Embedded Method Research Articles

Modified embedded atom method potential for Fe-Al intermetallics mechanical strength: A comparative analysis of atomistic simulations

The structural and mechanical properties of Fe-Al compounds (FeAl, Fe2Al, Fe3Al, FeAl2, FeAl3, Fe2Al5) have been studied using modified embedded atom method (MEAM) potentials. The equilibrium lattice constants, formation enthalpies, and elastic properties have been investigated and compared with other studies. The calculated lattice constants show good agreement with the embedded atom method (EAM) and density functional theory (DFT) calculations and with experiments. All Fe-Al compounds are mechanically stable according to the elastic constants restrictions. The calculated bulk modulus of the compounds does not show a linear relation with Fe concentration, which is most probably caused by the mechanical anisotropy of Fe-Al compounds. However, comparison of the Fe-Al mechanical properties of MEAM, DFT and EAM-based approaches and experiments show non-consistent differences, which reflects uncertainties with several of these methods, due to assumptions and simplifications imposed during calculations. In general, DFT calculations are closer to experimental observations than semi-empirical potentials. Comprehensive comparisons are made based on theoretical and experimental methodologies.

Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds.

Quantum-mechanics-(QM)-based simulations now routinely aid in understanding and even discovering new chemistries involving molecules and materials exhibiting desired functionalities. Ab initio correlated wavefunction (CW) theories systematically improve QM methods, with many exhibiting high accuracy. However, execution of CW methods requires expensive computations that typically scale poorly with system size. Divide-and-conquer approaches partition large systems into smaller fragments; a lower level of theory treats fragment interactions while a preferred higher level of theory describes the important fragment. These methods offer ways to incorporate CWs into chemical simulations of large systems, e.g., biomolecules, surfaces, large inorganic clusters, bulk crystals, etc. Here we propose a partitioning protocol that utilizes capping atoms to saturate severed covalent bonds at fragment interfaces and density functional embedding theory (DFET) to describe fragment interactions. The capping groups in each fragment provide an ad-hoc potential that approximates the effects of the environment. An embedding potential optimized via DFET then serves as an augmentation of the capping group to simulate the effects of the environment. We concurrently use an auxiliary fragment (a separate system comprised of only the combined capping groups) to account for, and thereby correct, the electron density contributions of all the capping groups added to all of the fragments. This method depends only on the capped-subsystem and auxiliary-fragment electron densities, forgoing, as with the original DFET developed for metallic systems, orbital-based projector approaches that determine a nonlocal action of the embedding potential onto the fragment electrons. By using an auxiliary fragment, the method maintains a purely electron-density-dependent embedding potential, substantially lessening the cost and leading to simpler implementation. Here, we demonstrate the utility of our capped-DFET and ensuing capped embedded CW method in two contrasting systems, namely, an organic molecule and an ionic metal oxide cluster.

Mathematical Problem Solving Ability: Comparison of Guided Discovery Learning Model and Problem Based Learning Model

The purpose of this study was to examine the achievement of mathematical problems solving ability between students who acquired learning with Problem Based Learning model with students who acquired learning with Guided Discovery Learning model. This research was a mixed method research with concurrent embedded method. This research consists of experimental group that obtained learning with Guided Discovery Learning method and Problem Based Learning method. The population of this research was a junior high school students in Tanah Datar regency, West Sumatera with sample of grade VIII. Data analysis was done quantitatively and qualitatively. Quantitative analysis was used to calculate the difference in the achievement of mathematical problem solving ability, while qualitative data analysis was used to strengthen the result of quantitative analysis. The results showed that the percentage of the achievement of mathematical problem solving ability of students who obtained learning with Guided Discovery Learning method and Problem Based Learning method is moderate, whereas there is no difference in the achievement of significant mathematical problem solving ability between the two experimental classes.

Vacancy and phonon dispersion properties of Be, Co, Hf, Mg, and Re by modified embedded atom method potentials.

The modified embedded atom method (MEAM) potentials improved by Jin et al. (Appl. Phys. A120 (2015), p. 189) were applied to calculate the mono- and bi-vacancy properties as well as the phonon dispersions for hexagonal close-packed (HCP) metals Be, Co, Hf, Mg, and Re. We expressed the formulas for calculating the mono- and bi-vacancy properties by the molecular static (MS) method based on the MEAM potentials for HCP metals. The lattice dynamics (LD) method and the MEAM potentials were adopted to calculate the phonon dispersion properties. The calculation results show better agreement with the experimental data than the previous calculations by using the unimproved embedded atom model.

LAMMPS implementation of rapid artificial neural network derived interatomic potentials

While machine learning approaches have been successfully used to represent interatomic potentials, their speed has typically lagged behind conventional formalisms. This is often due to the complexity of the structural fingerprints used to describe the local atomic environment and the large cutoff radii and neighbor lists used in the calculation of these fingerprints. Even recent machine learned methods are at least 10 times slower than traditional formalisms. An implementation of a rapid artificial neural network (RANN) style potential in the LAMMPS molecular dynamics package is presented here which utilizes angular screening to reduce computational complexity without reducing accuracy. For the smallest neural network architectures, this formalism rivals the modified embedded atom method (MEAM) for speed and accuracy, while the networks approximately one third as fast as MEAM were capable of reproducing the training database with chemical accuracy. The numerical accuracy of the LAMMPS implementation is assessed by verifying conservation of energy and agreement between calculated forces and pressures and the observed derivatives of the energy as well as by assessing the stability of the potential in dynamic simulation. The potential style is tested using a force field for magnesium and the computational efficiency for a variety of architectures is compared to a traditional potential models as well as alternative ANN formalisms. The predictive accuracy is found to rival that of slower methods. Additionally, the transferability of the formalism is demonstrated by correctly predicting the Mg phase diagram include the pressure dependence on melting temperature and the presence of a high pressure BCC phase.

E-learning in a pandemic era

The COVID-19 pandemic contributed to a digital economy transition by emphasizing the importance of digital infrastructure while exposing the digital gaps between countries and communities. Inequalities in digital access has impeded educational gains made towards achieving the sustainable development goals. While major studies have been carried out on digital learning there is currently a dearth of knowledge on the digital gaps and needs of students in remote communities in Nigeria. To this end, this study explored the digital gaps and needs of rural secondary schools in remote communities and its implications on e-learning across 6 Nigerian states during the COVID-19 era. The study adopted a concurrent embedded mixed method design approach to collect data from rural secondary schools from 6 Nigerian states. Findings from the study identified lack of ICT strategies and policies in Nigeria, socio-economic status, poor internet connectivity, electricity, and a high poverty level as the major drivers of digital gaps in remote communities.

Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data.

.Purpose: Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models.Approach: We assessed 77 low-grade gliomas and glioblastomas multiform patients publicly available in TCGA and TCIA. Radiomics features were extracted from multiparametric MRI images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery) and different regions-of-interest (enhancing tumor, nonenhancing tumor/necrosis, and edema). A method developed to find variables that are part of causal structures was used for feature selection and compared with an embedded feature selection approach commonly used in radiomics/radiogenomics studies, across two different scenarios: (1) leaving data from a center as an independent held-out test set and tuning the model with the data from the remaining centers and (2) use stratified partitioning to obtain the training and the held-out test sets.Results: In scenario (1), the performance of the proposed methodology and the traditional embedded method was AUC: 0.75 [0.25; 1.00] versus 0.83 [0.50; 1.00], Sens.: 0.67 [0.20; 0.93] versus 0.67 [0.20; 0.93], Spec.: 0.75 [0.30; 0.95] versus 0.75 [0.30; 0.95], and MCC: 0.42 [0.19; 0.68] versus 0.42 [0.19; 0.68] for center 1 as the held-out test set. The performance of both methods for center 2 as the held-out test set was AUC: 0.64 [0.36; 0.91] versus 0.55 [0.27; 0.82], Sens.: 0.00 [0.00; 0.73] versus 0.00 [0.00; 0.73], Spec.: 0.82 [0.52; 0.94] versus 0.91 [0.62; 0.98], and MCC: versus , whereas for center 3 was AUC: 0.80 [0.62; 0.95] versus 0.89 [0.56; 0.96], Sens.: 0.86 [0.48; 0.97] versus 0.86 [0.48; 0.97], Spec.: 0.72 [0.54; 0.85] versus 0.79 [0.61; 0.90], and MCC: 0.47 [0.41; 0.53] versus 0.55 [0.48; 0.60]. For center 4, the performance of both methods was AUC: 0.77 [0.51; 1.00] versus 0.75 [0.47; 0.97], Sens.: 0.53 [0.30; 0.75] versus 0.00 [0.00; 0.15], Spec.: 0.71 [0.35; 0.91] versus 0.86 [0.48; 0.97], and MCC: 0.23 [0.16; 0.31] versus. . In scenario (2), the performance of these methods was AUC: 0.89 [0.71; 1.00] versus 0.79 [0.58; 0.94], Sens.: 0.86 [0.80; 0.92] versus 0.43 [0.15; 0.74], Spec.: 0.87 [0.62; 0.96] versus 0.87 [0.62; 0.96], and MCC: 0.70 [0.60; 0.77] versus 0.33 [0.24; 0.42].Conclusions: This proof-of-concept study demonstrated good performance by the proposed feature selection method in the majority of the studied scenarios, as it promotes robustness of features included in the models and the models’ generalizability by making used imaging data of different scanners or with sequence parameters.

Adaptive Stepsize Control for Extrapolation Semi-Implicit Multistep ODE Solvers

Developing new and efficient numerical integration techniques is of great importance in applied mathematics and computer science. Among the variety of available methods, multistep ODE solvers are broadly used in simulation software. Recently, semi-implicit integration proved to be an efficient compromise between implicit and explicit ODE solvers, and multiple high-performance semi-implicit methods were proposed. However, the computational efficiency of any ODE solver can be significantly increased through the introduction of an adaptive integration stepsize, but it requires the estimation of local truncation error. It is known that recently proposed extrapolation semi-implicit multistep methods (ESIMM) cannot operate with existing local truncation error (LTE) estimators, e.g., embedded methods approach, due to their specific right-hand side calculation algorithm. In this paper, we propose two different techniques for local truncation error estimation and study the performance of ESIMM methods with adaptive stepsize control. The first considered approach is based on two parallel semi-implicit solutions with different commutation orders. The second estimator, called the “double extrapolation” method, is a modification of the embedded method approach. The introduction of the double extrapolation LTE estimator allowed us to additionally increase the precision of the ESIMM solver. Using several known nonlinear systems, including stiff van der Pol oscillator, as the testbench, we explicitly show that ESIMM solvers can outperform both implicit and explicit linear multistep methods when implemented with an adaptive stepsize.

Atomistic aspects of the temperature effect on fracture toughness of a silicon single crystal

The temperature effect on the fracture toughness of a Si single crystal was explored through molecular dynamics simulations over the temperature range of 1–1000 K. A sharp crack model with the (010)[001] (plane)[front] crystallographic system was considered, in which the interatomic force was characterized using the modified embedded atom method potential. The computational experiment of a mode-I fracture was carried out employing the displacement based on the mode-I near tip field, and the fracture toughness was evaluated using the critical stress intensity factor for the onset of crack growth. The results showed that the fracture toughness in the temperature range of 1–750 K slightly decreased with increasing temperature, where the effect of lattice trapping was reduced at the elevated temperature. However, when the temperature was higher than 800 K, the fracture toughness increased resulting from the nucleation of nanovoids in front of the crack tip. Detailed atomistic analysis showed that, in the small-scale yielding limit, the temperature effect on the hydrostatic component of the local crack tip field was important to crack-tip plasticity.

TEACHERS’ PERCEPTION OF THE IMPACT OF INSTRUCTIONAL STRATEGIES ON CHILDREN WITH READING DIFFICULTY IN GHANA

The study investigated teachers’ perception of the impact of instructional strategies on pupils with reading difficulties in selected districts in the Central Region of Ghana. A concurrent embedded mixed method design was used for this study. Proportionate stratified sampling procedure was used to select ninety-five (95) upper primary teachers for the study. The response rate was 100% representing 95 teachers. Data were collected using a questionnaire and interview schedules. Statistical tools used in data analysis were mainly frequency distribution and percentages, means and standard deviations and independent samples t-test, however, Braun and Clarke (2006) thematic analytic approach was used to analyse the qualitative data. The study revealed that Teachers had negative perceptions about pupils with reading difficulties. Also, role play, read aloud, direct instruction and group activities were the frequently used strategies teachers used in teaching pupils with reading difficulties. The study recommends that Ghana Education Service in collaboration with all the head teachers should organise a workshop on reading difficulty for teachers in the three districts. Further, the Ministry of Education through the Ghana Education Service should find means of providing resources to support teaching of reading in the schools in all the districts.

TEACHERS’ PERCEIVED ATTITUDES TOWARD THE IMPLEMENTATION OF 21ST CENTURY SKILLS AT PRIMARY SCHOOL

21st century education requires teachers to be able to understand the 21st century skills and apply them in daily classroom practice. Although necessary for teachers, research on investigating the teachers’ perceived attitudes toward the implementation of the 21st century skills for teaching English in primary schools in Bali is still limited. This study aimed to investigate primary school’s English teachers’ perceived attitudes toward the implementation of 21st-century skills. The design of this study was a sequential embedded mixed method with dominantly used qualitative design. This study used 3 English teachers and two headmasters from 2 primary schools in Tabanan regency as the subject. The data were collected by using a questionnaire and interview to gain the data of teachers’ perceived attitudes toward the implementation of the 21st century skills. From the data that have been obtained, primary English teachers gave positive attitudes toward 95,8% of the skills in the implementation of 21st century skills. The interview section showed that the teacher lacks understanding of 21st century skills because of a lack of in-depth information about 21st century skills. Moreover, the teachers have to be aware of 21st century skills in the assessment as one of the concepts that are adapted for Kurikulum 2013.

GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies.

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method—threshold gradient descent regularization (TGDR)—stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes—GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.

I6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.

DNA N6-methyladenine (6mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .

Copers and Noncopers Use Different Landing Techniques to Limit Anterior Tibial Translation After Anterior Cruciate Ligament Reconstruction.

Background:At 1 year after anterior cruciate ligament reconstruction (ACLR), two-thirds of patients manage to return to sports (copers), whereas one-third of patients do not return to sports (noncopers). Copers and noncopers have different muscle activation patterns, and noncopers may not be able to control dynamic anterior tibial translation (ATTd) as well as copers.Purpose/Hypothesis:To investigate whether (1) there is a positive correlation between passive ATT (ATTp; ie, general joint laxity) and ATTd during jump landing, (2) whether ATTd is moderated by muscle activating patterns, and (3) whether there is a difference in moderating ATTd between copers and noncopers. We hypothesized that patients who have undergone ACLR compensate for ATTd by developing muscle strategies that are more effective in copers compared with noncopers.Study Design:Controlled laboratory study.Methods:A total of 40 patients who underwent unilateral ACLR performed 10 single-leg hops for distance with both legs. Lower body kinematic and kinetic data were measured using a motion-capture system, and ATTd was determined with an embedded method. Muscle activity was measured using electromyographic signals. Bilateral ATTp was measured using a KT-1000 arthrometer. In addition, the Beighton score was obtained.Results:There was no significant correlation between ATTp and ATTd in copers; however, there was a positive correlation between ATTp and ATTd in the operated knee of noncopers. There was a positive correlation between the Beighton score and ATTp as well as between the Beighton score and ATTd in both copers and noncopers in the operated knee. Copers showed a negative correlation between ATTd and gastrocnemius activity in their operated leg during landing. Noncopers showed a positive correlation between ATTd and knee flexion moment in their operated knee during landing.Conclusion:Copers used increased gastrocnemius activity to reduce ATTd, whereas noncopers moderated ATTd by generating a smaller knee flexion moment.Clinical Relevance:This study showed that copers used different landing techniques than noncopers. Patients who returned to sports after ACLR had sufficient plantar flexor activation to limit ATTd.

Thermodynamic properties of fcc metals using reparameterized MEAM potentials

The modified embedded atom method (MEAM) potentials of Jin et al. (Appl Phys A 120:189, 2015) are reparameterized through fitting the available experimental data and ab initio results of several physical properties better for seven face-centered cubic metals, Ag, Al, Au, Cu, Ni, Pd, and Pt. Results for the structure stabilities, point defects, and phonon dispersion properties are in better agreement with the measured values than those from the original MEAM potentials, showing the reliability of the reparameterized potentials. Also, the thermodynamic properties for these metals are studied within the quasi-harmonic approximation. Calculation results for the temperature dependence of lattice constants, thermal expansion coefficients, molar heat capacities at constant volume and/or constant pressure, isothermal and adiabatic bulk moduli, Gruneisen parameters, and Debye temperatures are compared to those from the earlier embedded atom methods and ab initio method calculations as well as the experimental data.

Formation enthalpies and dilute heats of HCP-HCP disordered binary alloys: modified ones of embedded atom method potentials.

The formation enthalpies and the dilute heats of HCP-HCP disordered binary alloys were evaluated by employing the improved ones of the modified analytic embedded atom method (EAM) potentials for HCP metals. We calculated the formation enthalpies according to the concentration of alloy elements for 36 kinds of HCP-HCP disordered binary alloys by using the modified ones of embedded atom method potentials for HCP metals proposed by Jin et al. (Appl. Phys. A120, 2015, 189), Johnson's alloy potential model, and Vegard's law. We derived the formulas to calculate the dilute heats of HCP-HCP binary alloys and evaluated the dilute heats for 56 kinds of HCP-HCP disordered binary alloys. The present results of the formation enthalpies and the dilute heats for HCP-HCP binary alloys are basically consistent with the experimental data, the first principle calculations, and the calculations by Miedema theory. Our results agree with the available experimental results better than the modified analytic EAM calculation results.

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.

Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Preparation of MoSi2 Coating on Mo Substrate for Oxidation Resistance by a Facile Method

A MoSi2 coating layer with a MoB protected layer was simply prepared by using a combined method, which involved slurry painting and an embedded method via a hot press sintering process. The microstructure and phase composition of the coatings were investigated and determined. The experimental time and the B addition are considered as important factors to be discussed in this study. All the results proved that when B is added, it was found that the formation of MoB played an important role of a diffusion barrier to Si and hindered the production of the poor oxidation resistance phases (Mo5Si3 and Mo3Si).

Printed silicone pneumatic actuators for soft robotics

A silicone-based embedded additive manufacturing method, called Rapid Liquid Printing (RLP) is applied to the fabrication of soft pneumatic actuators to investigate and demonstrate its potential for applications in soft robotics. This process is shown to improve on traditional silicone casting and additive manufacturing of elastomers, the two main manufacturing methods used in soft robotics, by offering complete design freedom at high speed without compromising material properties. Contrary to existing silicone printing techniques, RLP uses commercially available materials and a simple robotic arm or gantry CNC machine to print large structures in a supporting gel at a rate of four times the speed of state-of-the-art multi-material printers. To determine the applicability of RLP for soft robotics and in the production of soft actuators, a benchmark testing procedure for pneumatic linear actuators is developed. A linear actuator design is manufactured using three techniques: silicone casting, multi-material polyjet printing, and RLP. These actuators are tested for elongation and fatigue behavior through cycling and force. Rapid liquid printed actuators perform comparably for elongation range and exceed the two other methods for repeatability and longevity. A complex, multi-chambered demonstrator is designed from the actuator geometry to showcase the advantages of RLP versus existing silicone printing methods including speed, scale, and part complexity. These results demonstrate the applicability of RLP to the field of soft robotics and pave the way for its further implementation in the manufacturing of soft pneumatic actuators.

Left behind? The effects of digital gaps on e-learning in rural secondary schools and remote communities across Nigeria during the COVID19 pandemic

The COVID19 pandemic has contributed to a digital economy by emphasising the importance of digital infrastructure while exposing the digital gaps between countries and communities. For example, during the period of COVID19, schools were closed with no option of online learning due to a deficit of infrastructure around educational technologies. Inequalities in digital access have impeded educational gains made towards achieving the Sustainable Development Goals (SDGs). Though significant studies have been carried out on digital learning, there is currently a dearth of knowledge on students' digital gaps and needs in remote communities in Nigeria. To this end, this research study explored the digital gaps and needs of rural secondary schools in remote communities and its implications on e-learning across 6 Nigerian states, namely Kwara, Ekiti, Ebonyi, Bayelsa, Adamawa, and Kano during the COVID19 era. The study adopted a concurrent embedded mixed method design approach to collect data from rural secondary schools from 6 Nigerian states. Findings from the study identified a lack of ICT strategies and policies in Nigeria, socioeconomic status, poor internet connectivity, electricity, and a high poverty level as the primary drivers of digital gaps in remote communities. Therefore, addressing the digital gaps among students in remote parts of Nigeria will be crucial to achieving the targets of SDGs, particularly SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030.