Academic Analytics: Mapping the Genome of the University

Ferreira, Sérgio André; Andrade, António

doi:10.1109/rita.2014.2340019

Cited by 10 publications

(6 citation statements)

References 4 publications

(4 reference statements)

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“…Academic analytics sometime use interchangeably with learning analytics by some authors, but lately these two have been differentiated as follows: academic analytics is at institutional level and for the benefit of administrators and learning analytics is at course level and for the benefit of instructors and students or learners [3], [8], [9].…”

Section: Academic Analyticsmentioning

confidence: 99%

“…The use of academic analytics is currently at infancy stage [3]. Academic analytics can be used for enrollment management and improvement of teaching and learning.…”

Section: Academic Analyticsmentioning

confidence: 99%

“…Output from predictive model will be used for designing intervention program to assist at-risk students improve their performance. Author [3], [13], [14], [15], [16] said that early warning notification system can be developed for administrators, instructors and students for student performance monitoring.…”

Section: Academic Analyticsmentioning

confidence: 99%

“…There are many definitions for analytics have been used by different authors, for example analytics is decision making based on data where information is used to support and justify decision at all levels of the company [3]. Gartner uses term analytics to describe statistical and mathematical data analysis that cluster, segment, scores and predict what scenario is most likely to happen [4].…”

Section: Introductionmentioning

confidence: 99%

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Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

et al. 2019

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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.

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Section: Academic Analyticsmentioning

confidence: 99%

“…The use of academic analytics is currently at infancy stage [3]. Academic analytics can be used for enrollment management and improvement of teaching and learning.…”

Section: Academic Analyticsmentioning

confidence: 99%

Section: Academic Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

et al. 2019

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“…These structures and the different actors of the HEIs are in need of Analytics systems with different scales and granularities. Ferreira and Andrade summarize the use of Analytics at the macro and micro scales, emphasizing the complementary relationship between them [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques

Conde

García‐Peñalvo

Gómez-Aguilar

et al. 2015

IEEE R. Iberoamericana Tecnologias Aprendizaje

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The application of the Information and Communication Technologies to teaching and learning processes is linked to the development of new tools and services that can help students and teachers. Learning platforms are a clear example of this. They are very popular tools in eLearning contexts and provide different kinds of learning applications and services. In addition, these environments also register most of the interactions between the learning process stakeholders and the system. This information could potentially be used to make decisions but usually it is stored as raw data, which is very difficult to understand. This work presents a system that employs visual learning analytic techniques to facilitate the exploitation of that information. The system presented includes several tools that make possible to explore issues such as: when interaction is carried out, which contents are the most important for users, how they interact with others, etc. The system was tested in the context of a software engineering subject, taking into account the stored logs of five academic years. From this analysis it is possible to see how visual analytics can help decision-making and in this context how it helps to improve educational processes.

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Reference Model Supported in Academic Analytic for the Collection and Analysis of Data from Students and Teachers

Simanca

Garrido

Carreño

et al. 2020

Lecture Notes in Electrical Engineering

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Academic Analytics: Mapping the Genome of the University

Cited by 10 publications

References 4 publications

Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques

Reference Model Supported in Academic Analytic for the Collection and Analysis of Data from Students and Teachers

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