Predicting Academic Outcomes: A Survey from 2007 Till 2018

Alturki, Sarah; Hulpuș, Ioana; Stuckenschmidt, Heiner

doi:10.1007/s10758-020-09476-0

Cited by 26 publications

(22 citation statements)

References 35 publications

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“…Based on Alturki et al (2020), the features that are used for predicting academic achievement can be classified into three categories. They are: (i) demographics, (ii) pre-enrollment features, and (iii) postenrollment features.…”

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

“…They are: (i) demographics, (ii) pre-enrollment features, and (iii) postenrollment features. Although the demographical features are heavily used for predicting academic achievement, the extent to which they are useful is unclear (Alturki et al, 2020). One of the top used features in this category is gender (Aulck et al, 2016;Daud et al, 2017;Garg, 2018;Kovačić, 2010;Osmanbegović & Suljic, 2012;Shakeel & Butt, 2015).…”

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

“…Using pre-enrollment features to predict students' academic achievement is significant, especially if the prediction is to be performed at an early stage (Alturki et al, 2020). Previous GPA is one of the most popular used features for predicting academic success (Abu Saa, 2016;Aluko et al, 2018;Garg, 2018;Huang & Fang, 2013;Kabakchieva, 2013;Kovačić, 2010;Osmanbegović & Suljic, 2012;Pal & Pal, 2013;Thai-Nghe et al, 2007).…”

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

“…Using post-enrollment features for predicting students' academic achievement can maximize the prediction accuracy as such features represent students' current situation in the program (Alturki et al, 2020). The achievement of the previous semester, which is part of the total GPA, has been used in many studies to predict students' success (e.g.…”

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

“…Such prediction brings many advantages, for instance, providing valuable feedback, offering recommendations, supporting personalized learning, and strategically planning educational programs (Hellas et al, 2018). There are three types of predictions in higher education: (i) predicting students' academic performance or GPA at a degree level; (ii) predicting students' failure or drop out of a degree; and (iii) predicting students' results on particular courses (Alturki et al, 2020). In this study, we address the first type.…”

Section: Introductionmentioning

confidence: 99%

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Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Alturki

2021

JITE:IIP

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Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

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Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

Section: Related Work On Predictions In Higher Educationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Alturki

2021

JITE:IIP

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Predicting Academic Performance of High School Students

Dinh-Thanh

Thi-Ngoc-Diem

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms

Lünich,

Keller,

Marcinkowski

2023

Tech Know Learn

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Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students’ fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality, equity, and need. Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 $$\times$$ × 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against potential risks, and establish continuous feedback through ongoing consultation with all stakeholders.

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Predicting Academic Outcomes: A Survey from 2007 Till 2018

Cited by 26 publications

References 35 publications

Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Predicting Academic Performance of High School Students

Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms

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