Aat

Graf, Sabine; Ives, Cindy; Rahman, Nazim; Ferri, Arnold

doi:10.1145/2090116.2090145

Cited by 44 publications

(9 citation statements)

References 4 publications

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“…The tests performed by the authors showed that the accuracy of the prediction by the RNN is higher than 90% using the registration data until the 6th week and that, compared with the multiple regression analysis, the RNN is effective for the early prediction of the final notes. Graf et al (2011) presented an Academic Analysis Tool (AAT) developed in the Moodle Analytics project that incorporates functionality to analyze data related to students' behavior in the learning systems. AAT is a software application that allows users to access and analyze student behavior data in learning systems, also it allows users to extract detailed information about how students interact and learn in online courses using a learning system.…”

Section: Resultsmentioning

confidence: 99%

“…Mixed graph of terms, obtained by using the latent directory allocation approach, as a tool for the classification of feelings. Graf et al (2011) Academic analysis tool developed in the Moodle Analytics project.…”

Section: Clarizia Et Al (2018)mentioning

confidence: 99%

“…The benefits can be categorized into two aspects: 1) methods and techniques and 2) intelligent services. The benefits categorized as methods and techniques are: software frameworks for developing prediction models (Olivé et al, 2018), educational data mining system (Lara et al, 2014), pipeline infrastructure (Chaffai et al, 2017), student performance prediction structure (Qu et al, 2018), system oriented to analysis of results (Zorrilla et al, 2010), algorithms (Ros et al, 2017), methods based on a latent statistical class (Sorour et al, 2015), application of data mining using computational techniques (Peñafiel et al, 2018), mixed chart of terms (Clarizia et al, 2018), opinion mining platform (Spatiotis et al, 2018), data mining techniques such as grouping and association (Islam et al, 2019), statistical machine learning technique (Hussain et al, 2018), distributed feedback analysis engine for Big Data (Altaf et al, 2019), neural networks to predict performance (Olanrewaju et al, 2016), learning style prediction method, system that uses (Yang et al, 2014), voting methodology (Kotsiantis et al, 2010), based learning behavioral model (Chang and Chu, 2010), clustering algorithms (Omar and Abdesselam, 2017), method for predicting students' final grades by an RNR (Okubo et al, 2017), academic analysis tool (Graf et al, 2011), complementary techniques -agglomerate hierarchical cluster, epistemic network analysis and process mining (Rayón et al, 2014), learning analysis methodology (Uzir et al, 2020), method based on Learning Analytics (Guo et al, 2020) and explanatory model of student behavior (Jovanović et al, 2020).…”

Section: Gq2 -What Benefits Have Been Obtained For Students Teachers and Managersmentioning

confidence: 99%

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Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Silva¹,

Dias²,

Barbosa³

et al. 2021

Informatics in Education

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Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

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Section: Resultsmentioning

confidence: 99%

Section: Clarizia Et Al (2018)mentioning

confidence: 99%

Section: Gq2 -What Benefits Have Been Obtained For Students Teachers and Managersmentioning

confidence: 99%

See 1 more Smart Citation

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Silva¹,

Dias²,

Barbosa³

et al. 2021

Informatics in Education

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show abstract

“…It is integrated into the Moodle interface as Bookmarklet, but only shows real‐time visualization of discussion forum activity. AAT or Academic Analytics Tool accesses and analyses students' behavior data in learning systems. Its objective is to allow the identification of difficult or inappropriate learning material in order to significantly contribute to the design of improved student support activities and resources.…”

Section: Background and Related Toolsmentioning

confidence: 99%

“…These tools are more specific and easier to be used by non‐expert users in DM than general or traditional DM tools . In this way, instructors are able to discover, in an easy way, hidden information about students’ behavior, and how students learn in the courses in a single and user‐friendly interface .…”

Section: Introductionmentioning

confidence: 99%

MDM tool: A data mining framework integrated into Moodle

Luna

Castro

Romero

2017

Comp Applic In Engineering

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The interest in developing Learning Analytics tools that can be integrated into the well-known Moodle course management systems is increasing nowadays. These tools generally provide some type of basic analytics and graphs about users' interaction in the course. However, they do not enable a varied set of Data Mining techniques to be applied, such as approaches for classification, clustering, or association. To address this issue, a new and freely available Moodle Data Mining tool, named MDM, has been proposed in this paper. The proposed tool eases the whole knowledge discovery process, including tasks such as selection, data pre-processing, and data mining from Moodle courses. The proposed MDM tool has been developed in PHP programming language, so it can be easily integrated into Moodle as a module for a specific course. Its main features and architecture are described in depth, and a tutorial is also provided as a practical way of using the MDM interface. Finally, some experimental results using a real-life sample dataset of mechanical engineering students are analyzed. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:90-102, 2017; View this article online at wileyonlinelibrary.com/ journal/cae;

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Data mining in education

Romero

Ventura

2012

WIREs Data Min & Knowl

657

377

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Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Business and Industry

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Aat

Cited by 44 publications

References 4 publications

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

MDM tool: A data mining framework integrated into Moodle

Data mining in education

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