Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective.

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Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

Electronics ◽

10.3390/electronics10212620 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2620

Author(s):

María Consuelo Sáiz-Manzanares ◽

Raúl Marticorena-Sánchez ◽

Javier Ochoa-Orihuel

Keyword(s):

Machine Learning ◽

Student Satisfaction ◽

Learning Outcomes ◽

Educational Data Mining ◽

Learning Technologies ◽

Machine Learning Techniques ◽

Self Regulated Learning ◽

Learning Techniques ◽

Automatic Feedback ◽

Teaching Learning

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

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Rethinking the selection of learning technologies

Australasian Journal of Educational Technology ◽

10.14742/ajet.2343 ◽

1989 ◽

Vol 5 (2) ◽

Author(s):

John G. Hedberg

Keyword(s):

Information Technologies ◽

Learning Task ◽

Learning Technologies ◽

Learning Activities ◽

Conceptual Representations ◽

Modern Learning ◽

Teaching Learning ◽

Selection Of

<span>In this paper, the criteria for selecting modern learning technologies are discussed and it is suggested that four teaching/learning activities might form the basis for selection combined with a number of types of conceptual representations. The most important aspects for a designer are the match between learning task and its ability to be presented or manipulated by the learner using a decreasing range of information technologies.</span>

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Computing and ICT Literacy

Technology Literacy Applications in Learning Environments ◽

10.4018/978-1-59140-479-8.ch003 ◽

2005 ◽

pp. 37-48

Author(s):

Antonio Cartelli

Keyword(s):

Data Mining ◽

Action Research ◽

Learning Process ◽

Ict Literacy ◽

Research Strategies ◽

Data Mining Techniques ◽

Web Technologies ◽

Computing Skills ◽

Teaching Learning ◽

School Curricula

Three main questions guided the author in the writing of this chapter: Is there the need for a widespread and in-depth ICT literacy in mankind? What has to be meant for ICT literacy? And are there special problems in students’ learning of ICT topics? And last but not least: How can ICTs themselves improve teachers’ work and students’ learning on ICTs? The introduction answers the first question and shows how difficult the search can be for solutions to the problem of the digital divide. The answer to the second question comes from a short survey of the experiences that some institutions made for the introduction of basic computing skills and ICT literacy in school curricula. In the meantime the problems that the students usually meet while attending computer programming and ICT literacy courses are described. Finally the author reports the results of some experiences involving the use of ICTs in teaching and describes how he arrived to hypothesize the adoption of action research strategies, of Web technologies and data mining techniques for the monitoring of the teaching-learning process and its improvement.

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Computing and ICT Literacy

Information Communication Technologies ◽

10.4018/978-1-59904-949-6.ch235 ◽

2008 ◽

pp. 3338-3347

Author(s):

Antonio Cartelli

Keyword(s):

Data Mining ◽

Action Research ◽

Learning Process ◽

Ict Literacy ◽

Research Strategies ◽

Data Mining Techniques ◽

Web Technologies ◽

Computing Skills ◽

Teaching Learning ◽

School Curricula

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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Journal of Visualized Experiments ◽

10.3791/62103 ◽

2021 ◽

Author(s):

María Consuelo Sáiz Manzanares ◽

René Jesús Payo Hernanz ◽

María José Zaparaín Yáñez ◽

Gonzalo Andrés López ◽

Raúl Marticorena Sánchez ◽

...

Keyword(s):

Data Mining ◽

Eye Tracking ◽

Learning Processes ◽

Behavioral Analysis ◽

Data Mining Techniques ◽

Tracking Technology

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Application of data mining techniques for identifying the holistic athlete's characteristics

PsycEXTRA Dataset ◽

10.1037/e548052012-458 ◽

2007 ◽

Author(s):

Stavroula Psouni ◽

Dimitris Psounis

Keyword(s):

Data Mining ◽

Data Mining Techniques

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

ACMIT Proceedings ◽

10.33555/acmit.v1i1.13 ◽

2019 ◽

Vol 1 (1) ◽

pp. 121-131

Author(s):

Ali Fauzi

Keyword(s):

Data Mining ◽

Business Intelligence ◽

Direct Investment ◽

Capital Investment ◽

Export Market ◽

Added Value ◽

Knowledge Generation ◽

Management Scenarios ◽

Data Mining Techniques ◽

Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i10.68 ◽

2017 ◽

Vol 6 (10) ◽

pp. 60

Author(s):

S. K. Saravanan ◽

G. N. K. Suresh Babu

Keyword(s):

Data Mining ◽

Credit Card ◽

Data Transfer ◽

Study Data ◽

Time Requirement ◽

Literature Study ◽

Data Mining Techniques ◽

Online Transactions ◽

Secured Data ◽

Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

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