Development of a Bahiagrass \u3cem\u3ePaspalum Notatum\u3c/em\u3e Flugge With Increased Short-Day Biomass

Low herbage productivity of subtropical grasses during the short-day winter months of October through to March can place a severe burden on livestock producers in Southeastern U.S. Researchers at the University of Florida (Sinclair et al., 2001) hypothesised that the decrease in forage production might result from physiological dormancy induced by short day length. A study using artificial lights to extend the day length demonstrated that maintaining the day length at 15 hr during the short-day length period increased \u27Pensacola\u27 bahiagrass P. notatum Flugge saure Parodi forage yield 122% when compared with normal photoperiod (Mislevy et al., 2001). A Pensacola-derived bahiagrass population was selected for increased vegetative growth under short-day length using restricted recurrent phenotypic selection for three cycles (UF Cycle 3) to increase forage yield. Plants that comprise this population were less sensitive to short photoperiod and produced increased forage mass during the short days. The objective of this clipping study was to evaluate forage production and forage nutritive value of UF Cycle 3 compared with selected standard entries during short and long day length periods

Extended Daylength to Increase Fall/Winter Yields of Warm-Season Perennial Grasses

Low forage production in fall/winter months is a severe limitation for dairy and beef cattle producers in the southeastern U.S. It was hypothesized that shrt daylengths during these months induce a physiological dormancy in grasses. Four grasses [Pensacola bahiagrass, Paspalum notatum Flugge; Tifton 85 and Florakirk bermudagrass, Cynodon dactylon (L.); Florona stargrass, C. nlemfuensis Vanderyst var. nlemfuensis] were subjected to extended daylengths during the winter/fall months in a field test. Pensacola bahiagrass and Tifton 85 bermudagrass showed especially dramatic increases in forage yield during the fall/winter season under the extended daylength. Genetic elimination of daylength sensitivity in these grasses appears to be a viable option for increasing year-round forage production

Photoperiod Response in Pensacola Bahiagrass

Photoperiod response has been found to influence the growth and development of \u3ePensacola\u27 derived bahiagrass (Paspalum notatum Flugge var. saure Parodi). Four selection cycles [\u3ePensacola= (Cycle 0), Cycle 4, \u3eTifton 9\u27 (Cycle 9) and Cycle 23] resulting from recurrent restricted phenotypic selection (RRPS) of spaced-plants, were field grown in 1999 and 2000, to study photoperiod sensitivity among genotypes. Two day-length treatments were imposed on the field grown plants. One treatment, used only natural light. The second treatment imposed an extended day-length treatment using Quartz-halogen lamps, installed in the field during the fall and winter, to extend day-length to15 hours. The top growth of individual plants was harvested three times during the fall and winter seasons and stolon spread was measured in mid February, 2000. Top growth was increased by the extended day-length treatment for Pensacola and RRPS Cycle 4 in all three harvest dates. Top growth of Tifton 9 was unaffected by the extended light for the September harvest, but increased in the late October and late January harvests. RRPS Cycle 23 plants grown under natural light, out-yielded the plants grown under extended light treatment, for the first two harvests. There were no differences in yields of RRPS Cycle 23 plants from extended or natural light from the January harvest. The later cycles, Tifton 9 and RRPS Cycle 23, were less sensitive to day-length, than RRPS Cycles 0 and 4. Extended daylength, for all cycles, dramatically reduced stolon spread by nearly half that of the plants grown under natural light. Results from this experiment demonstrate a high sensitivity in growth and development of Pensacola-derived bahiagrass to day-length

Comparing Recent Organizing Templates for Test Content between ACS Exams in General Chemistry and AP Chemistry

Two different versions of “big ideas” rooted content maps have recently been published for general chemistry. As embodied in the content outline from the College Board, one of these maps is designed to guide curriculum development and testing for advanced placement (AP) chemistry. The Anchoring Concepts Content Map for general chemistry from the ACS Exams Institute is a component of a larger content map for the four-year undergraduate curriculum. This article compares the structure and content in these two maps to provide perspective on the current nature of the general chemistry curriculum. This contribution is part of a special issue on teaching introductory chemistry in the context of the AP chemistry course redesign

Developing a multiple-document-processing performance assessment for epistemic literacy

The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory

Exploratory Analysis in Learning Analytics

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods

Affective processes as network hubs

The practical problems of designing and coding a web-based flight simulator for teachers has led to a ‘three-tier plus environment’ model (COVE model) for a software agent’s cognition (C), psychologicsal (O), physical (V) processes and responses to tasks and interpersonal relationships within a learning environment (E). The purpose of this article is to introduce how some of the COVE model layers represent preconscious processing hubs in an AI human-agent’s representation of learning in a serious game, and how an application of the Five Factor Model of psychology in the O layer determines the scope of dimensions for a practical computational model of affective processes. The article illustrates the model with the classroom-learning context of the simSchool application (www.simschool.org); presents details of the COVE model of an agent’s reactions to academic tasks; discusses the theoretical foundations; and outlines the research-based real world impacts from external validation studies as well as new testable hypotheses of simSchool

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item‐level non‐response

In low‐stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low‐stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non‐response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item‐by‐examinee level by assuming different data‐generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test‐taking behaviour. An illustration of the model by means of an application to real data is presented