Higher Education and Emerging Technologies: Shifting Trends in Student Usage

This study serves as an update to a previous study by Sam Houston State University librarians about the use and preferences of Internet, communication, and educational technologies among students. Since the previous study was initiated in 2010, the iPad has made its debut and significantly altered the educational technology landscape. In this new landscape, this study investigates student usage of such technologies as instant messaging, cell phones, e-readers, social networking, RSS feeds, podcasts, and tablets. In addition, this study aims to determine which technologies students prefer the library to utilize for a variety of services, such as reference assistance or book renewals, and which technologies may not be worth the investment, such as geosocial networking. The information gained from this survey is intended to provide guidance for libraries looking to provide services utilizing the most popular technologies with the most efficient use of resources. Survey results show an increasing use and dependence on educational technologies and a desire for basic library services to be available on a variety of platforms and technologies.     

Development of a supported self-directed learning approach for anatomy education

The ability to deliver sufficient core anatomical knowledge and understanding to medical students with limited time and resources remains a major challenge for anatomy educators. Here, we report the results of switching from a primarily didactic method of teaching to supported self-directed learning for students studying anatomy as part of undergraduate medicine at the University of Edinburgh. The supported self-directed approach we have developed makes use of an integrated range of resources, including formal lectures and practical sessions (incorporating gross anatomy specimens, medical imaging technologies, anatomical models, clinical scenarios, and surface anatomy workstations). In practical sessions, students are provided with a custom-made workbook that guides them through each session, with academic staff, postgraduate tutors, and near-peer teaching assistants present to deal with misunderstandings and explain more complicated topics. This approach retains many of the best attributes of didactic teaching but blends them with the advantages associated with self-directed learning approaches. The switch to supported self-directed learning-initially introduced in 2005-resulted in a significant improvement in anatomy examination scores over the subsequent period of five years, manifesting as an increase in the average anatomy practical spot examination mark, less students failing to obtain the pass mark and more students passing with distinction. We conclude that the introduction of supported self-directed learning improved students' engagement, leading to deeper learning and better understanding and knowledge of anatomy.     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.