CS260: Lecture 2

1. CS260: Lecture 2 John Canny Fall 2006

2. Human Learning • Why study human learning in HCI?

3. Why Study Human Learning? A: People learn to use new systems periodically A: As people gain familiarity with systems, they evolve their use of them. A: Learning science is one of the most-studied areas in social science, with effective success metrics. A: Learning is not an isolated mental process, its part of most everyday “knowledge work” practices (micro-genesis). A: To better understand adult, child and unschooled users. A: Because formal education strongly shapes the way people think.

4. Lev Vygotsky Vygotsky was an extraordinary scholarwho studied Law, and taught Literature, History of Art and Psychology by age 22. Vygotsky pursued a social perspective and took it very far, developing theories of knowledge, development, and education that were profoundly influential.

5. Vygotsky in Education Vygotsky is (with Piaget) the leading education theorist of the early 20th century. Vygotsky’s social theory of learning – Like Piaget he argued that children learn by constructing their own understanding of the world they experience. In contrast to Piaget, he insisted that “the world” experienced by children is a social, rather than a natural one. i.e. games, toys, and books are social constructions that embody social norms and expectations for the child.

6. Vygotsky – Genetic method Another of Vygotsky’s ideas is his “genetic” domains: • Onto-genesis: Development by an individual • Socio-historical: Development of the society • Phylo-genesis: Development of the (human) species • Micro-genesis: Creation of ideas & concept learning His social theory involves the interplay between 1. and 2. Thus Vygotsky’s approach interleaves methods that would be regarded as both scientific and humanistic.

7. Vygotsky’s Genetic Principle Vygotsky’s genetic principle has extraordinary implications. Among other things, it implies a socio-historical approach to understanding human behavior. This approach is still popular among researchers in Management and Cooperative Work. And much more…

8. Power laws One of the puzzling findings in human behavior is the presence of ubiquitous “power law” processes. A power law probability distribution has the form: p(x) = 1/rank(x)a Where a is typically between 0 and 3 and is often very close to 1. Power laws are not “natural” from a statistical point of view. Something very special must be happening to cause them.

9. Power laws Processes that exhibit power-law statistics: The probabilities of words in the English language Word probabilities in any reasonably large corpus Number of links into a web site, click-thru of web sites Citations in academic journals Paths that users take walking through a house Products purchased from a vendor Size of cities, sizes of companies Stock market returns, trades, volumes Incomes of people… many others “One of the strongest [nontrivial] facts in social sciences”

10. Genetic processes and Power laws Power laws arise (and were first studied) in genetics. If there are k “types” we assume each type gives birth, mutates or dies with some probability. With a variety of choices of parameters, this process gives rise to power law distributions. For many choices of parameters,the power is 1.

11. Genetic processes and Power laws While genetic processes may explain many power law phenomena, they can do more than that. Just as in classical genetics, a genetic process implies a “phylogenic” tree exists for the objects in a domain. We can guess the ancestry of an item from its current popularity, the popularity of its parent, and the separation time. This can help us understand the adoption and learning of activities.

12. Learning and existing knowledge • Learning is a process of (genetically) building new knowledge using existing knowledge. • Knowledge is not acquired butconstructed out of existing“materials”. • The process of applying existingknowledge in new settings is called Transfer.

13. ZPD • Learning is layered and incremental. • In real societies, learners are helped by others. • In fact learners have a “zone” of concepts they can acquire with help. • This is the Zone of Proximal Development (ZPD).

14. Back to learning.. • Example: Who knows what this is? 100k

15. Back to learning.. • Example:

16. Learning new applications • Applications are designed to fit in ordinary users’ ZPD. • In most cases, you can’t assume that there is human available to help a user learn the new system. • A tutorial help system can provide much of this support.

17. Learning new applications • People learn best by doing (constructing new knowledge). • Using a system exposes a user’s conceptual models of how it works, and allows them to diagnose mistakes. • A tutorial help system should be able to recognize and respond to common user misunderstandings.

18. Learning and experience • Learning is most effective when it connects with the learner’s real-world experiences. • The knowledge that the learner already has form those experiences serves as a foundation for knew knowledge.

19. Learning and transfer • Transfer is certainly enhanced by similarity between the old and new contexts. • What other factors should affect transfer?

20. Transfer and understanding • Transfer depends on thorough learning in the first situation (learning with understanding*). • The more thorough the understanding in the first situation, the more easily knowledge will transfer.

21. Understanding • By understanding we mean that a person has a mental model of why a thing behaves as it does. • This model allows the person to predict how the thing behaves in other situations, and to “explain” their reasons for that conclusion.

22. Transfer and Generality • Generality of existing knowledge: has the learner already seen it applied in several contexts?

23. Transfer and Motivation • Motivation: is the new knowledge useful or valuable? • Motivation encourages the user to visualize use of the new knowledge, and to try it out in new situations. • Students are usually motivated when the knowledge can be applied to everyday situations.

24. Transfer and Abstraction • Is the existing knowledge abstract or specific? • Abstract knowledge is packaged for portability. Its built with virtual objects and rules that can model many real situations. • E.g. clipart

25. Summary Factors affecting transfer: • Real-world experience • Degree of Understanding (the earlier concept) • Generality of the earlier knowledge • Motivation • Abstraction

26. Metacognition • Metacognition is the learner’s conscious awareness of their learning process. Metacognitionhelps transfer

27. Metacognition • Strong learners carefully manage their learning. • For instance, strong learners reading a textbook will pause regularly, check understanding, and go back to difficult passages. • Weak learners tend toplough through theentire text, then realize they don’tunderstand and startagain. Have I learnedthis yet?

28. Metacognition • Another very good strategy is to predict the next main point in an argument before you read it: • “What would a user interview be like?” • “What techniques will improve learning”? • Then when you see thereal answer, the newknowledge will tie withreal experience – theexperience you just had. Let me guess what’s comingnext..

29. Structuring Learning • A similar strategy is very effective for teaching. • Ask students to work on a problem first, trying out their own approaches. • Then provide an explanation (a set of principles to explain the problem’s behavior). Reading, lecture Problem, lecture Problem work only

30. Structuring Learning • Again this gives students some rich and immediate experience with the problem. • When the explanation is given, students can relate the new information with the experience they just had. Reading, lecture Problem, lecture Problem work only

31. Break

32. Piaget: Stages of learning • Piaget observed very systematic progression of knowledge in young children through stages: • Sensori-motor (acting, observing, remembering) • Semiotic or symbolic (naming things) • “Concrete” operations (relationships, transformations) • Propositional or formal thought

33. Sensori-motor stage (< 2 years) • Conditioned behaviors, and first hand-eye coordination. • Grasping, manipulating things. • Some indirect manipulation. • Object persistence.

34. Semiotic stage (>1.5 years) • Children continue to play with “missing” objects, and may use gesture to invoke them. • This soon turns to imaginary play. • Drawing. • Speech – naming first the things that are present. • Then referring to things thatare not present, and to the past and future.

35. Concrete thought (2-7,7-11 years) • Concrete thought: a system of (real) objects, relationships, and operations on them. • Children “understand” things by being able to relate them to similar things, and to predict the consequences of their actions. • They can plan and act to achieve a desired outcome.

36. Concrete thought • But early concrete thought is still tied to direct experience – it is not “de-centered.” • E.g. children in this stage can navigate through their neighborhood, changing their route if needed. • i.e. they can mentally model and predict the results of their actions. • But they cannot indicate that route abstractly, say on a map.

37. Concrete thought • Concrete thought includes rich spatial and temporal relationships. • Visual design is a “concrete” process.

38. Formal thought (11+ years) • Objects and operations no longer need to relate to the world. Things don’t need to be true or consistent. Thinking is a “game”. • “Operations” are more abstract, and often complementary e.g. joining-separating. • Children learn a number of principles, like reversibility, proportion, chance.

39. Formal thought caveats • Researchers have found that the transition to formal thought is not as reliable as Piaget had thought. • Many features of this stage are missing in children who do not attend school. • This stage corresponds with the transition from learning from experience (pre-school), to learning from texts (school).

40. Aside: Design for Developing Regions Users in developing regions often have limited formal schooling. This leads to systematic challenges: • Users are not comfortable with formal objects such as “a red square”, i.e. they do not treat objects as sets of attributes, but as concrete things. • Graphic (picture representations) are OK, but should not be too abstract. • Users favor representations of people doing actions. • Users expect consistency and realism.

41. Formal thought (7+ years) • Side-effects of abstract representations: • Context disappears – things are just true or false everywhere. • Rules are very powerful, and both the rules and the reasoning must be accurate, or false conclusions will be drawn. • Detail must be discarded or the rules may conflict.

42. Thought styles • Designers and other visually-oriented people usually favor concrete thought – context-dependent, rich representations. • Technologists and mathematically-oriented people favor formal thought – context independent, sparse representations, rich consequences.

43. A mismatch • Many interface researchers (technologists) tried to build UI design tools using abstract interface specs (UIMSes) • the designer specifies rules about the interface and the system finds a solution satisfying them. • Real designers hated this idea. They lost control over spatial relationships and overall layout which was lost in the rules.

44. Macro and micro-Piaget • Piagetian stages are often evident in learners’ acquisition of particular concepts. • i.e. the learner’s first experience is “sensori-motor” – if I do X, then Y happens. • They develop a language for naming the operations, objects, groups of objects etc. • They acquire concrete understanding of the system’s operation: I can change state X to Y using operation Z. • Finally, they may develop a formal understanding of how the system works (as explicit rules).

45. Piaget’s progression • The Piagetian progression can be a good model for the progression in learning new concepts, like how to use a computer program. • Look for a Sensori-motor  Symbolic  Concrete  Abstract progression in your own learning, and in your users’.

46. Inquiry cycles • Inquiry-based learning makes student’s meta-cognitive strategy explicit. • It also treats learning as a kind of scientific research.

47. Inquiry cycles • Question: a new problem for the learner • Hypothesis: Learner proposes a solution or a way to understand the problem better • Investigate: Learner figures a way to try out the hypothesis (often an experiment)

48. Inquiry cycles • Analyze: understand the results of the investigation. • Model: Construct a model or principle for what’s going on. • Evaluate: Evaluate the model, the hypothesis, everything that came before.

49. Inquiry cycles • See http://thinkertools.soe.berkeley.edu • Thinkertools uses software agents to personify the different stages in inquiry cycles. • The agents help scaffold the child through the cycle.

50. Scaffolding • Refers to the process of shaping the learner’s experience while learning, by creating a “scaffold” to guide their actions. • Generally, the teacher begins by doing most or all of the task. • The task is repeated, with the learner doing more and more of it. • Eventually, the learner does the entire task themselves – the scaffold is removed.