No Arabic abstract
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.
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).
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.
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 suggest one redefinition of common clusters of questions used to analyze student responses on the Force and Motion Conceptual Evaluation (FMCE). Our goal is to move beyond the expert/novice analysis of student learning based on pre-/post-testing and the correctness of responses (either on the overall test or on clusters of questions defined solely by content). We use a resources framework, taking special note of the contextual and representational dependence of questions with seemingly similar physics content. We analyze clusters in ways that allow the most common incorrect answers to give as much, or more, information as the correctness of responses in that cluster. Furthermore, we show that false positives can be found, especially on questions dealing with Newtons Third Law.
In this work, we analyse all existing data related to the number of incomers and outcomers (who actually obtain the degree) of the following courses offered at the Federal University of Santa Catarina: physics teaching, bachelor in physics, master of sciences in physics and doctorate in physics, corresponding to the 1998-2017 period, according to their availability. The data point towards a great male predominance (larger than 76%) and a huge evasion of both sexes (in average, less than 20% of the undergraduate incomers obtain the degree), the evasion being lower in the postgraduate courses and always slightly higher for women. The average number of incomers and outcomers per year decreases as the students advance from graduate to postgraduate courses, although many students in the postgraduate courses come from other institutions. The proportion of women decreases as the carrier advances. The results indicate the need of complementary studies that can help the identification of the causes of such a high evasion in order to minimize them.