Search Results (4)

This study examines the motivational and sustainability effects of an Anki-based, individualized project-based learning (PBL) model in an elementary Hindi language course. Conventional PBL approaches in language education typically rely on collaborative, production-focused tasks that can be demanding for novice learners and usually conclude when the final project is submitted, leaving little structured support for continued practice. In this study, script, vocabulary, expression, sentence patterns, and pronunciation are not treated as background work but defined as the core pedagogical problem. Over the semester, each learner builds and refines a personalized Anki deck—a multimedia flashcard system based on spaced repetition—designed to support Devanagari word and sentence recognition, pronunciation practice, listening comprehension, and vocabulary retention. Each student constructed an individual deck aligned with course content, selecting vocabulary items, creating example sentences, and developing personalized memory cues that matched their learning pace and needs. Motivation was measured with a modified Instructional Materials Motivation Survey (IMMS) using only positively worded items to enhance reliability. Results showed consistently high scores across all ARCS domains, particularly for Confidence (M = 3.86) and Satisfaction (M = 3.93). Female students reported higher average scores, but gender showed no association with motivational grouping. Strong correlations among ARCS dimensions indicated consistent engagement across motivational components. Cluster analysis identified two groups of learners: highly motivated learners who treated deck creation as an ongoing learning resource, and less motivated learners who still maintained scores above the neutral midpoint—engaged enough to manage typical beginner challenges. The findings suggest that Anki-based PBL can make project-based learning workable at the novice level. By positioning deck creation as both the problem students solve and the tool they build, the model integrates continuous, self-paced practice into the project structure rather than treating it as a one-time deliverable. This design responds to a familiar gap in beginner language instruction: what happens when formal scaffolding ends. Unlike conventional PBL, which concludes with project submission, this approach creates a resource learners can use independently over time, embedding ongoing vocabulary retention and autonomous practice into the learning experience itself. Full article

Fundus image is an image that captures the back of the eye (retina), which plays an important role in the detection of a disease, including diabetic retinopathy (DR). It is the most common complication in diabetics that remains an important cause of visual impairment, especially in the young and economically active age group. In patients with DR, early diagnosis can effectively help prevent the risk of vision loss. DR screening was performed by an ophthalmologist by analysing the lesions on the fundus image. However, the increasing prevalence of DR is not proportional to the availability of ophthalmologists who can read fundus images. It can lead to delayed prevention and management of DR. Therefore, there is a need for an automated diagnostic system as it can help ophthalmologists increase the efficiency of the diagnostic process. This paper provides a deep learning approach with the concatenate model for fundus image classification with three classes: no DR, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). The model architecture used is DenseNet121 and Inception-ResNetV2. The feature extraction results from the two models are combined and classified using the multilayer perceptron (MLP) method. The method that we propose gives an improvement compared to a single model with the results of accuracy, and average precision and recall of 91% and 90% for the F1-score, respectively. This experiment demonstrates that our proposed deep-learning approach is effective for the automatic DR classification using fundus photo data. Full article

In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study. Full article

Alternative methods for accurate in vitro assessment of skin and respiratory sensitizers are urgently needed. Sensitization is a complex biological process that cannot be evaluated accurately using single events or biomarkers, since the information content is too restricted in these measurements. On the contrary, if the tremendous information content harbored in DNA/mRNA could be mined, most complex biological processes could be elucidated. Genomic technologies available today, including transcriptional profiling and next generation sequencing, have the power to decipher sensitization, when used in the right context. Thus, a genomic test platform has been developed, denoted the Genomic Allergen Rapid Detection (GARD) assay. Due to the high informational content of the GARD test, accurate predictions of both the skin and respiratory sensitizing capacity of chemicals, have been demonstrated. Based on a matured dendritic cell line, acting as a human-like reporter system, information about potency has also been acquired. Consequently, multiparametric diagnostic technologies are disruptive test principles that can change the way in which the next generation of alternative methods are designed. Full article