A New Approach to Investigate Students\u2019 Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

The problem of taking a set of data and separating it into subgroups where the ele- ments of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we de- scribe and criticize the variables and parameters used to show the results of the clus- ter analysis methods

A quantitative method to analyse an open-ended questionnaire: A case study about the Boltzmann Factor

This paper describes a quantitative method to analyse an openended questionnaire. Student responses to a specially designed written questionnaire are quantitatively analysed by not hierarchical clustering called k-means method. Through this we can characterise behaviour students with respect their expertise to formulate explanations for phenomena or processes and/or use a given model in the different context. The physics topic is about the Boltzmann Factor, which allows the students to have a unifying view of different phenomena in different contexts

Czech machinery cluster and its role in sustainable development of Moravian-Silesian enterprises during the post-transformation era

The paper intends to contribute to the field of geographical economics by an extensive questionnaire survey carried out in Moravian-Silesian region, which represents one of territories of traditional industry in the Czech Republic. The purpose of this paper is to analyse and assess the co-operation among enterprises, educational institutions, and public administration from the perspective of sustainability in the Moravian-Silesian region during its post-transformation era. The article deals specifically with the Czech machinery cluster. The research question that lies behind the survey is as follows: Is the co-operation of entities present in the Czech machinery cluster beneficial to the parties involved? The contribution of the paper is in uncovering the role of this cluster in the sustainable development of Moravian-Silesian enterprises during post-transformation period. Since the Moravian-Silesian region is a typical old industrial region, which moreover underwent a difficult transformation process, there are numerous peculiarities in functioning of its enterprises. Machinery was traditionally one of the supportive pillars of regional industry and it is not surprising that the machinery cluster was created as the first one. Yet, regional characteristics lie behind specific trajectories towards economic sustainability. The above ways toward economic sustainability differ markedly from the concepts that are in vogue in developed western territories.Web of Science102art. no. 23

K-means Clustering to Study How Student Reasoning Lines Can Be Modified by a Learning Activity Based on Feynman\u2019s Unifying Approach

Research in Science Education has shown that often students need to learn how to identify differences and similarities between descriptive and explicative models. The development and use of explicative skills in the field of thermal science has always been a difficult objective to reach. A way to develop analogical reasoning is to use in Science Education unifying conceptual frameworks. In this paper we describe a 20-hour workshop focused on Feynman\u2019s Unifying Approach and the two-level system. We measure its efficacy in helping undergraduate chemical engineering students explain phenomena by applying an explanatory model. Contexts involve systems for which a process is activated by thermally overcoming a well-defined potential barrier. A questionnaire containing six open-ended questions was administered to the students before instruction. A second one, similar but focused on different physical content was administered after instruction. Responses were analysed using k-means Cluster Analysis and students\u2019 inferred lines of reasoning about the analysed phenomena were studied. We conclude that students reasoning lines seem to have clearly evolved to explicative ones and it is reasonable to think that the Feynman Unifying Approach has favoured this change

Are Virtual Learning Environments used to facilitate collaborative student learning activity? Findings of an institutional evaluation

Virtual Learning Environments (VLEs) are used extensively within higher education, primarily as an educational tool, but can also have additional functionality. There has been considerable debate, both internal to the university and in the external academic community, about the value of a VLE, e.g. MacLaren (2004), Sharp et al. (2005) and Conole and de Laat (2006). The focus of this debate is whether or not a VLE is primarily used as a transmissive tool, in which the teacher determines VLE content and communication and which tends to be teacher initiated while the student adopts a passive role (Jonassen & Land, 2000). Whilst a transmissive approach may be an important element in students’ learning experiences, there is little evidence to suggest such usage facilitates deep learning

Performance Evaluation of Quadratic Probing and Random Probing Algorithms in modeling Hashing Technique

In hashing technique, a hash table and hash map represent a data structure for a group of objects to map between key and value pairs, as the hash table is affected by collision and overflow. The hash table collision and overflow can be handled by searching the hash table in some systematic fashion for a bucket that is not full. In open addressing, quadratic and random probing are well-known probe sequence algorithms for collision and overflow resolution. Key density, loading density, loading factor, collisions, overflows, keys clustering, space complexity, and time complexity are the main factors that highly affect the two algorithms during hash table systematic probing. Therefore, this project is conducted to compare the quadratic probing and random probing challenge performance in terms of the key density, loading density, loading factor, overflows, collisions, keys clustering, space complexity, time complexity using step count, the order of magnitude, the worst case, the average case, and the best case. Comparing both algorithms was performed by collecting data from an online survey about the English language proficiency of 104 students. The compression result shows that the random probing algorithm has achieved similar performance compared to quadratic probing in terms of key density, loading density, loading factor, space complexity, order of magnitude, worst case, and average and best case. While the quadratic probing algorithm has recorded less time complexity using the step count method compared to the random probing algorithm. On the other hand, the random probing algorithm has recorded fewer overflows, collisions, and key clustering compared to quadratic probing. However, the study has recommended the quadratic probing algorithm for better time complexity performance and the random probing algorithm for better performance resolving overflows, collisions, and key clustering