Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that were mainly based on questionnaire surveys, recent literature suggests that unobtrusive digital data bring us unprecedented opportunities to study students' lifestyles in the campus. In this paper, we collect behavioural records from undergraduate students' ( = 18 960) smart cards and propose two high-level behavioural characters, orderliness and diligence. The former is a novel entropy-based metric that measures the regularity of campus daily life, which is estimated here based on temporal records of taking showers and having meals. Empirical analyses on such large-scale unobtrusive behavioural data demonstrate that academic performance (GPA) is significantly correlated with orderliness. Furthermore, we show that orderliness is an important feature to predict academic performance, which improves the prediction accuracy even in the presence of students' diligence. Based on these analyses, education administrators could quantitatively understand the major factors leading to excellent or poor performance, detect undesirable abnormal behaviours in time and thus implement effective interventions to better guide students' campus lives at an early stage when necessary.
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...
N 6-methyladenosine (m6A) modification of RNA is deposited by the methyltransferase complex consisting of Mettl3 and Mettl14 and erased by demethylase Fto and Alkbh5 and is involved in diverse biological processes. However, it remains largely unknown the specific function and mechanism of Fto in regulating adult neural stem cells (aNSCs). In the present study, utilizing a conditional knockout (cKO) mouse model, we show that the specific ablation of Fto in aNSCs transiently increases the proliferation of aNSCs and promotes neuronal differentiation both in vitro and in vivo, but in a long term, the specific ablation of Fto inhibits adult neurogenesis and neuronal development. Mechanistically, Fto deficiency results in a significant increase in m6A modification in Pdgfra and Socs5. The increased expression of Pdgfra and decreased expression of Socs5 synergistically promote the phosphorylation of Stat3. The modulation of Pdgfra and Socs5 can rescue the neurogenic deficits induced by Fto depletion. Our results together reveal an important function of Fto in regulating aNSCs through modulating Pdgfra/Socs5-Stat3 pathway.
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