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Register a new userPredicting time to graduation at a large enrollment American university
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The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tintos Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).
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Most STEM students experience the introductory physics sequence in large-enrollment (N $gtrsim$ 100 students) classrooms, led by one lecturer and supported by a few teaching assistants. This work describes methods and principles we used to create an effective flipped classroom in large- enrollment introductory physics courses by replacing a majority of traditional lecture time with in-class student-driven activity worksheets. In this work, we compare student learning in courses taught by the authors with the flipped classroom pedagogy versus a more traditional pedagogy. By comparing identical questions on exams, we find significant learning gains for students in the student-centered flipped classroom compared to students in the lecturer-centered traditional classroom. Furthermore, we find that the gender gap typically seen in the introductory physics sequence is significantly reduced in the flipped classroom.
Research is described on a system for web-assisted education and how it is used to deliver on-line drill questions, automatically suited to individual students. The system can store and display all of the various pieces of information used in a class-room (slides, examples, handouts, drill items) and give individualized drills to participating students. The system is built on the basic theme that it is for learning rather than evaluation. Experimental results shown here imply that both the item database and the item allocation methods are important and examples are given on how these need to be tuned for each course. Different item allocation methods are discussed and a method is proposed for comparing several such schemes. It is shown that students improve their knowledge while using the system. Classical statistical models which do not include learning, but are designed for mere evaluation, are therefore not applicable. A corollary of the openness and emphasis on learning is that the student is permitted to continue requesting drill items until the system reports a grade which is satisfactory to the student. An obvious resulting challenge is how such a grade should be computed so as to reflect actual knowledge at the time of computation, entice the student to continue and simultaneously be a clear indication for the student. To name a few methods, a grade can in principle be computed based on all available answers on a topic, on the last few answers or on answers up to a given number of attempts, but all of these have obvious problems.
This paper describes some of the results of a National Science Foundation Nanotechnology Undergraduate Education project that aims to establish a nanoscience and nanotechnology program at the University of North Dakota. The goal is to generate new interest in nanoscience and nanotechnology among engineering and science students and prepare them with the knowledge and skills necessary for the next generation of graduates to compete in the global market and contribute to the nanoscience and nanotechnology field. The project explored several aspects of student learning, including students motivations for investigating nanotechnology through interdisciplinary coursework. To collect this information, a survey was administered to students who enrolled to two nanoscience and nanotechnology courses. Data collected from the survey will be used to improve the design and delivery of future courses as part of constructing a complete nanoscience and nanotechnology curriculum.
We detail an experimental programme we have been testing in our university. Our Advanced Hackspace, attempts to give all members of the university, from students to technicians, free access to the means to develop their own interdisciplinary research ideas, with resources including access to specialized fellows and biological and chemical hacklabs. We assess the aspects of our programme that led to our community being one of the largest collectives in our university and critically examine the successes and failures of our trial programmes. We supply metrics for assessing progress and outline challenges. We conclude with future directions that advance interdisciplinary research empowerment for all university members.
In this study, we explored the extent to which problems and instructional strategies affect social cohesion and interactions for information seeking in physics classrooms. Three sections of a mechanics physics course taught at a Chilean University in Coquimbo were investigated. Each section had a weekly problem-solving session using different sets of well and/or ill-structured problems (i.e., algebra-based and open-ended problems respectively), as well as instructional strategies for guiding the problem-solving sessions. Data was collected on networks of information seeking and perceptions of good physics students, during a problem-solving session. We used social network analysis (SNA) for constructing variables while conducting the study. Results suggest that the teaching and learning strategies to guide problem-solving of well and ill-structured problems yield different levels of social interaction among classmates, and significant levels of activity in seeking out information for learning and problem-solving. While strategies for guiding problem-solving lend to significant differences for network connectivity, well and ill-structured physics problems predict similar levels of social activity.
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