[DCSB] Gregory Crane, Stella Dee, Maryam Foradi, Monica Lent, Maria Moritz (University of Leipzig) "Dynamic Syllabi for Historical Language Instruction"
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  • 1. Dynamic Syllabi for Historical Language Instruction Digital Humanities, University of Leipzig
  • 2. Dynamic Syllabi for Historical Language Instruction Part 1: Globalization and Localization Its relevance to historical languages and resulting challenges. Part 2: User Experience for eLearning The resulting web interface and user experience for learners. Part 3: Games, Graphs, and Data eLearning games: the data and system that drives it. 2
  • 3. Dynamic Syllabi for Historical Language Instruction Part 1: Globalization and Localization Supporting languages of the local learners Part 2: User Experience for eLearning The resulting web interface and user experience for learners. Part 3: Games, Graphs, and Data eLearning games: the data and system that drives it. 3
  • 4. Dynamic Syllabi for Historical Language Instruction Part 1: Globalization and Localization Supporting languages of the local learners Part 2: User Experience for eLearning Language independent functions Part 3: Games, Graphs, and Data eLearning games: the data and system that drives it. 4
  • 5. Dynamic Syllabi for Historical Language Instruction Part 1: Globalization and Localization Supporting languages of the local learners Part 2: User Experience for eLearning Language independent functions Part 3: Games, Graphs, and Data How do you build the backend? 5
  • 6. Latin and Greek Latin and Greek are taught in across Europe Primary and secondary school instruction in the national language à at least 24 national languages within Europe alone…. 6
  • 7. Globalization The process of making all the necessary technical, financial, managerial, personnel, marketing and other enterprise decisions necessary to facilitate international business. Being global = Providing materials for each language that are suitable for learning historical languages based on their native language. 7
  • 8. “Omni-local” instead Respecting and enhancing local cultures and variation. 8
  • 9. Why be Global? Historische Sprachen eLearning Projekt Scaife Digital Library Perseus Digital Library 9
  • 10. Why be Global? Historische Sprachen eLearning Projekt Scaife Digital Library Perseus Digital Library 10
  • 11. Internationalization and Localization Internationalization is the process of enabling a product at a technical level for localization. Historische Sprachen Localization is the process of modifying eLearning products or services to account for Projekt differences in distinct markets. Source: LISA (The Localization Industry Scaife Perseus Standards Association) Digital Digital Library Library 11
  • 12. Localization: Linguistic Issues Almost any product or service that will be sold to individuals who do not speak the language in which it was created will require linguistic adaptation. Adaptation of the content for Croatian and Persian speakers, Comparison: •  Explaining, what is a Dative case for Persian speakers !== Croatian speakers don’t need this, because they have 7 cases in their language (Bulgarians have none!), BUT Croatians don’t have a definite article. •  No need for explaining the function of participles for Persian speakers !== Croatians need to know, what a participle is. 12
  • 13. Localization Challenges Physical Issues Beyond translation, localization often involves physical modification to products or services in order to be acceptable in the local market. Business and Cultural Issues Local business and cultural issues can affect all aspects of product design and localization: e.g. numbers, names, colors and graphics. C = L 13 Technical Issues Supporting local languages may require special attention and planning at the engineering stage: e.g. right to left direction, date formats, separators in the numbers.
  • 14. Technical Issues: Date Formats Europe: 03.12.2013 US: 12/03/2013 Islamic Countries: 29.01.1435 Iran: 1392/09/12 Chinese Calendar: Jia-Zi(Rat) (11th month), 1, 4711 14
  • 15. Interlingua: L1-independent display of a sentence 15
  • 16. But you can’t avoid the L1 of the learner! •  You cannot avoid translating L2 to L1 during learning the language •  Translation helps dynamic learning •  Using translation doesn’t mean, going back to the grammar-translation method •  It is not a learning method itself, but it could be combined with other methods. 16
  • 17. Translation strategies in the learning process Literary English Literal English Literal Persian 17
  • 18. Translation: Clarification •  We are not talking about literary translations (i.e., free translations that capture the spirit of the original but do not follow the original closely). •  The purpose of translation is to learn and to demonstrate what you have learned – more literal, more applied •  Translation is the skill to be used to develop language understanding •  We also DO need a lot of new translations in many languages •  We are doing collaborative translation by named individuals, not an anonymous crowd. •  We need a FIRST direct translation of Plato’s Republic into Persian. 18
  • 19. Question Knowing other languages is not always a good point: The help systems are so good that you can translate without learning (e.g., you have aligned Greek/English, morpho-syntactic annotations, dictionaries, commentaries and then you translate into Persian!) How do you internalize knowledge of the language? 19
  • 20. eLearning User Experience (UX) Localization and a graph-based backend are both important components that ultimately make our goal eLearning user experience possible 20
  • 21. What exactly is UX? User Experience includes… Usability Accessibility System Performance Interaction Design Utility 21 21 Graphic Design
  • 22. UX for eLearning eLearning presents some interesting user experience challenges such as: •  Improve understanding and retention of learning materials •  Teach users novel interactions required for novel learning tasks like treebanking and alignment •  Accommodate the wide variety of learning goals for different types of users based on their interests 22
  • 23. UX for eLearning eLearning also presents some interesting user experience opportunities such as: •  Personalize a user’s learning experience; go beyond customization •  Provide detailed and immediate feedback for users based on their responses to exercises •  Visualize user progress in a way that shows how what they have learned maps directly to their target corpus 23
  • 24. Acquainting the User with our System •  Based on a traditional Greek textbook (John William White’s First Greek Book) our learning materials are divided into lessons. •  Immediately gives the user a sense of place, and progress as it relates to Ancient Greek grammar. The interface clearly communicates, 24 “Start Here.”
  • 25. Providing Goals and Feedback •  Show a user what they’ll accomplish in the lesson. •  As the user progresses through the lessons, they see that the things they learn are directly related to their target corpus. 25
  • 26. Visualize Progress within the Target Corpus •  Since the system itself is optimized for a target text, it quickly becomes clear how a relatively small number of vocabulary words and grammar rules helps a user make huge strides in learning in a short time. 26
  • 27. New Interactions for New Kinds of Learning •  We start slowly to introduce the concept of treebanking. •  Provide feedback while the user is building the tree, until they are comfortable with the new interaction. •  Give the user specific corrections once they’ve submitted an answer. 27
  • 28. Enhancing the UX going forward •  Use recorded metrics to discover the ways people learn and retain information most effectively. •  Personalize the interface and experience more acutely. •  Use richly annotated text to provide numerous examples of grammatical constructions and vocabulary words in context. •  Provide further texts, from which users can learn. 28
  • 29. Goal Interactive and dynamic learning + more and better feedback for students Games cover every stage in the workflow of a digital edition Transcription + Structural Markup Aligned Translation Linguistic Annotation Identifying Named Entities 29
  • 30. Motivation Transcription + Structural Markup Aligned Translation •  Practise typing by Captchas •  Fill in missing word (forms) •  Identify OCR errors •  Align new translation •  Identifying Named Entities Linguistic Annotation Suggest correction for existing translations •  •  30 Identify the morphology of a given word and context Identify the syntactic function of a word (treebanking) •  Who/where/what is it? •  Uncover ethnicities, locations, events in ancient texts
  • 31. Data – Intersection Bruce Robertson, Federico Boschetti Francesco Mambrini Leif Isaksen, Gabriel Bodard 31
  • 32. Data Preprocessing Syntax Morphology Alignment Preprocessing/ Format Normalization 32 STORAGE
  • 33. RDB – Why not? Modelling this ER model as RDB schema means: •  1 table per entitiy and •  1 table per relationship → at least 30 (gave up after 7 tables) •  Adding new model components means: either rebuild the db or put high effort into persistence and integrity 33
  • 34. Data Preprocessing Representation STORAGE GRAPH 34
  • 35. Why Graphs? What: Entity •  Entities and relationships •  Nodes and the way they relate (to the world) to each other as edges Relationship Entity How: •  Scalable Entity •  Additive 35 Relationship
  • 36. Graph Performance Performance stays stable when dealing with highly connected data RDBs then require join-intensive queries where performance slows down with growing dataset Not so with graphs, because queries are localized to a portion of graph traversed to satisfy that query 36
  • 37. Graph Performance - traversals (shortest paths, exists a path, etc.) are more performant than in RDBs (huge joins) - existence of well-performing algorithms (e.g. Dijkstra) on graphs Depth RDBS exec. time Neo4j exec. time Records returned 1 0.016 0.01 ~2500 2 30.267 0.168 ~110,000 3 1543.505 1.359 ~600,000 4 Unfinished 2.132 ~800,000 from: Ian Robinson, Jim Webber, Emil Eifrem. Graph Databases. O'Reilly Media. 2013. p. 20 37
  • 38. Benchmarks Client: Virtual Client Ubuntu 12.0.4 on (1 core, 2 GB RAM from the host) Host: Windows 7 Professional Intel Core i5-2400 CPU 2 cores á 3,10 Ghz, 4GB RAM Depth Neo4j exec. time Records Returned 2 ~0.5 ~200 2 ~0.6 ~500 2 ~0.8 ~ 5,000 38
  • 39. Data Additivity Add new kinds of relationships, nodes & subgraphs to existing structure without affecting application functionality. 39
  • 40. Data Additivity Add new kinds of relationships, nodes & subgraphs to existing structure without affecting application functionality. 40 40
  • 41. Data Queries Return every word with the “POS” property “noun” START doc=node(*) MATCH (doc)-[:CONTAINS_SENT]->(sent)-[:CONTAINS]->(w) WHERE HAS (w.pos) AND w.pos=“n” ins nta RETURN DISTINCT w.cts sentence co document word 41
  • 42. Data Queries Return every word of a sentence that contains at least one word with the POS property “verb” START s=node(*) MATCH (s)-[:CONTAINS]->(w) WHERE HAS (w.pos) AND w.pos=“v” WITH s MATCH (s)<-[:BELONGS_TO]-(w2) RETURN s, w2 ORDER BY w2.cts ASC 42
  • 43. Data Queries Return every word of a sentence that contains at least one word with the POS property “verb” learned by the user “John” during the first week of the semester. START s=node(*) MATCH(s)-[:CONTAINS]->(w)<-[:CONTAINS]-(submission)<-[:SUBMITTED]-(u) WHERE HAS (w.pos) AND w.pos=“v” AND u.name=“John” AND submission.time < 24.1.2014 WITH s MATCH (s)<-[:BELONGS_TO]-(w2) RETURN s, w2 ORDER BY w2.cts ASC 43
  • 44. Thank you Questions? 44