Building Data Science Capabilities into University Data Warehouse to Predict Graduation

EUNIS 2018 Congress, Tuesday 5 June - Friday 8 June 2018, Centre de Conférences, UPMC, Sorbonne Université, Paris. Proceedings. EUNIS European University Information Systems, Paris, 2018The discipline of data science emerged to combine statistical methods with computing. At Aalto University, Finland, we have taken first steps to bring educational data science as a part of daily operations of Management Information Services. This required changes in IT environment: we enhanced data warehouse infrastructure with a data science lab, where we can read predictive model training data from data warehouse database and use the created predictive models in database queries. We then conducted a data science pilot with an objective to predict students’ graduation probability and time-to-degree with student registry data. Further ethical and legal considerations are needed before using predictions in daily operations of the university.Peer reviewe

BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS

This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems

Developing A New Decision Support System for University Student Recruitment

This paper investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The paper describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs’ analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The paper investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a DW structure and knowledge management tools within the decision making framework. 2000 records have been used to build and test the data mining techniques within the KDD process. The records were drawn from the Arab Academy for Science and Technology and Maritime Transport (AASTMT) students’ database (DB). Moreover, the paper has analyzed the key characteristics of DW and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a DW for the Egyptian Universities that handle their admission and registration related archival data. The decision makers’ potential benefits of the DW within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MS-SQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUSTAN Graphics (5.0) by CLUSTAN software will also be used as a clustering package. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems

Higher Education Meets Business Intelligence

Abstract In an ever-changing market powered by user satisfaction and financial success, Higher Education institutions must focus on data analytics to improve student satisfaction and business processes. This project underlines the importance of using a powerful data analytics tool to accomplish these goals. Many Higher Education institutions already collect the necessary data in order to predict and determine key changes but still pull this information from multiple databases in individual reports without overlapping benefit or any level of efficiency. The previous systems increase the risk of user error and limit the ability for multiple departments to collaborate and gain insights found through the combination of reports pulled from a campus-wide data source. Through a review of case studies and hands-on use of IBM Cognos data analytics tool, this study addresses the already acknowledged, and also personally obtained, benefits of Business Intelligence in real world scenarios unique to Higher Education. Exceptional data management and accessibility create opportunities for improved student retention rates leading to stronger departments and higher graduation rates. While improving student retention, student satisfaction increases and the institution often attracts more motivated and qualified students experiencing an increase in admission rates. Many Higher Education Institutions are also using Business Intelligence (BI) tools to pull reports leading to options for overall cost reduction. These cuts come in the form of smarter buildings and also fewer professionals needed for creating the BI reports. This project includes the following sections: Introduction, Background, Statement of the Problem, Business Component, Technology Component, Results, and Conclusion

Artificial Intelligent Enabled Supply Chains as a Competitive Advantage

The focus of this paper is on the topics of artificial intelligence and supply chain management and how artificial intelligence-enabled supply chains provide organizations with competitive advantages. The supply chain’s adoption of data collection technologies as part of digital transformation and movements of industry 4.0 creates a strong foundation for artificial intelligence analytics. Artificial intelligence has three branches sensing and interacting, decision-making, and learning. Each branch uses its algorithms and serves a different purpose for the business. Artificial intelligence-enabled supply chains create unique, inimitable competitive advantages that fit Michael Porter’s five forces

Modeling a Longitudinal Relational Research Data System

A study was conducted to propose a research-based model for a longitudinal data research system that addressed recommendations from a synthesis of literature related to: (1) needs reported by the U.S. Department of Education, (2) the twelve mandatory elements that define federally approved state longitudinal data systems (SLDS), (3) the constraints experienced by seven Midwestern states toward providing access to essential educational and employment data, and (4) constraints reported by experts in data warehousing systems. The review of literature investigated U.S. government legislation related to SLDS and protection of personally identifiable information, SLDS design and complexity, repurposing business data warehouse systems for educational outcomes research, and the use of longitudinal research systems for education and employment outcomes. The results were integrated with practitioner experience to derive design objectives and design elements for a model system optimized for longitudinal research. The resulting model incorporated a design-build engineering approach to achieve a cost effective, obsolescence-resistant, and scalable design. The software application has robust security features, is compatible with Macintosh and PC computers, and is capable of two-way live connections with industry standard database hardware and software. Design features included: (1) An inverted formal planning process to connect decision makers and data users to the sources of data through development of local interactive research planning tools, (2) a data processing module that replaced personally identifiable information with a system-generated code to support the use of de-identified disaggregate raw data across tables and agencies in all phases of data storage, retrieval, analysis, visualization, and reporting in compliance with restrictions on disclosure of personally identifiable information, (3) functionality to support complex statistical analysis across data tables using knowledge discovery in databases and data mining techniques, and (4) integrated training for users. The longitudinal research database model demonstrates the result of a top down-bottom up design process which starts with defining strategic and operational planning goals and the data that must be collected and analyzed to support them. The process continues with analyzing and reporting data in a mathematically programmed, fully functional system operated by multiple level users that could be more effective and less costly than repurposed business data warehouse systems

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Designing and Implementing a Data Warehouse using Dimensional Modeling

As a part of the business intelligence activities initiated at the University of New Mexico (UNM) in the O ce of Institutional Analytics, a need for a data warehouse was established. The goal of the data warehouse is to host data related to students, faculty, sta , nance data and research and make it readily available for the purposes of university analytics. In addition, this data warehouse will be used to generate required reports and help the university better analyze student success activities. In order to build real-time reports, it is essential that the massive amounts of transactional data related to university activities be structured in a way that is op- timal for querying and reporting. This transactional data is stored in relational databases in an Operational Data Store (ODS) at UNM. But for reporting purposes, this design currently requires scores of database join operations between relational database views in order to answer even simple questions. Apart from a ecting per- formance, i.e., the time taken to run these reports, development time is also a factor, as it is very di cult to comprehend the complex data models associated with the ODS in order to generate the appropriate queries. Dimensional modeling was employed to address this issue. Dimensional mod- eling was developed by two pioneers in the eld, Bill Inmon and Ralph Kimball. This thesis explores both methods and implements Kimball\u27s method of dimensional modeling leading to a dimensional data mart based on a star schema design that was implemented using a high performance commercial database. In addition, a data integration tool was used for performing extract-transform-load (ETL) operations necessary to develop jobs and design work ows and to automate the loading of data into the data mart. HTML reports were developed from the data mart using a reporting tool and performance was evaluated relative to reports generated directly from the ODS. On average, the reports developed on top of the data mart were at least 65% faster than those generated from directly from the ODS. One of the reason for this is because the number of joins between tables were drastically reduced. Another reason is that in the ODS, reports were built against views which when queried are slower to perform as compared to reports developed against tables

Advising the whole student: eAdvising analytics and the contextual suppression of advisor values

Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies