Evanthia Faliagka Maria Rigou Spiros Sirmakessis Qualities of good teachers Teachers are distinguished as liked or disliked based on three criteria academic qualifications relationship with students and personality traits ID: 575739
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Slide1
Identifying great teachers through their online presence
Evanthia Faliagka, Maria Rigou, Spiros SirmakessisSlide2
Qualities of good teachers
Teachers are distinguished as liked or disliked based on three criteria: academic qualifications, relationship with students and personality traitsTraits that yield positive educational resultsConscientiousness (efficient, not lazy, thorough)Agreeableness (warm, forgiving, sympathetic)
Openness
to experience
(curious, imaginative, excitable)
Extroverted
(
sociable
, enthusiastic, forceful,
positive
)
Emotionally stable (calm, self-confident, not shy)
Big-Five personality model (NEO PI-R)Slide3
Previous work
Academic qualifications of teachers come from:CVs and accompanying documentsLinkedInTeacher personality is typically evaluated through special purpose questionnaires/testsInterviewsRecruiting teachers could be performed online provided that we can have some unbiased feedback on teacher personalitySocial web data (from
Facebook
, Twitter, LinkedIn, etc.) can be the source of such feedback especially in the case of
active users
if we can interpret
social web activities
in terms of
personality traits
demonstrated. Slide4
Proposed system
A system that automates candidate teacher pre-screening process providing an overall candidate rankingbased on supervised learningand automatically extracting applicant
personality
measures
from tweets
and
Fb
posts
This approach has been implemented as a web based teacher evaluation systemSlide5
Architecture
Candidate
teachers
School
director
CV data
Login to
Login to
Position
requirements
Extracting candidate’s skills
Calculating personality traits
Data mining algorithms
Ranked list
of teachers
Applicant education, work experience and
loyalty are
directly extracted from LinkedIn
Personality traits of the candidate are assessed by analyzing posts to Twitter and
FbSlide6
Academic qualifications
Education (in years of formal academic training)Work experience (in years of working at relevant job positionsLoyalty (average number of years spent per job)Slide7
Personality mining
Judging a teacher’s personality (or ANY personality) is a hard problem for automated e-recruitment systems
We
focus on the extroversion personality trait
It is reflected through language use in written speech
It is discriminated through text analysis
It is
a crucial
characteristic in teacher
personality
The
emotional positivity and social orientation of
a person,
both directly extracted from
LIWC frequencies
, can act as predictors of
the extroversion trait
Linguistic Inquiry and Word Count systemSlide8
Calculation of teacher extroversion
To find which words are mentioned most frequently by the candidate we analyze the raw text of tweets and Facebook
posts
The words identified are input to the
TreeTagger
tool for lexical analysis and lemmatization
Then using the
LIWC
dictionary the system classifies the canonical form of word output by the
TreeTagger
A dictionary of word stems classified in certain psycholinguistic categories
We
calculate the LIWC
extroversion
score E
E is estimated directly
from LIWC scores, by summing the emotional positivity
score and the social orientation scoreSlide9
Calculation of teacher extroversion
Finally, we use the regression model which was trained in a previous work of ours that predicts the candidates’ extroversion from their LIWC scores in the {posemo, negemo, social} categories
E
=
S
+1.335*
P
- 2.25*
N
Where:
E is the
extroversion score
S
the frequency of social
words
P
the frequency of positive emotion works N the frequency of negative emotion wordsSlide10
Login pageSlide11
Experience miningSlide12
Ranking
Our system uses machine learning algorithmsIt requires a training set as an inputIt automatically builds the ranking model It calculates the final scoring function h(x)It returns the final ranked list of teachers by applying the learned function to sort themSlide13
Learning-to-rankSlide14
Pilot Scenario
42 teachers logged in to our system as candidates for a job position in a private elementary schoolThe job position was also announced through the systemTeachers were also evaluated manually on their academic qualifications and interviewed for assessing their personality by the school directorAutomated extroversion scores were compared to the interview results referring to each teacher’s extroversion
The data collected
are to be
used as the training set for the ranking algorithmSlide15
Pilot Scenario
Grading scale for the personality extroversion score: 0-5We used Weka to test the correlation of the scores output from the system (i.e. model predictions) with the actual scores assigned by the directorComparison metrics
(
system
vs
director):
Overlap size of the top-k list
Correlation coefficient of the top-k
candidates
Mean absolute
difference
of top-k candidates’ ranks
k=8
Top-k
Correlation
Ranking
error
Candidates
6 (
75%
)
0.72
2,6Slide16
Conclusions
The proposed system could be of practical value in speeding up the teacher recruitment process Automating qualifications and personality assessmentFuture work:Use larger training sets Instead of manual character assessment
, use special
questionnaires
and train the system with their results
Use additional
social network metrics
(LinkedIn endorsements and recommendations, no of re-tweets,
Fb
likes and shares, etc)
Incorporate
automated assessment of
teacher scores
in
more personality traits
(agreeableness, openness
to experience, etc) Slide17
Thank you!