Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community

Computer-based technology and student engagement: a critical review of the literature

Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites (Facebook and Twitter), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook. Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement. © 2017, The Author(s)

Development and optimization of quantitative PCR for the diagnosis of invasive aspergillosis with bronchoalveolar lavage fluid

Background: The diagnosis of invasive pulmonary aspergillosis (IPA) remains challenging. Culture and histopathological examination of bronchoalveolar lavage (BAL) fluid are useful but have suboptimal sensitivity and in the case of culture may require several days for fungal growth to be evident. Detection of Aspergillus DNA in BAL fluid by quantitative PCR (qPCR) offers the potential for earlier diagnosis and higher sensitivity. It is important to adopt quality control measures in PCR assays to address false positives and negatives which can hinder accurate evaluation of diagnostic performance. Methods: BAL fluid from 94 episodes of pneumonia in 81 patients was analyzed. Thirteen episodes were categorized as proven or probable IPA using Mycoses Study Group criteria. The pellet and the supernatant fractions of the BAL were separately assayed. A successful extraction was confirmed with a human 18S rRNA gene qPCR. Inhibition in each qPCR was measured using an exogenous DNA based internal amplification control (IAC). The presence of DNA from pathogens in the Aspergillus genus was detected using qPCR targeting fungal 18S rRNA gene. Results: Human 18S rRNA gene qPCR confirmed successful DNA extraction of all samples. IAC detected some degree of initial inhibition in 11 samples. When culture was used to diagnose IPA, the sensitivity and specificity were 84.5% and 100% respectively. Receiver-operating characteristic analysis of qPCR showed that a cutoff of 13 fg of Aspergillus genomic DNA generated a sensitivity, specificity, positive and negative predictive value of 77%, 88%, 50%, 96% respectively. BAL pellet and supernatant analyzed together resulted in sensitivity and specificity similar to BAL pellet alone. Some patients did not meet standard criteria for IPA, but had consistently high levels of Aspergillus DNA in BAL fluid by qPCR. Conclusion: The Aspergillus qPCR assay detected Aspergillus DNA in 76.9% of subjects with proven or probable IPA when the concentrated BAL fluid pellet fraction was used for diagnosis. There was no benefit from analyzing the BAL supernatant fraction. Use of both extraction and amplification controls provided optimal quality control for interpreting qPCR results and therefore may increase our understanding of the true potential of qPCR for the diagnosis of IPA.Supported by NIH grant R01 AI054703 from the National Institute of Allergy and Infectious Diseases

Common TNF-α, IL-1β, PAI-1, uPA, CD14 and TLR4 polymorphisms are not associated with disease severity or outcome from Gram negative sepsis

<p>Abstract</p> <p>Background</p> <p>Several studies have investigated single nucleotide polymorphisms (SNPs) in candidate genes associated with sepsis and septic shock with conflicting results. Only few studies have combined the analysis of multiple SNPs in the same population.</p> <p>Methods</p> <p>Clinical data and DNA from consecutive adult patients with culture proven Gram negative bacteremia admitted to a Danish hospital between 2000 and 2002. Analysis for commonly described SNPs of tumor necrosis-α, (TNF-α), interleukin-1β (IL-1β), plasminogen activator-1 (PAI-1), urokinase plasminogen activator (uPA), CD14 and toll-like receptor 4 (TLR4) was done.</p> <p>Results</p> <p>Of 319 adults, 74% had sepsis, 19% had severe sepsis and 7% were in septic shock. No correlation between severity or outcome of sepsis was observed for the analyzed SNPs of TNF-α, IL-1β, PAI-1, uPA, CD14 or TLR-4. In multivariate Cox proportional hazard regression analysis, increasing age, polymicrobial infection and haemoglobin levels were associated with in-hospital mortality.</p> <p>Conclusion</p> <p>We did not find any association between TNF-α, IL-1β, PAI-1, uPA, CD14 and TLR4 polymorphisms and outcome of Gram negative sepsis. Other host factors appear to be more important than the genotypes studied here in determining the severity and outcome of Gram negative sepsis.</p

University student engagement inventory (USEI): psychometric properties

Academic engagement describes students’ investment in academic learning and achievement and is an important indicator of students’ adjustment to university life, particularly in the first year. A tridimensional conceptualization of academic engagement has been accepted (behavioral, emotional and cognitive dimensions). This paper tests the dimensionality, internal consistency reliability and invariance of the University Student Engagement Inventory (USEI) taking into consideration both gender and the scientific area of graduation. A sample of 908 Portuguese first-year university students was considered. Good evidence of reliability has been obtained with ordinal alpha and omega values. Confirmatory factor analysis substantiates the theoretical dimensionality proposed (second-order latent factor), internal consistency reliability evidence indicates good values and the results suggest measurement invariance across gender and the area of graduation. The present study enhances the role of the USEI regarding the lack of consensus on the dimensionality and constructs delimitation of academic engagement.Jorge Sinval received funding from the William James Center for Research, Portuguese Science Foundation (FCT UID/PSI/04810/2013). Leandro S. Almeida and Joana R. Casanova received funding from CIEd – Research Centre on Education, projects UID/CED/1661/2013 and UID/CED/1661/2016, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT. Joana R. Casanova received funding from the Portuguese Science Foundation (FCT) as a Doctoral Grant, under grant agreement number SFRH/BD/117902/2016.info:eu-repo/semantics/publishedVersio

Passerine Exposure to Primarily PCDFs and PCDDs in the River Floodplains Near Midland, Michigan, USA

House wren (Troglodytes aedon), tree swallow (Tachycineta bicolor), and eastern bluebird (Sialia sialis) tissues collected in study areas (SAs) downstream of Midland, Michigan (USA) contained concentrations of polychlorinated dibenzofurans (PCDFs) and polychlorinated dibenzo-p-dioxins (PCDDs) greater than in upstream reference areas (RAs) in the region. The sum of concentrations of PCDD/DFs (ΣPCDD/DFs) in eggs of house wrens and eastern bluebirds from SAs were 4- to 22-fold greater compared to those from RAs, whereas concentrations in tree swallow eggs were similar among areas. Mean concentrations of ΣPCDD/DFs and sum 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents (ΣTEQsWHO-Avian), based on 1998 WHO avian toxic equivalency factors, in house wren and eastern bluebird eggs ranged from 860 (430) to 1500 (910) ng/kg wet weight (ww) and 470 (150) to 1100 (510) ng/kg ww, respectively, at the most contaminated study areas along the Tittabawassee River, whereas mean concentrations in tree swallow eggs ranged from 280 (100) to 760 (280) ng/kg ww among all locations. Concentrations of ΣPCDD/DFs in nestlings of all studied species at SAs were 3- to 50-fold greater compared to RAs. Mean house wren, tree swallow, and eastern bluebird nestling concentrations of ΣPCDD/DFs and ΣTEQsWHO-Avian ranged from 350 (140) to 610 (300) ng/kg ww, 360 (240) to 1100 (860) ng/kg ww, and 330 (100) to 1200 (690) ng/kg ww, respectively, at SAs along the Tittabawassee River. Concentrations of ΣTEQsWHO-Avian were positively correlated with ΣPCDD/DF concentrations in both eggs and nestlings of all species studied. Profiles of relative concentrations of individual congeners were dominated by furan congeners (69–84%), primarily 2,3,7,8-tetrachlorodibenzofuran and 2,3,4,7,8-pentachlorodibenzofuran, for all species at SAs on the Tittabawassee and Saginaw rivers but were dominated by dioxin congeners at upstream RAs

Putting Youth on the Map: A Pilot Instrument for Assessing Youth Well-Being

Extant measures of adolescent well-being in the United States typically focus on negative indicators of youth outcomes. Indices comprised of such measures paint bleak views of youth and orient action toward the prevention of problems over the promotion of protective factors. Their tendency to focus analyses at a state or county geographic scale produces limited information about localized outcome patterns that could inform policymakers, practitioners and advocacy networks. We discuss the construction of a new geo-referenced index of youth well-being based on positive indicators of youth development. In demonstrating the index for the greater Sacramento, California region of the United States, we find that overall youth well-being falls far short of an optimal outcome, and geographic disparities in well-being appear to exist across school districts at all levels of our analysis. Despite its limitations, the sub-county geographic scale of this index provides needed data to facilitate local and regional interventions

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)