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The uptake of Massive Open Online Courses (MOOCs) has been substantial and they continue to flourish as an educational delivery model. Much of the available literature regarding student retention and completions in MOOCs comes from university-designed courses. The Vocational Education and Training (VET) sector has lagged behind its university counterparts as VET pedagogy relies heavily on skills acquisition and does not lend itself as easily to MOOC delivery. This paper analysed the attributes of 366,099 enrolled learners and 132,867 learners that were designated as 'starters' from 263 Australian universities, 27 transnational universities and 73 VET MOOCs to compare the differences in learning environments and to build a VET MOOC learner profile. The findings indicated that higher levels of VET MOOC learners were retained when compared to university MOOCs. It also revealed eight course design factors that may contribute to increased learner retention and completions for VET MOOCs.

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance. [ABSTRACT FROM AUTHOR]

Aim/Purpose: The primary use of electronic health records (EHRs) is in the care of the individual patient. Secondary research uses employ information in EHRs for purposes beyond that of care of the individual. Secondary research uses may broadly be divided into studies which focus on improving care and treatment of individuals and those which aim to increase knowledge about disease causes, associations and prevalence at a population level. This paper provides a review of studies that have used EHRs to increase knowledge at a population level. It examines the methods used, types of research conducted, difficulties and challenges faced and implications for future research and mental health research in particular. Method: A review was undertaken based on a search for peer-reviewed and recently (i.e. since 2005) published articles with full-text available online. Findings/Results: The studies which have used EHRs to increase knowledge have predominantly involved; (1) data mining to identify biomarkers and gene–disease associations, (2) epidemiological research using linked/merged health records and (3) surveillance, prediction and alerts for diseases/illnesses. The principal methodological challenges identified were data quality, discrepancies/inconsistencies in data and interoperability of EHRs. Conclusions: Despite the challenges faced in secondary usage of EHRs, significant research has been undertaken and researchers have proposed and tested various approaches to address methodological issues. The study methods employed in other fields of medical research can be extrapolated to study issues of significance to mental health using EHRs. [ABSTRACT FROM PUBLISHER]

Exposure to phobic stimuli in subjects with specific phobia typically results in increased anxiety, ranging from mild to severe, followed by gradual habituation. The Internet is a candidate medium for the delivery of phobic stimuli to phobic subjects, such as pictures, video clips or computer animations. Delivery of such images in home settings warrants careful attention to the range and time course of anxiety responses elicited, and to tailoring of progression through hierarchies of images. The agency of the user is paramount, they need to have the final say at all stages of exposure as to whether to proceed or not. We have incorporated solutions to these requirements in the design of an internet-based exposure program (FEARDROP). This employs a database repository of pictures and videos of phobic stimuli. Images are called up by the user engaging a tracking circle with their mouse and following it around the screen. The image fades out if the circle is not followed, a form of ‘dead man's brake’. Anxiety responses are measured at intervals on a visual analogue scale and graphed for the user. Initial results show substantial habituation to spider pictures within minutes, with a controlled comparison to video images in progress. [ABSTRACT FROM AUTHOR]

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance.

Exposure to phobic stimuli in subjects with specific phobia typically results in increased anxiety, ranging from mild to severe, followed by gradual habituation. The Internet is a candidate medium for the delivery of phobic stimuli to phobic subjects, such as pictures, video clips or computer animations. Delivery of such images in home settings warrants careful attention to the range and time course of anxiety responses elicited, and to tailoring of progression through hierarchies of images. The agency of the user is paramount, they need to have the final say at all stages of exposure as to whether to proceed or not. We have incorporated solutions to these requirements in the design of an internet-based exposure program (FEARDROP). This employs a database repository of pictures and videos of phobic stimuli. Images are called up by the user engaging a tracking circle with their mouse and following it around the screen. The image fades out if the circle is not followed, a form of 'dead man's brake'. Anxiety responses are measured at intervals on a visual analogue scale and graphed for the user. Initial results show substantial habituation to spider pictures within minutes, with a controlled comparison to video images in progress.