Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks

Al-Luhaybi, Mashael; Yousefi, Leila; Swift, Stephen; Counsell, Steve; Tucker, Allan

doi:10.1007/978-3-030-23204-7_3

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worthwhile to consider how to create special network structures to make effective predictions based on the characteristics of educational data. Some studies that can propose their thinking about the target problem and use it to optimise the algorithm are more encouraging (Al-Luhaybi et al, 2019;Olive et al, 2019).…”

Section: Methods For Course Failure Predictionmentioning

confidence: 99%

“…Hlosta et al (2017) make predictions of academic performance based on a series of models, such as XGBoost, logistic regression, and SVM with different kernels, and demonstrate that XGBoost outperforms other models. Al-Luhaybi et al (2019) propose a bootstrapped resampling approach for predicting academic performance through taking into consideration the bias issue of educational datasets. The experimental results verify the effectiveness of its algorithm.…”

Section: Methods For Course Failure Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

View full text Add to dashboard Cite

Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

show abstract

Section: Methods For Course Failure Predictionmentioning

confidence: 99%

Section: Methods For Course Failure Predictionmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

View full text Add to dashboard Cite

show abstract

“…While overlay models have been used broadly across a variety of academic domains, more research efforts continue to devote in the direction of improving their representational power and prediction accuracy. For instance, dynamic Bayesian networks (DBNs) were proposed to model the hierarchy and relationships of different learning domain skills [35], [36]. Perceived as a composite model of multiple hidden Markov models (HMM), a DBN not only represents a specific skill of a given learning domain similar to the traditional BKT, but also models the dependencies between different skills via conditional probabilities.…”

Section: A Overlay-based Modelingmentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

View full text Add to dashboard Cite

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-theart review of 11 years of research (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

show abstract

“…This study showed that the resample Sample Bootstrapping technique increased the accuracy value from 82.13% to 85.96%. Research from Al-Luhaybi et al [17] also used resampling with the Sample Bootstrapping method to classify student datasets at Brunel University. The accuracy increased after the resampling technique was carried out from 75.59% to 93.1%.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Sample Sample Bootstrapping for Resampling Pap Smear Single Cell Dataset

Desiani

Affandi²,

Andhini

et al. 2022

LKJITI

View full text Add to dashboard Cite

The purpose of this study was to determine how the effect of using Bootstrapping Samples for resampling the Harlev dataset in improving the performance of single-cell pap smear classification by dealing with the data imbalance problem. The Harlev dataset used in this study consists of 917 data with 20 attributes. The number of classes on the label had data imbalance in the dataset that affected single-cell pap smear classification performance. The data imbalance in the classification causes machine learning algorithms to produce poor performance in the minority class because they were overwhelmed by the majority class. To overcome it, The resampling data could be used with Sample Bootstrapping. The results of the Sample Bootstrapping were evaluated using the Artificial Neural Network and K-Nearest Neighbors classification methods. The classification used was seven classes and two classes. The classification results using these two methods showed an increase in accuracy, precision, and recall values. The performance improvement reached 10.82% for the two classes classification and 35% for the seven classes classification. It was concluded that Sample Boostrapping was good and robust in improving the classification method.

show abstract

Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks

Cited by 5 publications

References 10 publications

Educational Anomaly Analytics: Features, Methods, and Challenges

Educational Anomaly Analytics: Features, Methods, and Challenges

Student Modeling and Analysis in Adaptive Instructional Systems

Implementation of Sample Sample Bootstrapping for Resampling Pap Smear Single Cell Dataset

Contact Info

Product

Resources

About