Session 3: Thursday 11 February, 11am-12pm

Lacking a vaccine, Governments have turned to behavioral strategies to stop the spread of COVID-19, for example, mask-wearing, physical distancing, and lockdown policies. Tracking technologies that monitor who you have been in contact with and when this contact occurred, offer a different solution. These technologies allow people to maintain their normal social activities with some assurance that they will be notified if they make contact with an infected individual. However, the effectiveness of these strategies relies upon their acceptance and uptake among the population, and people's willingness to identify as infected with COVID-19 should the need arise. We conducted a series of nationally representative surveys in Australia and overseas to assess attitudes towards three hypothetical mobile tracking technologies: a Government App, the Apple/Google Exposure Notification System, and a telecommunication network tracking option. Where possible, we then compared these hypothetical results to the uptake of real-world apps, such as Australia's COVIDSafe app. We discuss our findings with reference to the observed gap between public health intentions and public health behaviors.

Computational thinking and programming are part of mandatory curricula around the world, including many Australasian nations. Teachers across most levels of schooling now must teach these skills to students. Teaching these skills presents many challenges for teachers, as they typically were not part of their initial teacher education. There are a variety of professional learning opportunities for teachers to learn these skills, but there is a lack of research evaluating the impact of these opportunities. One of the challenges of evaluating professional learning opportunities is the lack of standardised instruments for relevant measures of impact. One measure that is often used in studies of professional learning is teachers’ self-efficacy. To measure self-efficacy in computational thinking, Bean et al. (2015) created and validated the Teachers’ Self-Efficacy in Computational Thinking (TSECT) instrument. In this talk we will present results from evaluating our programs’ impact using the TSECT instrument. Our results show that, generally, TSECT measures were low before the programs but much higher after, and that both sustained and short programs can have a positive impact on teachers’ self-efficacy in computational thinking. We will also discuss how we have used learning derived from measuring TSECT to improve the programs’ scope and delivery.

There has been a subtle shift in child development research, with researchers examining the beneficial role of the father in a child’s development. A particular focus has been on the role that father-child play interactions may have in supporting children’s emotion regulation/emotional control skills. Rough-and-tumble play (RTP) is one of the preferred play interactions between fathers and their children and can be viewed as a proxy of the father-child relationship. Our past work has shown that higher quality RTP (i.e. play that was warm, sensitive, and controlled) was associated with fewer behavioural problems in children. However, few studies have examined the impact of paternal mental health on the father-child relationship and child development. The aim of the current study was two-fold. First, we aimed to examine whether paternal mental health impacts the father-child relationship. Second, we aimed to examine whether paternal mental health and/or particular aspects of the father-child relationship were related to children’s emotional control. Fifty-three Australian fathers and their 4-7 year-old child participated in this study. Correlational analyses showed significant negative relationships between paternal stress as measured by the DASS and RTP quality, child engagement, and dyadic connectedness. Children’s emotional control problems showed significant positive relationships with father’s overall parenting stress, and two father-child relationship measures (father negative regard and father detachment). There was also a significant negative relationship between children’s emotional control problems and father’s positive regard during play. A stepwise regression analysis revealed that both father detachment during play and father’s overall parenting stress were significant predictors of children’s emotional control problems (R2=.215). The results of this study will be discussed in terms of the importance of fathers in child development.

We have demonstrated that when people generate difficult numerical estimations and predictions, they approximate Benford’s law (BL) (Burns & Krygier, 2014; Burns et al, under review; Burns & Chi, 2020; Chi, 2020). We are exploring the cognitive processes behind this phenomenon using the mathematics of BL as a source of hypotheses. Berger and Hill (2020) describe three basic facts regarding sequences of random variables that converge on BL: (i) Powers of every continuous; (ii) Products of random samples for every continuous distribution; (iii) When random samples are taken from random distributions that are chosen in an unbiased way, then the combined samples converge to BL. Translating these characteristics into cognitive processes suggests either processes based on selecting from a distribution, or some form of combining information. These make contrasting predictions for variables with Gaussian distributions because random samples from a Gaussian will not fit BL. We tested this by having participants estimate the weight of pictured animals. The resulting estimates were a good fit to BL arguing against selection from a single distribution. Understanding BL could be a tool for understanding numerical estimation and prediction in general, and the decisions based on such judgments.

Scheduling theory concerns the development of policies determining the optimal allocation of resources to a set of tasks. Scheduling problems have been studied extensively in the context of operations research and computer science, where optimal policies have been established for many cases, but almost no research has examined how people perform with respect to these optimal policies. We conducted several experiments in which each task is a random dot motion (RDM) judgment, with difficulty determined by the coherence of the motion. Participants were presented with 4 or more RDM's, which they click on to complete by indicating the direction of motion in that RDM. RDM’s varied in coherence and consequently, difficulty and average completion time. We are concerned with the order in which RDM’s are selected for completion. Scheduling is more optimal when RDMs of different difficulty are presented in fixed locations compared to random and when there is a time deadline compared to no deadline. That is, there are more participants who exhibit optimal or near optimal responding when location is fixed and there is a deadline.