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Development of the Personalized Recommender System COsys for Career Orientation

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Page 1: Development of the Personalized Recommender System COsys for Career Orientation

“Do you think that the young students possess the listed competencies?” (to the managers) “Do you think that you possess the listed competencies?” (to the students) The suggested competencies for voting: technical – available technical skills, including skills for

programming and skills for working with hardware devices;

functional – abilities for performing of concrete engineering activities related to the job position,

social – competences related to the human behavior and effective communication and socialization,

global – abilities for working in multicultural team, meta – ability for knowing how to learn, how to adapt,

how to assess

Classes Description Content-based It recommends resources that a user has read or

has liked in the past Collaborative-based

It recommends resources that are read and liked by users with similar profiles compared to the current user

Demographic-based

It recommends resources that are read and liked by users with similar demographic profiles

Knowledge-based

It recommends knowledge in a given domain

Learning path-based

It recommends knowledge formed in learning paths

Social societies-based

The recommendations are produced as consequences of social relations and communications of a user

Hybrid It includes two or more from the above mentioned techniques

COsys modules Registration - creation of an account in COsys system and an individual student profile preparation Competences Analysis - proposes a quiz and makes an analysis about the existing student’s competences Recommender - generates recommendations with a suitable learning path to this student Market Information - includes search engine and the possibility for looking of available job positions Access to Information Resources - connects a student to the information resources –how a CV to be written, how a motivational letter to be constructed or what are the steps for interview preparation Learning Sources - suggests two types of learning sources that could improve student knowledge in a given domain

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Ada

ptiv

ity

Will

ingn

ess t

o w

ork

Ethi

c

Effic

ienc

y

Com

pute

r sk

ills

Lead

ersh

ip sk

ills

Mat

hem

atic

al s

kills

Soci

al sk

ills

Mot

ivat

ion

Prof

essi

onal

skill

s

Com

mun

icat

ion

Criti

cal t

hink

ing

Off

ice

skill

s

Entr

epre

neur

ship

Selli

ng sk

ills

Crea

tivity

Tech

nica

l ski

lls

Clie

nt se

rvic

ing

Self-

man

agem

ent

Fore

ign

lang

uage

s

Employers

Students

Browser Web

server

Dispatcher

Controllers Action

View

Active

record Recommendable

Resque

RDBMS

SQLite

Redis

WEBrick

Ruby

Gem app

like/dislike Changes in

like/dislike status

DDeevveellooppmmeenntt ooff tthhee PPeerrssoonnaalliizzeedd RReeccoommmmeennddeerr SSyysstteemm CCOOssyyss ffoorr CCaarreeeerr

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AAIIMM to present the developed COsys as a personal and social-oriented learning environment forcing students to perform self-analysis of their current competences and to learn by recommendations

MMEETTHHOODDOOLLOOGGYY (1) development of survey tools for analysis of the most important and critical competences for every individual student (2) detailed exploration and analysis of the needed competences of an employee for successful realization, (3) COsys functionality description and design (4) examination of existing algorithms for recommendation generation (5) system implementation through Ruby on Rails, WEbrick web server, SQLite RDBMS, Redis digital repository.

Survey tools

1Competences analysis

2

Conclusion:

As it can be seen different institutions in our society are looking for highly qualified and well trained staff ready to perform specialized technical work. One step for achieving that is ensuring a communication gate between employees and employers. We assert that the presented work has the ability to support career development of students and their personal progress in lifelong learning aspect.

Functionality3Recommender

algorithms

4

Implementation5

For contacts: Malinka Ivanova, [email protected] Tsvetelina Atanasova, [email protected]