@article{MAKHILLJMC202115225012,
    title = {Remote Emotional Quotient Assessment of UTME Candidates as Alternative to PUTME
Screening in Nigerian Institutions of Higher Learning},
    journal = {Journal of Mobile Communication},
    volume = {15},
    number = {2},
    pages = {20-25},
    year = {2021},
    issn = {1990-794x},
    doi = {jmcomm.2021.20.25},
    url = {https://makhillpublications.co/view-article.php?issn=1990-794x&doi=jmcomm.2021.20.25},
    author = {Olebara},
    keywords = {Classification tree,likert scale analysis,eq models,assessment putme,utme,jamb,university,admission,emotional quotient and intelligent quotient},
    abstract = {This study proposes test of Emotional Quotient
(EQ) of UTME candidates by Universities they have
applied to, as alternative to duplicate Intelligent Quotient
test (Post UTME) presently practiced. The proposed
assessment system was tested by designing a
questionnaire which asks EQ questions and uses Student’s
response on whether they gained Merit or Supplementary
admission and tagged academic ability. EQ ordinal
questions were summed and transformed into categorical
questions that is used to derive interaction with
independent variables. Performance of EQ as Dependent
while demographic information was used as Independent
variable was derived using classification tree. Similarly,
association with Student’s admission type as dependent
variable while EQ items and demographic data is
independent variable was also derived. The result of
Classification Tree with CHAID algorithm returns Study
Type as variable with highest priority to dependent
variable, with p-value of 0.000 and Chi-Square of 26.203.
Age and EQ item were also shown to have statistical
significance with Age having a p-value of 0.000 and Chi
Square of 27.251. Older participants have higher Merit
admission than Supplementary admission. EQ item with
ability characteristic was captured in node 3 alongside
age. Participants who Agree to this item have higher
Merit admission than those who do not. Alternative
Hypothesis is accepted and Null hypothesis rejected.
Performance prediction using Admission Type returned
overall 76.5% accuracy while prediction using EQ and
dependent variable while other demographics were input
ad independent variables returned 68.8% performance
accuracy. Also, the choice of Mixed Model for Testing
EQ seems to be ideal since performance of both types
(68.8% for Self-Report and 75.5% for Ability) and not too
far apart. The researcher therefore recommends the use of
Previous works (Results) as well as Remote Emotional
Assessment System (REAS) as alternative to PUTME.}
    }