Orderliness predicts academic performance: behavioural analysis on campus lifestyle

Cao, Yi; Gao, Jian; Lian, Defu; Rong, Zhihai; Shi, Jiatu; Wang, Qing; Wu, Yifan; Yao, Huaxiu; Zhou, Tao

doi:10.1098/rsif.2018.0210

Cited by 73 publications

(60 citation statements)

References 52 publications

(69 reference statements)

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“…Existing works explored this task mainly through data mining technology, statistical analysis, and other machine learning methods to achieve the academic performance prediction. For example, Cao et al [1] proved that there was a significant correlation between the regularity of campus life and academic achievement by defining quantitatively two high-level behavior characteristics, orderliness and diligence. A recent study [12] examined the performance of a broad range of classification techniques to find a qualitative model for the student performance prediction problem by using students' behavior data.…”

Section: Student Performance Predictionmentioning

confidence: 99%

“…Owing to the development of information management systems (such as the student card system) in colleges and universities, it becomes convenient and easy for teachers to collect and analyze students' behaviors, which is one of the most important approaches to learn students' learning and living habits on campus. For example, a student who wants to get a high Grade Point Average (GPA) score may live a very regular life (such as going to the library at certain times) [1,2], since she needs to work hard on her selected courses. The students' behaviors tell us whether they intend to spend more time on studies [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Section: Student Performance Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

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“…The study by Crede et al [19] concludes that attendance is the most accurate known predictor of academic performance; see [20] for a more detailed analysis of the impact of class attendance on academic performance based on the CNS data. Cao et al [21] analyzed behavioral data from the digital records of nearly 19,000 students' smart cards, such as entering and leaving the library, having a meal in the cafeteria, or taking a shower in the dormitory. They conclude that the students' orderness (regularity of daily activities) is a strong predictor of academic performance.…”

Section: Related Workmentioning

confidence: 99%

“…They conclude that the students' orderness (regularity of daily activities) is a strong predictor of academic performance. Our approach shares some similarities with [21], but the key difference is that we have investigated not only individual behavior but also the students' social environment.…”

Section: Related Workmentioning

confidence: 99%

Academic performance and behavioral patterns

et al. 2018

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Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

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“…Orderliness is another important narrow trait of conscientiousness, which reflects the tendency to keep things organized and tidy [11] and has been reported another important factor that can affect academic performance [2,3,18,25]. Orderliness can be measured as the regularity of activities, such as having breakfast, taking a shower, etc.…”

Section: Orderlinessmentioning

confidence: 99%

Predicting Academic Performance for College Students

Yao

Lian

Cao

et al. 2019

ACM Trans. Intell. Syst. Technol.

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Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from small sample size and social desirability bias. In this paper, we collect longitudinal behavioral data from 6, 597 students' smart cards and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by social influence theory, we analyze the correlation between each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. The experiments on a large-scale realworld dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors. :2 H. Yao et al. students' academic performance. It has been demonstrated that physical status [23, 29], intelligence quotient [9] and even socioeconomic status [38] are correlated with academic performance. However, these characteristics are relatively stable over the long run and are difficult to change via educational management.Comparatively, more studies are focused on the perspectives of psychology and behavior, partially due to the possibility of intervening on student's mentation and behavior. Extensive experiments about the correlation between the big-five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) and academic performance have been reported [2,25,33], uncovering conscientiousness as one of the strongest predictors. Behaviors like class attendance [8], lifestyle [34] and sleep habit [10] are also highly associated with academic performance. However, almost all of these results are obtained from questionnaires or self-reports, which usually suffer from small sample size and social desirability bias, resulting in the difficulty to draw a valid and solid conclusion.Thanks to the development of information technology, there is a growing trend to augment physical facilities with sensing, computing and communication capabilities in modern universities. These facilities provide an unprecedented opportunity to collect real-time digital records about students' campus activities in an unobtrusive way and to reveal behavioral predictors for academic performance. In this paper, mainly through campus smart card, we collect 6, 597 students' longitudinal behavio...

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Orderliness predicts academic performance: behavioural analysis on campus lifestyle

Cited by 73 publications

References 52 publications

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Academic performance and behavioral patterns

Predicting Academic Performance for College Students

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