Browsing by Author "Barbu, Marian"
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- Characterization of engineering student profiles at european institutions by using speet it-toolPublication . Vilanova, Ramon; Vicario, Jose; Prada, Miguel Angel; Barbu, Marian; Dominguez, Manuel; Pereira, Maria Maria; Podpora, Michal; Spagnolini, Umberto; Alves, Paulo; Paganoni, AnnaThe international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. The provided tools are freely available to anyone that has academic data to explore. The paper will present the architecture that is behind the presented IT tool, input data needed to operate and main functionalities as well as examples of use to show how academic data can be interpreted.
- Data mining tool for academic data exploitation: graphical data analysis and visualizationPublication . Prada, Miguel Angel; Dominguez, Manuel; Morán, Antonio; Vilanova, Ramon; Vicario, José; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Barbu, Marian; Torrebruno, Aldo; Spagnolini, Umberto; Paganoni, AnnaThe vast amount of data collected by higher education institutions and the growing availability of analytic tools, makes it increasingly interesting to apply data mining in order to support educational or managerial goals. The SPEET (Student Profile for Enhancing Engineering Tutoring) project aims to determine and categorize the different profiles for engineering students across Europe, in order to improve tutoring actions so that they help students to achieve better results and to complete the degree successfully. For that purpose, it is proposed to perform an analysis of student record data, obtained from the academic offices of the Engineering Schools/Faculties of the institutions. The application of machine learning techniques to provide an automatic analysis of academic data is a common approach in the fields of Educational Data Mining (EDM) and Learning Analytics (LA). Nevertheless, it is often interesting to involve the human analyst in the task of knowledge discovery. Visual analytics, understood as a blend of information visualization and advanced computational methods, is useful for the analysis and understanding of complex processes, especially when data are nonhomogeneous or noisy. The reason is that taking advantage of the ability of humans to detect structure in complex visual presentations, as well as their flexibility and ability to apply prior knowledge, facilitates the process aimed to understand the data, to identify their nature, and to create hypotheses. For that purpose, visual analytics uses several strategies, such as preattentive processing and visual recall, that reduce cognitive load. But a key feature is the interactive manipulation of resources, which is used to drive a semi-automated analytical process that enables a dialog between the human and the tool. During this human-in-the-loop process, analysts iteratively update their understanding of data, to meet the evidence discovered through exploration. This report documents the steps conducted to design and develop an IT Tool for Graphical Data Analysis Visualization within the SPEET1 ERASMUS+ project. The proposed goals are aligned with those of the project, i.e., to provide insight into student behaviors, to identify patterns and relevantfactors of academic success, to facilitate the discovery and understanding of profiles of engineering students, and to analyze the differences across European institutions. And the intended use of the tool is to provide support to tutoring. For that purpose, the concepts and methods used for the visual analysis of educational data are reviewed and a tool is proposed, which implements approaches based on interaction and the integration of machine learning. For the implementation details and validation of the tool, a data set has been proposed. It only includes variables present in a typical student record, such as the details of the student (age, geographical information, previous studies and family background), school, degree, courses undertaken, scores, etc. Although the scope of this data set is limited, similar data structures have recently been used in developments oriented to the prediction of performance and detection of drop-outs or students at risk. In the third chapter, the report presents, describes and structures the academic data set which is used as a basis for the visual analysis. Chapter 4 reviews the concepts, goals and applications of visual data exploration, specifically of interactive visual analytics in the framework of educational data mining. Chapter 5 discusses visual analysis methods that are interesting for the proposed goals, which include providing insights of behaviors, patterns and factors of success, both locally and across European institutions. The proposed methods are initially presented and, later, applied to subject of study. The last chapter describes the tool implementation. For that purpose, the design and the technologies used for its implementation are presented, the availability of the tool is discussed, and a short user guide is included.
- Data mining tool for academic data exploitation: literature review and first architecture proposalPublication . Barbu, Marian; Vilanova, Ramon; Lopez Vicario, José; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Ángel Prada, Miguel; Morán, Antonio; Torreburno, Aldo; Marin, Simona; Tocu, RodicaUsing data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets. Data itself is not new. Data has always been generated and used to inform decision-making. However, most of this was structured and organised, through regular data collections, surveys, etc. What is new, with the invention and dominance of the Internet and the expansion of digital systems across all sectors, is the amount of unstructured data we are generating. This is what we call the digital footprint: the traces that individuals leave behind as they interact with their increasingly digital world. Data analytics is the process where data is collected and analysed in order to identify patterns, make predictions, and inform business decisions. Our capacity to perform increasingly sophisticated analytics is changing the way we make predictions and decisions, with huge potential to improve competitive intelligence. These examples suggest that the actions from data mining and analytics are always automatic, but that is less often the case. Educational Data Mining (EDM) and Learning Analytics (LA) have the potential to make visible data that have heretofore gone unseen, unnoticed, and therefore unactionable. To help further the fields and gain value from their practical applications, the recommendations are that educators and administrators: • Develop a culture of using data for making instructional decisions; • Involve IT departments in planning for data collection and use; • Be smart data consumers who ask critical questions about commercial offerings and create demand for the most useful features and uses; • Start with focused areas where data will help, show success, and then expand to new areas; • Communicate with students and parents about where data come from and how the data are used; • Help align state policies with technical requirements for online learning systems. This report documents the first steps conducted within the SPEET1 ERASMUS+ project. It describes the conceptualization of a practical tool for the application of EDM/LA techniques to currently available academic data. The document is also intended to contextualise the use of Big Data within the academic sector, with special emphasis on the role that student profiles and student clustering do have in support tutoring actions. The report describes the promise of educational data mining (seeking patterns in data across many student actions), learning analytics (applying predictive models that provide actionable information), and visual data analytics (interactive displays of analyzed data) and how they might serve the future of personalized learning and the development and continuous improvement of adaptive systems. How might they operate in an adaptive learning system? What inputs and outputs are to be expected? In the next sections, these questions are addressed by giving a system-level view of how data mining and analytics could improve teaching and learning by creating feedback loops. Finally, the proposal of the key elements that conform a software application that is intended to give support to this academic data analysis is presented. Three different key elements are presented: data, algorithms and application architecture. From one side we should have a minimum data available. The corresponding relational data base structure is presented. This basic data can always be complemented with other available data that may help to decide or/and to explain decisions. Classification algorithms are reviewed and is presented how they can be used for the generation of the student clustering problem. A convenient software architecture will act as an umbrella that connects the previous two parts. The document is intended to be useful for a first understanding of academic data analysis. What we can get and what we do need to do. This is the first of a series of reports that taken all together will provide a complete and consistent view towards the inclusion of data mining as a helping hand in the tutoring action.
- Data mining tool for academic data exploitation: publication report on engineering students profilesPublication . Barbu, Marian; Vilanova, Ramon; Vicario, José; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Kawala-Janik, A.; Prada, Miguel Angel; Dominguez, Manuel; Spagnolini, Anna; Fontana, L.This report summarizes the findings of the project SPEET. It relies on the initial document generated as Intellectual Output #1 and the results obtained by application of the IT tools developed in Intellectual Output #2, and Intellectual Output #3 to the academic data provided by the partner institutions. The main objectives of applying analytic techniques to evaluate the academic data sources can be categorized as follows: Improve Student Results; Create Mass-customized Programs; Improve the Learning Experience in Real-time; Reduce Dropouts and Increase Results.
- Data mining tool for academic data exploitation: selection of most suitable algorithmsPublication . Vicario, José; Vilanova, Ramon; Bazzarelli, M.; Paganoni, Anna; Spagnolini, Umberto; Torrebruno, Aldo; Prada, Miguel Angel; Morán, Antonio; Dominguez, Manuel; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Barbu, MarianSPEET project is aimed at exploiting the potential synergy among the huge amount of academic data actually existing at universities and the maturity of data science in order to provide tools to extract information from students’ data. A rich picture can be extracted from this data if conveniently processed. The purpose of this project is to apply data mining algorithms to process this data in order to extract information about and to identify student profiles. In this document, the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The document starts by addressing the motivation of the development of data mining tools along with the considerations taken into account for academic data gathering. These considerations include the proposed unified dataset format and some details about confidentiality issues. Next, the students’ clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Besides, a discussion of obtained results when considering data belonging to the different SPEET project’s partners is addressed. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated.
- Data-driven tool for monitoring of students performancePublication . Vilanova, Ramon; Dominguez, Manuel; Vicario, José; Prada, Miguel Angel; Barbu, Marian; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Spagnolini, Umberto; Paganoni, AnnaIn today's education, school success is defned as ensuring achievement for every student. To reach this goal, educators need tools to help them identify students who are at risk academically and adjust instructional strategies to better meet these students' needs. Student progress monitoring is a practice that helps teachers use student performance data to continually evaluate the effectiveness of their teaching and make more informed instructional decisions. This paper reflects the main output of the SPEET project as an IT tool that implements specific algorithms developed to deal with the basic problems tackled in the project: Classification, Clustering and Drop-out Prediction.
- Educational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degreesPublication . Prada, Miguel Angel; Dominguez, Manuel; Vicario, Jose Lopez; Alves, Paulo; Barbu, Marian; Podpora, Michal; Spagnolini, Umberto; Pereira, Maria João; Vilanova, RamonThis paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.
- SPEET: an international collaborative experience in data mining for educationPublication . Vilanova, Ramon; Vicario, José; Prada, Miguel Angel; Barbu, Marian; Dominguez, Manuel; Pereira, Maria João; Podpora, Michal; Spagnolini, Umberto; Alves, Paulo; Paganoni, AnnaThis paper presents the collaborative experience that is under development as the European ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring). This project goal emerges from the potential synergy among a) the huge amount of academic data actually existing at the academic departments of faculties and schools, and b) the maturity of data science in order to provide algorithms and tools to analyse and extract information from the available large amount of data. A rich picture can be extracted from this data if conveniently processed. The main purpose of this project is to apply data mining algorithms to process this data in order to extract information about and to identify student profiles. Some examples of the student profile we are referring to within the project scope is, for example: Students that finish degree on time, Students that are blocked on a certain set of subjects, Students that leave degree earlier, etc
- SPEET: software tools for academic data analysisPublication . Vilanova, Ramon; Vicario, José; Prada, Miguel Angel; Barbu, Marian; Dominguez, Manuel; Pereira, Maria João; Popdora, Michal; Spagnolini, Umberto; Alves, Paulo; Paganoni, AnnaThe international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. This work reflects the outputs of the SPEET project in relation to the data mining tools, specific algorithms developed to deal with the basic problems tackled in the project: Classification, Clustering and Drop-out Prediction.
- SPEET: visual data analysis of engineering students performance from academic dataPublication . Dominguez, Manuel; Vilanova, Ramon; Prada, Miguel Angel; Vicario, José; Barbu, Marian; Pereira, Maria João; Podpora, Michal; Spagnolini, Umberto; Alves, Paulo; Paganoni, AnnaThis paper presents the steps conducted to design and develop an IT Tool for Visual Data Analysis within the SPEET (Student Profile for Enhancing Engineering Tutoring) ERASMUS+ project. The proposed goals are to provide insight into student behaviours, to identify patterns and relevant factors of academic success, to facilitate the discovery and understanding of profiles of engineering students, and to analyse the difierences across European institutions. For that purpose, the concepts and methods used for the visual analysis of educational data are reviewed and a tool is proposed, which implements approaches based on visual interaction.