Advisor(s)
Abstract(s)
The 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.
Description
Keywords
Academic analytics Learning analytics Big data edication Educational data minig Student profile Dropout prevention
Citation
Prada, Miguel; Dominguez, Manuel; Morán, Antonio; Vilanova, Ramon; Vicario, Jose; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Barbu, Marian; Torrebruno, Aldo; Spagnolini, Umberto; Paganoni, Anna (2018). Data mining tool for academic data exploitation: graphical data analysis and visualization. ERASMUS + KA2/KA203