A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering

This chapter describes a design-based research project that developed an early warning system for an undergraduate engineering mentoring program. Using near real-time data from a university’s learning management system, we provided academic advisors with timely and targeted data on students’ academic progress. We discuss the development of the early warning system and detail how academic advisors used it. Our findings point to the value of providing academic advisors with performance data that can be used to direct students to appropriate sources of support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107974/1/Krumm_etal_2014_LA.pd

Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict

Horizontal DNA transfer (HDT) is a pervasive mechanism of diversification in many microbial species, but its primary evolutionary role remains controversial. Much recent research has emphasised the adaptive benefit of acquiring novel DNA, but here we argue instead that intragenomic conflict provides a coherent framework for understanding the evolutionary origins of HDT. To test this hypothesis, we developed a mathematical model of a clonally descended bacterial population undergoing HDT through transmission of mobile genetic elements (MGEs) and genetic transformation. Including the known bias of transformation toward the acquisition of shorter alleles into the model suggested it could be an effective means of counteracting the spread of MGEs. Both constitutive and transient competence for transformation were found to provide an effective defence against parasitic MGEs; transient competence could also be effective at permitting the selective spread of MGEs conferring a benefit on their host bacterium. The coordination of transient competence with cell-cell killing, observed in multiple species, was found to result in synergistic blocking of MGE transmission through releasing genomic DNA for homologous recombination while simultaneously reducing horizontal MGE spread by lowering the local cell density. To evaluate the feasibility of the functions suggested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals carrying Streptococcus pneumoniae. This revealed the frequent within-host coexistence of clonally descended cells that differed in their MGE infection status, a necessary condition for the proposed mechanism to operate. Additionally, we found multiple examples of MGEs inhibiting transformation through integrative disruption of genes encoding the competence machinery across many species, providing evidence of an ongoing "arms race." Reduced rates of transformation have also been observed in cells infected by MGEs that reduce the concentration of extracellular DNA through secretion of DNases. Simulations predicted that either mechanism of limiting transformation would benefit individual MGEs, but also that this tactic's effectiveness was limited by competition with other MGEs coinfecting the same cell. A further observed behaviour we hypothesised to reduce elimination by transformation was MGE activation when cells become competent. Our model predicted that this response was effective at counteracting transformation independently of competing MGEs. Therefore, this framework is able to explain both common properties of MGEs, and the seemingly paradoxical bacterial behaviours of transformation and cell-cell killing within clonally related populations, as the consequences of intragenomic conflict between self-replicating chromosomes and parasitic MGEs. The antagonistic nature of the different mechanisms of HDT over short timescales means their contribution to bacterial evolution is likely to be substantially greater than previously appreciated

Physics, Astrophysics and Cosmology with Gravitational Waves

Gravitational wave detectors are already operating at interesting sensitivity levels, and they have an upgrade path that should result in secure detections by 2014. We review the physics of gravitational waves, how they interact with detectors (bars and interferometers), and how these detectors operate. We study the most likely sources of gravitational waves and review the data analysis methods that are used to extract their signals from detector noise. Then we consider the consequences of gravitational wave detections and observations for physics, astrophysics, and cosmology.Comment: 137 pages, 16 figures, Published version <http://www.livingreviews.org/lrr-2009-2

A Framework to Support Interdisciplinary Engagement with Learning Analytics

Learning analytics can provide an excellent opportunity for instructors to get an in-depth understanding of students’ learning experiences in a course. However, certain technological challenges, namely limited availability of learning analytics data because of learning management system restrictions, can make accessing this data seem impossible at some institutions. Furthermore, even in cases where instructors have access to a range of student data, there may not be organized efforts to support students across various courses and university experiences. In the current chapter, the authors discuss the issue of learning analytics access and ways to leverage learning analytics data between instructors, and in some cases administrators, to create interdisciplinary opportunities for comprehensive student support. The authors consider the implications of these interactions for students, instructors, and administrators. Additionally, the authors focus on some of the technological infrastructure issues involved with accessing learning analytics and discuss the opportunities available for faculty and staff to take a multi-pronged approach to addressing overall student success.https://scholarworks.wm.edu/educationbookchapters/1045/thumbnail.jp

Policy Matters: Expert Recommendations for Learning Analytics Policy

Interest in learning analytics (LA) has grown rapidly among higher education institutions (HEIs). However, the maturity levels of HEIs in terms of being ‘student data-informed’ are only at early stages. There often are barriers that prevent data from being used systematically and effectively. To assist higher education institutions to become more mature users and custodians of digital data collected from students during their online learning activities, the SHEILA framework, a policy development framework that supports systematic, sustainable and responsible adoption of LA at an institutional level, was recently built. This paper presents a mix-method study using a group concept mapping (GCM) approach that was conducted with LA experts to explore essential features of LA policy in HEI in contribution the development of the framework. The study identified six clusters of features that an LA policy should include, provided ratings based on ease of implementation and importance for each of the six themes, and offered suggestions to HEIs how they can proceed with the development of LA policies

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

Student engagement and perceptions of blended-learning of a clinical module in a veterinary degree program.

Blended learning has received much interest in higher education as a way to increase learning efficiency and effectiveness. By combining face-to-face teaching with technology-enhanced learning through online resources, students can manage their own learning. Blended methods are of particular interest in professional degree programs such as veterinary medicine in which students need the flexibility to undertake intra- and extramural activities to develop the range of competencies required to achieve professional qualification. Yet how veterinary students engage with blended learning activities and whether they perceive the approach as beneficial is unclear. We evaluated blended learning through review of student feedback on a 4-week clinical module in a veterinary degree program. The module combined face-to-face sessions with online resources. Feedback was collected by means of a structured online questionnaire at the end of the module and log data collected as part of a routine teaching audit. The features of blended learning that support and detract from students’ learning experience were explored using quantitative and qualitative methods. Students perceived a benefit from aspects of face-to-face teaching and technology-enhanced learning resources. Face-to-face teaching was appreciated for practical activities, whereas online resources were considered effective for facilitating module organization and allowing flexible access to learning materials. The blended approach was particularly appreciated for clinical skills in which students valued a combination of visual resources and practical activities. Although we identified several limitations with online resources that need to be addressed when constructing blended courses, blended learning shows potential to enhance student-led learning in clinical courses

Re-Patterning Sleep Architecture in Drosophila through Gustatory Perception and Nutritional Quality

Organisms perceive changes in their dietary environment and enact a suite of behavioral and metabolic adaptations that can impact motivational behavior, disease resistance, and longevity. However, the precise nature and mechanism of these dietary responses is not known. We have uncovered a novel link between dietary factors and sleep behavior in Drosophila melanogaster. Dietary sugar rapidly altered sleep behavior by modulating the number of sleep episodes during both the light and dark phase of the circadian period, independent of an intact circadian rhythm and without affecting total sleep, latency to sleep, or waking activity. The effect of sugar on sleep episode number was consistent with a change in arousal threshold for waking. Dietary protein had no significant effect on sleep or wakefulness. Gustatory perception of sugar was necessary and sufficient to increase the number of sleep episodes, and this effect was blocked by activation of bitter-sensing neurons. Further addition of sugar to the diet blocked the effects of sweet gustatory perception through a gustatory-independent mechanism. However, gustatory perception was not required for diet-induced fat accumulation, indicating that sleep and energy storage are mechanistically separable. We propose a two-component model where gustatory and metabolic cues interact to regulate sleep architecture in response to the quantity of sugar available from dietary sources. Reduced arousal threshold in response to low dietary availability may have evolved to provide increased responsiveness to cues associated with alternative nutrient-dense feeding sites. These results provide evidence that gustatory perception can alter arousal thresholds for sleep behavior in response to dietary cues and provide a mechanism by which organisms tune their behavior and physiology to environmental cues