TECHNOLOGICAL EDUCATIONAL INSTITUTE OF THESSALY
ΚΝΟWLEDGE MANAGEMENT for SOFTWARE PROJECT
MANAGEMENT USING ONTOLOGIES AND SOCIAL NETWΟRKS
ONSOCIAL
SP19-D4 Open Software Project Case Study Contact Person: Panos Fitsilis (
[email protected]) May 2013 Final
Table of Contents Table of Contents .......................................................................................................................2
1Project Description ...................................................................................................................3
2Project Team Organization .......................................................................................................6 2.1Project Organization ..........................................................................................................6 2.2Required profiles and skills................................................................................................7
3Skills mapping to ontology .......................................................................................................7
4Case study scenario ..................................................................................................................9 5Conclusions.............................................................................................................................15
1
Project Description
The Educational Content Methodology & Technology Laboratory (E-CoMeT Lab) is a subproject of the Hellenic Open University project and It is funded by the European Union (European Social Fund - ESF) and national funds, through the Public Investment Programme (PIP). The mission of E-CoMeT Lab is the production of standards for educational material and the development of distance education and continuous students' assessment methods. The main activities of E-CoMeT Lab are: 1. Creating (development, production, publishing), making accessible and ready for use (retrieval and sharing), and maintaining (storage and archiving, adjusting to technological changes) educational content and material (digital and printed).
2. Developing and evolving methods for distance education using modern educational theories and all the technological developments that ensure their efficient application. 3. Exploiting and producing software technology solutions and Information Technology and Communications (ICT) applications in education and in the production of audiovisual content.
The E-CoMeT Lab supports the above activities by: 1. Conducting basic and applied research.
2. Observing the technological developments and adopting best practices.
3. Supporting the analysis, design, development and support of ICT integrated solutions and applications.
4. Providing consulting services and solutions and applications assessment services. 5. Providing training, dissemination and effects diffusion services.
6. Providing digital educational material, methodologies and applications certification services.
The activities of E-CoMeT Lab are organized into work packages and tasks. The WP1 contains developmental tasks for the support of the educational work of the Hellenic Open University. WP1 contains the following tasks:
Task1: Improvement of the quality of the educational material
Task3: Certification of the printed and digital educational material and of the educational material development processes.
Task2: Technical support for the development of new educational material
Task4: Methodological support of the development of new educational material. Task5: Development and operation of a digital repository of training material.
Task6: Broader application of training methodologies and of the use of educational services. Task7: Requirements analysis, continuous evaluation and adjustment of material and applications. Task8: Development and operation of an integrated digital space of educational services. Task9: New services of the integrated digital educational space.
Task10: Development of learning activities in the digital educational space.
Task11: Development of virtual classrooms and educational services using web conferencing technologies. Task12: Innovative activities of E-CoMeT lab. Task13: Transfer of best practices.
Task14: Development of modern learning resources.
For this case study we will focus on Task9 and Task11 of WP1.
The objective of task 9 is the development of the following new services:
Generation and presentation of comprehensive comparative analysis of the grades of students projects for individual courses of the HOU thematic units. Collection, analysis and presentation of data about attendances of meetings between students and tutors and exams. Access to students registry information from mobile devices.
Development of functionality for the creation of courses and assignment of courses to tutors. Development of functionality for scheduling educational activities for the graduate and postgraduate programs of HOU.
The objectives of task 11 are the following:
Development of user friendly web conferencing applications and content
Technical support of web conferencing and teleconferencing applications, software and equipment.
Acquisition of teleconferencing software licenses and modern equipment such as video cameras, smart blackboards e.t.c.
Integration of web conferencing and teleconferencing software and content into HOU integrated digital educational space.
The applications, the software and the equipment will be used for providing teleconferencing services to HOU students and tutors for conducting distant meetings. Those services will be available through the integrated digital educational space, while selected tele-lectures will be recorded and archived in the digital repository in order to be integrated with learning activities.
2
Project Team Organization
2.1 Project Organization
E-CoMeT Lab employs a total of 21 technical staff members. Staff memebers are organized into teams respectively with the tasks they work for. As we mentioned in previous section for this test case we will focus on Task9 and Task11. The team of Task 9 has 4 members, while one person is working on Task 11. In the following paragraphs we describe the job position of each staff member. For privacy reasons we will refer to each member by its initials.
Task9 team members:
KC: KC participates in Task9 as a software engineer and application developer. His main task is to deveolp applications that enchance the traditional classroom with
computational intelligence in order to improve the participation of students (distant and in person) in the learning process. He also creates applications for
authentication services and user state management in smart classrooms.
KN: KN participates in Task9 as a software engineer and he is engaged in
infrastructure services support and development. He has participated in the
implementation of Midleware for the transfer of Ubinsence information. He also supports existing infrastructure serviceis and develops new services that are
essential for supporting essential and new services of the integrated digital space
(e.g. installation of Ubisnese platform, installation of EDX platform, participation in
installation and configuration of communication infrastructure for smart classrooms
ant remote management, installation and translation of the educational tool CanvasLMS, D-Space customization and extension. )
PA: PA participates in Task9 as a software developer and he provides technical
support, customization and development for the HOU LMS system that is based on
the Moodle platform. He participates in Moodle customization, development of new services for HOU students and tutors and supports HOU students.
MD: MD participates in Moodle updating and customizin, as well as in developing new services for HOU students.
Task11 team members:
CI: CI is currently the sole member of Task11 team. He supports and upgrades the CENTRA teleconference system.
2.2 Required profiles and skills
Form the project and tasks documentation and the CVs of the members of the two teams
that we have described in the previous section, we were able to analyse the technical skills
and the education that are required for each one of the two project tasks. While the project documentation did not provide any information about social or other skills we could not
extract any requirement regarding non-technical skills. After analyzing and grouping the
requirements for each task we concluded that Task9 and Task11 require people with skills on a combination of software engineering, software development and technical aspects. Task 9 is software engineering oriented thus it requires engineering or post-graduate
education. It requires advanced skills related to Web Applications Development such as
HTML, CSS, JavaScipt, as well as modern web technologies like Web2.0, AJAX, XML and web applications development frameworks. Task9 mainly involves customization and support of HOU LMS that is based on Moodle and of other web based services for HOU students and teachers. Moodle as well as most of other applications and services are based on the
Linux/Apache/Php/Mysql stack, so background on these technologies is also required. Task11 is mostly about installing, supporting and integrating existing teleconference
applications, thus it requires more technical skills than Task9. The required education is of graduate or college level, while experience on using and administering the Centra
teleconference system, as well as software for audio and video editing is required. Good knowledge of polpular web browsers (IE, firefox, chrome, safari, opera), of office
productivity suites (MS Office and Openoffice), and of web communication and conferencing systems (lotus sametime) is also required.
3
Skills mapping to ontology
After identifying and grouping the education and technical skills that are required for our tasks and the education and technical skills that our current staff members have, we
inserted them to the OnSocial PM ontology. OnSocial PM Ontology uses the classification of competences and skills defined in Human Resources Management Ontology. Thus we
extended the HRM Competence ontology in order to map the required skills. We refined the “Web_related” skills class of HRM Skill ontology by adding the classes “CMS” for skills
regarding Content Management Systems, “Framework” for skills regarding web applications
development frameworks and “LMS” for skills regarding Learning Management Systems. We
also extended the “Specific_Application” class with the subclasses “Teleconference_
Application”, “Video_Editting_Application” and “Web_borwser”. Then we created an
instance of the corresponding Skill sub-class for each identified skill. Figure 1 shows a part of the competences and skills class hierarchy and figure 2 shows the individuals of
“Components_technology_or_library”, “Office_Suite_Application” and “Web_Browser” classes.
Figure
1: Part of the competences and skills class hierarchy. ICT_Skill subclasses are shown. Web_related and
Figure
2: The required skills that belong to the classes "Components_technology_or_library", "Office Application" and The required types of education for tasks 9 and 11 were also mapped into the ontology.
Required education types were represented as instances of “Education_Type” class. Figure 3
Figure
shows the individuals that correspond to the required education types.
4
Case study scenario
For our case study scenario we want to add two new employees in our project's staff. We
want to find one new member for the team that works for Task9 and one new member for the team that works for Task11. We followed the following steps:
Step 1: We inserted the information about our project, existing teams and team members in Onsocial PM Ontology. We defined the technical skills and the education type that each team member has and that each task requires using the “has_competence” and
“requires_technical_skill” object properties. We created an instance of the “Project” class for EcoMeT lab WP1 project, an instance of the “Task” class for each task, an instance of “Team” class for each team and an instance of “Person” class for each one of the team members.
Figure 4 shows the main relations between the project, the teams, the tasks and the
Figure
4: The instances of ECoMeT project, Tasks 9 and 11, the corresponding teams, and the members of these teams. Is-a relations are shown with solid lines, while object property relations between the instances are shown with
employees as defined in the ontology.
Figure 5 shows the technical skills requirements of Task11 and Task9 as defined in the
ontology using the “requires_technical_skill” object property.
Figure
5: Task11 and Task9 "Task" instances and their relations to "Technical_Skill" instances through the object
After populating the ontology with the project's employees, tasks and skills we where able
to visualize useful information about the relation between the current employees' skills and
the requirements of the task they worked for. For example figure 6 visualizes the skills of KC that works for Task9 compared to the technical skill requirements of Task9.
Figure
6: KC works for Task9. His competences cover 3 technical skill requirements of Task9: "PHP", "XML" and "Moodle"
Step 2: We inserted in our ontology the technical skills and education info from the CVs of
two candidates, BS and SN. We also used Onsocial software to collect the profile information of the friends of current employees from facebook and linkedin social networking systems.
We were able to collect 271 profiles from social networks and insert them as instances in the ontology. We collected information about the education, the previous jobs and the skills of the friends of our employees. 63 distinct skills where detected. 150 new assertions relating
Person instances with Education instances, 71 new assertions relating Person instances and
competence instances and 1069 new assertions relating Person instances with Job instances where added in the ontology. The 71 identified assertions relating persons and skills where about only two Person instances while no skills information could be retrieved about the other profiles of the social network. Similarly education information for only 4 Person
instances was retrieved from the social network. The information about previous jobs did
not contain any comprehensive information about the job position or the industry sector of the corresponding company and thus was of little value.
Figure 7 visualizes the competences of a candidate person that was retrieved from the social network, while figure 8 visualizes the education info retrieved for the same person.
Figure
Figure
Step 3: Finally, we enabled the ontology reasoner and queried the ontology for the
candidates that best cover the technical skills and education requirements of each task. Based on the results of our queries we computed three types of ranks:
Technical Skill Suitability Rank: The number of task's technical skill requirements that
Team Technical Skill Supplement Rank: The number of task's technical skill
the person covers with its competences.
requirements that the person covers with its competences and are not covered by the current team members.
Education Suitability Rank: The number of task's education type requirements that the person covers with its education.
The computed ranks are shown on table 1 . Task9 Person
Technical Skill Suitability Rank
Team Technical Skill Supplement Rank
BS
9
3
SN
6
1
Person_b2ZwmF75mF
5
1
Person
Education Suitability Rank
SN
1
Person_YV55r8WDcU
1
Person_b2ZwmF75mF
1 Task11
Person
Technical Skill Suitability Rank
Team Technical Skill Supplement Rank
SN
2
2
Person
Education Suitability Rank
Person_641434249
2
Person_100000169424087
1
SN
1
Table 1: Ranks computed for the testcase.
Using SPARQL queries we were able to get more detailed information about the
competences of each candidate and how they cover each task's requirements. Figure 9
shows the technical skills of BS in contrast with the requirements of Task9 and Figure 10
shows the education of Person_b2ZwmF75mF in contrast with the education requirements of Task9. Figure
9: Visualization of BS technical skills and Task9's technical skill requirements. 9 technical skills of BS are also
Image
10: Visualization of Person_b2ZwmF75mF education and Task9
education requirements. EducationType_MSc a requirement that
5
Conclusions
We used the OnSocial PM-Ontology and OnSocial software for determining the most
appropriate candidates for filling two positions for two tasks on HOU project. We inserted
into OnSocial ontology 5 employee profiles, two candidate profiles based on their CVs, and 271 candidate profiles that where retrieved from Facebook and Twitter social networking
systems. Our ontology was populated with 1 Project, 2 Tasks, 1 Team, 278 Persons, 74 Skills, 7 Education Types, 313 Companies and 1069 Jobs. The properties of the company and the job records that were retrieved from the social networking systems where not appropriate
for identifying any useful information related to the candidate's skills. Most of the Company records contained only the company name, while most job records contained only an
alphanumeric job id. Similarly, while 271 profiles and 63 Skills where retrieved from the social networking systems and mapped into the ontology, we where able to retrieve a “has_competence” relation for only 2 profiles.
Despite the poor data input from the social networking systems, we where able to compute
suitability ranks for our candidates and get enhanced information from the ontology through SPARQL queries.
For Task9, 3 candidates cover some of the tasks' technical skill requirements. Two of them are the ones whose profiles where inserted from their CVs and one is among those
retrieved by the social networking systems. Three candidates also match Task9's education requirements. BS and Person_b2ZwmF75mF match both technical skill and education
requirements. Among them BS has a higher Technical Skill Suitability Rank and a higher
Team Technical Skill Supplement Rank, thus he is the most suitable candidate for Task9. Only candidate SN covers some of Task11's technical skill requirements. He also matches the educational requirements of the task and has a positive Team Technical Skill Supplement
Rank, that means that he has skills that are not covered by the current employees. Thus SN is the only appropriate candidate for Task11.
