Times Higher Education is developing plans for a pioneering ranking focused on universities’ impact on society, to be launched at THE’s Innovation and Impact Summit in South Korea in April 2019.
Confirming that the 2019 Innovation and Impact Summit will be held in partnership with one of the world’s leading research and technology universities, Korea Advanced Institute of Science and Technology (KAIST), Phil Baty, THE’s editorial director for global rankings, also announced that THE has begun to collect new data on university-business interactions, with plans to consult on a range of new performance metrics for a ranking to be launched and debated at the summit.
“Times Higher Education is delighted to be building on its 15 years of experience in global rankings and data analysis to develop, in full partnership with the global university community, new performance indicators in this exciting emerging area. After exploring some of our ideas at our inaugural Innovation and Impact Summit in 2017 with the Hong Kong Polytechnic University, there is no better place to scrutinise and explore the resulting datasets and analyses than at one of the world’s most high-impact institutions, KAIST, in a country that completely transformed its economy in a matter of decades through research and innovation.”
Currently, THE collects data on the research income universities attract from business and industry, which forms one of 13 performance indicators in its World University Rankings. But a new pilot data collection exercise has been initiated in parallel to data collection for the World University Rankings, covering:
Income from business consultancy
Turnover of all active spin-off activities
Number of active spin-off companies (active for at least three years) – broken down by those with some institutional ownership and those not owned by the institution.
The data could be combined with a range of existing datasets, for example on university-industry co-authored research publications, and patent and licensing data, to form a final ranking.
Using correlational analyses and multiple regressions, this study uses U.S. News & World Report’s (USNWR) 2016 college rankings data and data from the National Center for Education Statistics’ (NCES) Integrated Postsecondary Education Data System (IPEDS) to examine variables that explain institutional peer assessment score and rank. This study focused on the 97 institutions included in USNWR’s 2016 Best Regional Universities (South) ranking list.
Analyses in this study addressed four major foci: 1) correlations between USNWR subfactor data values and selected IPEDS proxies, 2) IPEDS variables that explained variance in peer assessment score, 3) IPEDS variables that explained variance in rank, and 4) the extent to which rank could be predicted based on these results.
The results of this study indicated three main findings. First, USNWR subfactors with direct or indirect IPEDS proxies were highly correlated with the identified proxies. Second, more than 85% of variation in peer assessment score could be explained by five or fewer proxy variables, which differ dependent upon institution sector (private or public). Third, more than 85% of variation in institutional ranking could be explained by five proxy variables and without the inclusion of the peer assessment score subfactor. Collectively, findings suggest USNWR rankings are no more than a reflection of institutional outcomes and financial resources.
Global universities are subject to the academic ranking every year. One of the common ranking types that are applied annually is called the Academic Ranking of World Universities (ARWU). It developed by a team of researchers and experts. The ARWU is composed of a set of common criteria related to academic tasks and it does not include any indication or factor relevant to the recent technology, such as the websites of universities. Actually, there is a lack to find out the relationship between universities’ global ranking and their website features. Therefore, this research aimed at updating the current ranking model by adding a new criterion reflexing the websites’ features related to its contents and structure. This research focuses on universities as two classes ranked and unranked. This process includes extract, analyze websites’ datasets, visualize the initial results, study the relationship and the significant differences between the two classes if found, and modify the ARWU by updating the criteria list & their weights. A special S/W tool applied to analyze websites and to extract the required data. This research contributes to modify and enhance the ARWU model to be more comprehensive than the current one. The involvement of universities’ websites in the ranking process will encourage universities to improve their websites to achieve a higher-ranking level amongst leading universities. Furthermore, it gives a good chance for all universities to participate in the global ranking competition, especially the universities that have excellent outcomes and perfect websites.