The objective of this study was to assess the effectiveness of the Health Promoting Schools (HPS) framework in improving the health and well-being of students and their academic achievement.

The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement

https://www.nature.com/articles/s41598-021-03867-8.pdf

Predicting students’ performance in e-learning using learning process and behaviour data

Educational data mining to predict students' academic performance: A survey study

Digital transformation provides new opportunities for the green development of enterprises. Clarifying the impact and mechanism of digital transformation on enterprise pollution reduction has important policy implications for the synergistic development of digitalization and greenization. This paper explores the impact of digital transformation on enterprise pollution abatement using data from Chinese manufacturing enterprises and fixed effects models. The empirical results show that digital transformation contributes to enterprise pollution reduction. Furthermore, the pollution reduction role of digital transformation is more pronounced in enterprises with high degrees of pollution intensity. Digital transformation acts on pollution reduction through productivity improvement and scale expansion, while a mechanism for green technology innovation does not exist. Additionally, industry attributes and external environment heterogeneity affect the pollution abatement role of digital transformation. Enterprises can achieve stronger pollution abatement from digital transformation when they belong to high-polluting industries, high-tech industries, areas with high environmental regulation (HER), and cohorts. The paper provides new micro evidence that digital transformation promotes pollution reduction, enriches the literature on the environmental impact of digital transformation, and provides beneficial insights for enterprises to drive pollution reduction in the era of the digital economy. 

Enterprise pollution reduction through digital transformation? Evidence from Chinese manufacturing enterprises

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.

Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

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 article, 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.

/pdf/academic-performance-prediction-based-on-multisource-3guiuxny92.pdf

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

Digital transformation and carbon performance: evidence from firm-level data

Digital data trails from disparate sources that cover different aspects of student life are being stored daily on most modern university campus. However currently it remains a challenge to (i) combine these data into a holistic view of a student; (ii) use this to predict academic performance accurately; (iii) and take advantage of the prediction to drive behavioral change and support student positive engagement with the university. To initially alleviate this problem, a framework named Augmented Education (AugmentED) is proposed in this chapter. In our study, (1) firstly, the experiment is conducted based on a real-world campus dataset from college students (N = 156), aggregating multisource data that consists of a small private online courses (SPOC) platform, usage of smart cards (e.g., library entry, meal, and consumption), Wi-Fi detection records, and central storage (e.g., gender, age, and class schedule). Specially, to gain a deep insight into the features leading to excellent or bad performance, on the one hand, three novel metrics (e.g., Lyapunov exponent) that measure the regularity of campus lifestyles are estimated; on the other hand, LSTM-based features that represent the dynamic changes of temporal lifestyle patterns are extracted by means of long short-term memory (LSTM). (2) Secondly, a machine learning-based intelligent algorithm is developed to predict academic performance. (3) Finally, visualized feedback is designed, potentially enabling students (especially for at-risk students) to optimize their interactions with the university and achieve study-life balance.

Augmented: Academic Performance Prediction Based on Digital Campus

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Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

Digital transformation and carbon performance: evidence from firm-level data

Augmented: Academic Performance Prediction Based on Digital Campus