Managing plagiarism in programming assignments with blended assessment and randomisation.

Plagiarism is a common concern for coursework in many situations, particularly where electronic solutions can be provided e.g. computer programs, and leads to unreliability of assessment. Written exams are often used to try to deal with this, and to increase reliability, but at the expense of validity. One solution, outlined in this paper, is to randomise the work that is set for students so that it is very unlikely that any two students will be working on exactly the same problem set. This also helps to address the issue of students trying to outsource their work by paying external people to complete their assignments for them. We examine the effectiveness of this approach and others (including blended assessment) by analysing the spread of similarity scores across four different introductory programming assignments to find the natural similarity i.e. the level of similarity that could reasonably occur without plagiarism. The results of the study indicate that divergent assessment (having more than one possible solution) as opposed to convergent assessment (only one solution) is the dominant factor in natural similarity. A key area for further work is to apply the analysis to a larger sample of programming assignments to better understand the impact of different features of the assignment design on natural similarity and hence the detection of plagiarism

MOCDroid: multi-objective evolutionary classifier for Android malware detection

Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial Anti-Virus. Android is one of the target architectures where these problems are specially alarming, due to the wide extension of the platform in different everyday devices.The detection is specially relevant for Android markets in order to ensure that all the software they offer is clean, however, obfuscation has proven to be effective at evading the detection process. In this paper we leverage third-party calls to bypass the effects of these concealment strategies, since they cannot be obfuscated. We combine clustering and multi-objective optimisation to generate a classifier based on specific behaviours defined by 3rd party calls groups. The optimiser ensures that these groups are related to malicious or benign behaviours cleaning any non-discriminative pattern. This tool, named MOCDroid, achieves an ac-curacy of 94.6% in test with 2.12% of false positives with real apps extracted from the wild, overcoming all commercial Anti-Virus engines from VirusTotal

Building Innovative Online Korean and Japanese Courses: A Pilot on Technology- Enhanced Curriculum Development

Our pilot project created blended/online courses to accommodate the growing needs of precollegiate and collegiate students interested in learning Korean and Japanese. In the initial phase, we conducted a survey of students’ experiences with and perceptions about blended/online Asian language learning. We found a general lack of familiarity with, and moderate resistance toward, online language learning modes. With learner attitudes in mind, we developed online modules for beginning Korean and Japanese courses. In this article, we report the survey results and the process of developing these innovative blended and online modalities of content delivery, focusing on the strengths of the modules and the unforeseen development challenges. The impacts that these technology-enhanced environments may have on student perceptions of transactional distance and tele-/copresence are explored. We suggest that transforming conventional East Asian language courses into blended/online modes is not only feasible but also beneficial for foreign language teaching and learning

Evaluation of a Desktop 3D Printed Rigid Refractive-Indexed-Matched Flow Phantom for PIV Measurements on Cerebral Aneurysms

Purpose Fabrication of a suitable flow model or phantom is critical to the study of biomedical fluid dynamics using optical flow visualization and measurement methods. The main difficulties arise from the optical properties of the model material, accuracy of the geometry and ease of fabrication. Methods Conventionally an investment casting method has been used, but recently advancements in additive manufacturing techniques such as 3D printing have allowed the flow model to be printed directly with minimal post-processing steps. This study presents results of an investigation into the feasibility of fabrication of such models suitable for particle image velocimetry (PIV) using a common 3D printing Stereolithography process and photopolymer resin. Results An idealised geometry of a cerebral aneurysm was printed to demonstrate its applicability for PIV experimentation. The material was shown to have a refractive index of 1.51, which can be refractive matched with a mixture of de-ionised water with ammonium thiocyanate (NH4SCN). The images were of a quality that after applying common PIV pre-processing techniques and a PIV cross-correlation algorithm, the results produced were consistent within the aneurysm when compared to previous studies. Conclusions This study presents an alternative low-cost option for 3D printing of a flow phantom suitable for flow visualization simulations. The use of 3D printed flow phantoms reduces the complexity, time and effort required compared to conventional investment casting methods by removing the necessity of a multi-part process required with investment casting techniques

Family engagement and compassion fatigue in alternative provision

© 2021 The Author. Published by Taylor & Francis. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1080/13603116.2021.1938713In a sector largely ignored in policy and the public imagination, Alternative Provision works to care for and educate children for whom mainstream schooling does not work. Central to their mission is the engagement of families, often seen as both the cause of their child’s difficulties and the solution to their successful educational re-engagement. Practitioners within Alternative Provision work within sophisticated strategies of family engagement, from regular communication to the more intensive interventions of home visits, supporting families with everything from filling in forms to cleaning, from managing outbursts to sourcing furniture. With the majority of families living within contexts of deprivation, many have life histories containing trauma, trauma that Alternative Provision Practitioners listen to, confront and, often, internalise, risking ‘compassion fatigue’. This article focuses on the potential for compassion fatigue within family engagement in Alternative Provision, beginning with the impact on practitioners. It then discusses the role of leadership in building an assemblage of organisation interventions to both mitigate compassion fatigue and maximise ‘compassion satisfaction’, the fulfilment that comes from empathic work. Finally, it examines how compassion satisfaction could mitigate the deleterious impact of vicarious trauma.Published onlin

Effect of Algorithm-Based Therapy vs Usual Care on Clinical Success and Serious Adverse Events in Patients with Staphylococcal Bacteremia: A Randomized Clinical Trial

Importance: The appropriate duration of antibiotics for staphylococcal bacteremia is unknown. Objective: To test whether an algorithm that defines treatment duration for staphylococcal bacteremia vs standard of care provides noninferior efficacy without increasing severe adverse events. Design, Setting, and Participants: A randomized trial involving adults with staphylococcal bacteremia was conducted at 16 academic medical centers in the United States (n = 15) and Spain (n = 1) from April 2011 to March 2017. Patients were followed up for 42 days beyond end of therapy for those with Staphylococcus aureus and 28 days for those with coagulase-negative staphylococcal bacteremia. Eligible patients were 18 years or older and had 1 or more blood cultures positive for S aureus or coagulase-negative staphylococci. Patients were excluded if they had known or suspected complicated infection at the time of randomization. Interventions: Patients were randomized to algorithm-based therapy (n = 255) or usual practice (n = 254). Diagnostic evaluation, antibiotic selection, and duration of therapy were predefined for the algorithm group, whereas clinicians caring for patients in the usual practice group had unrestricted choice of antibiotics, duration, and other aspects of clinical care. Main Outcomes and Measures: Coprimary outcomes were (1) clinical success, as determined by a blinded adjudication committee and tested for noninferiority within a 15% margin; and (2) serious adverse event rates in the intention-to-treat population, tested for superiority. The prespecified secondary outcome measure, tested for superiority, was antibiotic days among per-protocol patients with simple or uncomplicated bacteremia. Results: Among the 509 patients randomized (mean age, 56.6 [SD, 16.8] years; 226 [44.4%] women), 480 (94.3%) completed the trial. Clinical success was documented in 209 of 255 patients assigned to algorithm-based therapy and 207 of 254 randomized to usual practice (82.0% vs 81.5%; difference, 0.5% [1-sided 97.5% CI, -6.2% to ∞]). Serious adverse events were reported in 32.5% of algorithm-based therapy patients and 28.3% of usual practice patients (difference, 4.2% [95% CI, -3.8% to 12.2%]). Among per-protocol patients with simple or uncomplicated bacteremia, mean duration of therapy was 4.4 days for algorithm-based therapy vs 6.2 days for usual practice (difference, -1.8 days [95% CI, -3.1 to -0.6]). Conclusions and Relevance: Among patients with staphylococcal bacteremia, the use of an algorithm to guide testing and treatment compared with usual care resulted in a noninferior rate of clinical success. Rates of serious adverse events were not significantly different, but interpretation is limited by wide confidence intervals. Further research is needed to assess the utility of the algorithm. Trial Registration: ClinicalTrials.gov Identifier: NCT01191840