Download A Competency Framework Model to Assess Success

Journal of Scientific & Industrial Research
Vol. 75, November 2016, pp. 662-666
A Competency Framework Model to Assess Success Pattern
For Indian Faculties A NLP Based Data Mining Approach
R K Banu1* and R Ravanan2
*1
Sathyabama University, Chennai,Tamil Nadu, India,
Département of Statistics, Presidency College, Tamil Nadu, India,
2
Received 19 January 2016; revised 08 July 2016; accepted 20 September 2016
Faculties who help us grow as people are responsible for imparting some of life’s most important lessons. We learn
through them, through their commitment to excellence and through their ability to make us realize our own personal growth.
The researchers look at the effectiveness by number of ways of assessing faculties. In our research work we analyzed and
assessed the success pattern of college faculties based on Neuro-Linguistic Programming (NLP), a branch of Behavioral
Psychology of the modern day. Using NLP Tools we pick up Behavior and Response Patterns in people in different life
situations. The response patterns may vary in different contexts. Hence the patterns are checked in various contexts. The
reports generated out of this assessment helps to identity their core competencies and the areas of improvement for their
professional growth.
Keywords: Data mining, NLP, Sequence pattern mining, Prefix Span algorithm, Competency
Introduction
Data Mining may generate thousands of patterns
A pattern is interesting if it is easily understood by
humans, valid on new or test data with some degree of
certainty, potentially useful, novel, or validates some
hypothesis that a user seeks to confirm People
develop different patterns that work well in certain
contexts 1 These patterns determine their capabilities
and skills-sets, attitudes and preferences, beliefs
and values. Each pattern has its own merits and
demerits..Data Mining is a process of sorting through
large amounts of data and taking out only relevant
information. The term Data Mining is often used
to separate the process of discovery and prediction.
Forecasting and predicting models helps the
prediction of future events.2 Neuro-Linguistic
Programming (NLP) is the science of modeling the
patterns of human behavior. NLP explores the inner
workings of the human mind: how we think, how we
develop our desires, goals and fears and how we
motivate ourselves, make connections, and give
meaning to our experiences.3Everyone has a mixture
of strengths and preferences.It is known as VAK
Analysis (Visual, Auditory & Kinesthetic). The
questions are based on VAK 4. Sequences are
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*Author for Correspondence
E-mail:[email protected]
common, occurring in any metric space that facilitates
either total or partial ordering 5 Sequence pattern
mining is from given a set of sequences, we can
find the complete set of frequent subsequence.
The sequential pattern mining problem was first
introduced by Agrawal and Srikant in 6.Given a set of
sequences, where each sequence consists of a list
of elements and each element consists of a set of
items, and given a user-specified min support
threshold, sequential pattern mining is to find all of
the frequent subsequences, i.e., the subsequences
whose occurrence frequency in the set of sequences is
no less than min support 7. The perception about
teachers varies from student to student on their
teaching quality. Students may relate teachers
personality. Some students like the approach and
others may not. 8 In our research work We are trying
to prove that , the competency ratio is high, The
faculty able to handle any kind of student and prepare
them to meet the industry expectation once they
complete their course. We identify the patterns
exhibited by faculty and match it with the required
competencies defined in Faculty assessment job role.
Individual report can be printed immediately after the
test. . In current era, the subject knowledge alone
would not help the Faculty in a current competitive
environment. . the Skills required for the Faculty in
the current trend is discussed in this paper which
BANU & RAVANAN: A FRAMEWORK MODEL FOR FACULTIES
helps the Faculty to identify their current strength and
the reports generated out of this assessment deals with
the areas of improvement to perform in a better way
to attain international standard with best teaching
practice and methods. The faculty details collected
from various educational institutions. The assessment
questions are based on NLP. The list of competencies
and association rules set are discussed in Section 2.
The issues in the existing system , the methodology,
algorithm used to measure the competency is
discussed in Section 3. The results and discussions are
in Section 4. Finally concluded in Section 5.
Competency Framework
The Faculty assessment problem is computationally,
unwidely becoming huge and difficult to produce the
performance evaluation with the increasing database
in a size.9 Therefore , we propose a heuristic
algorithm for finding the performance of the Faculty.
The aim of the algorithm is to reduce the larger
database into smaller database and also minimize the
cost. The following assumptions were defined in this
framework by referring.10 The main role of the work
is designing the competency assessment using NLP,
There are 10 competencies are assumed. Each
competency have 5 literals. The literals have 5 options
are named as A,B,C,D,E . The assumed competencies
are listed in (Table 1). Association Analysis aims at
analyzing data to identify event occurrence Mining
Table 1—List of competencies
Competency Name
Description
Presentation skill
Ability to present and persuade the
students effectively.
Student Facilitation
Ability to trust and support the belief of
the students that they can do well.
Continuous learning Ability to gather relevant information to
enhance knowledge and capabilities.
Curriculum planning Ability to plan a sequence of learning
and Scheduling
experience, teaching methods and
assessment criteria.
Research Skill
Ability to systematically gather and
interpret scientific data to gain new
insights.
Creativity
Ability to create new ideas and styles to
awaken student creativity.
Positive Feedback
Ability to monitor the performance of the
students and give supportive feedback..
Work Coordination
Ability to combine and integrate different
elements for smooth functioning.
Democratic
Ability to involve other stake holders in
Decision making
decision making process.
Requirement
Ability to identify the needs, analyze the
Analysis
impact, understand fully the consequences
and avoid conflicting issues.
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association rules searching for interesting patterns
among items in given data set.11 The Association rules
are performed in two stages . One, The discovery of
frequent set of items from the projected database.
Second one is generating the association rules from
the item sets.12,13,14 The following association rules are
framed, which gives Support Count as Threshold
value These values are used to find the success rate
of each competency.
A( X, “2”) ⇒ SP of Individual Competency ( “ A” )
B( X, “2”) ⇒ SP of Individual Competency (“B “)
C( X, “2”) V ( A( X, “1” ) ^ B( X, “1”) ^ C(X,”1”) ^
D(X,”1”)) ⇒ SP of Individual
Competency (“C “)
D( X, “2”) ⇒ SP of Individual Competency (“D“)
E( X, “2”) ⇒ SP of Individual Competency (“E“)
Where, X is a literal and A,B,C,D, and E are candidates.
The selection of success pattern process done
alphabetically. Using data mining technique the
success rate of an individual is calculated.
Methodology
This section describes the working principles of the
proposed algorithms with an illustration in order to
determine success pattern and find excellence of the
organizations. First the issues identified in the
existing system.
Issues
Many tools are available to measure Faculties
performance. Most of the research works are based on
the direct questions such as Time management,
Curriculum Planning , Student’s feedback about the
Faculty, Students Result. Few tools are based on a
concept called DISC (Dominance, Inducement,
Submission and Compliance D- type individuals
mostly task-oriented and good decision makers. They
usually focus on results and the bottom-line. I- type
individuals are outgoing and people-oriented. They
tend to influence and inspire people. They usually
focus on fun and talking. S- type individuals are
reserved and people-oriented. They tend to support
people. Focus on peace and harmony. C-type
individuals are reserved and task-oriented. They tend
to be more cautious. Focus on facts and rules or
multiple intelligences (Rhythmic / musical, Visual,
Verbal, Logical, Bodily kinesthetic, Interpersonal,
Intrapersonal, existential, Naturalistic). These tools
will measure limited patterns and will give a 30-40
pages generic report about a person. With that it is
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J SCI IND RES VOL 75 NOVEMBER 2016
difficult to match a person’s capability with the skill
sets required in teaching. In our research work , the
questions are based on NLP, With reference to ISCO
standards10 the individual competencies are indented
and measured in a Faculty, Each competencies have 5
attributes totally 50 attributes Identifying the strengths
and the area of improvement which supports the
professional growth and development of a teacher.
Helps the institution to retain competent teacher and
protect students form incompetent teachers.
Heuristic Algorithm
In this Algorithm, initially construct the database
based on the assessment. The assessments are taken
from the different educational institutions. The data
are collected for computing the success rate of the
teaching faculty. Second, The association rules are
formed in section 2.This is mainly used for finding
the success rate of the given competency. The third
step of the algorithm, The given database is scanned
and taken the projected data by using prefixspan
algorithm which is used to reduce the database size.
PrefixSpan algorithm is created for mining in
projected databases. In this study our database is a
long continuous sequence. In this heuristic algorithm
is used PrefixSpan algorithm for projecting the
database and to mine a continuous sequence database.
Significant issue of PrefixSpan algorithm’s main
attribute is that PrefixSpan only grows longer sequential
patterns from the shorter frequent this was our useful
source of idea. One particularly important problem in the
area of sequential rate mining is the problem of
discovering all subsequences that appear on a given
sequence database and have minimum support threshold
that are defined in association rule. The difficulty is in
figuring out what sequences to try and then efficiently
finding out which of those are frequent15. Patterngrowth methods are a more recent approach to deal with
sequential pattern mining problems. The key idea is to
avoid the candidate generation step altogether, and to
focus the search on a restricted portion of the initial
database. PrefixSpan is the most promising of the
pattern-growth methods and is based on recursively
constructing the patterns, and simultaneously, restricting
the search to projected databases16
Algorithm 1
Finding the competency of individual
Step 1. Construct the database based on assessment
and add one empty column as success rate(SR).
Step 2. Set all association rules as threshold.
Step 3. The prefix span algorithm for getting the
projected database .
3.1 Project all the demand prefixed with X
3.2 Scan the demands once and find frequent single
candidate pathY that could be merged with X
and generate a candidate cycle X’ as X+Y
3.3 Output X’ as a candidate cycle
3.4 For each X’, construct X’ projected demand
S|X’
Step 4. Apply the association rule and set the
competency in the projected database that is reflected
in the main database also.
Step5. Repeat the process until there is no data
available in the database.
The Algorithm 1 is repeated for all the persons in
the organization and generates the new database
which is contained number of person and success rate
of the each competency. The second part of the
algorithm is finding the success pattern of the each
person which is used to provide the overall efficiency
of educational institutions. But this is not applicable
for only educational institution; it is also used in any
organization for finding the performance of the staff.
Algorithm 2
Finding the success pattern ratio
Step 1.construct new database with ‘n ‘person and 10
competency from algorithm1.
Step 2.Replace success rate A as 10,B as 9,C as 8,D
as 7,E as 6.
Step 3.Setup attributes weightage for competency.
(Table 2a)
Step 4.Calculate Success pattern ratio =
SR∕k=110CW.
Step 5.Find the categories of each person based on
assumption given in Table2a.
To achieve success pattern of an individual. The
following assumptions are taken Such as A = 10,
B = 9, C = 8 , D -= 7 and E =6 respectively for each
person to arrive final table.The competencies
identified in Table 1 are measured using algorithm1..
The success pattern ratio is calculated by given
attribute weightage in table 2a. Using algorithm 2.
The individual category derived based on Table 2.b.
This Modified prefixspan algorithm(Finding the
competency of individual) is mainly in the
construction of projected database and multiple
scans.The cost of constructing projected database
can be minimized by creating pseudo-projections in
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BANU & RAVANAN: A FRAMEWORK MODEL FOR FACULTIES
memory, so that they can be processed faster than the
other mining methods and prefixspan algorithm also.
Table 2 (a)—Attribute Weight age of Competency
S. No
1
2
3
4
5
6
7
8
9
10
Competency Name
Attribute
Weightage
Presentation skill
Student Facilitation
Continuous learning
Curriculum planning and Scheduling
Research Skill
Creativity
Positive Feedback
Work Coordination
Democratic Decision making
Requirement Analysis
Results and Discussions
By applying Apriori Association rule, they made
decision about how the teachers are efficient in
conducting workshops, Seminars and conferences17.
In our work. We analyzed the top competencies of a
faculty who can fit for their job role as a teacher with
Modified Prefixspan Algorithm with minimum cost
and time. Which will help the management to decide
whether they had a right choice in their recruitment
process to fulfil the expectation of student community
to meet the industry requirements. Suggestions also
discussed to improve their skill to compete in current
era. Using the algorithm 1 & 2. The final table 3 is
arrived. The competency Graph obtained is given in
(Fig.1). The process is repeated and done for almost
1000 faculties across various educational institutions
in various discipline such as Arts, Science and
4
3
3
4
3
4
4
4
3
4
2 (b)—Individual Competency Category
Success Pattern Ratio
Category
91 -100
81-90
71-80
61-70
Below 60
Over skilled
Skilled
Fit
Can Consider
Must Improve
Table 3—Success Pattern for 20 Faculties
Faculty
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
36
36
36
36
28
32
32
40
40
40
40
40
28
36
32
36
40
36
24
36
2
24
21
18
27
21
21
30
27
27
18
27
27
24
18
27
27
24
18
27
27
3
27
18
27
27
18
24
27
27
27
21
30
24
21
21
27
24
21
21
21
24
4
32
36
32
36
24
32
40
32
36
28
40
36
32
28
36
36
32
40
40
36
5
24
24
27
24
18
18
30
30
27
24
30
27
24
21
30
27
24
30
24
30
6
32
36
24
32
28
32
28
28
36
28
32
36
32
24
32
36
32
36
32
36
7
32
36
32
32
28
28
32
32
36
40
40
32
32
24
36
32
32
24
40
32
8
32
32
36
32
28
32
32
36
36
28
40
32
32
24
40
32
32
24
40
32
9
24
24
30
27
18
21
24
30
27
18
24
24
24
18
24
24
24
18
24
24
10
32
32
36
36
24
28
32
32
36
36
36
36
32
28
36
36
32
28
36
36
Total
295
295
298
309
235
268
307
314
328
281
339
314
281
242
320
310
293
275
308
313
SP
8.19
8.19
8.28
8.58
6.53
7.44
8.53
8.72
9.11
7.81
9.42
8.72
7.81
6.72
8.89
8.61
8.14
7.64
8.56
8.69
Fig.1—Competency Graph for 20 faculties
SP Ratio
81.94
81.94
82.78
85.83
65.28
74.44
85.28
87.22
91.11
78.06
94.17
87.22
78.06
67.22
88.89
86.11
81.39
76.39
85.56
86.94
Category
Skilled
Skilled
Skilled
Skilled
Can Consider
Fit
Skilled
Skilled
Over Skilled
Fit
Over Skilled
Skilled
Fit
Can Consider
Skilled
Skilled
Skilled
Fit
Skilled
Skilled
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J SCI IND RES VOL 75 NOVEMBER 2016
Engineering Faculties. Profile compatibility gives an
understanding of the overall capability of an
individual to deliver the expected performance in a
specific job. The projected database once scanned for
the competency, Next time, it will verify the
remaining data from the main table. The process gets
over in the Initial scan itself. The time and cost is
reduced due to this. Our Modified Prefixspan
Algorithm works efficiently.
Conclusion
Our research work helps the institution to identify
and measure the individual competency and helps to
keep right person in a right job. This is an eye opener
for an individual faculty to identify the areas where
they lack in their competency and scope for
improvement. Once understand the reality then the
faculty who is comes under the category, “Fit” can
become skilled”, “Skilled” become “Over Skilled”.
4
5
6
7
8
9
10
11
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