Comparison of applications for educational data mining in Engineering Education

Buenaño-Fernández, Diego; Luján‐Mora, Sergio

doi:10.1109/edunine.2017.7918187

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…Based on the previous research (Jormanainen and Sutinen 2013;Delibasic et al 2013;Kovanović et al 2017;Zhang and Qin 2018;Slater et al 2017;Asif et al 2017;Fernández and Luján-Mora 2017;Khan et al 2019), we made an assumption that end-users benefit from the open-ended EDM process in the form of more interpretable and contextual support and decision making tools. We have presented in this paper a study that was conducted to asses whether the AUI method and the implemented Neural N-Tree would increase its accuracy over time when the end-users were adjusting the predictive models.…”

Section: Discussionmentioning

confidence: 99%

Augmented intelligence in educational data mining

Suhonen

Jormanainen

Tukiainen

2019

Smart Learn. Environ.

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Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple studies have shown how the adjusting and even creating the decision tree classifiers help EDM end-users to better comprehend the dataset and the context where the data has been collected. To harness the power of such open-ended approach in EDM, we introduce a novel Augmented Intelligence method and a cluster analysis algorithm Neural N-Tree. These contributions allow EDM end-users to analyze educational data in an iterative process where the knowledge discovery and the accuracy of the predictive model generated by the algorithm increases over time through the interactions between the models and the end-users. In contrast to other similar approaches, the key in our method is in the model adjusting and not in parameter tuning. We report a study where the potential EDM end-users clustered data from an education setting and interacted with Neural N-Tree models by following Augmented Intelligence method. The findings of the study suggest that the accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the study indicates that the knowledge discovery is possible through AUI.

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Section: Discussionmentioning

confidence: 99%

Augmented intelligence in educational data mining

Suhonen

Jormanainen

Tukiainen

2019

Smart Learn. Environ.

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“…It basically contains applications for data pre-processing, association rules, classification, clustering, regression and visualization [1]. Engineering under-graduate students of three different cohorts of three different technologies with three different courses have been studied comparatively to trace their pedagogical progress using sessional (pre-examination marks) in particular courses.…”

Section: Data Preprocessing and Methodologymentioning

confidence: 99%

“…The Weka (Waikato Environment for Knowledge Analysis) tool (Knowledge Explorer) is the useful GUI (Graphical User Interface) tool with the collection of major machine learning algorithms coded in java [1], [14], [20]. It basically contains applications for data pre-processing, association rules, classification, clustering, regression and visualization [1].…”

Section: Data Preprocessing and Methodologymentioning

confidence: 99%

“…Diego Buenaño Fernández et al [1] have compared three open source tools (Weka, Rapid Miner, and Knime) on different academic records of students. Analysis based on three engineering programs (Network and Telecommunications Engineering, Computer and Information Systems Engineering and Electronics and Information Networks Engineering.)…”

Section: Related Workmentioning

confidence: 99%

“…There are various applications used in educational environment in the form of archives, images, blogs, audios, videos, artifacts, scientific documents, meta-data or online hyperlinks or in various other formats [1]. Educational institutes with variety of educational data like student attendance records, examination records, fees records, personal information, etc., entails to be managed and tracked time to time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Engineering Students Pedagogical Progress

Mahboob¹,

Syed²,

Ur³

et al. 2018

ijacsa

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Abstract-Students' pedagogical progress plays a pivotal role in any educational institute in order to pursue imperative education. Educational institutes, Universities, Colleges implement various performance measures in order to keep analyzing and tracking progress of students to cultivate benefits of education in a better way. There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions. This study aims to analyze and track engineering under graduate student's records to judge quality education, student motivation towards learning, and student pedagogical progress to maintain education at high quality level and predicting engineering student's forthcoming progress. Different engineering discipline students' (of three different cohorts) data have been analyzed for tracing current as well as future pedagogical progress based on their sessional (preexamination) marks. In this research, the classification techniques by k-nearest neighbor, Naïve Bayes and decision trees are applied to evaluate different engineering technologies student's performance and also there are different methodologies that can be used for data classification.

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The use of tools of data mining to decision making in engineering education—A systematic mapping study

Buenaño-Fernández

Villegas-Ch

Luján‐Mora³

2019

Comp Applic In Engineering

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In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas.

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Comparison of applications for educational data mining in Engineering Education

Cited by 15 publications

References 7 publications

Augmented intelligence in educational data mining

Augmented intelligence in educational data mining

A Comparative Study of Engineering Students Pedagogical Progress

The use of tools of data mining to decision making in engineering education—A systematic mapping study

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