Where and how 3D printing is used in teaching and education

The emergence of additive manufacturing and 3D printing technologies is introducing industrial skills deficits and opportunities for new teaching practices in a range of subjects and educational settings. In response, research investigating these practices is emerging across a wide range of education disciplines, but often without reference to studies in other disciplines. Responding to this problem, this article synthesizes these dispersed bodies of research to provide a state‐of‐the‐art literature review of where and how 3D printing is being used in the education system. Through investigating the application of 3D printing in schools, universities, libraries and special education settings, six use categories are identified and described: (1) to teach students about 3D printing; (2) to teach educators about 3D printing; (3) as a support technology during teaching; (4) to produce artefacts that aid learning; (5) to create assistive technologies; and (6) to support outreach activities. Although evidence can be found of 3D printing‐based teaching practices in each of these six categories, implementation remains immature, and recommendations are made for future research and education policy.This work was supported by the Engineering and Physical Sciences Research Council [number EP/K039598/1]

Exploring Which Agile Principles Students Internalize When Using a Kanban Process Methodology

This paper reports on a case study of the Agile Kanban project methodology, which while growing in popularity, has had far less analysis on its usefulness in the classroom as compared to other frameworks such as Agile Scrum. Our study provides insight into why the Kanban methodology is useful by mapping student comments about the methodology to the twelve principles laid down in the Agile Manifesto. Our analysis identified two key agile principles that help to explain the value of Kanban. Specifically, we found that the students focused on self-organizing teams and reflection at regular intervals, and that these two principles led to improved team communication and coordination. Our findings are useful for those looking to use or define a process management methodology for student teams as well as others exploring the more general challenge of incorporating agile into the classroom

Implications for automation assistance in unmanned aerial system operator training

2012 Summer.Includes bibliographical references.The integration of automated modules into unmanned systems control has had a positive impact on operational effectiveness across a variety of challenging domains from battlefields and disaster areas to the National Airspace and distant planets. Despite the generally positive nature of such technological progress, however, concerns for complacency and other automation-induced detriments have been established in a growing body of empirical literature derived from both laboratory research and operational reviews. Given the military's demand for new Unmanned Aerial System (UAS) operators, there is a need to explore how such concerns might extend from the operational realm of experienced professionals into the novice training environment. An experiment was conducted to investigate the influence of automation on training efficiency using a Predator UAS simulator developed by the Air Force Research Laboratory (AFRL) in a modified replication of previous research. Participants were trained in a series of basic maneuvers, with half receiving automated support only on a subset of maneuvers. A subsequent novel landing test showed poorer performance for the group that received assistance from automation during training. Implications of these findings are discussed

The Impact of AI on Teaching and Learning in Higher Education Technology

Thanks to AI, students may now study whenever and wherever they like. Personalized feedback on assignments, quizzes, and other assessments can be generated using AI algorithms and utilised as a teaching tool to help students succeed. This study examined the impact of artificial intelligence in higher education teaching and learning. This study focuses on the impact of new technologies on student learning and educational institutions. With the rapid adoption of new technologies in higher education, as well as recent technological advancements, it is possible to forecast the future of higher education in a world where artificial intelligence is ubiquitous. Administration, student support, teaching, and learning can all benefit from the use of these technologies; we identify some challenges that higher education institutions and students may face, and we consider potential research directions

Improving Introductory Computer Science Education with DRaCO

Today, many introductory computer science courses rely heavily on a specific programming language to convey fundamental programming concepts. For beginning students, the cognitive capacity required to operate with the syntactic forms of this language may overwhelm their ability to formulate a solution to a program. We recognize that the introductory computer science courses can be more effective if they convey fundamental concepts without requiring the students to focus on the syntax of a programming language. To achieve this, we propose a new teaching method based on the Design Recipe and Code Outlining (DRaCO) processes. Our new pedagogy capitalizes on the algorithmic intuitions of novice students and provides a tool for students to externalize their intuitions using techniques they are already familiar with, rather than with the syntax of a specific programming language. We validate the effectiveness of our new pedagogy by integrating it into an existing CS1 course at California Polytechnic State University, San Luis Obispo. We find that the our newly proposed pedagogy shows strong potential to improve students’ ability to program

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

Personalizing education with algorithmic course selection

The work presented in this thesis utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, topics or modules) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The original research contribution of this thesis is an algorithm that can generate a schedule of courses with consideration of a student\u27s profile, minimization of cost, and complete adherence to institution requirements. The research problem at hand - a constrained optimization problem with potentially conflicting objectives - is solved by first identifying a minimal sets of courses a student can take to graduate and then intelligently placing the selected courses into available semesters. The distinction between the proposed approach and related studies is in its simultaneous achievement of the following: guaranteed fulfillment of curricular requirements; applicability to both traditional and non-traditional curricula; and flexibility in nomenclature - semantics are extracted from syntax to allow the identification of relevant content, despite differences in course or topic titles from one institution to the next. The course selection algorithm presented is developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with the assurance that recommended selections are always valid. With this algorithm, PERCEPOLIS can recommend the entire trajectory that a student could take to graduation, as opposed to just the next semester, and it does so with consideration of course or topic availability --Abstract, page iii