The application of artificial intelligence principles to teaching and training*

This paper compares and contrasts the use of AI principles in industrial training with more normal computer‐based training (CBT) approaches. A number of applications of CBT are illustrated (for example simulations, tutorial presentations, fault diagnosis, management games, industrial relations exercises) and compared with an alternative approach using AI. An evaluation of the relative merits of the two approaches will be given. Existing CBT packages are used to illustrate the points raised and the emphasis of the arguments will be on the effectiveness of AI and CBT in terms of both cost and learning. The position of AI applications within CBT is discussed, as is the task of getting started in applying these techniques.     

Supporting English second-language learners in disadvantaged contexts: learning approaches that promote success in mathematics

In the Australian context, children living in disadvantaged circumstances, whose second language is English, are one of the groups at risk of failing in mathematics. This paper explores the impact purposely developed learning activities (Representations, Oral Language and Engagement in Mathematics Learning activities) have on pupils' mathematics outcomes in the first three years of formal schooling (Foundation, Year 1 and Year 2). The participating group (n = 461) comprised 328 English language learners and 133 mainstream pupils. To ascertain the impact of the learning experience, pre- and post-tests were conducted at the commencement and completion of each school year. The results indicate that all of the children significantly improved, with English as a second language pupils showing the greatest gains, and achieving norm-referenced expectations for their age.     

Reducing Risk for Substance Use by Economically Disadvantaged Young Men: Positive Family Environments and Pathways to Educational Attainment

Using prospective, longitudinal data spanning 10 years (age = 10–20) from a study of 295 economically disadvantaged males, the current investigation evaluated a developmental model that links early family environment and later educational aspirations, extracurricular activities, and educational attainment to substance use in early adulthood. The results indicate that a positive family environment during adolescence (low family conflict, high family warmth, and effective child management) predicted educational involvements during adolescence that promoted educational attainment during early adulthood. Finally, higher levels of educational attainment were associated with less substance use in early adulthood, even after controlling for adolescent substance use. These findings suggest that positive parenting promotes educational achievements that increase resilience to substance use for economically disadvantaged males.     

Early manifestations of intellectual performance: Evidence that genetic effects on later academic test performance are mediated through verbal performance in early childhood

Intellectual performance is highly heritable and robustly predicts lifelong health and success but the earliest manifestations of genetic effects on this asset are not well understood. This study examined whether early executive function (EF) or verbal performance mediate genetic influences on subsequent intellectual performance, in 561 U.S.-based adoptees (57% male) and their birth and adoptive parents (70% and 92% White, 13% and 4% African American, 7% and 2% Latinx, respectively), administered measures in 2003–2017. Genetic influences on children's academic performance at 7 years were mediated by verbal performance at 4.5 years (β = .22, 95% CI [0.08, 0.35], p = .002) and not via EF, indicating that verbal performance is an early manifestation of genetic propensity for intellectual performance.     

Developmental trajectories of behavioral inhibition from infancy to age seven: The role of genetic and environmental risk for psychopathology

The present study leveraged data from a longitudinal adoption study of 361 families recruited between 2003 and 2010 in the United States. We investigated how psychopathology symptoms in birth parents (BP; M_age = 24.1 years; 50.5–62.9% completed high school) and adoptive parents (AP; M_age = 37.8 years; 80.9% completed college; 94% mother–father couples) influenced children's behavioral inhibition (BI) trajectories. We used latent growth models of observed BI at 18 and 27 months, and 4.5 and 7 years in a sample of adopted children (Female = 42%, White = 57%, Black = 11%, Multi-racial = 21%, Latinx = 9%). BI generally decreased over time, yet there was substantial variability in these trajectories. Neither BP nor AP psychopathology symptoms independently predicted systematic differences in BI trajectories. Instead, we found that AP internalizing symptoms moderated the effects of BP psychopathology on trajectories of BI, indicating a gene by environment interaction.     

An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control

An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control (ILC) is proposed in this paper. The developed analog ILC method and the high-gain property tracker design methodology are first combined to significantly reduce learning epochs and overcome the initial condition shift problem and discontinuous reference input in the traditional ILC. Besides, the proposed ILC improves the transient response and decreases the rate of weighting matrices Q$Q$ to R$R$ under the traditional linear quadratic tracker design. First, the off-line observer/Kalman filter identification (OKID) is used to determine the appropriate (low-) order system parameters and state estimator for the physical system with unknown system equation, so that the model-based PD-type ILC can be implemented for practical applications. Then, to improve the transient response and decrease the control effort, the proportional difference type (PD-type) ILC algorithm is combined with the high-gain property linear quadratic tracker (LQT) design to construct the high performance tracker for the model-based sampled-data systems. Furthermore, the discrete-time version high performance tracker design for the unknown stochastic sampled-data system via the iterative learning control method is proposed in this paper based on the Euler method and the digital redesign approach. Finally, some examples are given for illustrating the effectiveness of the proposed method.