Linda Corrin

Technological diversity: An investigation of students' technology use in everyday life and academic study

Generational generalisations regarding how students interact with technology have been used in recent times to prompt calls for radical changes to the delivery of teaching in higher education. This article reports on a study aimed to investigate first‐year students’ technology access and usage in two contexts of use: everyday life and academic study. A survey was delivered to first‐year students across seven faculties of an Australian university during the second semester of the 2008 academic year. A total of 470 respondents met the criteria for this study. The findings suggest a wide diversity of usage of technologies with the usage rates of technology in academic study being generally lower than those in everyday life. These findings indicated that generational generalisations are not useful in informing the design of learning and teaching in higher education. However, there are questions regarding reliability of current survey‐based methods to examine students’ technology use and the level of diversity discovered across both contexts of use. This suggests that further in‐depth research into how students shape technology to suit their lives is required to gain a greater understanding of how technology can effectively support teaching and learning.

A conceptual framework linking learning design with learning analytics

In this paper we present a learning analytics conceptual framework that supports enquiry-based evaluation of learning designs. The dimensions of the proposed framework emerged from a review of existing analytics tools, the analysis of interviews with teachers, and user scenarios to understand what types of analytics would be useful in evaluating a learning activity in relation to pedagogical intent. The proposed framework incorporates various types of analytics, with the teacher playing a key role in bringing context to the analysis and making decisions on the feedback provided to students as well as the scaffolding and adaptation of the learning design. The framework consists of five dimensions: temporal analytics, tool-specific analytics, cohort dynamics, comparative analytics and contingency. Specific metrics and visualisations are defined for each dimension of the conceptual framework. Finally the development of a tool that partially implements the conceptual framework is discussed.

Digital Natives: Exploring the Diversity of Young People’s Experience with Technology

The concept of ‘digital natives’, based on assumptions of high technology literacy of the current generation of students, has triggered extensive discussion and debate in relation to technology use in higher education. Whilst several previous studies have demonstrated that generational views of technology literacy and engagement are not useful to the planning of future teaching and learning developments in higher education (Helsper and Eynon 2009; Kennedy et al. 2008; Bennett and Maton 2010), the digital natives discussion has eventually led to research offering a greater insight into the reality of students’ engagement with technology. From the non-empirical foundations of the digital natives concept through initial quantitative studies and now towards new in-depth qualitative studies, a greater understanding is being developed of the diversity that exists around students’ adopt and use of technology. This chapter reports on a study which aims to further the understanding of the motivations, attitudes and practices of young people in relation to technology. Eight student case studies are presented which provide an in-depth exploration of the stories behind students’ choices and uses of technology across the contexts of their everyday life and academic study.

An innovative OSCE clinical log station: a quantitative study of its influence on Log use by medical students

BackgroundA Clinical Log was introduced as part of a medical student learning portfolio, aiming to develop a habit of critical reflection while learning was taking place, and provide feedback to students and the institution on learning progress. It was designed as a longitudinal self-directed structured record of student learning events, with reflection on these for personal and professional development, and actions planned or taken for learning.As incentive was needed to encourage student engagement, an innovative Clinical Log station was introduced in the OSCE, an assessment format with established acceptance at the School. This study questions: How does an OSCE Clinical Log station influence Log use by students?MethodsThe Log station was introduced into the formative, and subsequent summative, OSCEs with careful attention to student and assessor training, marking rubrics and the standard setting procedure. The scoring process sought evidence of educational use of the log, and an ability to present and reflect on key learning issues in a concise and coherent manner.ResultsAnalysis of the first cohort’s Log use over the four-year course (quantified as number of patient visits entered by all students) revealed limited initial use. Usage was stimulated after introduction of the Log station early in third year, with some improvement during the subsequent year-long integrated community-based clerkship. Student reflection, quantified by the mean number of characters in the ‘reflection’ fields per entry, peaked just prior to the final OSCE (mid-Year 4). Following this, very few students continued to enter and reflect on clinical experience using the Log.ConclusionWhile the current study suggested that we can’t assume students will self-reflect unless such an activity is included in an assessment, ongoing work has focused on building learner and faculty confidence in the value of self-reflection as part of being a competent physician.

What data and analytics can and do say about effective learning

The collection and analysis of data about learning is a trend that is growing exponentially in all levels of education. Data science is poised to have a substantial influence on the understanding of learning in online and blended learning environments. The mass of data already being collected about student learning provides a source of greater insights into student learning that have not previously been available, and therefore is liable to have a substantial impact on and be impacted by the science of learning in the years ahead. However, despite the potential evident in the application of data science to education, several recent articles, e.g., 3 have pointed out that student behavioural data collected en masse do not holistically capture student learning. Rogers contends that this positivist view of analytics in education is symptomatic of issues in the social sciences more broadly. While there is undeniable merit in bringing a critical perspective to the use of data and analytics, we suggest that the power and intent of data science for understanding learning is now becoming apparent. The intersection of the science of learning with data and analytics enables more sophisticated ways of making meaning to support student learning.

DesignLAK17: quality metrics and indicators for analytics of assessment design at scale

Notions of what constitutes quality in design in traditional on-campus or online teaching and learning may not always translate into scaled digital environments. The DesignLAK17 workshop builds on the DesignLAK16 workshop to explore one aspect of this theme, namely the opportunities arising from the use of analytics in scaled assessment design. New paradigms for learning design are exploiting the distinctive characteristics and potentials of analytics, trace data and newer kinds of sensory data usable on digital platforms to transform assessment. But, characteristics of quality assessment design need to be reconsidered, and new metrics for capturing quality are required. This symposium and workshop focuses on what might be appropriate quality metrics and indicators for assessment design in scaled learning. It aims to build a community of interest round the topic, to share perspectives, and to generate design and research ideas.

Learning design and feedback processes at scale: stocktaking emergent theory and practice

Design for learning in scaled courses is shifting away from replication of traditional on-campus or online teaching towards exploiting the distinctive characteristic and potentials of scale to transform both teaching and learning. Scaled learning environments such as MOOCs may represent a new paradigm for teaching. This workshop involves consideration of the how learning occurs in scaled environments, and how learning designers and analysts can assist. It will explore questions at the heart of effective learning design, using expert panelists and collaborative knowledge-building techniques to arrive at a stocktake of thinking.

The Connected Community of Practice in Educational Technology: A Model for Future Networked Professional Development?

While intentionally created communities of practice have continued to grow within the higher education context, simultaneously an organic ‘networked’ community has grown amongst those involved in researching and implementing educational technology internationally. This global community, which we refer to as the “educational technology community”, represents a new and emerging type of community of practice (CoP) that is not bound by geographic location or a need for synchronous contact between members. This chapter will examine this organically evolving educational technology community as a model for future networked CoPs. This analysis will lead to a possible model for future networked CoPs which will align with current thinking about networked learning and professional development (e.g. Sloep in Technology-enhanced professional learning: processes, practices and tools. Routledge, New York, 2014). The overall aim of the chapter therefore is to explore possible future methods of professional development through networked learning in higher education.