This study investigated the feasibility of adopting an automatic scoring system (ASS) in a domestic English-speaking education context. Scope, test items, assessment criteria, scoring methods, and reporting strategies of six overseas English-speaking tests utilizing ASSs were examined. Moreover, a comparative analysis was conducted to identify disparities between ASS-based and non-ASS-based speaking tests. Findings were: 1) some ASS-based tests utilized ASS technology throughout the assessment, while others adopted a hybrid scoring system involving human raters; 2) compared to non-ASS-based tests, ASS-based tests used more test items targeting low-level skills such as sound and forms but fewer test items targeting conversation and discourse level skills; 3) pronunciation, fluency, and vocabulary were widely employed as evaluation criteria with sparse use of organization, content, and task completion in most ASS-based tests; 4) differences were minimal in assessment criteria application and score calculation between ASS-based and non-ASS-based tests; and 5) some ASS-based tests provided criteria-specific results and feedback with total scores and proficiency levels.

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.