How to Do Things With Words

How to ... be a reflective practitioner.

Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.

A Survey of Clustering Data Mining Techniques

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

미국의 “전국 수학 교사 협의회”(National Council of Teachers of Mathematics, NCTM)는 1989년부터 〈학교 수학의 교육과정과 평가 규준〉(1989), 〈수학 가르침(교수)의 전문성 규준〉(1991), 〈학교 수학의 평가(시험) 규준〉(NCTM, 1995), 〈학교 수학의 원리와 규준〉(2000)을 출판하여 미국의 수학 교육 의 전망(목표, 나아갈 길)과 규준(실행 지침)을 제시하였다. 수학 교사들로 구성된 미국의 NCTM은 학생, 학부모, 학교 행정가 등 많은 사람들과 힘을 합하여 모든 학생들에게 수준 높은 수학 교육을 받을 수 있는 여건(환경, 기회)을 조성하는 데 구심점의 역할을 하였다. 한편 많은 관련 단체들은 여러 배경과 능력을 가진 학생들이 전문성을 지닌 교사(특수 교사를 일컫는 밀이 아니다. 수학 교과를 이해하고 수학의 전문성과 특수성을 가르칠 수 있는 일반 교사를 일컫는 말이다.)로부터 미래를 대비해 평등하고, 진취적이며, 지원이 잘 이루어지고, 공학 도구(IT)가 잘 갖춰진 환경에서 중요한 수학적 아이디어를 이해하면서 학습할 수 있는 수학 교실(미국에서는 우리나라처럼 수학 교사가 수학 시간에 학생의 방(교실: Homeroom)에 찾아가지 않고 학생들이 선생의 방(수학 교실: Classroom)을 찾아온다. 전형적인 수학 교실의 사진은 2쪽에 나와 있다.)을 만들기 위해 함께 힘썼다. NCTM에서 출간한 여러 규준들은 우리나라의 제6차와 제7차 교육과정에도 큰 영향을 미쳤다. 이 글에서는 NCTM(2000)에서 제시한 학습 원리를 간단히 살펴본 다음 이를 중심으로 현재 미국 수학 교육의 교수ㆍ학습 이론의 동향을 살펴본다.

미국 NCTM의 Principles and Standards for School Mathematics에 나타난 수학과 교수,학습의 이론

Economic pressures on universities and the emergence of new technologies have spurred the creation of new systems for delivering engineering laboratories in education, in particular simulations and remote-access laboratory systems. Advocates of simulation argue that physical labs needlessly consume university space and students' time. However, proponents of hands-on laboratories argue that student engineers should be exposed to real environments. Remote laboratories have appeared as a third option. These laboratories are similar to simulation techniques in that they require minimal space and time, because the experiments can be rapidly configured and run over the Internet. But unlike simulations, they provide real data. Studying the relative effectiveness of these modes of delivering student laboratories is complex, for the underlying technology of the laboratory is just one of many possible factors that could influence effectiveness. For example, the interface to the equipment may be of importance, as might the discussions students have among themselves. This paper presents a model for testing the relative effectiveness of engineering laboratories in education that takes account of these and other factors. The results are presented for an assessment study comparing versions of remote labs versus hands-on labs in a junior-level mechanical engineering course on machine dynamics and mechanisms. The results suggest that students learned lab content information equally well from both types of laboratories, and that they have a realistic understanding and appreciation of the practical advantages of remote laboratories.

A model for evaluating the effectiveness of remote engineering laboratories and simulations in education

Laboratories play a crucial role in the education of future scientists and engineers, yet there is disagreement among science and engineering educators about whether and which types of technology-enabled labs should be used. This debate could be advanced by large-scale randomized studies addressing the critical issue of whether remotely operated or simulation-based labs are as effective as the traditional hands-on lab format. The present article describes the results of a large-scale (N = 306) study comparing learning outcomes and student preferences for several different lab formats in an undergraduate engineering course. The lab formats that were evaluated included traditional hands-on labs, remotely operated labs, and simulations. Learning outcomes were assessed by a test of the specific concepts taught in each lab. These knowledge scores were as high or higher (depending on topic) after performing remote and simulated laboratories versus performing hands-on laboratories. In their responses to survey items, many students saw advantages to technology-enabled lab formats in terms of such attributes as convenience and reliability, but still expressed preference for hands-on labs. Also, differences in lab formats led to changes in group functions across the plan-experiment-analyze process: For example, students did less face-to-face work when engaged in remote or simulated laboratories, as opposed to hands-on laboratories.

/pdf/constructing-reality-a-study-of-remote-hands-on-and-4uc5q4fr3u.pdf

Constructing reality: A study of remote, hands-on, and simulated laboratories

The category utility hypothesis holds that categories are useful because they can be used to predict the features of instances and that the categories that tend to survive and become preferred in a culture (basic-level categories) are those that best improve the category users' ability to perform this function. Starting from this hypothesis, a quantitative measure of the utility of a category is derived. Application to the special case of substitutive attributes is described. The measure is used successfully to predict the basic level in applications to data from hierarchies of natural categories and from hierarchies of artificial categories used in category-learning experiments. The relationship of the measure to previously proposed indicators of the basic level is discussed, as is its relation to certain concepts from information theory. Categorization is one of the most basic cognitive functions. Why is the ability to categorize events or objects important to an organism? An obvious answer to this question is that categories are important because they often have functional significance for the organism. Another familiar answer is that grouping objects into categories allows for efficient storage of information about these groups of objects. One purpose of this article is to explore connections between these two answers regarding the utility of categories.

/pdf/explaining-basic-categories-feature-predictability-and-2krej340lc.pdf

Explaining Basic Categories: Feature Predictability and Information

Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories

Advocates of hands-on laboratories and advocates of simulation have debated for years. Proponents of hands-on laboratories argue that student engineers need to be exposed to the physical experiences-and the uncertainties-of real environments. Advocates of simulation argue that physical labs are wasteful-they tie up badly needed space, and consume student's time in menial set-up and tear-down procedures. Now remote laboratories have appeared as a third option. These laboratories are similar to simulation techniques in that they require minimal space and time, because the experiments can be rapidly configured and run over the Internet. But unlike simulations, they provide real data. It is unknown what the relative effectiveness of hands-on, simulated, and remote laboratories is. This paper presents a model for testing this relative effectiveness, and discusses the results of a preliminary assessment study comparing versions of remote labs versus hands-on labs in a junior-level mechanical engineering course on machine dynamics and mechanisms.

James E. Corter

Papers

A model for evaluating the effectiveness of remote engineering laboratories and simulations in education

Constructing reality: A study of remote, hands-on, and simulated laboratories

Explaining Basic Categories: Feature Predictability and Information

Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories

Remote versus hands-on labs: a comparative study