PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

This study aimed to investigate the impact of implementing team-based learning (TBL) in postpartum nursing simulation practical education for nursing college students. Methods: The study design was a non-equivalent control group pretest-posttest quasi-experimental design. 128 nursing students divided into two groups: 61 in the experiment group and 67 in the control group. During the winter break in January 2023, students participating in simulation practicals were assigned to the control group, while students participating in simulation practicals during the regular semester (April 2023) were assigned to the experimental group, to prevent crossontamination between the groups due to experimental treatment. Both groups completed selfdministered questionnaires to assess self-directed learning abilities, collaborative self-efficacy, academic achievement, and learning satisfaction. Results: The experimental group showed significantly better compared to the control group, the experimental group showed higher levels of academic achievement and learning satisfaction. Conclusion: It was evident that TBL applied to postpartum nursing simulation practical education is a pedagogical teaching strategy that enhances academic achievement and learning satisfaction. It is necessary to develop and apply team-based simulation practical education not only for challenging obstetric cases but also for labor and delivery nursing, antepartum nursing, and other related areas in clinical practice.

The purpose of this study was to evaluate the effectiveness of a practical English program for college students which had been administered both on- and off-line. After over 1,400 freshmen took two TOEIC-based courses consecutively, questionnaires were administered to measure the students’ satisfaction of the program and their perceived usefulness of on-line learning. The effectiveness of the English program was measured through the improvement in the students’ TOEIC scores, their perceived usefulness of online learning, and their satisfaction with the program. The results showed a statistically significant increase in the students’ TOEIC scores in both semesters, with greater improvement in the second semester as compared to the first. Although the students’ overall perception of the usefulness of e-learning was in the middle on the scale, those who perceived online learning as useful were shown to have spent more time studying for the courses outside class, thereby improving their TOEIC scores and enhancing their level of satisfaction with the program. Pedagogical and research implications are suggested.