In order to evaluate the role of cause of death on the grief responses of parentally bereaved young and middle-aged adults, 400 individuals completed measures assessing their experiences and feelings surrounding the loss of a parent. Respondents included 247 young adults and 155 middle-aged adults. Cause of death was categorized as acute or anticipated with 209 participants reporting the parent’s death as acute, while anticipated death was reported by 191 individuals. Results suggested that gender of the adult child and age level of the participant were important factors contributing to the grief response, and women were found to have more difficulty adjusting to the loss of a parent as well as demonstrating a more intense grief response. Young adults were found to be more impacted by the loss of a parent than were middle-aged adults. Those who were single or separated were similarly more impacted versus those who were married, where more young adults were single/separated and more middle-aged adults were married. Cause of death was only mildly influential in influencing responses to parental loss and did not interact with other studied variables. These results point to the importance of support from others in coping with a parent’s death as well as for the counseling of bereaved persons who may be at risk for difficulties in coping with the death of a parent and enable a more precise understanding of individual grief processes across the adult lifespan.

Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. 54.3%). We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model’s accuracy. Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.

In this study, classroom interaction is addressed by using automatic speech recognition (ASR) for automatic analysis of teacher talk. Educational research on classroom interactions and talk is mainly based on manual analysis, which, although quite accurate, is time consuming and expensive. As there are already many ways to harness the power of automatic approaches to analyse the content of talk, the next step is the automatic detection of different types of talk. In this study, the latter includes the automatic detection of teacher questions and their differentiation into closed and open types. The data consists of ASR text outputs of 25 physics lessons on the same topic, each 90 minutes long, from 25 different science teachers. The interplay between human- and machine-led approaches resulted in promising steps taken towards automatization. Future possibilities for automatic analysis of classroom talk and the implications for teaching and learning are discussed.

The transition from high school to postsecondary studies entails many challenges for students. Thus, postsecondary institutions have developed Summer Bridge Programs (SBP) to support students in this transition by improving academic and psychosocial preparedness while boosting general confidence and attitude toward the university or college. The EMBER program in the Faculty of Engineering at McMaster University aims to help students entering Engineering programs succeed academically and socially. The program is divided into three parts: self-paced online learning modules, synchronous online tutorials, and four-day in-person workshops. This paper explores students’ experiences and self-reported levels of confidence and preparedness. Three anonymous surveys were administered to students participating in EMBER. The initial results show that students’ self-reported confidence and preparedness levels increased after attending EMBER. Most students valued all parts of the experience. Future research will focus on assessing the extent to which the EMBER program improves academic outcomes.