The Promotion of Graduate Programs through Clustering Prospective Students

Croda, Rosa María Cantón; Gibaja-Romero, Damián-Emilio; Castillo-Villar, Fernando-Rey

doi:10.9781/ijimai.2019.07.001

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Cantón-Croda et al [9] analyzed privately enrolled prospective students to develop better promotional strategies of academic programs, principally at graduate levels, using data collected from online sources. The clustering technique using the k-means algorithm was used to determine the market of student segment-based strategies.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Investigating learning outcomes in engineering education with data mining

Mahboob

Ali

Laila

2020

Comp Applic In Engineering

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“…Also, the same procedure used to check the financial sustainability of the institution, which outputted the age and city are crucial to promoting graduate programs. At the same time, marital status and sex do not impact the decision of students in the institution [23]. Additionally, three decision tree variants (J48, Random Tree, and REP tree) applied to identify the academic performance based on social, demography, educational, motivation, study skills, and personal relationships.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Predicting Attitude of Indian Student’s Towards ICT and Mobile Technology for Real-Time: Preliminary Results

et al. 2020

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This paper proposed a novel futuristic approach to support the educational informatics and overcome the conventional system of attitude measure. For this, we presented a significant predictive model to identify the attitude of students towards technology. The present approach not only explored the impact of the technology but also predicted an opinion of students. The concept of an online awareness model may overcome the traditional method. We have performed the descriptive and inferential statistics to predict the attitude of Indian students towards the ICTMT in university education with primary data samples. Factor Analysis (FA) using Principal Component Analysis (PCA) has extracted the prominent two components with nine features for technology benefits and six features for the technology use. The SQuareRoot (SQRT) and Log transformations have been used to decrease the Skewness, and it has also improved the association between attitude and educational benefit. Overall reliability of the gathered data sample size of 163 calculated 0.957 provided with Cronbach alpha test. This study has used a Pearson Correlation (PC) for exploring the technology impact and the Linear Regression Model (LRM) for the prediction. The LRM has substantiated that the educational benefit explained the attitude significantly, and a significant positive association discovered using the PC with a value of 0.75. The LRM model projected nine most significant educational benefits of ICTMT, which affected the attitude of the Indian student towards technology. We have proposed technology aids in building online predictive model wires real-time prediction.

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