This study explores links between attributes of computing students, such as prior programming experience (PE) and gender, with expectations for success and the perception of challenges. Using Expectancy-Value Theory (EVT), we investigate their major intentions and the impact of these factors post-CS1. Data was gathered using surveys at the beginning and end of an introductory programming course, focusing on demographics, expectations of success, and perceptions of challenges. Application status for the computing major was also recorded. Our results revealed that men and students with PE generally perceived greater potential for success and reported facing fewer challenges. In contrast, women and students without PE more often indicated concerns about intellectual ability and perceived challenges less positively. Notably, while gender appears in the preceding results, an intersectional analysis indicates that PE is the central factor. PE is also linked to persistence in the field of computing. Our results further highlight the importance of providing students with opportunities to develop experience, as it can help shape their expectations, perceived challenges, and retention in computing.

Regular practice of gratitude has the potential to enhance psychological wellbeing and foster stronger social connections among young adults. However, there is a lack of research investigating user needs and expectations regarding gratitude-promoting applications. To address this gap, we employed a user-centered design approach to develop a mobile application that facilitates gratitude practice. Our formative study involved 20 participants who utilized an existing application, providing insights into their preferences for organizing expressions of gratitude and the significance of prompts for reflection and mood labeling after working hours. Building on these findings, we conducted a deployment study with 26 participants using our custom-designed application, which confirmed the positive impact of structured options to guide gratitude practice and highlighted the advantages of passive engagement with the application during busy periods. Our study contributes to the field by identifying key design considerations for promoting gratitude among young adults.

Many students are first exposed to computing in a programming course such as CS1. This course affects their understanding of computing and may affect their intention to major in the program. We investigate the intention to major in computing in relation to demographic factors and factors related to academic success. We deployed surveys at the beginning and end of a CS1 course to gauge students' level of prior experience in programming, elicit demographic factors such as gender and parental education level, and identify their intention to major in computing. Grades from CS1 and CS2 were also collected. Our results suggest that most students do not change their intention to major in computing after taking CS1. Students who were more likely to intend to major in programming at the beginning of the course were those with prior experience, those who identified as men, or students who had a parent with a bachelor's or post-grad degree. We also find that students' grades correlate to their change in program intention. This reinforces the need to change perceptions about computing early, prior to CS1.

As artificial intelligence (AI) became a part of daily life, it has become important to determine user competence in using AI technology. Here, we propose the concept of AI literacy and develop a quantitative scale for obtaining accurate data regarding the AI literacy of ordinary users. We first identified the primary core constructs of AI literacy, including awareness, use, evaluation, and ethics. Next, we generated 65 items to capture these four constructs; only 31 items were retained after a three-step content validation process. Then, we conducted a survey, and collected two samples of data. By reducing the number of items using the first sample and performing reliability and validity tests on the second sample, we obtained a 12-item instrument for the quantitative measurement of AI literacy. The results confirmed that the proposed four-construct model is an adequate representation of AI literacy. Further, AI literacy is significantly related to digital literacy, attitude towards robots, and users’ daily usage of AI. This study will not only aid researchers in understanding how user competence in using AI technology affects human–AI interactions but will also help designers develop AI applications that are aligned with the AI literacy levels of the target users.

In time-varying multipath channels, the performance of polarization technologies in wireless networks such as polarization diversity, polarization multiplexing and polarization modulation is susceptible to polarization mode dispersion (PMD). PMD is an important element to describe the depolarization effects of wireless channels and is useful in polarized signal processing. We can use PMD information to choose modulation modes which can improve energy efficiency, or to choose power allocation model which can improve data transmission rate. Therefore, this paper presents a PMD estimation algorithm based on selection combining to obtain PMD information of time-varying multipath channels. The proposed algorithm can be divided into two steps. Firstly, to improve the accuracy of estimation, we use selection combining at the receiver, which choose signals having higher SNR from horizontal and vertical component. Then, We use selected signals to estimate multipath delay. Secondly, we compute PMD directly by using delay spread and max delay which can be calculated from multi-path delay. The performance of the proposed PMD estimation algorithm is evaluated by the mean square error (MSE). Theoretical analysis and simulation show that the proposed algorithm have better estimation performance compared with PMD estimation algorithm using single component.

To better understand users’ technology utilisation behaviour, a construct named technology dependence is suggested; as well a technology dependence model is proposed and empirically tested. Based on the literature on marketing, information systems’ utilisation, and psychology, a comprehensive set of constructs and hypotheses are compiled with a methodology for testing them in this paper. A questionnaire was designed and data were collected from 255 users of smartphones in Korea, mainly consisting of students and academics. Structural equation modelling was then applied to analyse the data. The result indicated that engagement is the strongest indicator of technology dependence along with user satisfaction. User satisfaction is affected by engagement and responsiveness of the technology. And all three dimensions of interactivity, namely, control, communication, and responsiveness, are revealed to be significant indicators of engagement. Based on the findings, the model develops useful insights into the factors that influence technology dependence and provides new ideas in understanding technology utilisation.