This PowerPoint was presented at the Emerging Learning Design 2014 Conference. We discuss the NSF funded iSECURE project and beta testing preliminary data. Slides of the tool are included.

1. Developing the Ultimate Course
Search Tool to Increase Retention
and Learning
Edina Renfro-Michel, Ph.D., LPC, ACS
renfromichee@mail.montclair.edu
Sailume Walo, MA, LPC
waloroberts1@mail.montclair.edu
Montclair State University
Vincent Oria, Ph.D.
New Jersey Institute of Technology
vincent.oria@njit.edu

2. Agenda
• Millennials!
• Our NSF Grant
• UCS and Learning Preferences
• What We Have Done and Are Doing
• In the Future….
• Implications for Higher Education
• The Tool!

3. Millennials
• Multitask
• Have Short Attention
Spans
• Tend to be Visual
Learners
• Bore Easily
• Want Instant
Gratification
• Want Control Over
Their Learning
• Have an Expectation
to Achieve
• Lack Self-Reflection
Skills
• Need Individualized
Educational
Opportunities

4. Our NSF Grant - iSECURE
•Ultimate Course Search (UCS)
• Use all digital Material
• Textbook
• Articles, PowerPoint Presentations,
etc.
• Lecture Videos
• Create Searches
• Integrate Learning Preferences

5. iSECURE
• Multimedia data often relies on annotation
However, manual annotation is tedious
• How to effectively and efficiently search such a
huge amount of heterogeneous data?

6. Multimedia Retrieval Problem
• Given a query (either image or video), retrieve
relevant items from a (large) set of multimedia files

7. Image/Video Representation
• Each keypoint descriptor is in high dimensional space
• Each image/keyframe is represented as a sparse set of
points, each point in high dimensional space
• Search: matching between two images will take
considerable time!!

9. Powerpoint Presentation
Indexing and Search
• Indexing
• Stemmed text is indexed with their name, value
pairs(slidenumber, slidetitle, slidetext)
• The set of slides belonging to a presentation file are mapped
relationally to that presentation along with the values of
presentation title and presentation filename
• The keywords will be stored as an inverted index (Given a
keyword, the set of slides(and presentations) in which the
keyword is found is mapped as an inverted listing)
• Search
• The presentations(slides in actual) are searched for matches in
keywords
• A presentation’s relevancy is calculated by the following :
• The score of each slide that belong to the same presentation is summed up
• The presentations are ranked according to their total score of relevancy
(higher to lower)

10. Lecture Video
• In order to find where the slide exists in a video, we need
to segment the lecture video
• We determine the transition of videos that are currently
recorded using a software
• For the videos recorded prior, we can use image
processing techniques to get the transition
• Once transition is found we can easily determine the
duration of the slide segment of video

11. Textbook
• We use the textbook back index and the ontology to form
our index
• The book’s index words and page numbers of the word’s
occurrences are organized as the following XML
<Textbook title = ‘textbookTitle’ >
<Keyword>
<Word> index word </Word>
<PageNumber>1,2,3 </PageNumber>
<Keyword>
</Textbook>
• The above information is indexed using Apache Lucene

12. Our Objectives for UCS
• Create a program that will accurately
search all electronic course materials
• Integrate UCS into Courses
• Help students understand learning
preferences as connected to UCS
• Create a user friendly, clean interface
• Determine the effectiveness of the tool

13. The Research
• Developed the Ultimate Course Search
Tool – Beta
• Begun Collecting Data in a Security Course
• Control and Experimental
• Demographics, Pre-Post Test, ILS

14. Learning Preferences
• Index of Learning Preferences (Felder &
Soloman, 1993)
Four Types of Learners
• Active – Reflective
• Sensing – Intuitive
• Visual – Verbal
• Sequential - Global

15. Your Results
• ACT_______________________________________REF
11a 9a 7a 5a 3a 1a 1b 3b 5b 7b 9b 11b
• SEN________________________________________INT
11a 9a 7a 5a 3a 1a 1b 3b 5b 7b 9b 11b
• VIS________________________________________VRB
11a 9a 7a 5a 3a 1a 1b 3b 5b 7b 9b 11b
• SEQ________________________________________GLO
11a 9a 7a 5a 3a 1a 1b 3b 5b 7b 9b 11b

16. Student Learning Preferences
Control
• 2 Active
• 12 Reflective
• 11 Sensing
• 3 Intuitive
• 13 Visual
• 1 Verbal
• 7 Sequential
• 7 Global
Experimental
• 10 Active
• 9 Reflective
• 15 Sensing
• 4 Intuitive
• 17 Visual
• 2 Verbal
• 12 Sequential
• 7 Global

17. Student Demographics
Control
• N = 28
• Mean Age = 23.8
• Year in School = 3.54
• Gender
• Female = 4
• Male = 24
• Racial/Ethnic Identifiers
• African American/Black = 5
• American Indian or Alaska = 0
• Asian = 3
• Caucasian/White = 12
• Hispanic/Latino = 9
• Pacific Isl/Native Hawiian = 1
• Other = 4
• No Answer = 3
Experimental
• N = 21
• Mean Age = 23.19
• Year in School = 3.52
• Gender
• Female = 1
• Male = 20
• Racial/Ethnic Identifiers
• African American/Black = 2
• American Indian or Alaska = 1
• Asian = 6
• Caucasian/White = 6
• Hispanic/Latino = 8
• Pacific Isl/Native Hawiian = 1
• Other = 5
• No Answer = 0

18. Survey Feedback: How did the
students using UCS?
• Search for Information/specific words & terms
• Review video podcast lectures
• As a reference
• To study for the Midterm and Final
• To help complete class projects

19. Survey Feedback: What did the
students like about UCS?
• User friendly
• Search engine
• Fast and accurate
• Search exact words
• Tabs and specific information
• Search Videos
• Searches lead to a lot of information
• Helped Students Understand Concepts
• Made studying easier
• Able to better understand material covered in class

20. Pre and Post Test Results
Control
• Pre Test Mean = 9.39
• Standard Dev = 2.25
• Post Test Mean = 12.13
• Standard Dev = 2.29
• Change in Scores = 2.74
Experimental
• Pre Test Mean = 9.10
• Standard Dev = 2.16
• Post Test Mean = 11.70
• Standard Dev = 3.09
• Change in Scores = 2.60

21. Implications for Higher Education
• Reduce attrition
• Increase clarity of course organization
• Increase student interaction with materials
• Individualize learning
• Create connections within and between courses

22. In the Future….
• In the Fall – integrate into Hybrid Graduate
Counseling course and MSU Computer Science
Course
• UCS will have individual learning preferences
assessment and information accessible in-tool
• UCS will be integrated into a course
management system – like Moodle or Canvas

23. In the Future….
• UCS will Learn from the Students – and Provide
Search Terms in Preferential Order Based on
Learning Preferences and Previous Viewings of
Material
• UCS will be linked to a database of courses
• More buttons! UCS will have the ability to add
more types of materials – with more buttons

24. Questions?
• Our Youtube Channel:
http://bit.ly/1imcF8o
• This Presentation on Slideshare:

25. The Tool!