The notion of what constitutes a ‘quality’ university has been challenged by the 2014 Gallup-Purdue Survey (Great Jobs, Great Lives: The 2014 Gallup-Purdue Index Report, Gallup, Inc., 2014). This survey of 30,000 US university alumni revealed that engagement and feelings of well-being beyond the university and into the workplace have little to do with the prestige of the university and much to do with having caring professors and being afforded opportunities for experiential learning. The Survey has shifted the focus from what university professors value to what students value. Assuming universities are interested in what students think, the issue then becomes one of assessing ‘value added’, and this paper examines one university’s approach to addressing this issue.
Selection of Student Achievement is conducted every year, starting from the level of Study Program, Faculty, to University, which then rank one will be sent to Kopertis level. The criteria made for the selection are Academic and Rich Scientific, Organizational, Personality, and English. In order for the selection of Student Achievement is Objective, then in addition to the presence of the jury is expected to use methods that support the decision to be more optimal in determining the Student Achievement. One method used is the Promethee Method. Preference Ranking Organization Method for Enrichment Evaluation (Promethee) is a method of ranking in Multi Criteria Decision Making (MCDM). PROMETHEE has the advantage that there is a preference type against the criteria that can take into account alternatives with other alternatives on the same criteria. The conjecture of alternate dominance over a criterion used in PROMETHEE is the use of values in the relationships between alternative ranking values. Based on the calculation result, from 7 applicants between Manual and Promethee Matrices, rank 1, 2, and 3, did not change, only 4 to 7 positions were changed. However, after the sensitivity test, almost all criteria experience a high level of sensitivity. Although it does not affect the students who will be sent to the next level, but can bring psychological impact on prospective student’s achievement.
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.