Developing an Instrument to Examine Preservice Teachers' Pedagogical Development

National and international reform documents have forged blueprints for advancing science education. Coursework for preservice teachers needs to correspond to these documents by providing learning experiences that develop preservice teachers' capabilities to plan and implement reform measures. Using a pretest–posttest design, responses from 59 2nd-year preservice teachers from the same university were compared after involvement in an elementary science pedagogy coursework. The survey, which was linked to the course outcomes (constructs) and multiple indicators, measured the preservice teachers' perceptions of their development towards becoming elementary science teachers. A pretest–posttest survey linked to course outcomes can be employed to assess perceived pedagogical development of preservice teachers, which can inform further teaching practices for implementing science education reform agendas

Irrigation scheduling of peanuts using plant based sensing.

Temperature is of extreme importance in all areas of plant growth. Canopy temperatures can be measured by infrared thermometry. The Forward Looking Infra-Red (FLIR) One heat sensing camera was chosen to measure the canopy temperature of a peanut crop grown in a red ferrosol at the Bundaberg Research Facility. This camera was matched to an iPhone S6. Canopy temperatures were recorded for two treatments, irrigation using the FLIR One technology and dry. Solar Noon (12:00 – 2:00pm) canopy temperature in the irrigated treatment averaged 28.6 - 32° C and the canopy temperature in the dry treatments were between 30 and 36° C. The camera angle for gathering canopy temperatures of the FLIR One camera should be at 75°. There were no visual canopy wilting symptoms displayed by the water stressed peanut crop canopy. When using thermal imaging you should manage the peanut crop to maintain the crop canopy temperature in irrigated peanut plants from 2-7°C below air temperature or below 35°C. The results of this study indicate that the FLIR One camera can measure peanut canopy temperatures and can be used as a tool to indicate that the crop may need irrigating before visually observing plant stress symptoms such as wilting. New Apps and software have been developed since the start of the project that have increased the value of the FLIR One camera as a tool for farmers to use to manage crop canopy temperatures They can provide information to develop precision irrigation and to help reduce irrigation variability across the field. The FLIR One camera can assist growers to schedule irrigations for their peanut crops

A reciprocal test of perceptions of teaching quality and approaches to learning: A longitudinal examination of teaching-learning connections

Biggs' Presage–Process–Product (3P) model provides a exible model for testing hypotheses about intra-psychic and contextual e ects on student learning processes and outcomes; however, few empirical studies have e ectively tested the longitudinal and reciprocal e ects implied by the model. The current study provides an empirical test of theorised reciprocal relationships operating over time implied by the 3P model between perceived teaching quality and approaches to learning. The current study examines a longitudinal sample of Japanese university students (n = 1348; female = 404) from 18 degree programmes. Data from a reciprocal latent model were analysed using structural equation modelling. Modelling identi ed signi cant reciprocal e ects between teaching quality and deep approaches to learning. Deep (positively) and surface (negatively) predicted annualised GPA (moderate and large e ects, respectively). Consistent with a systems theory perspective on teaching and learning, longitudinal results supported hypothesised reciprocal relationships between perceptions of teaching quality and approaches. Implications for theory and practice are discussed

Transfer of academic staff learning in a research-intensive university

In both Australia and abroad, there is an increasing pressure towards professionalisation of university teaching, with the expectation that academic development courses, such as the Graduate Certificate in Education Studies (higher education), lead to better teaching and learning practices. However, the knowledge, skills and/or attitudes that educators intend students to learn may not transfer successfully back to the workplace. This may occur for a variety of reasons, including individual characteristics of the learner (e.g. ability, motivation), and situational characteristics (e.g. the climate for transfer, including adequacy of resources and peer/manager support). The present study investigates the impact of these factors on teaching staff in a research-intensive university. Two in-depth case studies, followed by thematic analysis of 15 Graduate Certificate alumni interviews regarding post-course experiences, revealed that qualities of the work environment played significant roles in interviewees’ postcourse attitudes, intentions and activities related to the transfer of learning. Implications for encouraging transfer under similar circumstances are discussed.postprin

Exploring the impact of pedagogic approaches in technology practice upon the construction of feminine identity

Females participate to a limited extent in science, engineering and technology (SET) industries that are central to innovation and building national economies. The causes of this under representation, in part, have their roots embedded in how females perceive school mathematics, science and technology subjects as being inconsistent with their gender identity. A participatory action research methodology was used to investigate the effect of two different pedagogical approaches for teaching middle school mathematics and science through technology practice on female students’ attitudes to SET. Quantitative and qualitative data related to enjoyment, intention to undertake further such study, perceived usefulness and interest in career options involving SET, and perceptions of the investigative nature of the two approaches, were sought using, interviews, classroom observations, and a modified survey instrument. The findings indicated that female students responded in a more positive manner when careful scaffolding and the establishment of explicit linkages between the construction activity and mathematics principles were part of the pedagogical approach. In addition, there were specific types of projects that females found authentic. The implications of these findings for SET syllabus authors, pre- and inservice teacher educators, and classroom teachers are explored

Understanding students’ instrumental goals, motivation deficits and achievement: Through the Lens of a Latent Profile Analysis

Building on the future oriented and regulated nature of instrumental goals, Lens and colleagues developed a 2 (proximal-distal) x 2 (internal-external) motivational framework. The current study aimed to test this framework from a person-centred perspective, while equally taking into account students’ lack of motivation as to extend the empirical and theoretical borders of the model. Latent Profile Analyses were used to test the viability of two to five motivational profiles among Japanese second-year students (N = 781). A solution with three latent subgroups fitted the sample best, explaining 6% to 62% of the variance in the measured variables. The profiles were labelled “low future oriented motivational profile”, “average motivated profile”, and “highly motivated profile”. The highly motivated subgroup reported the most adaptive pattern of motivation and highest levels of deep level learning, while few differences were found for surface learning and GPA. Theoretical and practical implications are discussed.published_or_final_versio

An investigation on the best-fit models for sugarcane biomass estimation by Linear Mixed-Effect Modelling on Unmanned Aerial Vehicle-Based Multispectral Images: a case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products, accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth. This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles (UAVs). The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments. Individual spectral bands and different combinations of the plots, growth stages, and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling. A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution. The results showed that utilizing Green, Blue, and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates. Additionally, the combination of plots and growth stages outperformed all the candidates of random effects. The proposed model outperformed the Multiple Linear Regression (MLR), Generalized Linear Model (GLM), and Generalized Additive Model (GAM) for wet and dry sugarcane biomass, with coefficients of determination (R2) of 0.93 and 0.97, and Root Mean Square Error (RMSE) of 12.78 and 2.57 t/ha, respectively. This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices (VIs) in mature growth stages

Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images

Early prediction of sugarcane crop yield would benefit sugarcane growers and policymakers by allowing for timely decisions. The primary objective of this study was to reduce reliance on satellite images and improve early prediction of sugarcane yield at row level by using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery. To our knowledge, no previous study has evaluated the performance of multispectral UAV-derived vegetation indices in sugarcane crops at the crop row level. In this study, we used UAV mapping on 48 rows of sugarcane at three main growth stages (early, middle, and mature) over three growing seasons. A secondary objective was to predict future sugarcane yields at the earliest possible stage of growth. The results showed that the optimal growth stage for all 23 VIs varied, but the middle stage, from mid-March to early May, was the most prevalent. Further detailed analysis in the middle stage revealed that March was the best month for predicting future sugarcane yields when compared to April and May. This result is approximately a month earlier than previous studies in the same region. Following two stages of feature selection, such as Pearson correlation analysis and stepwise feature selection, a novel cross-validation methodology based on a generalized linear model trained and tested the yield prediction models on various combinations of the VIs. This novel methodology improves model accuracy by avoiding overfitting and over complexity caused by interdependent VIs, and then validates the model generality using previously unseen data. The best performance was achieved by combining the Normalized Difference RedEdge (NDRE) and the Green–Red Normalized Difference Vegetation Index (GRNDVI) at March. These results help growers and decision-makers benefit from early row-level yield forecast, six months before harvest, if UAV mapping is available