A case study of knowledge discovery on academic achievement, student desertion and student retention

Salazar, Addisson; Gosalbez, J.; Bosch, Ignacio; Vergara, Luis

doi:10.1109/itre.2004.1393665

Cited by 23 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification of dropout and non-dropouts student overall rate was 15.79% and 10.36% [18]. The author analyzed the different factors with effect of academic performance of average mark, retention and desertion [17]. The data set considered of 25000 students consists of 16 attributes of different aspects such as age, gender, faculty, test mark etc.…”

Section: Predictive Data Mining Model In Higher Educationmentioning

confidence: 99%

Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

Jayakumar¹,

Parameswari*²,

Akila³

2019

IJRTE

View full text Add to dashboard Cite

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

show abstract

Section: Predictive Data Mining Model In Higher Educationmentioning

confidence: 99%

Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

Jayakumar¹,

Parameswari*²,

Akila³

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…2.4 A case study of knowledge discovery on academic achievement, student desertion and student retention -A.Salazar, J.Gosalbez, I.Bosch, R.Miralles, L.Vergara [9] The large amount of data that is generated by tests can be used to enhance development of students by roping it with Data mining techniques to answer questions based on students development and factors affecting it. It can give boost to methods designed to improve students' academic performance.…”

Section: Complete the Sentencementioning

confidence: 99%

“…The test results generate large amount of relative data that can be used to extract knowledge about students learning and a comparative performance amongst them is obtained. This is done by mining the data using various data mining techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Students Performance based on Formative Assessment using Data Mining

Gogri¹,

Shaikh²,

Iyengar³

2013

IJCA

View full text Add to dashboard Cite

This paper proposes a system for performance assessment of students and explores how formative and summative assessment can be used to assess the performance. Proposed system focuses on formative assessment during the learning process. Computer based interactive Multiple choice question exam is proposed for formative assessment and the data generated is used for data mining. Paper discusses about the importance of detailed and instant feedback in learning process and the reduced workload on instructor in the proposed system.

show abstract

“…Various researchers have applied data mining in different areas of education, such as enrollment management (Gonzlez and DesJardins, 2002;Chang, 2006;Antons and Maltz, 2006), graduation (Eykamp, 2006;Bailey, 2006), academic performance (Naplava and Snorek, 2001;Pardos et al, 2006;Vandamme, 2007;Ogor, 2007), gifted education Im et al, 2005), web-based education (Minaei-Bidgoli et al, 2003), retention (Druzdzel and Glymour, 1994;Sanjeev and Zytkow, 1995;Massa and Puliafito, 1999;Stewart and Levin, 2001;Veitch, 2004;Barker et al, 2004;Salazar et al, 2004;Superby et al, 2006; Sujitpara-…”

Section: Data Mining In Educationmentioning

confidence: 99%

“…In this study, GPA was the most significant predictor of persistence. Salazar et al (2004) used clustering algorithms and C4.5 to study graduate student retention at Industrial University of Santander, Colombia. The authors found that the high marks in the national pre-university test predicted a good academic performance, and that the younger students had higher probabilities of a good academic performance.…”

Section: Data Mining For Other Applicationsmentioning

confidence: 99%

Longitudinal study of first-time freshmen using data mining

Nandeshwar¹

View full text Add to dashboard Cite

LONGITUDINAL STUDY OF FIRST-TIME FRESHMEN USING DATA MINING Ashutosh R. Nandeshwar DEDICATION Dr. B. R. Ambedkar-the greatest social reformer of India (1891-1956) Only his unyielding determination, heroic efforts, and fearless fights could have led to the emancipation of the "untouchables" of India. and Mrs. Janabai Janbodhkar-my Aaji, meaning grandmother (-2004) Her integrity, honesty, wisdom, and independence have left an unforgettable impression on my mind. iii v viding valuable feedback and sugary treats. Of course, my sincerest appreciation goes to my parents and family members. Baba (Dad), thanks for encouraging me to prepare for the GRE again and apply to other US universities, although you were still recovering from your major accident and still had trouble forming correct words and meaningful sentences. Aai (Mom), thanks for your unconditional support and constant push to make the most of my life. I simply could not come this far without you two. Thanks to my brothers and sisters-in-law for your great help and guidance. Huge thanks to Utpalvarna, my dear wife, for believing in me and pushing me to achieve certain goals, and for your countless hours doing all the work without my help. Thanks to my kids, Asanga and Dinnaga, for teaching me new meaning of life and making my days and nights bright with your smiles and calls of "Da-Da."

show abstract

A case study of knowledge discovery on academic achievement, student desertion and student retention

Cited by 23 publications

References 2 publications

Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

Evaluation of Students Performance based on Formative Assessment using Data Mining

Longitudinal study of first-time freshmen using data mining

Contact Info

Product

Resources

About