analysis, we discovered that the star schema is the most implemented approach. The purpose of the mapping was to explore and identify the priority areas of research and the research gaps within the academic community. These gaps are a source of opportunities to start new lines of research.This work was supported by Universidad Tecnológica Equinoccial

Moving the IT Infrastructure to the Cloud

Cloud computing services are nowadays advertised as an emerging business model. Moreover, these services bring innovative solutions in a more sophisticated competitive market. But, the decision for their adoption could be significantly reduced due to organizations’ concerns related to security, privacy, and trust. The challenge involves such questions as where to start, which provider should the company choose or whether it is even worthwhile. Thus, this paper proposes an improved unified framework, based on a previous study where a 6 step process framework was introduced. This improved framework add one new step for security and control after the migration process. At the end, a 7 processes framework is proposed aimed to fulfill organizations’ concerns when decide to adopt cloud computing services with a follow-up step. This additional step intends to help IT directors to make sure everything is working properly in a methodological way, in order to achieve a successful cloud computing migration process. An effective solution that is gaining momentum and popularity for competitive organizations

Moving ERP Systems to the Cloud - Data Security Issues

This paper brings to light data security issues and concerns for organizations by moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has become the new trend of how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations

Mobile Learning as the Key to Higher Education Innovation: A systematic mapping

The study of educational innovations has attracted increasing attention from academics and researchers around the world. Educational innovation proposes the implementation of new approaches or practices that are beneficial and make an impact on individuals or academic communities. The current educational model of many higher education institutions (HEIs) was not designed for this generation of “digital natives”. For this reason, HEIs face the challenge of building teaching strategies that generate meaningful educational experiences. This research seeks to address this issue through a systematic mapping that includes empirical research papers from 2015 to 2020 that study innovations in educational practices using mobile devices. A qualitative and quantitative approach was applied using a four-stage research methodology to evidence innovation in higher education. After employing the selected methodology and applying all the exclusion criteria, 27 papers related to the research topic were identified. Mapping was also performed between the corpus of papers and five dimensions on educational innovation (the purpose of learning, the context of learning, the role of the teacher, the role of the learner, and the evidence of the outcome). The findings reveal that the role of the teacher is the dimension that is least analyzed in innovation initiatives, whereas the most analyzed dimension is the purpose of learning. The goal of this work was to explore and identify educational innovations and unveil uncovered fields of research to generate opportunities for new lines of research in educational innovation

A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education

Advances in science and technology, the Internet of Things, and the proliferation of mobile apps are critical factors to the current increase in the amount, structure, and size of information that organizations have to store, process, and analyze. Traditional data storages present technical deficiencies when handling huge volumes of data and are not adequate for process modeling and business intelligence; to cope with these deficiencies, new methods and technologies have been developed under the umbrella of big data. However, there is still the need in higher education institutions (HEIs) of a technological tool that can be used for big data processing and knowledge management (KM). To overcome this issue, it is essential to develop an information infrastructure that allows the capturing of knowledge and facilitates experimentation by having cleaned and consistent data. Thus, this paper presents a hybrid information infrastructure for business intelligence and analytics (BI&A) and KM based on an educational data warehouse (EDW) and an enterprise architecture (EA) repository that allows the digitization of knowledge and empowers the visualization and the analysis of dissimilar organizational components as people, processes, and technology. The proposed infrastructure was created based on research and will serve to run different experiments to analyze educational data and academic processes and for the creation of explicit knowledge using different algorithms and methods of educational data mining, learning analytics, online analytical processing (OLAP), and EA analytics

A Quick Guide for Using Microsoft Onenote as an Electronic Laboratory Notebook

[Abstract] Scientific data recording and reporting systems are of a great interest for endorsing reproducibility and transparency practices among the scientific community. Current research generates large datasets that can no longer be documented using paper lab notebooks (PLNs). In this regard, electronic laboratory notebooks (ELNs) could be a promising solution to replace PLNs and promote scientific reproducibility and transparency. We previously analyzed five ELNs and performed two survey-based studies to implement an ELN in a biomedical research institute. Among the ELNs tested, we found that Microsoft OneNote presents numerous features related to ELN best functionalities. In addition, both surveyed groups preferred OneNote over a scientifically designed ELN (PerkinElmer Elements). However, OneNote remains a general note-taking application and has not been designed for scientific purposes. We therefore provide a quick guide to adapt OneNote to an ELN workflow that can also be adjusted to other nonscientific ELNs

USO DE MICROSOFT ONENOTE COMO CUADERNO ELECTRÓNICO DE LABORATORIO

Los sistemas de registro y de reporte de datos son de gran interés, puesto que respaldan la reproducibilidad y transparencia científica. La investigación actual genera una gran cantidad de datos que ya no se pueden documentar utilizando cuadernos de laboratorio de papel (CLP). Los cuadernos electrónicos de laboratorio (CEL) podrían ser una solu-ción prometedora para reemplazar los CLP y promover la reproducibilidad científica y su transparencia. Anteriormente analizamos cinco CEL y realizamos dos encuestas para implementar un CEL en un instituto de investigación biomédica. Entre los CEL proba-dos, encontramos que Microsoft OneNote presenta numerosas características relacio-nadas con las mejores funcionalidades del CEL. Además, ambos grupos encuestados prefirieron OneNote sobre un CEL científico (Elements de PerkinElmer). Sin embargo, OneNote es una aplicación general para tomar notas que no ha sido diseñada para fi-nes científicos. Por lo tanto, en este trabajo proporcionamos varias pautas para adaptar OneNote a un flujo de trabajo experimental