Meta-learning: Can It Be Suitable to Automatise the KDD Process for the Educational Domain?

Zorrilla, Marta E.; García‐Saiz, Diego

doi:10.1007/978-3-319-08729-0_28

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…The methods are implemented by several algorithms, and several were used in the selected papers. The most widely used Decision Tree algorithm is C4.5 [Zorrilla and Garcia-Saiz 2014], [Romero et al 2013a], [Hu et al 2014], followed by Simple Cart [Zorrilla and Garcia-Saiz 2014], [Romero et al 2013a], [Hu et al 2014], and Random Trees [Romero et al 2008], [Romero et al 2013a], [Hu et al 2014 [Romero et al 2013a]. Other popular algorithms are: K-means clustering [Moradi et al 2014], [Jovanovic et al 2012], [Pardos et al 2012], [Mogus et al 2012], [Sorour et al 2014]; K-nearest neighbor (KNN) [Zorrilla and Garcia-Saiz 2014], [Kotsiantis et al 2010], [Minaei-Bidgoli et al 2003], [Gamulin et al 2016]; and JRip [Zorrilla and Garcia-Saiz 2014], [Márquez-Vera et al 2013].…”

Section: Q01 Which Data Mining Techniques and Methods Were Used?mentioning

confidence: 99%

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Felix¹,

Ambrósio²,

Lima³

et al. 2018

Anais Do XXIX Simpósio Brasileiro De Informática Na Educação (SBIE 2018)

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Virtual learning environments facilitate online learning, generating and storing large amounts of data during the learning/teaching process. This stored data enables extraction of valuable information using data mining. In this article, we present a systematic mapping, containing 42 papers, where data mining techniques are applied to predict students performance using Moodle data. Results show that decision trees are the most used classification approach. Furthermore, students interactions in forums are the main Moodle attribute analyzed by researchers.This systematic mapping aims to identify what type of analysis is being used to predict students' performance on Moodle, using data mining. It is organized as follows:

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Section: Q01 Which Data Mining Techniques and Methods Were Used?mentioning

confidence: 99%

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Felix¹,

Ambrósio²,

Lima³

et al. 2018

Anais Do XXIX Simpósio Brasileiro De Informática Na Educação (SBIE 2018)

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“…Regarding what meta-features to use, in general, measurable properties of data sets and algorithms are chosen, for instance, statistical or information-theoretical measures [47], landmarkers [48] or model properties such as the average ratio of bias, variance error, or their sensitivity to noise [49] among others. The data context and its complexity for learning task are also used [50].…”

Section: Related Workmentioning

confidence: 99%

“…We also consider extraction of meta-features in the sense of some recent works [57,50] that have used a set of data complexity metrics (provided by DCoL tool [58] which measures characteristics of the data independently of the learning method) as meta-features. A description of the DCoL meta-features is included in Appendix B.…”

Section: Extraction Of Meta-featuresmentioning

confidence: 99%

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Espinosa

García‐Saiz

Zorrilla

et al. 2019

Computer Standards & Interfaces

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Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers' recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework's workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework.

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“…The set of algorithms recommended for the new data set then coincided with the set of algorithms of its nearest neighbor. A more recent work that applied meta-learning to EDM was the paper by Zorrilla and Garcia 53 , were meta-learning is used to build a recommender that help instructors (as non-expert data miners) in applying the right DM algorithm on their data sets. It is also worth noting the work by Zapata et al 54 , where meta-learning techniques are used in the field of learning objects recommendation in order to automatically obtain or predict the final ratings.…”

Section: Related Workmentioning

confidence: 99%

Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

Olmo¹,

Romero²,

Gibaja³

et al. 2015

IJCIS

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Recommending classification algorithms is an open research problem the solution to which is of tremendous value for practitioners and non-experts data mining users such as educators. This paper proposes a new meta-learning framework for educational domains based on the use of multi-label learning for selecting the best classification algorithms in order to predict students' performance. In short, the framework considers an offline phase where statistical tests are performed to find the subset of algorithms that achieves the best performance over the repository of educational data sets. The subset of algorithms along with the meta-features extracted from the training data are used to generate a multi-label data set. A multi-label classifier is then trained and, in an online phase, this model is used to recommend the most suitable classification algorithms to be applied to new unseen data sets. This new multi-label metalearning approach has been applied to a repository of educational data sets generated from Moodle usage data. The results obtained show significant improvement compared with a previous nearest neighbor proposal, demonstrating the suitability of the new framework.

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Meta-learning: Can It Be Suitable to Automatise the KDD Process for the Educational Domain?

Cited by 8 publications

References 7 publications

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

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