The relationship between readiness for, and involvement in self-directed learning

The study examined the following research questions: (1) Are there predictive relationships between readiness for self-directed learning and the extent to which adults are involved in self-directed learning? (2) Which personal readiness factor(s) is(are) the best predictor(s) of high involvement in self-directed learning?;The Self-Directed Learning Readiness Scale (SDLRS) and the Adult Learning Projects Telephone Interview Schedule was administered to a random sample of sixty-five Iowa State University Cooperative Extension Service professional staff;Findings indicate there is a positive, predictive relationship between readiness and the number of self-planned projects conducted, as well as the amount of time spent on them;Self-concept as an effective, independent learner was identified as the readiness factor that best predicted the number of self-planned projects (R(\u272) = .20) and the time spent on them (R(\u272) = .42). After self-concept was accounted for, view of learning as a lifelong, beneficial process was the only other readiness factor which contributed to the prediction. At least five individual items on the SDLRS appear to be very effective (r(\u272) = 1.00) in predicting extent of involvement in self-planned projects;Readiness level did not differ with respect to gender, full versus part-time employment, program area, level of position, educational level beyond a bachelor\u27s degree, or job tenure;Extent of involvement in self-planned projects was found to differ with respect to tenure and level of position. Staff with less than two years and those with 6-10 years tenure engaged in more self-planned projects than their colleagues. Individuals with less than two years tenure also spent significantly more time on self-planned learning than other staff. State level staff devoted significantly more time to self-planned projects than county or area level staff;Findings suggest that the SDLRS can predict involvement in self-planned learning. However, its predictive ability is limited due to the large portion of unexplained variance in the number of self-planned projects and time spent on these projects. Recommendations for further research were identified, including possible revision of the SDLRS

Glider observations of thermohaline staircases in the tropical North Atlantic using an automated classifier

Thermohaline staircases are stepped structures of alternating thick mixed layers and thin high-gradient interfaces. These structures can be up to several tens of metres thick and are associated with double-diffusive mixing. Thermohaline staircases occur across broad swathes of the Arctic and tropical and subtropical oceans and can increase rates of diapycnal mixing by up to 5 times the background rate, driving substantial nutrient fluxes to the upper ocean. In this study, we present an improved classification algorithm to detect thermohaline staircases in ocean glider profiles. We use a dataset of 1162 glider profiles from the tropical North Atlantic collected in early 2020 at the edge of a known thermohaline staircase region. The algorithm identifies thermohaline staircases in 97.7 % of profiles that extend deeper than 300 m. We validate our algorithm against previous results obtained from algorithmic classification of Argo float profiles. Using fine-resolution temperature data from a fast-response thermistor on one of the gliders, we explore the effect of varying vertical bin sizes on detected thermohaline staircases. Our algorithm builds on previous work by adding improved flexibility and the ability to classify staircases from profiles with noisy salinity data. Using our results, we propose that the incidence of thermohaline staircases is limited by strong background vertical gradients in conservative temperature and absolute salinity.</p