Application of learning analytics using clustering data Mining for Students’ disposition analysis

Bharara, Sanyam; Sabitha, Sai; Bansal, Abhay

doi:10.1007/s10639-017-9645-7

Cited by 66 publications

(28 citation statements)

References 46 publications

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“…Learning analytics can be defined as the measurement, gathering, investigation and reporting of relevant data about student, and the learning process, and methods [41,42]. The research by Bharara et al [43] identified new metrics that are relevant to the learning process in order to develop a robust model for evaluating student performance. Using 4 categories of features; these are interactional, academic, the level of parent's participation in the education of their children, and demographic features.…”

Section: Related Literaturesmentioning

confidence: 99%

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Adekitan

Salau

2019

SN Appl. Sci.

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The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. Data mining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.

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Section: Related Literaturesmentioning

confidence: 99%

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Adekitan

Salau

2019

SN Appl. Sci.

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“…The formula to compute the value of centroids calculated by the Euclidean distance d is shown in Equation . Each cluster is defined by a group of centroids that are means of the students’ assessments of CLOs/PLOs achieved in each course in different clusters [1,2,4,15].

d (x, y) = \sqrt{{{(x 2 - x 1)}^{2} + (y 2 - y 1)}^{2}} .

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Section: Data Preparation and Methodologymentioning

confidence: 99%

“…Illustrating the proposed methodology for course learning outcomes/program learning outcomes (CLOs/PLOs) centroids that are means of the students' assessments of CLOs/PLOs achieved in each course in different clusters[1,2,4,15].…”

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confidence: 99%

Investigating learning outcomes in engineering education with data mining

Mahboob

Ali

Laila

2020

Comp Applic In Engineering

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Higher education institutions are catching up on their high competition and challenges are in their analysis productivity. The major challenge is to monitor and analyze student progress through learning outcomes in the curriculum. One of the approaches is the outcome-based education (OBE) model to deal with learning outcomes. OBE is an integral part of higher education institutions. The OBE system is a key step for accreditation in engineering education. OBE focuses on a student-centered approach. The OBE is not restricted to welldefined teaching strategies or direct evaluations but also encompasses indirect evaluations to help students achieve the intended outcomes. In this investigation, engineering students' data have been analyzed forming three distinct clusters to group students according to best, average, and worst achievement of learning outcomes in two different computer engineering courses generally taught in the early semesters in higher education institutions. A data mining clustering approach is used to segment students using k-means and k-medoids techniques. Clustering can be regarded as a data modeling technique that provides summary data that interact with multiple disciplines and plays an important role in a wide range of computer applications. The investigation comprises of two parts for analysis: one part of the analysis is the mid-term and final exam scores, the quiz and assignment results, the laboratory results, and the evaluation, together with the learning outcomes achieved, and the other part is the comparative analysis of learning outcomes achieved in both engineering courses clustering with the best, average, and worst attainments, respectively. In this investigation, the results obtained from clustering data points show that the same group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoid clustering for one course. On the other hand, a diverse group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoids clustering for another course.

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“…Lo anterior, según el citado estudio, produce en los estudiantes tristeza, desmotivación, frustración y, sobre todo, un deseo de abandonar la universidad. Otros estudios relacionados, que proponen estrategias para facilitar la educación y/o disminuir el abandono académico, pueden ser encontrados en Larraín y Zurita (2008) Por otro lado, y con miras a mejorar la permanencia escolar, en la actualidad existe una gran variedad de técnicas basadas en herramientas inteligentes, que permiten mejorar los procesos de aprendizaje; entre estas se encuentran (Bharara et al, 2018): el aprendizaje analítico, la inteligencia de negocios, la acción analítica, el análisis de web, la minería de datos educacional, el análisis académico, los sistemas de gestión de contenidos, los recursos educativos abiertos, los sistemas inteligentes de enseñanza, los sistemas de imitaciones, los sistemas de juegos y las técnicas de agrupamiento (K-means, C-means), la lógica difusa, las partículas inteligentes y las cadenas de Markov, entre otras. Estas herramientas permiten descubrir patrones para orientar la toma de decisiones y mejorar los procesos de aprendizaje, con miras a disminuir el abandono académico.…”

Section: Introductionunclassified

Predicción de las principales variables que conllevan al abandono estudiantil por medio de técnicas de minería de datos

2020

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Application of learning analytics using clustering data Mining for Students’ disposition analysis

Cited by 66 publications

References 46 publications

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Investigating learning outcomes in engineering education with data mining

Predicción de las principales variables que conllevan al abandono estudiantil por medio de técnicas de minería de datos

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