Hypermedia and Hypertext as Agents of Change

1. Hypermedia and Hypertext as Agents of Change Dr. Christopher Staff (cstaff@cs.um.edu.mt) Dept. Computer Science & AI Faculty of Science http://www.cs.um.edu.mt/~cstaff/courses/med/med.ppt

2. Lecture Outline • This lecture… • Multimedia/hypermedia (ICT) have been vaunted as technologies which will change the way we learn for the better • But how good are we humans at making accurate predictions?

3. Lecture Outline • To understand the potential of multimedia/hypermedia, we will look at their history • And because this is about ICT in Education we will also briefly cover the history of Computer-Based Learning (Computer-Aided Instruction, Distance Learning, Intelligent Tutoring Systems, eLearning, …)

4. Lecture Outline • Next lecture… • Factors to consider in eLearning • Overview of Intelligent Tutoring Systems • Changing roles of student and tutor/teacher

5. Predictions Gone Wrong • "I think there is a world market for maybe five computers." - Thomas Watson, chairman of IBM, 1943

6. Predictions Gone Wrong • "There is no reason anyone would want a computer in their home."- Ken Olson, President, Chairman/Founder of Digital Equipment Corp.,1977.

7. Predictions Gone Wrong “Fellow men! I promise to show the means of creating a paradise within ten years, where everything desirable for human life may be had by every man in superabundance, without labour, and without pay; where the whole face of nature shall be changed into the most beautiful of forms, and man may live in the most magnificent palaces, in all imaginable refinements of luxury, and in the most delightful gardens; where he may accomplish, without labour, in one year, more than hitherto could be done in thousands of years.” - Writer, J.A. Etzler in 1842 about the steam engine.

8. Predictions Gone Wrong "De Forest has said in many newspapers and over his signature that it would be possible to transmit human voice across the Atlantic before many years. Based on these absurd and deliberately misleading statements, the misguided public...has been persuaded to purchase stock in his company...” - District Attorney, State vs. Lee de Forest, 1913. (de Forest invented the audion tube) (from http://www.foresight.org/News/negativeComments.html)

9. Predictions about Multimedia and Hypermedia in Education • Edutainment creates students eager to learn • Students can be given closer attention • Knowledge retention rates will be higher • Students can receive education wherever and whenever they like • Students can choose their own learning path • The demise of the classroom-based teacher

10. Who said this and about what? “Those who acquire it will cease to exercise their memory and become forgetful; they will rely on [it] to bring things to their remembrance by external signs instead of on their own resources… it shows great folly… to suppose that one can transmit or acquire clear and certain knowledge of an art through the medium …, or that … [they] can do more than remind the … [person] of what he knows on any given subject”

11. Plato, on Writing “Those who acquire it will cease to exercise their memory and become forgetful; they will rely on writing to bring things to their remembrance by external signs instead of on their own resources… it shows great folly… to suppose that one can transmit or acquire clear and certain knowledge of an art through the medium of writing, or that written words can do more than remind the reader of what he knows on any given subject” Plato, c. 360BC (cited in Russell, 2001)

12. The History of… • Hypermedia • Multimedia • Computer-based Learning

13. Some definitions • Multimedia “The co-occurrence of discrete and continuous data” We are multi-sensory beings We expect to receive information via multiple, simultaneous, complementary media

14. Some definitions • Hypertext “non-sequential writing” Nelson, T. 1987. Literary Machines. We are familiar with non-linear sequences of information (dictionary, encyclopaedia, newspaper) (Hypermedia = Hypertext + Multimedia)

15. Psychology of Perception • We have a narrow sense of perception • Our senses are tuned to alert us to “danger” • Processing information in our visual, aural, etc., fields is expensive • Our bodies are designed to process information efficiently and effectively

16. “Cost” of multimedia processing • Graphics: resolution x colour depth • E.g., 640 x 480 x 24 = 900KB • Video: res x colour depth x fps x duration • 640 x 480 x 24 x 25fps x 1 sec = 21.97MB

17. Principles • Hypertext/hypermedia should be designed for efficiency and effectiveness • Instead, information is “hidden” from us and we must make decisions about whether to read it • Can actually increase cognitive load! • Adaptive hypertext…

18. History of Hypertext • 1945: Vannevar Bush describes “memex” (Atlantic Monthly) • 1962: Engelbart describes AUGMENT • 1965: Ted Nelson coins the term “hypertext” • 1968: Van Dam + others at Brown University develop Hypertext Editing System and File Retrieval and Editing System

19. History of Hypertext • 1983: First 2nd generation hypertext systems - Intermedia and KMS • 1985: Peter Brown, University of Kent, develops first commercially available hypertext - Guide • 1987: Prototype of Nelson’s Project Xanadu • 1986-1990: More sophisticated hypertext systems developed

20. History of Hypertext • 1991: Tim Berners-Lee builds IP-based distributed hypertext system at CERN Develops UDI/URI, HTTP, and HTML… • 1993: Mosaic, first graphical Web browser, released • 2002: Work begins on Semantic Web • 2003: Proposals for Adaptive Web

21. Hypertext meets Multimedia • History of multimedia dependant mainly on wide-spread availability of “home” computers able to support non-textual data • Introduction of CD-ROM was an important step (cheaply available mass storage) • Hypermedia should allow links into and out of non-textual data…

22. Computer-based Learning • Early systems used “flash cards” • Students tested at end of session: failures were re-exposed to material • In 1987, Wenger criticises computer-based learning systems and identifies their implicit and explicit goals • Leads to development of modern Intelligent Tutoring Systems

23. Computer-based Learning • Wenger’s “dream” incorporates hypermedia: “imagine active books that can interact with the reader to communicate knowledge at the appropriate level, selectively highlighting the interconnectedness and ramifications of items, recalling relevant information, probing understanding, explaining difficult areas in more depth, skipping over seemingly known material ... intelligent knowledge communication systems are indeed an attractive dream” Wenger, E. 1987. Artificial Intelligence and Tutoring Systems

24. Computer-based Learning • It is little surprise, then, that modern Adaptive Hypertext Systems have some of their roots in Intelligent Tutoring Systems…

25. Agent of Change "ICT is not an end in itself or an agent of change by itself but when incorporated into a well managed change process it is a powerful enabler and amplifier." Bryn Jones, http://www.apple.com.au/education/k12/ade/biographies/. See also A learning technologies guidebook, http://purple.nd.edu.au/coms/bj/techplan/ltguide.htm

26. End of first lecture

27. Agent of Change "ICT is not an end in itself or an agent of change by itself but when incorporated into a well managed change process it is a powerful enabler and amplifier." Bryn Jones, http://www.apple.com.au/education/k12/ade/biographies/. See also A learning technologies guidebook, http://purple.nd.edu.au/coms/bj/techplan/ltguide.htm

28. Immediacy and Insatiability • New media, new communication platforms, mobile telephony, everything connected all the time… • Mediated experiences… • … have created an expectation that every desire, every wish, every demand, can and shall be satisfied instantly • The same expectations now exist in education…

29. ICT in the Classroom and eLearning

30. ICT in the Classroom • Introduced with the promise that children, normally in primary, secondary schools, will receive a better education • Not the focus of this lecture… • … but educational programmes are only as good as the designers who make them, and the teachers who use them • The mere presence of a computer in a classroom does not create better students…

31. “The future of interactive media in education… is that of communication tool” Radford, A. “The Future of Multimedia in Education”

32. eLearning • Connecting tutors and students who cannot be in the same place at the same time… • Giving remote students access to support • Shifting some of the responsibility for “education” onto computers

33. Factors • Student motivation/discipline • Student can communicate well in writing • Student potentially in control of their learning path • Tutor/Teacher as guide/learning manager • "Computer" responsible for knowledge transfer • Don't lose sight of the human element. We are social animals! • Computer vs. human in control of pedagogy • Many courses are designed by others and given to teachers to deliver (e.g., BTEC Edexcel at MCAST, NCC at Swatar, etc.)

34. Degrees of Support • From video streaming individual lectures… • … to fully-fledged ITS • From individual self-contained courses… • … to full degrees from on-line Universities

35. Ellul, 2001: My Virtual Classroom

36. Virtual University

37. Intelligent Tutoring Systems • Student learns by problem solving • ITS selects a problem • compares solution to student’s • Performs diagnosis based on the differences • Gives feedback, reassesses student and updates the student skills model • ITS also considers what the student needs to know, which part of the curriculum to teach next, and how to present the material • It then selects the next problem

38. Intelligent Tutoring Systems

39. Intelligent Tutoring Systems • ITS is responsible for: • Knowledge communication • Student assessment • Pedagogy

40. Intelligent Tutoring Systems • ITS must know the aims, objectives, goals of the student • ITS must know the current knowledge of the student • ITS must know about the domain • ITS must devise a plan to move the student from current state to desired state • ITS must be able to assess the student’s progress

41. Domain Expert • The ITS must know the domain • It must know how knowledge items are related • It must be able to represent inter-dependencies between knowledge items • Assumption that Domain Expert represents (complete) knowledge of the domain, and that the student’s goal is to acquire (all of) it

42. Domain Expert • Domain expertise must be machine processable, to allow system to “reason” • Common representation formats include: • If-Then rules • If-Then rules with uncertainty measures • Semantic networks • Frame-based Representations (Cases)

43. Modelling the student • Need to represent student’s knowledge • Need to record student’s progress • Ideally, when student makes mistakes, ITS can automatically discover why and take remedial action

44. Modelling the student • Student model can gather information implicitly or explicitly • Must compare student’s knowledge to the expert’s knowledge (usually to highlight differences) • If differences indicate misconceptions, ITS must decide how to take remedial action

45. Student Model Architectures • Overlay • Differential • Perturbation

46. Overlay Student Models • Teaching material represented as prerequisite hierarchy • Student’s knowledge assumed to be subset of expert’s • Goal is to move student’s knowledge to expert’s knowledge • No mechanism to differentiate between knowledge S doesn’t understand and S hasn’t yet been exposed to

47. Differential Student Models • Distinguishes between Knowledge S does not understand and K S has not been exposed to • Does not cater for misconceptions or “bugs”

48. Perturbation Student Models • Caters for K possessed by S which is not in Expert’s knowledge • E’s domain knowledge is extended with “bug library” • “Bugs” are typical mistakes made by students • Bug lib. can either be list of bugs (enumerative) or generated from an underlying cognitive theory (generative)

49. Student Model Diagnosis • Performance measuring • “Test” • Model tracing • Issue tracing • Expert systems

50. Model Tracing • Gives advice when student diverges from acceptable solution paths • Aims to change student behaviour