Explainable AI (XAI): Improving At-Risk Student Prediction with Theory-Guided Data Science, K-means Classification, and Genetic Programming

This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and causes, which facilitates instructional intervention guidance. Historically, low student retention has been prevalent across the globe as nations have implemented a wide range of interventions (e.g., policies, funding, and academic strategies) with only minimal improvements in recent years. In the US, recent attrition rates indicate two out of five first-time freshman students will not graduate from the same four-year institution within six years. In response, emerging AI research leveraging recent advancements in Deep Learning has demonstrated high predictive accuracy for identifying at-risk students, which is useful for planning instructional interventions. However, research suggested a general trade-off between performance and explainability of predictive models. Those that outperform, such as deep neural networks (DNN), are highly complex and considered black boxes (i.e., systems that are difficult to explain, interpret, and understand). The lack of model transparency/explainability results in shallow predictions with limited feedback prohibiting useful intervention guidance. Furthermore, concerns for trust and ethical use are raised for decision-making applications that involve humans, such as health, safety, and education. To address low student retention and the lack of interpretable models, this research explored the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve instruction and learning. More specifically, XAI has the potential to enhance the performance and explainability of AI/ML models predicting at-risk students. The scope of this study includes a hybrid research design comprising: (1) a systematic literature review of XAI and EDM applications in education; (2) the development of a theory-guided feature selection (TGFS) conceptual learning model; and (3) an EDM study exploring the efficacy of a TGFS XAI model. The EDM study implemented K-Means Classification for explorative (unsupervised) and predictive (supervised) analysis in addition to assessing Genetic Programming (GP), a type of XAI model, predictive performance, and explainability against common AI/ML models. Online student activity and performance data were collected from a learning management system (LMS) from a four-year higher education institution. Student data was anonymized and protected to ensure data privacy and security. Data was aggregated at weekly intervals to compute and assess the predictive performance (sensitivity, recall, and f-1 score) over time. Mean differences and effect sizes are reported at the .05 significance level. Reliability and validity are improved by implementing research best practices

The lasting effects of innovation on firm profitability: Panel evidence from a transitional economy

This study is the first to study the lasting effects of innovation on firm profitability in Vietnam. Using a unique panel dataset for the period 2005-2015, our results show that innovators achieve higher profit in comparison with non-innovating firms. The positive effects of innovation on firm profitability are observed not only in the short term but also in the longer term. The benefits of innovation for firm profitability can be seen in higher export probability, better productivity, better access to formal credit, and the ability to secure government support, but only after innovation

An experimental study and a proposed theoretical solution for the prediction of the ductile/brittle failure modes of reinforced concrete beams strengthened with external steel plates

An experimental study and a proposed theoretical solution are conducted in the present study to investigate the ductile/brittle failure mode of reinforced concrete beams strengthened with an external steel plate. The present experimental study has fabricated and tested six steel plate-strengthened RC beams and one non-strengthened RC beam under 4-point bending loads. The proposed theoretical model is then developed based on the observed experimental results to analyze the crack formation, to determine the distance between vertical cracks and to quantitatively predict the ductile/brittle failure mode of plate-strengthened RC beams. The experimental study shows that the failure mode is based on the sliding of concrete along with the external plate. This slip is limited between two vertical cracks, from which the maximum stress in the external steel is determined. Based on comparisons conducted in the present study, excellent agreements of the stresses/strains in soffit steel plates, crack distances, and system failure modes between the current theoretical solution and the previous and present experimental results are observed.&nbsp

Applying Improve Differential Evolution Algorithm for Solving Gait Generation Problem of Humanoid Robots

This chapter addresses an approach to generate 3D gait for humanoid robots. The proposed method considers gait generation matter as optimization problem with constraints. Firstly, trigonometric function is used to produce trial gait data for conducting simulation. By collecting the result, we build an approximation model to predict final status of the robot in locomotion, and construct optimization problem with constraints. In next step, we apply an improve differential evolution algorithm with Gauss distribution for solving optimization problem and achieve better gait data for the robot. This approach is validated using Kondo robot in a simulated dynamic environment. The 3D gait of the robot is compared to human in walk

An Assessment of Cough Medicine Dispensing Practice to Children Under Two Years Old in Pharmacies in Ho Chi Minh City Using Simulated-Patient Method

Over-the-counter (OTC) cough and cold medications (CCMs) have been used to treat the symptoms of upper respiratory infection in children for decades. The safety of CCMs in children has been questioned. The data on knowledge of pharmacists in supplying cough medicines for children under two years have been limited. This study aimed to evaluate the pharmacists’ dispensing decisions to manage the cough in children under two years old. A descriptive cross-sectional was carried out in 300 pharmacies in 15 districts in Ho Chi Minh City, Vietnam. The pharmacists were interviewed by a simulated patient. The results showed that, information that pharmacists actively asked the client about the patient and disease symptoms was limited. Most pharmacists did not provide adequate instructions and counsel about using drugs for clients. Only 22/300 (7.33%) of pharmacists appropriately provided cough medicines for children under 2 years old. The main reason of inappropriateness was the deficiency of knowledge about updated contraindication of N-acetylcysteine (93.17%). Pharmacists in pharmacies located in districts 3, 11 and Binh Thanh had higher rate of rational provision than those in other districts. A good and full understanding of the patient symptom helped the pharmacists supply cough medicines more reasonably. The limited caution of pharmacists and the low proportion of pharmacists updating contraindication of N-acetylcysteine should be considered as a warning sign in pharmacy practice in Ho Chi Minh City, Vietnam

The Role of Cultural and Institutional Distances in International Trade

Despite the effectiveness of the observed barriers such as taxes and quotas to adjust bilateral trade, they are still not well supported by governments in general and the World Trade Organization in particular. Therefore, in recent years, unobserved barriers have been critical tools to modify the trade flows between nations worldwide. China’s exports account for a massive proportion of global trade. However, the role of cultural and institutional distance in China’s trade flow has not been much explored. This study analyzes the impact of cultural and institutional differences on China's exports between 2006-2017 by adopting a system-GMM estimator. The main findings are, first, that cultural and institutional differences between China and its trading partners reduce China's exports. Second, cultural and institutional distances have the strongest influence on China's exports to high-income countries, followed by low-income countries, and finally middle-income countries. Third, manufactured products are the most sensitive to cultural and institutional distances. Based on these findings, several policies for China, as well as for emerging economies in general, are suggested for reducing cultural and institutional distances and boosting their exports. Doi: 10.28991/ESJ-2023-07-02-015 Full Text: PD