Exploring the Potentialities of Automatic Extraction of University Webometric Information
, , , , oraz | 21 lis 2020
O artykule
Article Category: Research Paper
Data publikacji: 21 lis 2020
Zakres stron: 43 - 55
Otrzymano: 20 lip 2020
Przyjęty: 09 lis 2020
DOI: https://doi.org/10.2478/jdis-2020-0040
Słowa kluczowe
© 2020 Gianpiero Bianchi et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1
Figure 2
Clusters obtained for Italian universities.
| Cluster1 (blue) | unito,unimi, polimi, unipd, unibo, unifi,unipi, uniroma1, unina |
| Cluster2 (red) | unisg, hunimed, iusspavia, unibz, sissa, imtlucca, sssup |
| Cluster3 (green) | univda,liuc,uninsubria,iulm,unisr,uniurb,unicam,unitus,lumsa,uniroma4,unicampus,unint. eu,unieurm,unilink,unicas,unisannio,uniparth,unisob.na,univaq,unite,unich,unimol,unifg,lum,unisale,unibas,unicz,unirc,unime,unikore,uniss |
| Cluster4 (yellow) | polito,unige,unibocconi,unicatt,unimib,unibg,unibs,unipv,unitn,univr,unive,iuav,uniud,uni ts,unipr,unimore,unife,univpm,unimc,sns,unisi,unipg,uniroma2,luiss,uniroma3,unisa,poliba,uniba,unical,unipa,unict,unica |
Selected indicators to be used for profiling Italian universities’ websites.
| No. | Indicator name | Source | Description | Rationale |
|---|---|---|---|---|
| 1. | Access to research | Scraping and mining | Number of research articles / log(number of professor) | Measures the ability to provide access to publications produced by the university, slightly normalized by the size of the institution |
| 2. | Access to content | Scraping and mining | Number of pdf, ppt, doc, rtf, ps / log(number of professor) | Measures the ability to provide consents, slightly normalized by the size of the institution |
| 3. | Orientation to external collaborations | Scraping and mining | Number of research institutions (IT+EU) and research-oriented companies mentioned in the website/log(number of professor) | Measures the ability of the website to provide a comprehensive description of the extent of on-going research (or Third-mission) collaborations, slightly normalized by the size of the institution |
| 4. | Access to information on teaching | Scraping and mining | Percentage of teachers providing their emails | Measures the possibility to easily get in touch with professors |
| 5. | Visibility | Search engine | Number of HTML pages pointing to the university website from external domains / log(number of professor) | Measures how the university website is visible from outside, slightly normalized by the size of the institution |
| 6. | Usability | Analytics | Percentage of contacts from mobile devices | Level of use by the mobile-oriented audience (largely including students). |
| 7. | Relevance | Analytics | (1/national ranking by visitors) / log(number of students) | Websites’ popularity at the national level, slightly normalized by the size of the institution |
| 8. | Intensity of use | Analytics | average time spent on the website / number of pages visited | A key indicator of website effectiveness: the more time is spent on the website, the more relevant will be the available contents |
| 9. | International orientation | Analytics | Percentage of foreign contacts | Popularity abroad as a condition to attract customers (incl. students) and partners |
| 10. | Direct access | Analytics | Percentage of direct accesses | Percentage of non-casual visitors as an indicator of popularity and ability to connect to a population of regular users |