Name
Abstract | ||
|---|---|---|
This article contains data from a group of users, divided into subgroups according to their levels of knowledge about technology. Statistical hierarchical and non-hierarchical clustering methods were studied, compared and used in the creations of the subgroups from the similarities of the skill levels with these users' technology. The research sample consists of teachers who answered online questionnaires about their skills in the use of software and hardware with an educational bias. The statistical methods of the grouping were performed and showed the possibilities of groupings of the users. The analysis of these groups allowed the identification of the common characteristics among the individuals of each subgroup. Therefore, it was possible to define two subgroups of users, one with skills in technology and another without skills in technology. The partial results of the research showed two main algorithms for grouping with 92% similarity from groups of users with skills in technology and the other with little skill, confirming the accuracy of the techniques discriminating against individuals. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.4018/IJTHI.2019040104 | INTERNATIONAL JOURNAL OF TECHNOLOGY AND HUMAN INTERACTION |
Keywords | Field | DocType |
Similarity of Profiles, Skills With Technology, Statistical Methods, Teacher Profiles, User Grouping | Data science,Computer science,Knowledge management | Journal |
Volume | Issue | ISSN |
15 | 2 | 1548-3908 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Kelsen de Oliveira | 1 | 0 | 1.01 |
| Max Brandão de Oliveira | 2 | 0 | 0.34 |
| Alex Sandro Gomes | 3 | 65 | 12.82 |
| Leandro Marques Queiros | 4 | 0 | 0.34 |