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This study investigates how the course format change caused by covid-19 pandemic affected learning behaviors and performance of college students enrolled in a large introductory history course. Clickstream log files capturing how students were interacting with online learning contents were analyzed to identify the learning behaviors of students before and after the mid-semester course format change. The non-parametric regression model was developed to examine the relationship between learning performance of students and course format change. Although the frequency of accessing learning resources decreased during the first three weeks after the course format change, it had a relatively small effect on the learning performance of students. The quantile regression model indicates the mid-semester course format change is associated with about 3.3% decrease in the learning performance of students. These results suggest that students were quite resilient and their learning during the pandemic was not as bad as we feared.

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images. [ABSTRACT FROM AUTHOR]

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10−2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Gamification, the use of game design elements in non-gaming context, has been introduced to education as a novel teaching and learning approach to engage learners. Current literature has focused on finding ways to afford the playfulness and gamefulness of gamification. This paper has studied college students' perceptions of pleasure in learning to explore the underlying aspects of enjoyment in their learning experiences. Based upon the playful experiences (PLEX) design framework (Arrasvuori et al., 2011), we distributed an online survey to 235 college students and used Principal Component Analysis (PCA) to find four underlying aspects of pleasurable learning experiences among the participants: instruction (Cronbach's a = 0.84), instructors' teaching styles (Cronbach's a = 0.87), learning effectiveness (Cronbach's a = 0.71), and collaboration (Cronbach's a = 0.71). This scale can be used for gamification designers to identify the key game design elements for their target learners. We have also examined the effects of gender, majors, computer skills, and gaming habits on participants' preferences for pleasure in learning. Majors resulted in statistically significant differences among the participants' attitudes towards collaboration (F(6, 212) = 5.84, p < 0.001, Eta-square = 0.14). The implications of the findings are also discussed in the paper.