1. NETWORK-BASED MODEL OF LEARNING
Akanksha Bapna, Aabhas Chauhan
10th January 2018
EVALDESIGN

2. Outline Objective Methodology Education Network Analysis
Data Preprocessing
Node and Relation Extraction Algorithm
Relation Filtering
Education Network Analysis
Limitations & Next Steps
Applications
Outline

3. Understand the complexity of education and learning processes
OBJECTIVE

4. Methodology

5. Database
From 1965 onwards
Data also consists of citations, keywords, full text, abstract
Only Abstracts
Full text: Over 400 GB data

6. Methodology

7. Preprocessing Peer reviewed article abstracts only (859,821)
Sentence Segmentation
Parallel creation of Test dataset of 5000 abstracts randomly selected
Reference dataset
Stanford CoreNLP toolkit analyzes individual sentences
Part of Speech (POS) tagging
Named Entity Recognition (NER)
Lemmatization (thinking, thought, thinks – think)
Dependency mapping

8. Sample Sentence
“Formatting magazines help students learn new computer skills and promote creativity.”
-Johnstone, C., Figueroa, C., Attali, Y., Stone, E., and Laitusis, C. (2013)*
* Results of a Cognitive Interview Study of Immediate Feedback and Revision Opportunities for Students with Disabilities in Large Scale Assessments. Synthesis Report 92.

9. Preprocessing: CoreNLP output

10. Develop Node Extraction Algorithm
Common Nouns
Magazines
Students
Creativity
Adjectives + Common Nouns
New Computer Skills
Noun Compounds
Formatting Magazines

11. Preprocessing: CoreNLP output

12. Develop Relation Extraction Algorithm
Relation – “Linking word(s)” between “subject” and “object”
Heuristic Rules
Avoid self loops
Avoid phrases like “author says”, “objective of the paper”
Check for conjunction and negations
Output Triplet
(subject, linking word(s), object)
(object, linking word(s), subject)

13. Node Extraction and Relation Extraction Algorithm output
S.No.
Triplet 1
(Formatting magazines, help, students) 2
(students, learn, new computer skills) 3
(Formatting magazines, help learn, new computer skills) 4
(students, promote, creativity) 5
(Formatting magazines, help promote, creativity)

14. Methodology

15. Relation Filtering Deep Learning-based Word2Vec model
Cosine Similarity Threshold
Based on F-measure
Increase/Decrease
Used sample Dataset B

16. Methodology

17. Relation Filtering Output
Triplet
Direction Cosine Similarity
(Threshold 0.04)
(Formatting magazines, help, students) 
(students, learn, new computer skills)
0.0500 
(Formatting magazines, help learn, new computer skills)
0.0459
(students, promote, creativity)
0.2922
(Formatting magazines, help promote, creativity)
0.1706

18. Output: Network of 88,411 nodes

19. Education is Complex!

20. The Network Complexity
S.No.
Degree of node 𝒅
Number of nodes 1
𝑑>100
147 2
100≥𝑑>50
210 3
50≥𝑑>10
1,604 4
𝑑≤10
86,450

21. The Network Output

22. (Student, Teacher) Relation
No. of intermediate Nodes
No. of Nodes 1
182 2
3,248 3
120,776

24. Summary ERIC Database: 1,596,398 article abstracts
Peer reviewed article abstracts: 859,821
Testing over Dataset B
Recall: 80%
Precision: 77%
F-measure: 79%
Cosine Similarity Threshold: (0.04, 0.15)
Final network: 1,25,522 (node, edge, node) triplets

25. Limitations & Next Steps
Specific to current corpus
Noise exists
Triplets like (students, promote, creativity) couldn’t be filtered out
Does not analyze inter-sentence relation
Does not take geography into account

26. Applications Design learning experiences Design interventions
Identify gaps in research
Self-evaluation platform for schools
School leaders - Efficient allocation of school resources
Teachers - Determine best topics for new class
Students – Explore learning concepts at own pace

27. THANK YOU!