Students performance modeling based on behavior pattern

Xi, Zhang; Sun, Guoqiang; Yong, Pan; Sun, Hao; He, Yu; Tan, Jia-li

doi:10.1007/s12652-018-0864-6

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…In recent years, nonlinear metrics have been increasingly applied to time series analysis [22,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59].…”

Section: ) Behavioral Change-nonlinear (Bc-nonlinear)mentioning

confidence: 99%

“…In [22], entropy is proposed to quantify the regularity/orderliness of students' behaviors, and it was demonstrated that a small entropy value generally leads to high regularity and high academic performance. Another example is entropy calculated based on a Hidden Markov Model (HMM) analysis [44], which is called HMM-based entropy for simplicity in our study. HMM-based entropy is proposed to quantify the uncertainty/diversity of students' behaviors, e.g., the uncertainty between the transition of different behaviors and the various activities that a behavior exhibits.…”

Section: ) Behavioral Change-nonlinear (Bc-nonlinear)mentioning

confidence: 99%

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Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

et al. 2021

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Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to (i) combine these data to obtain a holistic view of a student, (ii) use these data to accurately predict academic performance, and (iii) use such predictions to promote positive student engagement with the university. To initially alleviate this problem, in this paper, a model named Augmented Education (AugmentED) is proposed. In our study, (1) first, an experiment is conducted based on a real-world campus dataset of college students (N = 156) that aggregates multisource behavioral data covering not only online and offline learning but also behaviors inside and outside of the classroom. Specifically, to gain in-depth insight into the features leading to excellent or poor performance, metrics measuring the linear and nonlinear behavioral changes (e.g., regularity and stability) of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory (LSTM). (2) Second, machine learning-based classification algorithms are developed to predict academic performance. (3) Finally, visualized feedback enabling students (especially at-risk students) to potentially optimize their interactions with the university and achieve a study-life balance is designed. The experiments show that the AugmentED model can predict students' academic performance with high accuracy. INDEX TERMS academic performance prediction, behavioral pattern, digital campus, machine learning (ML), long short-term memory (LSTM)

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“…In recent years, nonlinear metrics have been increasingly applied to time series analysis [22,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59].…”

Section: ) Behavioral Change-nonlinear (Bc-nonlinear)mentioning

confidence: 99%

Section: ) Behavioral Change-nonlinear (Bc-nonlinear)mentioning

confidence: 99%

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The research topics covered in this special issue are wideranging, including context modelling and inference for smart environments (Alegre-Ibarra et al 2018; Mulero et al 2018; Nakahara and Beder 2018), localization tracking (Chen et al 2018;Xin et al 2018), optimization of energy consumption (Hammoud et al 2018), behavior mining and activity recognition (Han et al 2018;Zhang et al 2018), image analysis (Wu et al 2018), social event mining and mobile crowd sensing (Pan et al 2018;Park 2018), and behavior-based privacy (Tao et al 2018).…”

Section: Contributions Of This Issuementioning

confidence: 99%

“…While in 'Driving Behavior Modeling and Evaluation For Bus enter and leave Stop Process' (Han et al 2018) authored by Qingwen Han et al, special driving areas are distinguished by critical zone and a driving behavior model is constructed by using the bus enters and leave bus stop process. The approach includes driving behaviour establishment, good driving behavior discovery and current driving behavior evaluation; in paper 'Students Performance Modeling Based on Behavior Pattern' (Zhang et al 2018) authored by Xi Zhang et al a system modelling students' performance was established by using smart card readings to detect behaviour patterns and further extract statistical and relevance features for the performance modelling.…”

Section: Contributions Of This Issuementioning

confidence: 99%