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As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.HighlightsIntegrated approach was used to combine AI with learning analytics (LA) feedbackQuasi-experiment research was conducted to investigate student learning effectsIntegrated approach to foster student engagement, performances and satisfactionsParadigmatic implication was proposed for develop AI-driven learning analyticsClosed loop was established for both AI model development and educational application.

This paper introduces ADG-QPP (Adaptive Disturbance Generation), an unsupervised Query Performance Prediction (QPP) method designed specifically for dense neural retrievers. The underlying foundation of ADG-QPP is to measure query performance based on its degree of robustness towards perturbations. Traditional QPP methods rely on predefined lexical perturbations on the query, which only apply to sparse retrieval methods and fail to maintain consistent performance across different datasets. In our work, we address these limitations by perturbing the query by injecting disturbance leveraged by the focal network-based measurements including node-based, edge-based, and cluster-based metrics, into its neural embedding representation. Rather than applying the same perturbation across all queries, our approach develops an instance-wise disturbance for each query that is then used for its perturbation. Through extensive experiments on three benchmark datasets, we demonstrate that ADG-QPP outperforms state-of-the-art baselines in terms of Kendall τ , Spearman ρ , and Pearson’s ρ correlations.

Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. The in hand research work focuses on students’ grade and marks prediction utilizing supervised ML approaches. The data-set utilized in this research work has been obtained from the Board of Intermediate & Secondary Education (B.I.S.E) Peshawar, Khyber Pakhtunkhwa. There are 7 areas in BISEP i.e., Peshawar, FR-Peshawar, Charsadda, Khyber, Mohmand and Upper and Lower Chitral. This paper aims to examine the quality of education that is closely related to the aims of sustainability. The system has created an abundance of data which needs to be properly analyzed so that most useful information should be obtained for planning and future development. Grade and marks forecasting of students with their historical educational record is a renowned and valuable application in the EDM. It becomes an incredible information source that could be utilized in various ways to enhance the standard of education nationwide. Relevant research study reveals that numerous methods for academic performance forecasting are built to carryout improvements in administrative and teaching staff of academic organizations. In the put forwarded approach, the acquired data-set is pre-processed to purify the data quality, the labeled academic historical data of student (30 optimum attributes) is utilized to train regression model and DT-classifier. The regression will forecast marks, while grade will be forecasted by classification system, eventually analyzed the results obtained by the models. The results obtained show that machine learning technology is efficient and relevant for predicting students performance.

Predicting students’ academic performance has been a research area of interest in recent years, with many institutions focusing on improving the students’ performance and the education quality. The analysis and prediction of students’ performance can be achieved using various data mining techniques. Moreover, such techniques allow instructors to determine possible factors that may affect the students’ final marks. To that end, this work analyzes two different undergraduate datasets at two different universities. Furthermore, this work aims to predict the students’ performance at two stages of course delivery (20% and 50% respectively). This analysis allows for properly choosing the appropriate machine learning algorithms to use as well as optimize the algorithms’ parameters. Furthermore, this work adopts a systematic multi-split approach based on Gini index and p-value. This is done by optimizing a suitable bagging ensemble learner that is built from any combination of six potential base machine learning algorithms. It is shown through experimental results that the posited bagging ensemble models achieve high accuracy for the target group for both datasets.

Fracture networks constitute essential conduits for fluid and heat transport in enhanced geothermal systems (EGSs), yet their characterization is challenging due to the inherent geological complexities. This study develops an integrated inversion framework for effective fracture network characterization. The framework consists of a novel fracture network parameterization method, the covariance matrix adaptation‐evolution strategy (CMA‐ES) for fracture parameter inversion, and the embedded discrete fracture model (EDFM) for robust forward simulation of flow and transport in fractured reservoirs. The proposed fracture network parameterization method uses a background fracture network with fixed geometries and non‐uniform fracture apertures to approximate real‐world fracture networks. CMA‐ES is employed to infer fracture parameters by matching both conservative and sorptive tracer measurements, and multiple parallel CMA‐ES runs are executed to obtain an ensemble of model realizations for uncertainty assessment. Three synthetic EGS case studies with varying complexities demonstrate the effectiveness of the inversion framework in capturing major flow and transport characteristics in fracture networks. The long‐term thermal performances of the EGS reservoirs are appropriately predicted with the inferred fracture network models. This integrated framework offers a feasible solution for fracture network characterization and thermal performance prediction in EGS and also has potential applications in unconventional gas/oil reservoir explorations.

With the continuous development of production technology, the performance of glass-fiber-reinforced polymer (GFRP) bars is also changing, and some design codes are no longer applicable to new materials based on previous research results. In this study, a series of durability tests were carried out on a new generation of GFRP bars in laboratory-simulated seawater and a concrete environment under different temperatures and sustained loads. The durability performance of GFRP bars was investigated by analysing the residual tensile properties. The degradation mechanism of GFRP bars was also analysed by scanning electronic microscopy (SEM). Furthermore, the long-term performance of GFRP bars exposed to concrete pore solution under different stress levels was predicted using Arrhenius theory. The research results show that the degradation rate of GFRP bars was increased significantly at a 40% stress level. By comparing the test results, design limits, and other scholars’ research results, it is demonstrated that the GFRP bars used in this test have a good durability performance. It is found that the main degradation mechanism of the GFRP bars is the debonding at the fiber-matrix interface. In the range test, the effects of a 20% stress level on the degradation of GFRP bars were not obvious. However, the long-term performance prediction results show that when the exposure time was long enough, the degradation processes were accelerated by a 20% stress level.

Digital twin, a core technology for intelligent manufacturing, has gained extensive research interest. The current research was mainly focused on digital twin based on design models representing ideal geometric features and behaviors at macroscopic scales, which is challenging to accurately represent accuracy and performance. However, a numerical representation is essential for precision microstructures whose accuracy and performance are difficult to measure. The concept of a digital twin for an accurate representation, proposed in 2015, is still in the conceptual stage without a clear construction method. Therefore, the goal of accurate representation has not been achieved. This paper defines the concept and connotation of an accuracy and performance-oriented accurate digital twin model and establishes its architecture in two levels: geometric and physical. First, a geometric digital twin model is constructed by the contact surfaces distributed error modeling and virtual assembly with nonuniform contact states. Then, based on this, a physical digital twin model is constructed by considering the linear and nonlinear response of the structural internal physical properties to the external environment and time to characterize the accuracy and performance variation. Finally, the models are evaluated. The method is validated on microtarget assembly. The estimated values of surface modeling, center offset, and stress prediction accuracy are 94.22%, 89.3%, and 83.27%. This paper provides a modeling methodology for the digital twin research to accurately represent accuracy and performance, which is critical for product quality improvements in intelligent manufacturing. Research results can be extended to larger-scale precision structures for performance prediction and optimization.

Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

We propose a novel approach, referred to as contrastive disentangled representation for query performance prediction ( CoDiR-QPP ), to estimate search query performance by disentangling query content semantics from query difficulty. Our proposed approach leverages neural disentanglement to isolate the information need expressed in search queries from the complexities that affect retrieval performance. Motivated by empirical observations that varying query formulations for the same information need can significantly impact retrieval outcomes, we hypothesize that separating content semantics from query difficulty can enhance query performance prediction. Utilizing contrastive learning, CoDiR-QPP distinguishes between well-performing and poorly performing query variants, facilitating the estimation of a given query’s performance. Our extensive experiments on four standard benchmark datasets demonstrate that CoDiR-QPP outperforms state-of-the-art baselines in predicting query performance, offering improved semantic similarity computation and higher correlation metrics such as Kendall τ , Spearman ρ , and scaled Mean Absolute Ranking Error (sMARE).

In materials science, performance prediction of materials plays an important role in improving the quality of materials as well as preventing serious damage to the environment and threat to public safety. Traditional regression analysis models in materials science are not yet perfect, limited to capture nonlinearities of data and time-consumption for prediction, and have a poor ability to handle a large amount of data. This leads to a demand for analyses of materials data using novel computer science methods. In recent years, artificial neural networks (ANNs) are increasingly performing as a strong tool to establish the relationships among data and being successfully applied in materials science due to their generalization ability, noise tolerance and fault tolerance. In this paper, some typical ANN applications for predicting various properties (corrosion, structural, tribological and so on) of different materials serving multiple environments (atmosphere, stress, weld and so on) are reviewed. It highlights the significance of ANN in materials-related problems in separate sections arranged by the level of simplicity, ranging from simple ANN models alone to more complicated ANN models along with the hybrid use of other computing and input-ranking methods, and the trend of ANN in the context of materials science with some limitations.