A Survey on Educational Data Mining [2014-2019]

Aberbach, Hicham; Jeghal, Adil; Sabri, My Abdelouahed; Tairi, Hamid

doi:10.1109/iscv49265.2020.9204013

Cited by 28 publications

(21 citation statements)

References 7 publications

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“…Hicham et al [ 3 ] introduced an analytical hierarchical technique for assessing teachers' performance, which included both quantitative and qualitative analysis, to make the recommendations. Examining if the credential comparability design is appropriate by maintaining neutrality in subjective assessment by different social environments and colleges and whether it is feasible to achieve this objective.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Human listeners will be able to recognize the difference between conversations with machines and those with other humans if the system is able to detect it, and if it does not, we will confess to having artificial intelligence (AI). It was later in 1956 that John McCarthy came up with the most comprehensive definition of artificial intelligence [ 3 ]: “AI is based on the premise... the intelligence that a machine or a programme shows; every component of learning or any other quality.”…”

Section: Introductionmentioning

confidence: 99%

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Human Intelligence Analysis through Perception of AI in Teaching and Learning

Kshirsagar

Jagannadham²,

Alqahtani

et al. 2022

Computational Intelligence and Neuroscience

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Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students’ assistance, teaching, learning, and administration are also addressed in the discussion of students’ adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students’ attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Intelligence Analysis through Perception of AI in Teaching and Learning

Kshirsagar

Jagannadham²,

Alqahtani

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…One of the tasks of data mining is to group or cluster data without sample groups. Data mining is also the process of finding valuable correlations, patterns, and emerging trends in big data storage media using pattern recognition techniques such as statistical and mathematical techniques (Hicham, Jeghal, Sabri, & Tairi, 2020). Other terms often used include knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging (Lisnawati & Sinaga, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

Suwirya

Candiasa

Dantes

2022

CNAHPC

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The existence of an ATM location requires a placement evaluation that aims to support business and provide convenience and comfort to customers when using or transacting. This study aims to evaluate the placement of ATM locations using the K-Means method, and research using data obtained from data mining to obtain decisions that lead to not strategic, strategic, and very strategic ATM locations. This study uses data sourced from the BNI ATM database and performance data in one semester or six months, namely January to June 2021, as many as 121 ATM locations spread across the island of Bali. The application of the K-Means Algorithm in this study uses 6 clustering criteria, namely ATMs Usage, ATMs Fee-Based Income/ FBI, ATMs Service Level Agreement/ SLA, ATMs distance, competitor ATMs, and Business Distance. In addition to presenting calculations using spreadsheets, this research also produces implementations in web-based software. The results of alternative classifications based on K-Means on very strategic centroids of 27 locations or covering 22.31%, strategic several 77 locations or covering 63.64%, and non-strategic 17 locations or covering 14.05%. Although the location of ATMs that are classified as non-strategic criteria is quite small, this can be optimized by several strategic steps that can be taken by stakeholders within the company, such as evaluating the bank's business plan in 2022 and improving the supervision of machines at ATM locations.

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“…3. The concept of data mining is now used in a vast range of applications of all forms in the medical fields and security, financial data collection, media, and in particular, education [4]. For example, in the education field, the establishment can have the information required to take decisions before certain learners may drop out, or accurately select resources with a reliable estimation of how many learners may attend a specific course.…”

Section: Fig 3 Knowledge Discovery In Databases Processmentioning

confidence: 99%

“…They must help to make decisions. In recently, Educational Data Mining (EDM) is an autonomous research area with a set of machine and behavioral methods and analysis techniques to help understand learners and their teaching methods [4]; EDM develops strategies and applies techniques from machine learning, statistics, and data processing [5] to improve the quality of education. To enhance the level of learning, EDM builds solutions and implements approaches from machine learning and data analysis.…”

Section: Introductionmentioning

confidence: 99%

E-Learning Recommendation System for Big Data Based on Cloud Computing

Rahhali

Oughdir

Jedidi³

2021

Int. J. Emerg. Technol. Learn.

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In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.

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A Survey on Educational Data Mining [2014-2019]

Cited by 28 publications

References 7 publications

Human Intelligence Analysis through Perception of AI in Teaching and Learning

Human Intelligence Analysis through Perception of AI in Teaching and Learning

Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

E-Learning Recommendation System for Big Data Based on Cloud Computing

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