Shall the Student Rate the Professor?

PurposeThis paper explores the institutional and economic drivers of employability, as existing literature focuses on the individual and skills aspects, of employability. Tertiary institutions, possessing a strong academic reputation and standing amongst potential employers, will achieve high graduate employability, however when measured, this is not the case.Design/methodology/approachThis exploratory study builds on Santos' career boundary theory, recognising organisational boundaries; those related to the labour market, personal-aspects and finally, cultural boundaries (Santos, 2020). 37 Universities that provided their employability rate, within 12 months of graduation for 2020, are analysed. The Quacquarelli Symonds (QS) Ranking, measures drivers in terms of institutional reputation through survey responses, and partnerships with employers via research and placement data.FindingsThe regression explained 19% of the variation between the number of graduates being employed and the institutional and economic drivers. Universities in the same economic context, do not have the same number of employed students. Equally, those universities with the most favourable academic reputation, do not have the most employed student rate.Research limitations/implicationsOnly 37 universities provided all their employability data, thus, research with a larger sample will have to be conducted, but equally more needs to be done to establish why the smaller universities are unable to submit all the required data.Originality/valueAn exploratory understanding of the institutional and economic drivers of employability, is provided.

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2014 ASPA Teaching and Learning Conference

Political Science and Politics ◽

10.1017/s1049096513001881 ◽

2013 ◽

Vol 47 (01) ◽

pp. 237

Author(s):

Kimberly A. Mealy

Keyword(s):

Annual Meeting ◽

Graduate Student ◽

Teaching And Learning ◽

New Graduate ◽

Student Rate

APSA is looking forward to the 2014 APSA Teaching and Learning Conference (TLC). Registration opened in November 2013, including a new graduate student rate. The theme of the meeting is “Teaching Inclusively: Integrating Multiple Approaches into the Curriculum.” The meeting will be held on February 7–9, 2014 in Philadelphia. This will be the 11th annual meeting of the teaching conference.

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Shall the Student Rate the Professor?

The Journal of Higher Education ◽

10.1080/00221546.1940.11773040 ◽

1940 ◽

Vol 11 (3) ◽

pp. 146-154

Author(s):

Lily Detchen

Keyword(s):

Student Rate

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Lost Student Tracking in an Incomplete and Imprecise Information Environment Using Soft Computing Paradigm

International Journal of Artificial Life Research ◽

10.4018/ijalr.2012100103 ◽

2012 ◽

Vol 3 (4) ◽

pp. 32-48

Author(s):

Satya Ranjan Dash ◽

Susil Rayaguru ◽

Satchidananda Dehuri ◽

Sung-Bae Cho

Keyword(s):

Knowledge Base ◽

Soft Computing ◽

Partial Information ◽

Information Environment ◽

Attractive Alternative ◽

Symbolic Model ◽

Computing Model ◽

Computing Paradigm ◽

Student Rate ◽

Student Information

In a country like India, the growth rate of the number of academic institutions is at par with the lost student rate. Hence when a lost student is found we need to identify the student on the basis of information such as name of the student, institution name where he studies, class or branch of the student, etc. But the fact is that in most of the cases one never gets complete and precise information to identify a lost student. Hence, in such environment a soft computing model can be an attractive alternative to identify a lost student on the basis of imprecise or partial information. This paper presents a soft computing model for identifying lost student on the basis of imprecise and partial information. In this model student information is represented as a symbolic student object. Symbolic student object is further processed using a fuzzy symbolic model for identifying the lost student. The authors have devised a symbolic knowledge base which acts as a repository of information pertaining to student of different institutions that assist in creating student object and identifying the lost student. A fuzzy technique “symbolic similarity measure” is devised for generating symbolic student object and mapping the symbolic student object with student information present in knowledge base. This system has been tested scrupulously and an efficiency of above 90% has been achieved in identifying the lost student.

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