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

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

Inferring learning from big data:The importance of a transdisciplinary and multidimensional approach

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention