Dynamic Assessment Research Articles

Integrated SEAWAT model and GALDIT method for dynamic vulnerability assessment of coastal aquifer to seawater intrusion

Seawater intrusion (SI) has become a global issue exacerbated by intense anthropogenic activities and climate change. It is imperative to seek a synergistic strategy to reconcile environmental and economic benefits in the coastal regions. However, the intricate SI process and data scarcity present formidable challenges in dynamically assessing the coastal groundwater vulnerability. To address the challenge, this study proposed a novel framework that integrates the existing vulnerability assessment method (GALDIT) and variable-density groundwater model (SEAWAT). The future scenarios from 2019 to 2050 were investigated monthly under climate change (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5) and human activities (80 % and 50 % of current groundwater abstraction) in Longkou city, China, a typical coastal region subject to extensive SI, compared with the status quo in 2018. Results indicated that by 2050, the high vulnerability area, is in a narrow buffer within 1.2 km from the shoreline and exhibits minor changes while the salt concentration here increased by about 2700 mg/L compared with the current situation. The moderate vulnerability zone expands by about 30 km2, and the low vulnerable area decreases proportionally. The groundwater over-abstraction is identified as a more critical factor compared to the regional precipitation under climate change. When groundwater abstraction is reduced to 80 % of the current scale, the expansion rate of the moderate-vulnerable area slows down significantly, with an expansion area of only 18 km2 by 2050. Further reducing groundwater abstraction to 50 % of the current scale shifts the evolution trend of the medium-vulnerable area from expansion to contraction, with the area shrinking by about 11 km2 by 2050. The integrated vulnerability assessment framework can be applied not only in the similar coastal regions but also provides insights into other natural hazards.

Differential connectivity of the posterior piriform cortex in Parkinson’s disease and postviral olfactory dysfunction: an fMRI study

Olfactory dysfunction is a common feature of both postviral upper respiratory tract infections (PV) and idiopathic Parkinson’s disease (PD). Our aim was to investigate potential differences in the connectivity of the posterior piriform cortex, a major component of the olfactory cortex, between PV and PD patients. Fifteen healthy controls (median age 66 years, 9 men), 15 PV (median age 63 years, 7 men) and 14 PD patients (median age 70 years, 9 men) were examined with task-based olfactory fMRI, including two odors: peach and fish. fMRI data were analyzed with the co-activation pattern (CAP) toolbox, which allows a dynamic temporal assessment of posterior piriform cortex (PPC) connectivity. CAP analysis revealed 2 distinct brain networks interacting with the PPC. The first network included regions related to emotion recognition and attention, such as the anterior cingulate and the middle frontal gyri. The occurrences of this network were significantly fewer in PD patients compared to healthy controls (p = 0.023), with no significant differences among PV patients and the other groups. The second network revealed a dissociation between the olfactory cortex (piriform and entorhinal cortices), the anterior cingulate gyrus and the middle frontal gyri. This second network was significantly more active during the latter part of the stimulation, across all groups, possibly due to habituation. Our study shows how the PPC interacts with areas that regulate higher order processing and how this network is substantially affected in PD. Our findings also suggest that olfactory habituation is independent of disease.

A CFD-FEA coupled model for simulating dynamic response of offshore jacket platform under earthquake considering wind, wave, current and aftershock loads

Offshore platforms as the key equipment in marine oil and gas exploitation are facing challenges from various environmental conditions, such as winds, waves, currents and even earthquakes. Due to the combined action of those concurrent loads, there is potential risk of accidents. The aim of this paper is to systematically study the dynamic response and safety assessment of the large scaled offshore structures against the concurrent loads considering the fluid-structure interaction (FSI) effects. Three-dimensional numerical wave flume including a jacket platform is established by CFD software Fluent, based on which the simulation of wave generation and propagation is carried out. Then the FSI effects between waves and platform are analyzed using the CFD- FEA coupling method. Subsequently, the seismic ground accelerations are inputted into the platform, correspondingly the structural response is analyzed. Moreover, multiple influencing factors including earthquake direction and mainshock-aftershock sequence are discussed. The results show that the structure response under concurrent loads is not a simple linear superposition of different load. The seismic response would be underestimated if wave action is ignored when the earthquake acts on certain angles. The methodology proposed could be regarded as an effective way to analyze the FSI problem under realistic offshore environment.

Correction to: The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension

Correction to: The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension

Computational Fluid Dynamic Assessment of Patients with Congenital Heart Disease from 3D Rotational Angiography.

For congenital heart disease patients, multiple imaging modalities are needed to discern anatomy and functional information such as differential blood flow. During cardiac catheterization, 3D rotational angiography (3DRA) can provide CTA-like images, enabling anatomical information and intraprocedural guidance. We seek to establish whether unique aspects of this technique can also generate quantitative functional blood flow information. We propose that systematic integration of 3DRA imaging, catheter hemodynamic information, and computational fluid dynamics (CFD), can provide quantitative information regarding blood flow dynamics and energetics, without additional imaging or procedures. We report a single center retrospective feasibility study comprising four patients with 3DRA imaging and a complete set of hemodynamic data. 3DRA was processed and segmented to reconstruct vascular regions of interest (ROI), and a computational grid for CFD modeling of blood flow through the ROI was generated. Blood flow was simulated by integrating catheter hemodynamic data to devise boundary conditions at vascular ROI inlets and outlets. The 3DRA-based workflow successfully generated key computational outputs commonly used for cardiovascular applications, including flow patterns, distribution fractions, wall shear stress. Computational outputs obtained were as detailed and resolved as those obtained from more commonly used CT or MR angiography. Accuracy was confirmed by comparing computed flow distributions with measurements for 2 cases, showing less than 2.0% error from the measured data. Systematic integration of catheter hemodynamic information, 3DRA imaging, and CFD modeling, provides an effective and feasible alternative to obtain important quantitative blood flow information and visualization, without additional imaging.

A Dynamic Security Assessment Method for Ironmaking Plants Based on Cloud-Edge Collaboration in Reconstructed Networks

Security assessment of ironmaking plants is one of the crucial means to promote their sustainable development. However, the disparate nature of subsystems within these plants, along with network inconsistencies and isolated data, obstruct a thorough and timely security assessment. At the same time, it is impossible to achieve the sustainable development goals of reducing the adverse impact of safety on the environment, ensuring economic benefits and the health of employees. This study addresses the complexities of heterogeneous networks, disparate systems, and segregated data that are prevalent in traditional ironmaking plants; and a method to reconstruct the plant’s network and execute security assessments is proposed. This method involves coupling existing systems with new ones to create comprehensive data and resource pools by aggregating information from diverse sources. Subsequently, employing multiple regression models and optimized neural network models at the edge and central cloud facilitates dynamic assessment of security concerns. This method enables concurrent consideration of both regional and overall security analysis and decision-making within the plant. Through simulation testing of 27 functionalized module indicator datasets over the preceding 12 months at a specific ironmaking plant, the efficacy of the proposed theoretical methods and technological approaches in constructing security systems for ironmaking plants is substantiated.

Retraction Note: Assessment of fetal intraventricular diastolic fluid dynamics using ultrasound vector flow mapping

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12872-023-03524-0.

Exploring Land Use/Land Cover Dynamics and Statistical Assessment of Various Indicators

Current information on urban land use and surface cover is derived from the land classification of cities, facilitating accurate future urban planning. Key insights are driven by multi-year remote sensing data. These data, when analyzed, produce high-resolution changes on the Earth’s surface. In this context, publicly accessible Urban Atlas data are employed for the high-precision and high-resolution classification and monitoring of terrestrial surfaces. These datasets, which are useful for preserving natural resources, guiding spatial developments, and mitigating pollution, are crucial for monitoring changes and managing cities. This research aims to analyze and contrast land use and land cover (LULC) changes in Gaziantep (Turkey) between 2010 and 2018 using Urban Atlas data, and to investigate correlations between the city’s statistical data and LULC changes. Gaziantep’s urban dynamics were analyzed using Urban Atlas datasets from 2010 to 2015 and 2012 to 2018, the latter part of Copernicus, the European Earth Observation Programme. To understand the impact of LULC changes on urban landscapes, people, and the environment, official environmental and demographic statistics spanning four years were sourced and studied. The findings reveal a trend of agricultural and vacant lands evolving into residential and industrial zones, with such changes likely to increase in the near future, given the growth of building zones. While some land classes have shown consistent area values annually, residential and industrial zones have expanded in response to housing and employment demands. The most significant alterations have occurred in the last three years. Shifts in urban configurations align closely with migratory patterns, reflecting notable variations in factors like population, consumption, and pollution.

Evidential reasoning and lightweight multi-source heterogeneous data fusion-driven fire danger level dynamic assessment technique

Dynamic fire assessment under many uncertainties make danger level difficult to be evaluated reasonably. This investigation aims to establish a fire danger level dynamic assessment technique, which can be utilized to achieve automatically rules generation of fire assessment and multiple attributes-based fire danger level determination. In the technique, image segmentation with particle swarm optimization is used to generate the rules of fire assessment related to image type data automatically. Evidential reasoning with particle swarm optimization is used to generate the rules of fire assessment related to numeric type data automatically. Then, final fire danger level can be evaluated together with the help of the multi-source heterogeneous data. The main novelty of this investigation is that the developed multiple attributes-based assessment technique is not limited to the specific fire scenario, which is without resort to professional knowledges for determining the rules of fire assessment and training based on mass data in advance. In addition, the results of the case study of a tunnel fire support the ability of the technique, which can be used to achieve the reasonable fire danger level dynamic assessment with only the monitored data at the current time condition.

A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Presently, traffic safety analysis is mainly based on accident-based indicators, but they are incidental and small probability events, making safety analysis based on accident-related data difficult to achieve a comprehensive application in network size, and to realize risk assessment before accidents occur. To this end, this study innovatively develops a metric named Traffic Dynamic Operation Risk (TDOR) based on aggressive driving behaviors (ADBs) and traffic flow data for traffic safety evaluation. And Non-Negative Matrix Factorization (NMF) is adopted to explore the quantitative risk under the coupling of multiple risk factors. The results show that: 1) segments with high TDOR are always concentrated in a certain area and in a spatial distribution of Gaussian, and the peaks of these distributions are almost the assesses of transportation hubs or complex intersections; 2) traffic volume and coefficient of speed variation are the significant risk factors at ordinary sections and branches, while traffic volume and speed difference between upstream are the largest contributions at intersections; 3) roads with more accidents are associated with a higher TDOR, but the relationship between the two is not completely linearly correlated. The experimental results show that it is reasonable and feasible to use ADBs and traffic flow parameters to quantify traffic safety risks. Compared with other matrix factorization methods, the importance of variables based on NMF is more interpretable and more consistent with the change pattern of risk variables. The method proposed in this paper can change the previous traffic risk assessment pattern based on accident-related indicators and provide a basis for pre-accident risk prevention.

Dynamic Assessments of Word Reading Skills in Diverse School-Age Children: A Meta-Analysis

Purpose: Dynamic assessments evaluate ability to learn and may be less biased against children who historically have underperformed on static measures that evaluate acquired knowledge. This meta-analysis examined the validity of dynamic assessments of word reading skills (phonological awareness, sound–symbol knowledge, and decoding), as measured by the strength of their correlation with performance on word reading measures. The consistency of their validity across participant reading status (typically developing vs. at risk), language status (monolingual vs. bilingual), and age group (4–5 vs. 6–7 vs. 8–9 years) was also examined to determine whether this assessment approach is an equitable means of evaluating early word reading skills. Method: Electronic databases and preprint repositories were searched to identify studies that evaluated dynamic assessments of word reading skills conducted with children between the ages of 4 and 10 years. Pearson's correlation coefficients representing the relationship between dynamic assessments and word reading measures were extracted, and an overall pooled mean effect size was calculated using a random-effects meta-analysis with robust variance estimation. Subgroup analyses for participant reading status, language status, and age group were also conducted to determine whether there were significant differences in effect sizes across groups. Results: Thirty-five studies with 192 effect sizes were identified. Overall, dynamic assessments of word reading skills demonstrated strong correlations with word reading measures ( g = 0.49). Subgroup analysis by age indicated that dynamic assessments are significantly better suited to evaluate word reading in younger children (ages 4–5 and 6–7 years) than older students (ages 8–9 years; p = .0001). Validity was consistent across typically developing and at-risk participants and across bilinguals and monolinguals. There were few studies conducted with bilinguals despite dynamic assessment's purported application for this population. Conclusion: This meta-analysis provides preliminary evidence to support dynamic assessments as a valid and equitable means of evaluating word reading skills with diverse children aged 4–7 years. Supplemental Material: https://doi.org/10.23641/asha.25293046

An Approach for Personalized Dynamic Assessment of Chronic Kidney Disease Progression Using Joint Model.

Chronic kidney disease (CKD) poses significant challenges to public health and healthcare systems, demanding a comprehensive understanding of its progressive nature. Prior methods have often fallen short in capturing the dynamic and individual variability of renal function. This study aims to address this gap by introducing a novel approach for the individualized assessment of CKD progression. A cohort of 1042 patients, comprising 700 with stage 3a and 342 with stage 3b to stage 5 CKD, treated at a veteran general hospital in Taiwan from 2006 to 2019, was included in the study. A comprehensive dataset spanning 12 years, consisting of clinical measurements, was collected and analyzed using joint models to predict the progression to hemodialysis treatment. The study reveals that the estimated glomerular filtration rate (eGFR) can be considered an endogenous factor influenced by innate biochemical markers. Serum creatinine, blood pressure, and urinary protein excretion emerged as valuable factors for predicting CKD progression. The joint model, combining longitudinal and survival analyses, demonstrated predictive versatility across various CKD severities. This innovative approach enhances conventional models by concurrently incorporating both longitudinal and survival analyses and provides a nuanced understanding of the variables influencing renal function in CKD patients. This personalized model enables a more precise assessment of renal failure risk, tailored to each patient's unique clinical profile. The findings contribute to improving the management of CKD patients and provide a foundation for personalized healthcare interventions in the context of renal diseases.

Cine clips increase the detection of thyroid pyramidal lobe in routine thyroid sonogram

Background: Identification and resection of the thyroid pyramidal lobe is important for thyroid cancer surgery in order to prevent interval cancer in residual thyroid tissue. Purpose: The purpose of this study was to determine how often a thyroid pyramidal lobe is found in patients with and without previous thyroidectomy and to optimise the protocol for identifying thyroid pyramidal lobes during routine thyroid ultrasonography. Material and Methods: In this prospective study, a total of 1579 patients who received routine thyroid ultrasound scans at a single centre were enrolled. A dedicated standard scanning protocol was established containing both static images of the anterior neck superior to the thyroid as well as a transverse cine loop starting from the isthmus to the hyoid bone. The presence and features of thyroid pyramidal lobes were evaluated and compared. Results: Detection rate of thyroid pyramidal lobes in patients without thyroidectomy improved from 39.5% (480/1215) to 49.7% (640/1215) with protocol adding cine-loop as compared to protocol without cine-loop. The cine-loops were particularly helpful in the detection of thyroid pyramidal lobes when it is separated from the main lobe or in thyroidectomy patients. By adding assessment with cine-loop into the dedicated protocol, we have further detected different pathologies occurring on thyroid pyramidal lobes including benign and malignant solid nodules and pseudo-nodules of Hashimoto’s thyroiditis. Conclusion: The addition of dynamic assessment with cine-loop increases the detection rate of thyroid pyramidal lobes. By paying attention to the thyroid pyramidal lobe in pre-operative diagnostic sonographic images, we can help to avoid incomplete removal of the thyroid gland during thyroidectomy.

In-silico design novel phenylsulfonyl furoxan and phenstatin derivatives as multi-target anti-cancer inhibitors based on 2D-QSAR, molecular docking, dynamics and ADMET approaches

ABSTRACT A series of 31 hybrid of phenylsulfonyl furoxan and phenstatin (1a-j, 2a-j, 3a-j, 4 and 5) derivatives, were computationally studied as potential anti-cancer inhibitors against four cell lines, i.e. A2780, MDA-MB-231, HCT-116 and A549. In this work, the 2D-QSAR approach combining the multiple linear regression (MLR) model, and internal and external cross-validation, showed a satisfactory quality factor: R 2 = 0.85, 0.74, 0.82 and 0.75 for the four cell lines, respectively. The binding affinity of the hybrid agents towards the four 4GL7, 6GUE, 1M17 and 4XL7 anti-tumoral targets was further evaluated using molecular docking and dynamics simulations (0–200 ns). The dynamics assessment parameters indicated the formation of satisfactorily stable complexes. In addition, all considered data sets show that the best binding affinity, including the highest docking score, hydrogen bond energy and amino acid steric interactions are well predicted for the best-selected complexes. The developed 2D-QSAR model was leveraged to design and predict the biological activity of 12 new hybrid compounds (N1–N12) based on the best in-vivo inhibitor, namely, the 3 h ligand of the formula: (4-((1-(2-((4-((3crylamidophenyl)amino)quinazolin-2-yl)thio)acetyl)piperidin-4-yl)oxy)-3-(phenylsulfonyl)-1,2,5-oxadiazole 2-oxide). Multitargeting docking scores and dynamics simulations show that they exhibit satisfactorily potent anti-tumoral inhibition abilities towards the four proteins. Our in-silico outcomes would be combined with in-vitro and in-vivo studies to provide a perspective on the validation of their anti-cancer activity. In particular, the ADMET predictions indicate that four new designed ligands have demonstrated a good drug-like profile and can be considered prospective candidates for future anti-cancer therapies.

Point-of-care abdominal ultrasound in pediatric and neonatal intensive care units.

A spectrum of critical abdominal pathological conditions that might occur in neonates and children warrants real-time point-of-care abdominal ultrasound (abdominal POCUS) assessment. Abdominal radiographs have limited value with low sensitivity and specificity in many cases and have no value in assessing abdominal organ perfusion and microcirculation (Rehan et al. in Clin Pediatr (Phila) 38(11):637-643, 1999). The advantages of abdominal POCUS include that it is non-invasive, easily available, can provide information in real-time, and can guide therapeutic intervention (such as paracentesis and urinary bladder catheterization), making it a crucial tool for use in pediatric and neonatal abdominal emergencies (Martínez Biarge et al. in J Perinat Med 32(2):190-194, 2004) and (Alexander et al. in Arch Dis Child Fetal Neonatal Ed 106(1):F96-103, 2021). Conclusion: Abdominal POCUS is a dynamic assessment with many ultrasound markers of gut injury by two dimensions (2-D) and color Doppler (CD) compared to the abdominal X-ray; the current evidence supports the superiority of abdominal POCUS over an abdominal X-ray in emergency situations. However, it should still be considered an adjunct rather than replacing abdominal X-rays due to its limitations and operator constraints (Alexander et al. in Arch Dis Child Fetal Neonatal Ed 106(1):F96-103, 2021). What is Known: • Ultrasound is an important modality for the assessment of abdominal pathologies. What is New: • The evidence supports the superiority of abdominal POCUS over an abdominal X-ray in emergency abdominal situations in the neonatal and pediatric intensive care units.

Large-scale digital signatures of emotional response to the COVID-19 vaccination campaign

The same individuals can express very different emotions in online social media with respect to face-to-face interactions, partially because of intrinsic limitations of the digital environments and partially because of their algorithmic design, which is optimized to maximize engagement. Such differences become even more pronounced for topics concerning socially sensitive and polarizing issues, such as massive pharmaceutical interventions. Here, we investigate how online emotional responses change during the large-scale COVID-19 vaccination campaign with respect to a baseline in which no specific contentious topic dominates. We show that the online discussions during the pandemic generate a vast spectrum of emotional response compared to the baseline, especially when we take into account the characteristics of the users and the type of information shared in the online platform. Furthermore, we analyze the role of the political orientation of shared news, whose circulation seems to be driven not only by their actual informational content but also by the social need to strengthen one’s affiliation to, and positioning within, a specific online community by means of emotionally arousing posts. Our findings stress the importance of better understanding the emotional reactions to contentious topics at scale from digital signatures, while providing a more quantitative assessment of the ongoing online social dynamics to build a faithful picture of offline social implications.

Spatiotemporal Changes and Driving Analysis of Ecological Environmental Quality along the Qinghai–Tibet Railway Using Google Earth Engine—A Case Study Covering Xining to Jianghe Stations

The Qinghai–Tibet Railway is located in the most fragile and sensitive terrestrial ecosystem of the Qinghai–Tibet Plateau in China, and once the ecological environment is damaged, it is difficult to restore. This study, based on the Google Earth Engine platform, focuses on the section of the Qinghai–Tibet Railway from Xining to Jianghe. It utilizes Landsat series satellite imagery data from 1986 to 2020 to calculate the Remote Sensing Ecological Index (RSEI). This approach enables large-scale and long-term dynamic monitoring, analysis, and assessment of the ecological changes along the Qinghai–Tibet Railway corridor. The results indicate that (1) the average RSEI of the study area increased from 0.37 in 1986 to 0.53 in 2020, showing an overall trend of improvement. The ecological environment quality is mainly categorized as medium and good. (2) The quality of the ecological environment in the areas along the railway experienced fluctuations during different periods of railway construction and operation. From 1986 to 1994, after the first phase of the railway opened, the overall ecological environment showed a relative decline in quality. From 1994 to 2002, the ecological quality of 60% of the region saw slight improvements. During the extension construction of the second phase of the railway from 2002 to 2007, the regional ecology fluctuated again. However, from 2013 to 2020, during the operational period, a stable recovery trend was observed in the ecological environment. (3) The ecological environment in the study area is influenced by multiple factors. Different railway station areas exhibit strong spatial heterogeneity. The impact of single factors is significant, with the existence of spatial stratification and enhanced interactions among multiple factors. The strongest interactive effects are observed between land use types, the intensity of human activities, and temperature.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function.

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Greenhouse gas emissions benefits of the lightweight vehicle fleet in China: A dynamic fleet perspective

Lightweighting is believed to be a silver bullet to reduce greenhouse gas (GHG) emissions related to the road transportation. However, the fleet-scale and long-term GHG emissions estimation on the light-duty passenger vehicle (LDPV) fleet in China is still underexplored. A dynamic fleet-based life cycle assessment (LCA) model is developed to predict the annual GHG emissions attributable to the lightweight LDPV fleet in China at provincial level. With the most aggressive implementation of lightweighting, the cumulative mitigation potential of lightweight LDPV fleet from 2021 to 2050 is 0.72 Gt CO2 eq and 0.55 Gt CO2 eq using high-strength steel (AHSS) and aluminium (Al), respectively. Sooner implementation brings a higher cumulative reduction in GHG emissions. Delaying the implementation by five years would sacrifice 0.16 Gt CO2 eq ∼0.2 Gt CO2 eq cumulative reduction, and the loss would increase to 0.31 Gt CO2 eq ∼0.41 Gt CO2 eq when postpone the lightweighting by ten years.

Writing Woes of ESL Learners: Can Dynamic Assessment Be the Solution?

AbstractThis study explores the effectiveness of dynamic assessment (DA) in addressing grammatical writing difficulties in Pakistani English as a Second Language (ESL) learners' writing. Adopting an interactionist approach to DA, the research observes mediational moves by a teacher during the writing assessment process. The study employs a qualitative case study design, examining the interactional patterns and outcomes of DA for three higher secondary-level Pakistani ESL learners over an eight-week period. The findings indicate that the interactionist approach to DA is a promising method for improving the grammatical accuracy of Pakistani ESL learners, particularly in areas such as tenses, pronouns, and subject-verb agreement. The results of this study have significant implications for writing instruction in ESL contexts. Dynamic assessment can support the development of writing skills by providing ESL writers with opportunities to engage in distributive practice and receive feedback on their writing, resulting in a tailored and effective approach to writing instruction.