A multidimensional evaluation framework for personal learning environments

Evaluating highly dynamic and heterogeneous Personal Learning Environments (PLEs) is extremely challenging. Components of PLEs are selected and configured by individual users based on their personal preferences, needs, and goals. Moreover, the systems usually evolve over time based on contextual opportunities and constraints. As such dynamic systems have no predefined configurations and user interfaces, traditional evaluation methods often fall short or are even inappropriate. Obviously, a host of factors influence the extent to which a PLE successfully supports a learner to achieve specific learning outcomes. We categorize such factors along four major dimensions: technological, organizational, psycho-pedagogical, and social. Each dimension is informed by relevant theoretical models (e.g., Information System Success Model, Community of Practice, self-regulated learning) and subsumes a set of metrics that can be assessed with a range of approaches. Among others, usability and user experience play an indispensable role in acceptance and diffusion of the innovative technologies exemplified by PLEs. Traditional quantitative and qualitative methods such as questionnaire and interview should be deployed alongside emergent ones such as learning analytics (e.g., context-aware metadata) and narrative-based methods. Crucial for maximal validity of the evaluation is the triangulation of empirical findings with multi-perspective (end-users, developers, and researchers), mixed-method (qualitative, quantitative) data sources. The framework utilizes a cyclic process to integrate findings across cases with a cross-case analysis in order to gain deeper insights into the intriguing questions of how and why PLEs work

Effects of selective decontamination of digestive tract on mortality and acquisition of resistant bacteria in intensive care: a randomised controlled trial

Background Selective decontamination of the digestive tract (SDD) is an infection-prevention regimen used in critically ill patients. We assessed the effects of SDD on intensive-care-unit (ICU) and hospital mortality, and on the acquisition of resistant bacteria in adult patients admitted to intensive care. Methods We did a prospective, controlled, randomised, unblinded clinical trial, 934 patients admitted to a surgical and medical ICU were randomly assigned oral and enteral polymyxin E, tobramycin, and amphotericin B combined with an initial 4-day course of intravenous cefotaxime (SDD group n=466), or standard treatment (controls n=468). Primary endpoints were ICU and hospital mortality and the acquisition of resistant bacteria. Findings In the SDD group 69 (15%) patients died in the ICU compared with 107 (23%) in the control group (p=0.002). Hospital mortality was lower in the SDD groups than in the control group (113 [24%] vs 146 [31%], p=0.02). During their stay in intensive care, colonisation with gram-negative bacteria resistant to ceftazidime, ciprofloxacin, imipenem, polymyxin E, or tobramycin occurred in 61 (16%) of 378 SDD patients and in 104 (26%) of 395 patients in the control group (p=0.001). Colonisation with vancomycin-resistant enterococcus occurred in five (1%) SDD patients and in four (1%) controls (p=1.0). No patient in either group was colonised with meticillin-resistant Staphylococcus aureus. Interpretation In a setting with low prevalence of vancomycin-resistant enterococcus and meticillin-resistant S aureus, SDD can decrease ICU and hospital mortality and colonisation with resistant gram-negative aerobic bacteria