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• Quantifying fungal load supports efficient clinical management of tinea infections. • Automated dermatophyte quantification is, hitherto unexplored. • Rising tinea infections and drug resistance support automated quantification. • U-Net applied segmentation and pixel-based grading were the main steps followed. [Display omitted] Concerning the progression of dermatophytosis and its prognosis, quantification studies play a significant role. Present work aims to develop an automated grading system for quantifying fungal loads in KOH microscopic images of skin scrapings collected from dermatophytosis patients. Fungal filaments in the images were segmented using a U-Net model to obtain the pixel counts. In the absence of any threshold value for pixel counts to grade these images as low, moderate, or high, experts were assigned the task of manual grading. Grades and corresponding pixel counts were subjected to statistical procedures involving cumulative receiver operating characteristic curve analysis for developing an automated grading system. The model's specificity, accuracy, precision, and sensitivity metrics crossed 92%, 86%, 82%, and 76%, respectively. 'Almost perfect agreement' with Fleiss kappa of 0.847 was obtained between automated and manual gradings. This pixel count-based grading of KOH images offers a novel, cost-effective solution for quantifying fungal load. [ABSTRACT FROM AUTHOR]

This study introduces a custom-built RGB-D-based machine vision system designed to accurately estimate the mass and volume of whole kimchi cabbage (WC) and longitudinally cut kimchi cabbage (LCC). Given the pivotal role of kimchi cabbage (KC) in both Asian and Western diets, accurate post-harvest assessment of its mass and volume is critical for quality control, sorting, and pricing. Conventional manual measurements and visual estimations are laborious and inaccurate. Our research leveraged RGB-D data to refine machine learning models and enhance the extraction and analysis of 2D, 3D, and colorimetric features for a more reliable estimation approach. The results demonstrate that integrating 3D and colorimetric features markedly improves the estimation accuracy, with notable success in mass estimation for LCC (R² = 0.913, ratio of performance to deviation (RPD) = 3.38) and robust volume predictions for both cabbage types (R² > 0.90, RPD > 3). However, challenges such as potential over-exclusion of outer leaves in LCC and the need for more advanced WC mass estimation techniques have been identified. Future work will focus on refining the feature extraction methods and assessing various imaging environments to enhance the precision of mass and volume predictions across different forms of KC. • Novel RGB-D-based system accurately estimates mass and volume of kimchi cabbage. • Integration of 3D and colorimetric features enhances estimation accuracy. • Significant success in mass estimation for longitudinally cut cabbage (R² = 0.913). • Robust volume predictions achieved for both whole and cut cabbage (R² > 0.90). • Study addresses critical need for reliable post-harvest assessment methods. [ABSTRACT FROM AUTHOR]

In the education context, open-ended works generally entail a series of benefits as the possibility of develop original ideas and a more productive learning process to the student rather than closed-answer activities. Nevertheless, such works suppose a significant correction workload to the teacher in contrast to the latter ones that can be self-corrected. Furthermore, such workload turns to be intractable with large groups of students. In order to maintain the advantages of open-ended works with a reasonable amount of correction effort, this article proposes a novel methodology: students perform the corrections using a rubric (closed Likert scale) as a guideline in a peer-review fashion; then, their markings are automatically analyzed with statistical tools to detect possible biased scorings; finally, in the event the statistical analysis detects a biased case, the teacher is required to intervene to manually correct the assignment. This methodology has been tested on two different assignments with two heterogeneous groups of people to assess the robustness and reliability of the proposal. As a result, we obtain values over 95% in the confidence of the intra-class correlation test (ICC) between the grades computed by our proposal and those directly resulting from the manual correction of the teacher. These figures confirm that the evaluation obtained with the proposed methodology is statistically similar to that of the manual correction of the teacher with a remarkable decrease in terms of effort. [ABSTRACT FROM AUTHOR]

Neural foraminal stenosis (NFS) is the most common spinal disease in elderly patients, greatly affecting their quality of life. Efficient and accurate grading of NFS is extremely vital for physicians as it offers patients a timely and targeted treatment according to different grading levels. However, current clinical practice relies on physicians’ visual inspection and manual grading of neural foramina (NF), which brings the annoying inefficiency and inconsistency. A fully automated system is highly desirable but faces many technical challenges (e.g., the inefficiency in localizing NF candidates, and the severe ambiguities in grading). In this paper, an automated and accurate localization and grading clinical framework is proposed. By our framework, both localization and grading tasks are handled as multi-class classification problem: two-class classification (NF/non-NF) and four-class classification (normal/slight/marked/severe). To achieved it, a newly proposed saliency-biased Ncuts (SBNcuts) is utilized for efficient localization, and a novel task-aware structural representation learning (TASRL) model is developed for accurate localization and grading. Specifically, SBNcuts creatively incorporates saliency map as a preliminary guess of NF’s locations to refine the generated possible NF candidates with the preserved intact structure of NF. TASRL incorporates task labels (e.g., NF object label and four NFS grade labels) into manifold learning to obtain a discriminative, low-dimensional, and structural image representation, which enables similar appearance sharing among images with the same task label and different appearance among images with different task labels. The superior performance in localization and grading, with very high ( > 0.89) accuracy, specificity, sensitivity, and F-measure, have been demonstrated by experiments on 110 subjects. With our method, physicians could offer an efficient and consistent clinical grading for NFS. [ABSTRACT FROM AUTHOR]

The aim of the present research was to compare the use of multiple-choice questions (MCQs) as an examination method against the oral examination (OE) method. MCQs are widely used and their importance seems likely to grow, due to their inherent suitability for electronic assessment. However, MCQs are influenced by the tendency of examinees to guess answers, warranting research concerning scoring rules different from the simple positive-grades-only scores rule. Alternatively, OE is used in tertiary education, since it enables the assessment of intellectual capabilities and personal traits to a level not found in most other examination formats. However, the significant resource requirements of OE, especially in structured forms, might excessively strain the resources of academic institutions. In the present study, an MCQ test was given to examinees, in the framework of a computer-based learning system. The same examinees took also an OE possessing elements of structure, with three examiners concurrently and independently grading each of the examinees. In the MCQs examination a set of pairs of MCQs was composed. The MCQs in each pair were similar concerning the same topic, but this similarity was not evident for an examinee that did not possess adequate knowledge on the topic addressed in the questions of the pair. The scoring of the paired questions avoided the procedure of mixed-scoring, i.e., both positive and negative markings, while at the same time a pair-wise bonus/penalty scoring rule was adopted. The results of the “paired” MCQs examination, when using the pair-wise scoring rule, were statistically indistinguishable with the grades produced by the OE, when made to the same sample of students, on the same topics and with the same levels of difficult. Both the results of the paired MCQs examination, when using the pair-wise scoring rule, and the OE results differed significantly from those obtained by scoring the same MCQs using a positive-grades-only scoring rule that ignored the pairing of MCQs. [Copyright &y& Elsevier]

The aim of the present research was to compare the use of multiple-choice questions (MCQs) as an examination method, to the examination based on constructed-response questions (CRQs). Despite that MCQs have an advantage concerning objectivity in the grading process and speed in production of results, they also introduce an error in the final formulation of the score. The error is traced to the probability of answering a question by chance or based on an instinctive feeling, which does not enable the ascertainment of the knowledge of the whole background included in the question. In the present study, both MCQ and CRQ tests were given to examinees, in the framework of a computer-based learning system. Avoiding the procedure of mixed scoring, e.g. both positive and negative markings, a set of pairs of MCQs was composed. The MCQs in each pair were similar concerning the same topic, but this similarity was not evident for an examinee that did not possess adequate knowledge on the topic addressed in the questions of the pair. The examination based on these “paired” MCQs, by using a suitable scoring rule, when made to the same sample of students, οn the same topics and with the same levels of difficulty, gave results that were statistically indistinguishable with the grades produced by an examination based on CRQs, while both the “paired” MCQ test results and the CRQ test results differed significantly from those obtained from a MCQ test using positive-only scoring rule. [Copyright &y& Elsevier]

Abstract: The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa=.65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r =.92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning. [Copyright &y& Elsevier]

Practise is one of the most important steps in learning the art of computer programming. Unfortunately, human grading of programming assignments is a tedious and error-prone task, a problem compounded by the large enrolments of many programming courses. As a result, students in such courses tend to be given fewer programming assignments than should be ideally given. One solution to this problem is to automate the grading process such that students can electronically submit their programming assignments and receive instant feedback. This paper studies the implementation of one such automated grading system, called the Online Judge, in the School of Computing of the National University of Singapore for a compulsory first-year course that teaches basic programming techniques with over 700 students, describing the student reactions and behavior as well as the difficulties encountered. The Online Judge was also successfully employed for an advanced undergraduate course and an introductory high school course. [Copyright &y& Elsevier]