This mixed‐methods study explored adolescents’ (n = 682) feelings of social connection in the time of the COVID‐19 pandemic and examined potential risk (fear of missing out, problematic smartphone use) and protective (parent/peer attachment, smartphone use) factors to social disconnection. Data were collected from two schools in Canada using an online survey with questionnaires and open‐ended questions. Three themes regarding adolescents’ feelings of social connection during the pandemic were identified through thematic content analysis: (1) feeling socially connected, (2) feeling socially disconnected, and (3) feeling socially indifferent. Moreover, regression analysis identified secure peer attachments as a protective factor against social disconnection in the time of the COVID‐19 pandemic, while fear of missing out was identified as an independent risk factor.

With the increasingly ubiquitous use of smartphones in modern culture, particularly among young adults, recent research has focused on the behaviors, characteristics, and effects of smartphone use, with the evaluation of addictive features largely dominating work in this area. Given concerns around the application of the medical‐addiction model to such a novel, quasi‐normalized, and potentially functional dependence on smartphones, this paper explores the possibility of an alternative framework for understanding university students' relationships with their smartphones (i.e., that of attachment theory rather than addiction). Thus, this paper includes a review of the current state of the literature on problematic smartphone use and addiction before examining the use of an attachment theory framework to understanding this phenomenon. Specifically, the use of smartphones as attachment targets, characterized by the key features of attachment relationships (i.e., proximity seeking, secure base, safe haven, and separation anxiety), is explored and potential practical and theoretical implications of this approach are discussed. Through conceptualizing young adults' relationships with their smartphones within an attachment theory framework, this paper provides a well‐understood, meaningful, and biologically adaptive model with which to understand some of the alarming behaviors that have emerged alongside increasing rates of smartphone use, such as texting while driving, sleeplessness, and social isolation.

We present data from a larger longitudinal study, focusing on researchers’ and teachers’ collaborative design and implementation of assessments for learning (AfL), including student self-assessments, to support self-regulated learning (SRL) during classroom writing activities. We focus on students (N = 112) when they were in Grade 3. The study used a mixed method triangulation approach. Data include: detailed descriptions of classrooms with relatively high and low emphasis on SRL; students’ self-assessments of SRL; and students’ writing processes and products from a teacher-researcher co-constructed writing task. Students enrolled in classrooms with high emphases on SRL had more opportunities and support for SRL and AfL and, consequently, demonstrated more sophisticated self-assessments and higher levels of interest and task value than their peers in classrooms with low emphasis on SRL. Also, these learners demonstrated higher levels of self-regulation in writing tasks and higher quality writing products. Implications for research and practice are discussed.

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys; M_age = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use.