Fine-grained learning performance prediction via adaptive sparse self-attention networks

Wang, Xizhe; Mei, Xiaoyong; Huang, Qionghao; Han, Zhongmei; Huang, Changqin

doi:10.1016/j.ins.2020.08.017

Cited by 26 publications

(10 citation statements)

References 22 publications

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“…Fifty-four (87.70%) studies employed single intelligent models for predicting the attainment of learning outcomes. Remarkably, only eight studies (i.e., [60,65,66,80,84,93,96,101]) explored the use of hybrid intelligent models to improve the accuracy of academic perfor-mance predictions. Hybrid or ensemble classifiers involve the integration of heterogeneous learning techniques to boost the predictive performance [106].…”

Section: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

“…However, 18 studies were inconclusive about the strength of their findings. Only 5 (8.06%) studies reported using multiple datasets to verify the performance of their predictive models to check the consistency and validity of the learning outcomes predictions [65,80,86,96,102]. The remaining studies (91.93%) used only one dataset.…”

Section: Dominant Factors Predicting Student Learning Outcomesmentioning

confidence: 99%

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Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

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Section: Predictive Models Of Learning Outcomesmentioning

confidence: 99%

Section: Dominant Factors Predicting Student Learning Outcomesmentioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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“…Pandey and Karypis [28] proposed a self-attentive model for knowledge tracing (SAKT), which directly applied the transformer to capture long-term dependencies between students' learning interaction with few modifications, and achieved fairly good performance. Moreover, Wang et al [54] proposed an adaptive sparse self-attention network to generate the missing features and simultaneously obtained fine-grained prediction of student performance. Zhu et al [55] found there was a vibration problem in DKT and presented an attention-based knowledge tracing model to solve it, which also further used Finite State Automaton (FSA) to provide deep analysis about knowledge state transition.…”

Section: Attentive Knowledge Tracingmentioning

confidence: 99%

A Survey of Knowledge Tracing

Liu¹,

Shen²,

Huang³

et al. 2021

Preprint

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High-quality education is one of the keys to achieving a more sustainable world. The recent COVID-19 epidemic has triggered the outbreak of online education, which has enabled both students and teachers to learn and teach at home. Meanwhile, it is now possible to record and research a large amount of learning data using online learning platforms in order to offer better intelligent educational services. Knowledge Tracing (KT), which aims to monitor students' evolving knowledge state, is a fundamental and crucial task to support these intelligent services. Therefore, an increasing amount of research attention has been paid to this emerging area and considerable progress has been made. In this survey, we propose a new taxonomy of existing basic KT models from a technical perspective and provide a comprehensive overview of these models in a systematic manner. In addition, many variants of KT models have been proposed to capture more complete learning process. We then review these variants involved in three phases of the learning process: before, during, and after the student learning, respectively. Moreover, we present several typical applications of KT in different educational scenarios. Finally, we provide some potential directions for future research in this fast-growing field.

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“…It is a two-classifier used to estimate the probability that a sample comes from training data. GAN has the potential to generate "infinite" new samples in a distributed manner and has great application value in the fields of artificial intelligence, such as image, visual computing, and voice processing [22,23]. GAN provides a new direction for unsupervised learning and provides methods and ideas for processing high-dimensional data and complex probability distributions.…”

Section: Related Workmentioning

confidence: 99%

English Machine Translation Model Based on an Improved Self-Attention Technology

Pan

2021

Scientific Programming

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English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.

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Fine-grained learning performance prediction via adaptive sparse self-attention networks

Cited by 26 publications

References 22 publications

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

A Survey of Knowledge Tracing

English Machine Translation Model Based on an Improved Self-Attention Technology

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