A Data Mining Approach for Predicting Academic Success – A Case Study

Martins, Maria P. G.; Miguéis, Vera L.; Fonseca, D. S. B.; Alves, Albano

doi:10.1007/978-3-030-11890-7_5

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Major reviews of the state of the art [2][3][4][5] prove the importance and usefulness of EDM, as a tool for analysis and management support. According [6] the main goal of EDM is to generate useful knowledge which may ground and sustain decision-making targeted at improving student communities's learning as well as educational institution's efficiency. In fact, most EDM studies are subject to performance prediction and academic dropout, which identify the factors that can influence them and constitute fundamental aspects in the definition of management strategies focused on promoting success and preventing school dropout [5].…”

Section: Educational Data Miningmentioning

confidence: 99%

Modelling Academic Dropout in Computer Engineering Using Artificial Neural Networks

Camelo

Santos

Martins

et al. 2021

Advances in Intelligent Systems and Computing

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School dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difficult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classification, based on artificial neural networks, which allows the prediction, at the end of the first school year, of the propensity that the computer engineering students of a polytechnic institute in the interior of the country have for dropout. A differentiating aspect of this study is that it considers the classifications obtained in the course units of the first academic year as potential predictors of dropout. A new approach in the process of selecting the factors that foreshadow the dropout allowed isolating 12 explanatory variables, which guaranteed a good predictive capacity of the model (AUC=78.5%). These variables reveal fundamental aspects for the adoption of management strategies that may be more assertive in the combat to academic dropout.

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Section: Educational Data Miningmentioning

confidence: 99%

Modelling Academic Dropout in Computer Engineering Using Artificial Neural Networks

Camelo

Santos

Martins

et al. 2021

Advances in Intelligent Systems and Computing

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“…The staggering amount of data produced in educational institutions has led to the emergence of an independent research field called Educational Data Mining (EDM) (Liñan & Perez, 2015). Data mining has been used to predict a wide range of important educational outcomes, such as student success and performance (Martins, Miguéis, Fonseca, & Alves, 2019;Xing, 2019). The ability to accurately forecast students' future performance is seen as critical for carrying out appropriate pedagogical interventions in order to assure students' on-time and successful graduation.…”

Section:  Introductionmentioning

confidence: 99%

Predicting On-time Graduation based on Student Performance in Core Introductory Computing Courses using Decision Tree Algorithm

Casillano

2021

JPP

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Studi ini ditujukan untuk mengembangkan model yang akan memprediksi apakah seorang siswa akan lulus tepat waktu berdasarkan performa akademik mereka dalam mata kuliah pengantar komputasi. Metode: Proses data mining pendidikan digunakan dalam penelitian ini. Prosesnya dimulai dengan pengumpulan data pendidikan dan diakhiri dengan evaluasi model yang dikembangkan. Penelitian ini menggunakan decision tree algorithm. Temuan: Evaluasi model menghasilkan akurasi pengklasifikasian hingga 88,9% di mana jumlah total jawaban "Ya" (siswa yang lulus tepat waktu) adalah 52,49 yang diklasifikasikan dengan benar dan 3 salah diklasifikasikan sebagai "Tidak" dalam prediksi dan jumlah total jawaban "Tidak" (siswa yang tidak lulus tepat waktu) adalah 20,15 di antaranya diklasifikasikan dengan benar dan 5 salah diklasifikasikan dalam prediksi. Kesimpulan: Hasil penelitian dapat digunakan sebagai masukan dalam penyusunan bahan ajar baru dan perbaikan kurikulum yang akan membantu meningkatkan kinerja mahasiswa pada mata kuliah manajemen basis data. Model juga dapat digunakan sebagai alat untuk membantu mahasiswa lulus tepat waktu.

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“…After analyzing the literature on the use of this terminology in different subject fields, York et al identified six elements which define it namely: academic achievement; engagement in educationally purposeful activities; satisfaction; acquisition of desired knowledge, skills and competencies; persistence; attainment of educational outcomes, and post-college performance (p. 5). Furthermore, Education Data Mining (EDM) has been used for predicting a variety of crucial educational outcomes such as performance, success, satisfaction, and achievement (Calvet Llinan and Juan Perez, 2015;Dutt et al 2017;Anoopkumar and Rahman, 2016;Martins et al 2019;Willems et al 2019). Despite all these publications, it is still difficult for faculty to effectively apply known techniques to specific academic problems.…”

Section: Introductionmentioning

confidence: 99%

Teaching all students: A holistic and collaborative approach to increasing student success

Udeogalanya¹

2020

IJHEM

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All students who enroll have success as their main goal. However, most institutions focus their resources on programs for students on honor roll, Dean's list and those progressing academically. Little resources remain for those students who stumble. In 2015, 36.2% of white students, 22.5% of black students, and 15.5% of Hispanic students had completed four years of college. This shows a 13.7% gap between black and white students and a 20.7% gap between Hispanic and white students (Wellman, 2017). How do we close this gap in educational completion? This study believes that all students can learn. Consequently, there needs to be educational equity and the development of a basis for instruction and assessment of all students' learning outcomes. This paper represents an exploratory fundamental and qualitative research that aims to present a refocus on the role of faculty in teaching and learning to reach all students in classrooms. It examines a holistic and collaborative approach to increasing student success using evidence-based qualitative analysis of best practices. This approach has four component parts. Part 1 is the Holistic Component that involves engaging all students in the institution; communicating purposefully to them in a timely manner; and providing all-inclusive comprehensive support services (HC). This part develops and implements measurable benchmarks that motivate, encourage, and enable all students. Part 2 is the Collaborative Component which involves bringing six working teams together: faculty, industry, current majors, alumni, career services, and the community (CC). This part engages the team in maintaining a living curriculum that reflects the everchanging global economy. Part 3 is Celebration of Student Success (CSS) which entails the collaborative team owning each milestone, reaffirming teamwork while building trust and persistence. Part 4 is the Assessment of Student Progress (ASP) using the holistic and collaborative approach. The paper concludes that holistic and collaborative teamwork that includes, respects, and empowers all students is the key to reducing the college completion gaps that exist among blacks and Hispanic students.

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A Data Mining Approach for Predicting Academic Success – A Case Study

Abstract: The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 12 publications

References 11 publications

Modelling Academic Dropout in Computer Engineering Using Artificial Neural Networks

Modelling Academic Dropout in Computer Engineering Using Artificial Neural Networks

Predicting On-time Graduation based on Student Performance in Core Introductory Computing Courses using Decision Tree Algorithm

Teaching all students: A holistic and collaborative approach to increasing student success

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