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LCRec Tutorial
Learning Content Recommendation System
MINELab,
National Central University, Taoyuan, Taiwan (R.O.C)
Introduction
Learning Content Recommender (LCRec) helps user to find appropriate learning
contents based on given keywords of either intended job or skill.
Functionality
1. Search intended jobs: finding a list of job positions from
job seeker websites based on a given keyword of job title or skill
name from user using our recommender system.
2. Discover required skills: explore required skills of the
job position that user need to have.
3. Recommend learning contents: the automatic
generated fundamental learning contents of the required skill of the
job position.
Use case
The user want to study a “web developer”; what are skills the user need
to learn, and what are fundamental learning contents that user can getting
start from scratch?
user
by job title
Input keyword
“web developer”
List of required skills
with learning content
List of related job positions
Results
by skill title
Input keyword
“javascript”
Results
User can either search job position by keyword of job title or skill.
1. User input job title  Get result of job positions  link to required skills.
2. User input skill keyword  Get a list of required skills  link to related job positions.
search job position
Update &
display
How to use?
go to website: http://brs.minelab.tw
Overall steps
Select a user type
Search job position
by job title
Select a job position to see
a list of required skills
Search job position
by skill
Preview and download a recommended
learning content of the selected skill
Select a skill to see a list of
related job positions
Give a feedback
1
2
3
4
5
STEP
Step 1: Select a user type
1. Go to URL: http://brs.ninelab.tw
2. Read a LCRec description.
3. Choose the user type:
3.1. Professional – for IT professionals
/ experts.
3.2. Academic – for academics,
educators.
3.3. Student – for higher education
students, and others.
4. Click “CONTINUE”.
Step 2: Searching job position
by either job title or skill
1. Put searching
keyword of your
intended job in
the left text box
or put skill in the
right text box.
2. Click “GO”
button.
Step 3: Select a job position
to see the required skills
The searching result shows in two
boxes: a list of related job positon,
and a list of required skill regarding
to a selecting job position.
- User can see required skills
regards the job position by selecting
the intended job.
- User can see a list of job positions
related to a skill by selecting
intended skill.
- The result box will automatic
update information based on a
selecting job position or skill.
- User can see the job position
details by click on the job title that
listed below the result box.
Step 3: Select a job position
, List of job positions.
In the Job Positions box, you can see a
list of available positions.
For example, searching keyword
“PHP developer”.
The results show a job title and number
of required skills.
Result number 2 and number 3 are the
same job title but different number of
required skills.
Step 3: Select a job position
, List of required skills.
In the list of required skills, user
can see the skill title and a white
paper icon.
The icon is a shortcut which link
user to the recommended
learning contents.
Step 3: Select a job position
to see the required skills
After click on the job position title,
it will link to the job description
page.
This page show a list of required
skills of this job position, a job
description, a company name,
location, an estimated salary.
The user can preview a
recommended learning content by
click on the required skill.
Step 4: Preview & download
a recommended learning content
1. Click on a paper icon after the selected skill to preview a learning content.
2. Right click on the learning content > save as to your computer.
Step 5: Give a feed back
1. Click on tab menu “Survey”
2. Give your feedback of LCRec system by complete a questionnaire.
Thank you
MINELab,
National Central University, Taoyuan, Taiwan (R.O.C)
(R.O.C)

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Learning Content Recommender Tutorial V.2.1

  • 1. LCRec Tutorial Learning Content Recommendation System MINELab, National Central University, Taoyuan, Taiwan (R.O.C)
  • 2. Introduction Learning Content Recommender (LCRec) helps user to find appropriate learning contents based on given keywords of either intended job or skill.
  • 3. Functionality 1. Search intended jobs: finding a list of job positions from job seeker websites based on a given keyword of job title or skill name from user using our recommender system. 2. Discover required skills: explore required skills of the job position that user need to have. 3. Recommend learning contents: the automatic generated fundamental learning contents of the required skill of the job position.
  • 4. Use case The user want to study a “web developer”; what are skills the user need to learn, and what are fundamental learning contents that user can getting start from scratch? user by job title Input keyword “web developer” List of required skills with learning content List of related job positions Results by skill title Input keyword “javascript” Results User can either search job position by keyword of job title or skill. 1. User input job title  Get result of job positions  link to required skills. 2. User input skill keyword  Get a list of required skills  link to related job positions. search job position Update & display
  • 5. How to use? go to website: http://brs.minelab.tw
  • 6. Overall steps Select a user type Search job position by job title Select a job position to see a list of required skills Search job position by skill Preview and download a recommended learning content of the selected skill Select a skill to see a list of related job positions Give a feedback 1 2 3 4 5 STEP
  • 7. Step 1: Select a user type 1. Go to URL: http://brs.ninelab.tw 2. Read a LCRec description. 3. Choose the user type: 3.1. Professional – for IT professionals / experts. 3.2. Academic – for academics, educators. 3.3. Student – for higher education students, and others. 4. Click “CONTINUE”.
  • 8. Step 2: Searching job position by either job title or skill 1. Put searching keyword of your intended job in the left text box or put skill in the right text box. 2. Click “GO” button.
  • 9. Step 3: Select a job position to see the required skills The searching result shows in two boxes: a list of related job positon, and a list of required skill regarding to a selecting job position. - User can see required skills regards the job position by selecting the intended job. - User can see a list of job positions related to a skill by selecting intended skill. - The result box will automatic update information based on a selecting job position or skill. - User can see the job position details by click on the job title that listed below the result box.
  • 10. Step 3: Select a job position , List of job positions. In the Job Positions box, you can see a list of available positions. For example, searching keyword “PHP developer”. The results show a job title and number of required skills. Result number 2 and number 3 are the same job title but different number of required skills.
  • 11. Step 3: Select a job position , List of required skills. In the list of required skills, user can see the skill title and a white paper icon. The icon is a shortcut which link user to the recommended learning contents.
  • 12. Step 3: Select a job position to see the required skills After click on the job position title, it will link to the job description page. This page show a list of required skills of this job position, a job description, a company name, location, an estimated salary. The user can preview a recommended learning content by click on the required skill.
  • 13. Step 4: Preview & download a recommended learning content 1. Click on a paper icon after the selected skill to preview a learning content. 2. Right click on the learning content > save as to your computer.
  • 14. Step 5: Give a feed back 1. Click on tab menu “Survey” 2. Give your feedback of LCRec system by complete a questionnaire.
  • 15. Thank you MINELab, National Central University, Taoyuan, Taiwan (R.O.C) (R.O.C)