An Adaptive Distance Learning Environment for Language Teaching

1. PANEL: Intelligent Learning Tools; Adaptation to Non-stationary Environments; Learning & Evolution An Adaptive Distance Learning Environment for Language Teaching Alexandra Cristea Toshio Okamoto The University of Electro-Communications, Tokyo, Japan

2. English teacher system rationale • Lifting “the language barrier”. • present language teaching software • focuses on general items, irrelevant for academics. • little flexibility • academics are people with little time • important to build a user-oriented environment, with no time restrictions or other restrictions of physical nature.

3. Adaptability – up to where? • Adaptive environments should allow users to make decisions and correct directly the user model, or at least its effects • So, both lazy user, who prefers to be told things, and dynamic user, bored by standard ways, are satisfied. • Main question: balance between adaptability and user-driven software.

4. Our adaptation • Implicit (user tracing) & explicit (user input) • Symbolic (labels, pointers) & sub-symbolic (weights) • For more information: • “Student model-based, agent-managed, adaptive Distance Learning Environment for Academic English Teaching” (paper at IWALT)

5. Answers to questions from chair

6. (1) How do you define an ILT? What should be its basic features &   minimum requests? • An ILT should • serve for learning, so correspond to some specific or general learning goal • Intelligence in ILT should mean user adaptation; therefore, some minimal user modeling is necessary • Being a tool and not an environment, it can assure only a part of the learning goals, or be able to assure learning goal achievement only together with other tools • IEEE Learning Technology Standards Committee (LTSC)

7. (2) Share with us your experience in using ILTs. • Prof. De Bra, TUE, Netherlands, examples of adaptive learning environments: • http://wwwis.win.tue.nl/~debra/ • MyEnglishTeacher: • http://www.ai.is.uec.ac.jp/u/alex/MyEnglishTeacher/index.html • Etc.

8. Adaptation in educational systems • Adaptive presentation of educational material • providing prerequisite, additional or comparative explanations, • conditional inclusion of fragments, stretch-text, • providing explanation variants, reordering information, etc. • Adaptive navigation support • direct guidance, • sorting of links, • links annotation, link hiding, link disabling, link removal, • map adaptation, etc.

9. Pedagogical strategies Explanation Tutor-tutee Traditional: computer is teacher, user is student Learning companion computer-simulated learner, accompanying the user Learning by disturbing Learning with a simulated troublemaker. Learning by teaching Human student teaches the simulated companion. Learning with co-teacher Both simulated teacher and co-teacher What is user adaptation in ITS? • E.g.: switch among pedagogical strategies (cooperative strategy contexts – Frasson 1998). Within contexts, direct strategies exists, e.g: Learning by examples, learning by story-telling, learning by doing, learning by games, learning by analogy, discovery learning, learning by induction/ deduction, etc.

10. Learner & domain models • learner model • to switch between strategies; • In 1996, Greer pointed at the importance of offering adapted activities & appropriate feedback, favoring communication between students & offering assistance. • But: “student’s values, learning style metacognition and preferences regarding feedback” have to be correctly inferred • knowledge domain model • represents the model of the course contents knowledge • the student model has to be mapped on it

11. Layers in student models • latest student models have layered learner models: • knowledge & cognitive model level, wrapped by: • learning profile(curricula), wrapped by: • believability & emotional layer (which, if correctly interpreted, is supposed to point to the best learner-tailored pedagogical strategy - Abou-Jaude, Frasson, 1999)

12. Acquiring knowledge about learners • ask the learner (straightforward) single/multiple-choice questionnaires, where learner inputs preferences & opinion(s) about his/her knowledge level, learning profile, emotional profile, etc. • test the learner, to establish his/her profile (knowledge tests, IQ tests, even personality tests) • trace & interpret learner’s steps, choices & results during learning user’s into a learner model(most difficult)

13. Pros & Contras of knowledge acquisition methods • explicit information gathering : • info correct (if user knows him-/herself). • user-model building transparent to learner, who can directly influence it & correct misinterpretations. • implicit user tracing: • lets user concentrate on subject at hand • doesn’t prompt him/her with numerous questions. fine balance of modeling methods necessary.

14. Prediction: optimal solutions will imply a combination + fine tuning of fuzzy goals set: • user-friendliness, • lowuser overhead & • learning enhancement.

15. Advantages of ITS & user modeling on the Web • Servers can store large amounts of material & user modelsfrom tiny client machines • great number of (actual/ potential) users on Internet makes user modeling, average behavior interpretation, classifications, etc., more meaningful. (New:nation&region–oriented classification&adaptation) • Internet is loaded with (potentially) useful educational material using more than just local data & facilities

16. (3) Can an ILT go over the role of only assisting a human tutor? • Yes 

17. Automatic adaptation could become better than classrooms • Arguable: an adaptive system can perform better than a classroom teacher, who is bound to present a classroom average material • very convenient if a system makes correct assumptions, problematic when not • e.g., “smart” office software package from Microsoft

18. Advantages over classroom teaching • Classical classroom teaching method is • limited in time • learning is synchronous (unlike distance-learning) • A teacher always addresses average pupil (LE can be customized) • Media can enhance human aspect of course contents, ( believability level: smoothing transfer from face-to-face teaching / learning to learning in front of computer) • Media presentations can also contain extra clarifications, part of main contents, etc.

19. (4) Should ILTs be used only for ODL, or also in standard class teaching? What would be their specific tasks in the two cases? • Both. • The minimal task requirements and the definition is the same, as we are talking about tools and not systems or environments • (from the tools point of view, it can be used in collaboration with other tools, material, etc., generated automatically or by the teacher, for reaching a learning goal)

20. (5) How should an ILT evaluate the performance of a human learner? • Tracing and tests for finding the best learning path • Note: This is different from grading evaluation, which should be separated from the learning evaluation  • Accuracy • Influence on learning

21. (6) What are the expectations for the immediate and medium future? • Fischer 1999 noted: • new millennium: marked by changing of mindsets: • teacher:“sage on the stage”  “guide on the side” • Student:dependent, passive role  self-directed, discovery-oriented role • life-long learning • We have to prepare for the change. Intelligent, media-oriented distance learning environments are an answer. • But: focus should always be on learning enhancement and educational goals.

22. A last word … • It is: • difficult to break with old customs • dangerous to throw away old methodologies, just because they are old • But: • education is too important to be merely fashion oriented!