Vocabulary Recommendation for English ITS

1. Vocabulary Recommendation for English ITS

2. Intelligent Tutoring System • software packages that either teach or help human tutors to teach closed sets of knowledge to students • Shortcomings: Less Interactive, Less adaptive, Less Fun • Advantages • it can remember exactly what each student knows and what should be taught next, • it can employ limitless variation of approach to teach same lesson • Cheaper

3. Components of ITS Architecture Problem Solving Environment Student Model Domain Knowledge Pedagogical Module [Helander: Intelligent Tutoring System]

4. English ITS Grammar Reading Comprehension Vocabulary

5. Voca: [Brown, Automatic Question Generation for Vocabulary Assessment]

6. Gr: [Virvou, Student Modeling in and ITS for Passive Voice ….]

7. RC: [Mitkov, Computer-Aided Generation of Multiple-Choice Tests]

8. Vocabulary Assessment/Tutoring • Knowledge of Vocabulary is essential for all types of English ITS • Choice of Vocabulary for other types of English ITS should be carefully planned • Assessment and Tutoring of Vocabulary Command should be closely integrated • New Vocabulary should be wisely RECOMMENDED • Recommend words users will not know but will like to know

9. Recommender System • Specific type of information filtering system that attempts to recommend information items (films, music, news, web page etc.) that are likely to be of interest to user. [wikipedia] • Estimation of a user’s fondness of an item that has not been used by that user.

10. RecSys Approaches • Collaborative Filtering • Find users who have similar fondness on items with the target user. And recommend items that the users liked and not used by the target user. • Content-based System • Train a user model by teaching it the user’s fondness on items with set of features. And predict the user’s fondness on new items with set of known features of the item.

11. [Pazzani, Content-Based Recommendation Systems] • User Model Development • A supervised learning mechanism: “if an item has (f1, f2, f3….) features, the user liked it” • Decision Tree, Rocchio’s Algorithm, Linear classifier, Naïve Bayes etc. • Item representation • Find out (f1, f2, f3….) of all items • Find items with high probability of fondness of the target user

12. Content-Based Recommendation for Vocabulary • CBR: Content should have enough information to distinguish items user will like from items user won’t • Features of a word: document classification – to be further defined • Can be used to predict whether user likes to know the word or not

13. [Breese, Empirical Analysis of Predictive Algorithms for Collaborative Filtering] • User similarity calculation • Via Ratings on same items (Vector similarity, Pearson correlation coefficient etc.) • Scarcity problem: when only a few item ratings are shared among users

14. CF for Vocabulary Recommendation • Can be used to find words that target users do not know yet. • Word commonness can be used to address scarcity problem at the initial stage

15. Approach Problem Solving Environment Update user model New word recommendation Student Model Domain Knowledge Monitor User Model Support Recommendation Pedagogical Module

16. Approach • Problem Solving Environment • Assessment/Train via Web Contents • Determine Word Know or Not • Determine Content Like or Not • Domain Knowledge • Knowledge about vocabulary (WordNet) • Word Frequency

17. Approach • Student Model • Vocabulary Command • User Model Update as Progress • Monitored by Pedagogical Module to influence recommendation • Pedagogical Module • Recommend Unknown and Interesting Word

18. Way Ahead • Determine Algorithms to be Employed • Development Tools • NLTK, WordNet, Python • Evaluation • User Survey • Empirical Measurements