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This European Heart Rhythm Association (EHRA) Scientific Initiatives Committee, EHRA Young Electrophysiologists, Association of Cardiovascular Nursing and Allied Professionals, and European Society of Cardiology (ESC) Council on Stroke joint survey aimed to assess the interpretation of the CHA2DS2-VASc score components and preferred resources for calculating the score. Of 439 respondents, most were general cardiologists (46.7%) or electrophysiologists (EPs) (42.1%). The overall adherence to the ESC-defined scoring criteria was good. Most variation was observed in the interpretation of the significance of left ventricular ejection fraction and brain natriuretic peptide in the scoring for the ‘C’ component, as well as the ‘one-off high reading of blood pressure’ to score on the ‘H’ component. Greater confidence was expressed in scoring the ‘H’ component (72.3%) compared with the ‘C’ (46.2%) and ‘V’ (45.9%) components. Respondents mainly relied on their recall for the scoring of CHA2DS2-VASc score (64.2%). The three most favoured referencing resources varied among different professionals, with pharmacists and physicians relying mainly on memory or web/mobile app, whereas nurses favoured using a web/mobile app followed by memory or guidelines/protocol. In conclusion, this survey revealed overall good adherence to the correct definition of each component in scoring of the ‘C’, ‘H’, and ‘V’ elements of the CHA2DS2-VASc score, although the variation in their interpretations warrants further clarifications. The preferred referencing resources to calculate the score varied among different healthcare professionals. Guideline education to healthcare professionals and updated and unified online/mobile scoring tools are suggested to improve the accuracy in scoring the CHA2DS2-VASc score.

As educators we have grappled with strategies to teach referencing, a fundamental of digital information fluency. Despite a myriad of resources, errors are common and recurring (Jorgensen & Marek, 2013). Students frequently receive static information on referencing and no immediate feedback. This paper reflects on the development and evaluation of online resources for explicitly teaching APA referencing in the Faculties of Arts, Business and Science. We designed H5P resources that integrate into individual courses to encourage interactivity (Singleton & Charlton, 2019) and provide feedback for self-assessment (Nicol & Mcfarlane-Dick, 2006). According to Nicol and Macfarlane-Dick’s seven principles of good feedback practice, good referencing practices were first explained, before students were given an opportunity to practice those skills. This was followed by detailed automated feedback. Student performance is recorded so that teachers can begin dialogues and use feedback to improve teaching. The methodological approach taken aligns with Educational Design Research that aims to address real-life-learning issues through various cycles. Pre-interviews with teaching staff gave a better understanding of the expectations, support and students’ issues in referencing. Following these, students could access the tool and complete a questionnaire about their experiences. LMS and H5P analytics data added another dimension to quantitatively gauge engagement. Focus groups and interviews were conducted to ascertain the effectiveness and identify suggestions. The qualitative data results in rich descriptions. The data was analysed in an open-coding approach (Saldana, 2013) for themes that emerged in response to the main questions asked. Student voices provided evidence of identified themes and were indicators of the overall results. Staff insights about the challenges aligned with preliminary results that tertiary students’ previous APA referencing experiences are variable. LMS analytics showed good engagement and students completed the module in about 27 minutes. Feedback from the trial highlighted the benefits of interactivity and immediate feedback and led to amendments of the online resources. Participants commented, for example, “more practical activities”, and liked the final quiz for its instant feedback. As a result, additional activities were integrated. Some emerging themes from the trial were the relevance, consistency and practicality of the resources and indicated students’ willingness to use them for self-study. Feedback in Semester 1 confirmed those themes. From this round of feedback, the usefulness of the resources was highlighted. The evidence of effectiveness is summed up in that all participants would recommend the tool to peers. As tool development continues, we will present up-to-date data. Further, we consider how these tools might support more faculties and institutions, extending this research to include a suite of referencing resources.

As maker spaces, engineering design curricula, and other hands-on active learning tasks become more popular in science classrooms, it is important to consider what students are intended to take away from these tasks. Many teachers use engineering design tasks as a means of teaching students more general science principles. However, few studies have explored exactly how the design of these activities can support more generalized student learning and transfer. Specifically, research has yet to sufficiently investigate the effects of task design components on the learning and transfer processes that can occur during these kinds of tasks. This dissertation explores how various task manipulations and focusing processes affect how well students can learn and transfers science concepts from an engineering design task. I hypothesized that learning goals that focus students on the deep structure of the problem, and contrasting cases that help students notice that deep structure, would aid learning and transfer. In two experimental studies, students were given an engineering design task. The first study was a 2x2 between subjects design where goal where goal (outcome or learning) and reflection (on contrasting cases or the engineering design process) were manipulated. A subsequent second study then gave all students contrasting cases to reflect on, and only the goal manipulation was manipulated. Results showed that learning goals improved student performance on a transfer task that required students to apply the deep structure to a different engineering design task. In the second study, learning goals improved student performance on a transfer test. Transfer performance in both studies was predicted by the ability to notice the deep structure during the reflection on contrasting cases, even though noticing this structure did not differ by goal condition. Students with a learning goal valued the learning resources they were given more during the engineering design activity, and this perceived value of resources was linked to greater learning. A qualitative case study analysis was then conducted using video data from the second study. This case study investigated noticing processes during the building process, partner dialogue, and resource use. This analysis showed how high transfer pairs were better able to focus on the deep structure of the problem. Results suggest that what students noticed didn’t differ much between the various pairs. However, high transfer pairs were better able to focus on the deep structure through establishing a joint understanding of the deep structure, sustaining concentration on that deep structure during the cases reflection, referencing resources to identify features to test, and then systematically testing those features to identify their relevance. These processes are discussed in relation to how they differ in low transfer pairs. This dissertation consists of four chapters: an intro, two standalone journal articles, and a conclusion. The first chapter provides a conceptual framing for the two journal articles, and discusses the findings from these articles in conversation. The second chapter describes the two empirical studies investigating how task goals and contrasting cases affect learning, and transfer from an engineering design task. The third chapter describes the comparative case study of how mechanisms of focusing on the deep structure differ between high and low transfer pairs. Finally, the fourth conclusion chapter discusses the implications of the work from both of these papers.