What Makes Learning Analytics Research Matter

The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.

Remote Drilling Solution Delivers Consistency with the Use of an Automated Drilling Director: Case Studies from Kuwait

The Automated Drilling Director, a software application for drilling automation, integrates a physics-based model of the drilling system with machine learning and optimization algorithms to project the well path, monitor collision risk, manage vibrations, and control steering in real time automatically. With "intelligent" rotary steerable systems (RSSs), these steering decisions can be downlinked directly to the tool, thus, fully closing the loop around steering decision-making. Implementation of the Automated Drilling Director within a remote drilling center (RDC) enables the drilling operations to be conducted remotely and effectively with less rig site personnel. The resulting decisions are consistent and reliable, while a team of subject matter experts (SMEs) monitor the operations to optimize well assets, ensuring that the pre-job design of service (DoS) is executed properly. The validation of this innovative technology and approach in Kuwait, amongst others, opens the door to a new way of doing business, where resources, experience, and data are combined in the most efficient manner to improve consistency, as well as to maximize the value of the operators’ assets.

Closing the Loop - Recycling Plastic Waste

<p>New Zealand is ranked among the top nations in waste production, including a million tonnes of plastic waste. Currently, there are methods for recycling plastic within New Zealand but these methods can be expensive and time-consuming, resulting in most of the plastic being thrown into the landfill. Because plastic does not fully degrade, it ends up in the ocean and other waterways, poisoning the water with toxins. The purpose of this research is to provide a solution to reducing plastic waste by creating an alternative method of recycling that utilises new technologies such as additive manufacturing, to create a building material that fits into the concept of the circular economy. The findings of this research explored the recycling of plastic by collecting plastic waste such as PLA (Polylactic Acid) from old 3D printed models. The plastic was recycled into filament for additive manufacturing (AM) and used to print building tile, establishing an initial proof of concept for the use of recycled plastic as a potential building material.</p>

Closing the Loop - Recycling Plastic Waste

<p>New Zealand is ranked among the top nations in waste production, including a million tonnes of plastic waste. Currently, there are methods for recycling plastic within New Zealand but these methods can be expensive and time-consuming, resulting in most of the plastic being thrown into the landfill. Because plastic does not fully degrade, it ends up in the ocean and other waterways, poisoning the water with toxins. The purpose of this research is to provide a solution to reducing plastic waste by creating an alternative method of recycling that utilises new technologies such as additive manufacturing, to create a building material that fits into the concept of the circular economy. The findings of this research explored the recycling of plastic by collecting plastic waste such as PLA (Polylactic Acid) from old 3D printed models. The plastic was recycled into filament for additive manufacturing (AM) and used to print building tile, establishing an initial proof of concept for the use of recycled plastic as a potential building material.</p>