In moderate climates, the operation of windows is the most common way to control for thermal comfort. Window-opening behavior (WOB) is a complex process influenced by multiple factors, yet only simple bi-variate analyses between variables obtained from longitudinal datasets have been examined. The goal of this study is to investigate the effects of indoor and outdoor environmental parameters on WOB using a statistical modeling approach called “structural equation modeling.” The results show that the indoor environmental parameters, such as operative temperature and air velocity, mediated the relationship between the outdoor environmental parameters, such as outdoor air temperature and wind gust, and the WOB. The indoor wet-bulb globe temperature rose as the solar radiation increased, and subsequently, both parameters affected the WOB. Also, an increase in outdoor wind gust led to higher indoor air velocity, which in turn resulted in a lower chance of occupants opening the window. By enhancing our understanding of the relationship between these theoretical parameters, improved design strategies on the mediating parameters can be prioritized and communicated early in the building design phase leading to more informed design decisions.

Online learning has been gaining the attention of students and instructors in recent years, a phenomenon that has only increased now that most courses are being delivered online due to the ongoing COVID-19 pandemic. However, challenges remain for students to properly manage their time and achieve satisfactory grades in the online learning environment. In this study, we investigate how the online learning environment and varying levels of task complexity affect students’ procrastination and academic performance. We collected 157 college students’ online activity data. The results show that the students procrastinated more in the online learning environment than in the face-to-face setting; they likewise procrastinated more when completing high-complexity tasks than low-complexity tasks. A significant interaction effect shows that the students reacted differently to deadlines for high- and low-complexity tasks in the online learning environment than they did to deadlines in the face-to-face setting. Our findings highlight the importance of taking into account course assignment complexity when designing face-to-face and online classes.

Objective: The goal of this study is to model the effect of language use and time pressure on English as a first language (EFL) and English as a second language (ESL) students by measuring their eye movements in an on-screen, self-directed learning environment. Background: Online learning is becoming integrated into learners’ daily lives due to the flexibility in scheduling and location that it offers. However, in many cases, the online learners often have no interaction with one another or their instructors, making it difficult to determine how the learners are reading the materials and whether they are learning effectively. Furthermore, online learning may pose challenges to those who face language barriers or are under time pressure. Method: The effects of two factors, language use (EFL vs. ESL) and time constraints (high vs. low time pressure), were investigated during the presentation of online materials. The effects were analyzed based on eye movement measures (eye fixation rate—the total number of eye fixations divided by the task duration and gaze entropy) and behavioral measures (correct rate and task completion time). Results: The results show that the ESL students had higher eye fixation rates and longer task completion times than the EFL students. Moreover, high time pressure resulted in high fixation rates, short task completion time, low correct rates, and high gaze entropy. Conclusion and Application: The results suggest the possibility of using unobtrusive eye movement measures to develop ways to better assist those who struggle with learning in the online environment.

This paper investigated the effect of task complexity on time estimation in the virtual reality environment (VRE) using behavioral, subjective, and physiological measurements. Virtual reality (VR) is not a perfect copy of the real world, and individuals perceive time duration differently in the VRE than they do in reality. Though many researchers have found a connection between task complexity and time estimation under non-VR conditions, the influence of task complexity on time estimation in the VRE is yet unknown. In this study, twenty-nine participants performed a VR jigsaw puzzle task at two levels of task complexity. We observed that as task complexity increased, participants showed larger time estimation errors, reduced relative beta-band power at Fz and Pz, and higher NASA-Task Load Index scores. Our findings indicate the importance of controlling task complexity in the VRE and demonstrate the potential of using electroencephalography (EEG) as real-time indicators of complexity level.