Search Results (12)

Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article

Background/Objectives: Early handwriting and motor skills underpin later academic success, yet robust evidence for psychomotor programs in preschoolers is limited. This study examined whether participation in a six-week psychomotor intervention was associated with changes in graphomotor performance, writing proficiency, and motor competence in preschool children. Methods: This non-randomized quasi-experimental study included pre- and post-intervention assessments and a control group. Thirty-four preschoolers were assigned by class to an intervention group (n = 19) or a control group (n = 15). For six weeks, the intervention group completed three 50 min psychomotor sessions per week targeting fine- and gross-motor abilities, whereas the control group continued its usual school routine. Graphomotor performance (Pascual Graphomotor Test), writing proficiency (seven-item teacher scale) and motor competence (Movement Assessment Battery for Children-2) were assessed one week before and after the program. Results: The intervention group showed significant improvements in graphomotor performance and all motor-skill tasks assessed. Between-group comparisons of pre–post change favored the intervention group for the total graphomotor score (mean difference = −3.14 points, 95% CI [−4.59, −1.68]; p < 0.001; Hedges’ g = −1.48) and for all four motor-skill tasks (p ≤ 0.019). At post-intervention, pencil grip was the only writing proficiency item showing a significant between-group difference (p = 0.002; Cramér’s V = 0.617), whereas no significant differences were observed for the remaining items. Conclusions: A psychomotor intervention was associated with greater improvements in motor competence and graphomotor performance compared with the control condition. However, evidence for broader improvements in writing proficiency, including handwriting legibility, was limited. Although pencil grip differed between groups at post-intervention, this finding should be interpreted cautiously because the groups already differed at baseline. Full article

This mixed-methods study examined preschoolers’ digital home environment (DHE), parent–child digital and traditional literacy activities, children’s interest in literacy across modalities and parent and child perspectives on literacy practices. Participants included 121 Israeli parents of preschool-aged children and their children. Quantitative data were collected via parent questionnaires assessing joint digital literacy activities, general digital activities, parental involvement in selecting digital content, traditional literacy activities, and children’s interest in digital and traditional literacy. Qualitative data comprised parents’ open-ended responses about children’s digital media exposure and children’s perspectives on digital writing. Findings revealed positive associations between digital and traditional literacy practices in the home. Parent–child joint digital literacy activities emerged as the strongest predictor of children’s interest in digital literacy, beyond the effects of children’s age and traditional literacy practices. Conversely, parental involvement in selecting digital content was negatively associated with children’s interest in digital literacy activities. Qualitative findings indicated that parents perceived digital media use as offering educational opportunities while also raising developmental concerns, and placed strong emphasis on parental mediation and supervision. Children associated digital writing with learning, letters, and school-related literacy activities, while also linking computers with play and entertainment. Children’s preferences for handwriting versus keyboard writing were nearly equally divided, with explanations reflecting varied perceptions of convenience, enjoyment, and the meaning of writing in digital contexts. Together, these findings suggest that young children’s digital literacy experiences are shaped less by technology per se and more by the socially mediated interactions surrounding digital media use. Full article

Background/Objectives: The digital transition in school settings is reshaping children’s learning, writing practices, and mental health trajectories. This narrative review examines whether handwriting still matters in contemporary hybrid educational environments from a multidisciplinary perspective. Methods: Evidence from neuroscience, developmental psychology, educational sciences, pediatrics, and child psychiatry was narratively synthesized, with attention to handwriting, digital exposure, learning, emotional regulation, and vulnerable populations. Results: Handwriting uniquely integrates fine motor control, visuomotor coordination, orthographic processing, attention, and embodied cognition, supporting early literacy, memory consolidation, conceptual learning, and reflective writing. Conversely, excessive or poorly mediated digital exposure may interact with attentional fragmentation, sleep disruption, online stressors, problematic use, and internalizing symptoms, particularly in vulnerable children and adolescents. Digital tools remain essential for personalization, accessibility, and compensatory support, especially for students with neurodevelopmental or learning difficulties. Conclusions: Handwriting and digital technologies should not be framed as competing educational paradigms. A developmentally sensitive hybrid model is needed, preserving handwriting during key stages of literacy and self-regulation while integrating digital tools as purposeful, individualized resources for learning, inclusion, and school mental health promotion. Full article

Recognizing handwritten Arabic characters written by children via scanned or camera-captured images is a challenging task due to variations in writing style, stroke irregularity, and diacritical marks. Although deep learning has advanced this field, building reliable systems remains challenging. This study introduces a stacking ensemble framework for sensor-acquired handwriting data, enhanced with a dynamic confidence-thresholding mechanism designed to improve prediction reliability. The framework integrates three high-performing convolutional neural networks (ConvNeXtBase, DenseNet201, and VGG16) through a fully connected meta-learner. A key feature is the use of an optimized threshold that filters out uncertain predictions by maximizing the macro ${\mathrm{F}}_1$ score on validation data. The framework is evaluated on two benchmark datasets for children’s Arabic handwriting: Hijja and Dhad. The results demonstrate state-of-the-art performance, with an accuracy of $95.13\%$ and ${\mathrm{F}}_1$ score of $94.62\%$ on Hijja and an accuracy of $96.14\%$ and ${\mathrm{F}}_1$ score of $95.59\%$ on Dhad. Compared to existing methods, the proposed approach achieves more than a $3\%$ improvement in Hijja accuracy while maintaining robust performance across diverse character classes. These findings highlight the effectiveness of confidence-based stacking ensembles in enhancing reliability for Arabic handwriting recognition and suggest strong potential for automated educational assessment tools and intelligent tutoring systems. Full article

This study delves into the intricate realm of recognizing handwritten Arabic characters, specifically targeting children’s script. Given the inherent complexities of the Arabic script, encompassing semi-cursive styles, distinct character forms based on position, and the inclusion of diacritical marks, the domain demands specialized attention. While prior research has largely concentrated on adult handwriting, the spotlight here is on children’s handwritten Arabic characters, an area marked by its distinct challenges, such as variations in writing quality and increased distortions. To this end, we introduce a novel dataset, “Dhad”, refined for enhanced quality and quantity. Our investigation employs a tri-fold experimental approach, encompassing the exploration of pre-trained deep learning models (i.e., MobileNet, ResNet50, and DenseNet121), custom-designed Convolutional Neural Network (CNN) architecture, and traditional classifiers (i.e., Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP)), leveraging deep visual features. The results illuminate the efficacy of fine-tuned pre-existing models, the potential of custom CNN designs, and the intricacies associated with disjointed classification paradigms. The pre-trained model MobileNet achieved the best test accuracy of 93.59% on the Dhad dataset. Additionally, as a conceptual proposal, we introduce the idea of a computer application designed specifically for children aged 7–12, aimed at improving Arabic handwriting skills. Our concluding reflections emphasize the need for nuanced dataset curation, advanced model architectures, and cohesive training strategies to navigate the multifaceted challenges of Arabic character recognition. Full article

Handwriting is a complex perceptual motor task that requires years of training and practice before complete mastery. Its acquisition is crucial, since handwriting is the basis, together with reading, of the acquisition of higher-level skills such as spelling, grammar, syntax, and text composition. Despite the correct learning and practice of handwriting, some children never master this skill to a sufficient level. These handwriting deficits, referred to as developmental dysgraphia, can seriously impact the acquisition of other skills and thus the academic success of the child if they are not diagnosed and handled early. In this review, we present a non-exhaustive listing of the tools that are the most reported in the literature for the analysis of handwriting and the diagnosis of dysgraphia. A variety of tools focusing on either the final handwriting product or the handwriting process are described here. On one hand, paper-and-pen tools are widely used throughout the world to assess handwriting quality and/or speed, but no universal gold-standard diagnostic test exists. On the other hand, several very promising computerized tools for the diagnosis of dysgraphia have been developed in the last decade, but some improvements are required before they can be available to clinicians. Based on these observations, we will discuss the pros and cons of the existing tools and the perspectives related to the development of a universal, standardized test of dysgraphia combining both paper-and-pen and computerized approaches and including different graphomotor and writing tasks. Full article

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%. Full article

Dysgraphia is a learning disability that causes handwritten production below expectations. Its diagnosis is delayed until the completion of handwriting development. To allow a preventive training program, abilities not directly related to handwriting should be evaluated, and one of them is visual perception. To investigate the role of visual perception in handwriting skills, we gamified standard clinical visual perception tests to be played while wearing an eye tracker at three difficulty levels. Then, we identified children at risk of dysgraphia through the means of a handwriting speed test. Five machine learning models were constructed to predict if the child was at risk, using the CatBoost algorithm with Nested Cross-Validation, with combinations of game performance, eye-tracking, and drawing data as predictors. A total of 53 children participated in the study. The machine learning models obtained good results, particularly with game performances as predictors (F1 score: 0.77 train, 0.71 test). SHAP explainer was used to identify the most impactful features. The game reached an excellent usability score (89.4 ± 9.6). These results are promising to suggest a new tool for dysgraphia early screening based on visual perception skills. Full article

The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data. Full article

Augmented reality (AR) has been widely used in education, particularly for child education. This paper presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing. The app has two main features: (i) guided learning to teach users how to write the alphabet and (ii) on-screen and AR-based handwriting testing using machine learning. A learner needs to write on the mobile screen in on-screen testing, whereas AR-based testing allows one to evaluate writing on paper or a board in a real world environment. We implement a novel approach to use machine learning for AR-based testing to detect an alphabet written on a board or paper. It detects the handwritten alphabet using our developed machine learning model. After that, a 3D model of that alphabet appears on the screen with its pronunciation/sound. The key benefit of our approach is that it allows the learner to use a handwritten alphabet. As we have used marker-less augmented reality, it does not require a static image as a marker. The app was built with ARCore SDK for Unity. We further evaluated and quantified the performance of our app on multiple devices. Full article

Despite the widespread use of electronic devices for activities strictly connected to writing, several studies have proved the importance of developing handwriting, using paper and pen. One study conducted by Cutler and Graham demonstrated that the development of handwriting is so important that it may impact the academic success of a child. Moreover, different studies conducted by Graham have underlined that it is very important to give children clear and correct rules about handwriting from their early developmental stages. Effective handwriting instruction does not require a large amount of time. To see considerable returns, it is enough to spend just a few minutes every day teaching handwriting. To develop this skill, teachers need to feel prepared. The paper will propose a short excursus of suggestions dealing with how handwriting could be enhanced, underlining the importance of developing this skill from the first years of schooling as a tool aimed at preventing other possible future difficulties in the development of the person. Full article