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

Decoder-ROI based Versatile Video Coding for Multi-Object Tracking Vision Task

The video encoding standards High Efficiency Video Coding (HEVC) and, more recently, Versatile Video Coding (VVC) have introduced significant advancements in multimedia communication applications, such as video conferencing, broadcasting, and notably, E-learning. However, recent developments in artificial intelligence (AI) and big data have given rise to an urgent need for a specialized video encoding model designed specifically for image and video analysis applications using machine vision. In this paper, we propose a novel video encoding approach that effectively combines the ROI Coding algorithm and the VVC encoding model. The proposed method identifies regions of interest within video frames through fundamental and deep features. Based on this, we propose an adaptive compression method for each frame block, ensuring both the execution performance of machine learning applications and minimal data encoding requirements. To achieve new coding scheme without adding bitrate, New feature extraction approach are utilizing only decoded information (Decoder-ROI). The results demonstrate that the Decoder-ROI achieved significant compression rate improvement when compared to standard and relevant VCM schemes. Furthermore, ROI exploitation contributes to a 3.25\% reduction in encoding time compared to the baseline VVC encoding standard.

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

Do the internet, economic growth, and environmental quality spur people's happiness during Covid-19 pandemic?

Factors affecting people's happiness have attracted much attention in the context of the COVID-19 pandemic. This paper was conducted to investigate whether internet use, economic growth, and environmental quality improvements can ease psychological darns caused by the COVID-19 pandemic. In the context of Southeast countries, and by applying the Bayesian inference, the findings of this study are summarized: (i) internet use and economic growth positively drive people's happiness; (ii) the effect of environmental quality on happiness is unclear in the context of the COVID-19 pandemic. Based on this evidence, the study suggests practical implications for policymakers in boosting economic growth and enhancing people's happiness

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