Quantifying variability in predictions of student performance: Examining the impact of bootstrap resampling in data pipelines

Bertolini, Roberto; Finch, Stephen J.; Nehm, Ross H.

doi:10.1016/j.caeai.2022.100067

Cited by 11 publications

(8 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results demonstrate that the MVHGNN method outperforms the state-of-the-art ones evaluated on real campus student behavioral datasets [44]. Bertolini et al utilize bootstrapping to examine performance variability among five data mining methods (DMMs) and four filter preprocessing feature selection techniques for forecasting course grades for 3225 students enrolled in an undergraduate biology class [45]. Wu et al propose a novel knowledge tracing model based on an exercise session graph, named session graph-based knowledge tracing (SGKT).…”

Section: Plos Onementioning

confidence: 99%

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

2022

View full text Add to dashboard Cite

This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-MLP model for predicting student achievement is thus obtained. The training and verification of the EGWO-MLP prediction model are conducted based on the thirty attributes from the University of California (UCI) Machine Learning Repository dataset’s student performance dataset, including family features and personal characteristics. For the Mathematics (Mat.) subject achievement prediction, the EGWO-MLP model outperforms one model’s prediction accuracy, and the standard deviation possesses the stable ability to predict student achievement. And for the Portuguese (Por.) subject, the EGWO-MLP outperforms three models’ Mathematics (Mat.) subject achievement prediction through the training process and takes first place through the testing process. The results show that the EGWO-MLP model has made fewer test errors, indicating that EGWO can effectively feedback weights and biases due to the strong exploration and local stagnation avoidance. And the EGWO-MLP model is feasible for predicting student achievement. The study can provide reference for improving school teaching programs and enhancing teachers’ teaching quality and students’ learning effect.

show abstract

Section: Plos Onementioning

confidence: 99%

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

2022

View full text Add to dashboard Cite

show abstract

“…In recent decades, prediction and machine learning techniques have undergone rapid development and have been successfully applied in various fields, including education [6]- [8]. One popular prediction method is linear regression, which attempts to relate a linear relationship between an independent variable and a dependent variable [7], [9]. However, in some cases, linear regression may not be robust enough to cope with the complexity of student data [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…Education is a key factor in the development of individuals and society. In the context of education, it is important to understand the factors that influence students' success in achieving learning goals [7], [13]- [15]. Several previous studies have identified variables that potentially affect student success, such as attendance rate, number of study hours, student motivation, and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

Wahyuningsih,

Iriani,

Dwi Purnomo

et al. 2024

Comput. Sci. Inf. Technol.

View full text Add to dashboard Cite

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.

show abstract

“…A data pipeline itself is a series of data processing steps that begins with extracting raw data sets, processing the information, and managing that data in a systematic way, and then generating outputs at the end (Skiena, 2017). In education, data pipelines are utilized in order to develop early warning systems, predict student performance, and in data modeling for educational stakeholders (Ansari et al, 2017: Bertolini et al, 2021Bertolini et al, 2022;Schleiss et al, 2022). As of the time of this paper, to the best of the authors' knowledge, no publicly reported project has focused on the development of a comprehensive psychometric data pipeline for large-scale educational assessments.…”

Section: Introductionmentioning

confidence: 99%

A data pipeline for e-large-scale assessments: Better automation, quality assurance, and efficiency

SCHWARZ,

BULUT,

ANİFOWOSE

2023

International Journal of Assessment Tools in Education

View full text Add to dashboard Cite

The increasing volume of large-scale assessment data poses a challenge for testing organizations to manage data and conduct psychometric analysis efficiently. Traditional psychometric software presents barriers, such as a lack of functionality for managing data and conducting various standard psychometric analyses efficiently. These challenges have resulted in high costs to achieve the desired research and analysis outcomes. To address these challenges, we have designed and implemented a modernized data pipeline that allows psychometricians and statisticians to efficiently manage the data, conduct psychometric analysis, generate technical reports, and perform quality assurance to validate the required outputs. This modernized pipeline has proven to scale with large databases, decrease human error by reducing manual processes, efficiently make complex workloads repeatable, ensure high quality of the outputs, and reduce overall costs of psychometric analysis of large-scale assessment data. This paper aims to provide information to support the modernization of the current psychometric analysis practices. We shared details on the workflow design and functionalities of our modernized data pipeline, which provide a universal interface to large-scale assessments. The methods for developing non-technical and user-friendly interfaces will also be discussed.

show abstract

Quantifying variability in predictions of student performance: Examining the impact of bootstrap resampling in data pipelines

Cited by 11 publications

References 76 publications

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

A data pipeline for e-large-scale assessments: Better automation, quality assurance, and efficiency

Contact Info

Product

Resources

About