[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Hooshyar, Danial; Yang, Yeongwook

doi:10.1155/2021/7463631

Cited by 12 publications

(9 citation statements)

References 41 publications

(57 reference statements)

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“…In this article, a strong classifier model based on AdaBoost and decision tree data mining algorithm is constructed to classify abnormal traffic [27][28][29][30][31][32][33][34][35].…”

Section: E Experiments Amentioning

confidence: 99%

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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In order to better solve the problems of low efficiency, large consumption of human resources, and relatively low degree of intelligence in the abnormal data monitoring system of financial accounting in colleges and universities under the background of current accounting computerization, this article takes data mining and neural network algorithm as the technical basis to build the abnormal data monitoring system of financial accounting in colleges and universities. This article uses data mining algorithm and neural network analysis technology to process the original accounting information of colleges and universities, effectively eliminate invalid data, retain valuable data, and improve the detection efficiency of abnormal accounting data. The system test results show that the accuracy of identifying 50 abnormal situations in the original accounting data of colleges and universities is more than 90% by using data mining and neural network model.

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“…In this article, a strong classifier model based on AdaBoost and decision tree data mining algorithm is constructed to classify abnormal traffic [27][28][29][30][31][32][33][34][35].…”

Section: E Experiments Amentioning

confidence: 99%

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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“…The dataset includes up to 6199 examples or solutions collected between December 2019 and April 2022. While some part of the data was collected during experimental studies (e.g., [31], [32]), some learners played the game on their own. To complete the game, learners developed 20 or fewer solutions, depending on the game episode' conditions.…”

Section: Results and Findings A Datasetsmentioning

confidence: 99%

Modeling Learners to Early Predict Their Performance in Educational Computer Games

et al. 2023

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Data mining approaches have proven to be successful in improving learners´interaction with educational computer games. Despite the potential of predictive modelling in providing timely adaptive learning and gameplay experience, there is a lack of research on the early prediction of learners' performance in educational games. In this research, we propose an early predictive modelling approach, called GameEPM, to estimate learners' final scores in an educational game for promoting computational thinking. Specifically, the GameEPM approach models the sequence of learners' actions and then uses a limited sequence of the actions to predict the final score of the game for each learner. The findings from our initial trials show that our approach can accurately and robustly estimate the learners' performance at the early stages of the game. Using less than 50% of learners' action sequences, the cross-validated deep learning model achieves a squared correlation higher than 0.8 with a relative error of less than 8%, outperforming a range of regression models like linear regression, random forest, neural networks, and support vector machines. An additional experiment showed that the validated deep learning model can also achieve high performance while tested on an independent game dataset, showing its applicability and robustness in real-world cases. Comparing the results with traditional machine learning methods revealed that, in the validation and application phases, up to 0.30 and 0.35 R 2 gain is achieved in favor of the deep learning model, respectively. Finally, we found that while the lengths of action sequences influence the predictive power of the traditional machine learning methods, this effect is not substantial in the deep learning model.

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“…According to Macfadyen and Dawson [61], the primary objective of predictive modeling is to make early predictions and provide timely intervention. In the context of education, the prediction of students' performance and agency has the potential to enhance various educational systems (e.g., [62]). However, in practice, these predictive models struggle to make accurate and timely predictions during the initial stages of the learning process.…”

Section: Discussionmentioning

confidence: 99%

Learning Analytics in Supporting Student Agency: A Systematic Review

Hooshyar,

Tammets,

Ley

et al. 2023

Sustainability

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Student agency, or agency for learning, refers to an individual’s ability to act and cause changes during the learning process. Recently, learning analytics (LA) has demonstrated its potential in promoting agency, as it enables students to take an active role in their learning process and supports the development of their self-regulatory skills. Despite the growing interest and potential for supporting student agency, there have yet to be any studies reviewing the extant works dealing with the use of LA in supporting student agency. We systematically reviewed the existing related works in eight major international databases and identified 15 articles. Analysis of these articles revealed that most of the studies aimed to investigate student or educators’ agency experiences, propose design principles for LA, and to a lesser extent, develop LA methods/dashboards to support agency. Of those studies developing LA, none initially explored student agency experiences and then utilized their findings to develop evidence-based LA methods and dashboards for supporting student agency. Moreover, we found that the included articles largely rely on descriptive and diagnostic analytics, paying less attention to predictive analytics and completely overlooking the potential of prescriptive learning analytics in supporting agency. Our findings also shed light on nine key design elements for effective LA support of student agency, including customization, decision-making support, consideration of transparency and privacy, and facilitation of co-design. Surprisingly, we found that no studies have considered the use of LA to support student agency in K–12 education, while higher education has been the focal point of the LA community. Finally, we highlighted the fields of study and data visualization types that the studies mostly targeted and, more importantly, identified eight crucial challenges facing LA in its support of student agency.

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[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Cited by 12 publications

References 41 publications

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Modeling Learners to Early Predict Their Performance in Educational Computer Games

Learning Analytics in Supporting Student Agency: A Systematic Review

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