Data Mining for Learning Analytics: does lack of engagement always mean what we think it does?

Context and Objectives Learning Analytics (LA) has the potential to utilise student data to further the advancement of a personalized, supportive system of HE (Johnson et al., 2013). A number of LA systems are now being developed but there have been few studies that have analysed the usage of Virtual Learning Environments (VLE) in order to identify which analytics techniques and sources of data accurately reflect student engagement and achievement. Methods The interactions of 66 students with a Level 4 programming module on a VLE have been analysed via the simple K-means clustering algorithm to identify classes of behaviour and their characteristics. Results Two prominent classes were found with students achieving higher marks attending the lectures and tutorials more regularly and accessing all types of material on the VLE more frequently than students in the lower achieving cluster. However, there were a number of exceptions that had low levels of engagement that gained high marks and vice versa. Discussion A student’s prior experience and characteristics of their degree programme need to be taken into account to avoid incorrectly interpreting high and low levels of engagement. Conclusions The number of times students view online module materials will be an important factor for inclusion in any predictive LA models but must be able to take into account the differences in student backgrounds, delivery styles and subject

Contesting #StopIslam : the dynamics of a counter-narrative against right-wing populism

This paper sets out quantitative findings from a research project examining the dynamics of online counter-narratives against hate speech, focusing on #StopIslam, a hashtag that spread racialized hate speech and disinformation directed towards Islam and Muslims and which trended on Twitter after the March 2016 terror attacks in Brussels. We elucidate the dynamics of the counter-narrative through contrasting it with the affordances of the original anti-Islamic narrative it was trying to contest. We then explore the extent to which each narrative was taken up by the mainstream media. Our findings show that actors who disseminated the original hashtag with the most frequency were tightly-knit clusters of self-defined conservative actors based in the US. The hashtag was also routinely used in relation to other pro-Trump, anti-Clinton hashtags in the run-up to the 2016 presidential election, forming part of a broader, racialized, anti-immigration narrative. In contrast, the most widely shared and disseminated messages were attempts to challenge the original narrative that were produced by a geographically dispersed network of self-identified Muslims and allies. The counter-narrative was significant in gaining purchase in the wider media ecology associated with this event, due to being reported by mainstream media outlets. We ultimately argue for the need for further research that combines ‘big data’ approaches with a conceptual focus on the broader media ecologies in which counter-narratives emerge and circulate, in order to better understand how opposition to hate speech can be sustained in the face of the tight-knit right-wing networks that often outlast dissenting voices

Follow #eHealth2011: Measuring the Role and Effectiveness of Online and Social Media in Increasing the Outreach of a Scientific Conference

BACKGROUND: Social media promotion is increasingly adopted by organizers of industry and academic events; however, the success of social media strategies is rarely questioned or the real impact scientifically analyzed. Objective: We propose a framework that defines and analyses the impact, outreach, and effectiveness of social media for event promotion and research dissemination to participants of a scientific event as well as to the virtual audience through the Web. METHODS: Online communication channels Twitter, Facebook, Flickr, and a Liveblog were trialed and their impact measured on outreach during five phases of an eHealth conference: the setup, active and last-minute promotion phases before the conference, the actual event, and after the conference. RESULTS: Planned outreach through online channels and social media before and during the event reached an audience several magnitudes larger in size than would have been possible using traditional means. In the particular case of eHealth 2011, the outreach using traditional means would have been 74 attendees plus 23 extra as sold proceedings and the number of downloaded articles from the online proceedings (4107 until October 2013). The audience for the conference reached via online channels and social media was estimated at more than 5300 in total during the event. The role of Twitter for promotion before the event was complemented by an increased usage of the website and Facebook during the event followed by a sharp increase of views of posters on Flickr after the event. CONCLUSIONS: Although our case study is focused on a particular audience around eHealth 2011, our framework provides a template for redefining “audience” and outreach of events, merging traditional physical and virtual communities and providing an outline on how these could be successfully reached in clearly defined event phases

Transnational In-Group Solidarity Networks in the Case of #Hellobrother

This paper examines the dynamics of one hashtag, #hellobrother, shared on Twitter following the Christchurch terror attack on 15th March 2019. It was analysed as part of a larger study #Contesting Islamophobia: Representation and Appropriation in Mediated Activism which explores the potentials and limitations of counternarratives against Islamophobia on Twitter. Using three ‘trigger events’ (Awan, 2014), Brexit, the Christchurch terror attack, and the Covid pandemic as its starting point, the study analysed six weeks of tweets at different points in time. The data on #hellobrother demonstrates an affective response which, through the affordances of Twitter, gave rise to strong networks of transnational solidarity. It illustrates both the limitations of its potentially transient solidarities but also the capacity of social media to offer visibility to counternarratives, which at specific moments, following specific events can become normative.</jats:p

Implications for the design of a Diagnostic Decision Support System (DDSS) to reduce time and cost to diagnosis in paediatric shoulder instability.

BACKGROUND: Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis and improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability. METHODS: A modified nominal focus group technique, involving three clinical vignettes, was used to elicit physiotherapists decision-making processes. RESULTS: Twenty-five physiotherapists, (18F:7 M) from four separate clinical sites participated. The themes identified related to 'Variability in diagnostic processes and lack of standardised practice' and 'Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making'. CONCLUSION: No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed. Trial Registraty This was not a clinical trial and so no clinical trial registry is needed

A user-centred evaluation framework for the Sealife semantic web browsers

Background: Semantically-enriched browsing has enhanced the browsing experience by providing contextualised dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealife's main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure. / Methods: This paper describes a user-centred evaluation framework that was developed to evaluate the Sealife SWBs that elicited feedback on users' perceptions on ease of use and information findability. Three sources of data: i) web server logs; ii) user questionnaires; and iii) semi-structured interviews were analysed and comparisons made between each browser and a control system. / Results: It was found that the evaluation framework used successfully elicited users' perceptions of the three distinct SWBs. The results indicate that the browser with the most mature and polished interface was rated higher for usability, and semantic links were used by the users of all three browsers. / Conclusion: Confirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues

An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach

Identifying methods for monitoring foodborne illness: review of existing public health surveillance techniques

Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health ailment. Studies of this nature are scarce within the food safety domain, therefore identification and understanding of transferrable methods in other health-related fields are of particular interest. Methods: Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance. The title, abstract, and keyword fields of 5 databases were searched using predetermined search terms. A total of 5239 papers matched the search criteria, of which 145 were taken to full-text review—62 papers were deemed relevant and were subjected to data characterization and thematic analysis. Results: The majority of studies (40/62, 65%) focused on the surveillance of influenza-like illness. Only 10 studies (16%) used consumer-generated data to monitor outbreaks of foodborne illness. Twitter data (58/62, 94%) and Yelp reviews (3/62, 5%) were the most commonly used data sources. Studies reporting high correlations against baseline statistics used advanced statistical and computational approaches to calculate the incidence of disease. These include classification and regression approaches, clustering approaches, and lexicon-based approaches. Although they are computationally intensive due to the requirement of training data, studies using classification approaches reported the best performance. Conclusions: By analyzing studies in digital epidemiology, computer science, and public health, this paper has identified and analyzed methods of disease monitoring that can be transferred to foodborne disease surveillance. These methods fall into 4 main categories: basic approach, classification and regression, clustering approaches, and lexicon-based approaches. Although studies using a basic approach to calculate disease incidence generally report good performance against baseline measures, they are sensitive to chatter generated by media reports. More computationally advanced approaches are required to filter spurious messages and protect predictive systems against false alarms. Research using consumer-generated data for monitoring influenza-like illness is expansive; however, research regarding the use of restaurant reviews and social media data in the context of food safety is limited. Considering the advantages reported in this review, methods using consumer-generated data for foodborne disease surveillance warrant further investment