Computer-Supported Learning Environments

1. Computer-Supported Learning Environments Andy Carle acarle@cs.berkeley.edu CS 260 – Fall 2006

2. Outline • Review of learning principles • Design Patterns for Education • The Pedagogical Patterns Project • PACT • Constructionist Learning Systems: • Microworlds • Group Learning Systems • Peer Instruction Systems • Integrated Learning Environments

3. Building Understanding • Learning is a process of building new knowledge using existing knowledge. • Knowledge is not acquired in the abstract, but constructed out of existing materials. • Like any other human process, HCI researchers/practitioners seek to mediate learning via technology.

4. Constructivism • Piaget: Learners construct new knowledge from their experiences via cycles of accommodation and assimilation • Accommodation: The process of reframing one’s mental representation of the world to be in line with new experiences • Assimilation: Internalizing new experiences that fit the model one has already developed • Constructivism is not a pedagogy

5. Constructionism • A pedagogy designed to explicitly facilitate the learning methods suggested by constructivism • Developed by Seymour Papert and colleagues at MIT in the 1960s • Explicitly claims that the construction of external artifacts is critical to the building of internal models • Works even better with social artifacts

6. 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.

7. Scaffolding and ZPD • Scaffolding produces a steady progression through the learner’s ZPD (Zone of Proximal Development) ZPD Inaccessibletasks Solo tasks Scaffolded learning

8. Design Patterns • An abstraction of a commonly recurring design problem and its contextualized solution • Designed to inform users working in different contexts • Originated by Christopher Alexander in the study of architectural design problems • “Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice” - Alexander • A process by which ordinary people can capture the essence of a design decision by seeing how experts think about common problems in the domain Alexander, Ishikawa, & Silverstein, 1977.

9. The Pedagogical Patterns Project • Goals • Recreate the success of design patterns in architecture and software engineering in the space of pedagogical theory • Identify and disseminate context-neutral abstractions of best practices for teaching • Encourage instantiation of these patterns in diverse situations • Early work by Sharp, Manns, Prieto, and McLaughlin focused on teaching object-oriented programming concepts • Subsequent work by Joe Bergin extended the focus to general CS education • Pattern Format: • Description of the problem • Forces governing the application of the pattern • Description of the solution • Advice on implementing the solution Sharp et al., 2000. http://www.pedagogicalpatterns.org/

10. A Pedagogical Pattern: Early Warning • You teach a course in which ideas build upon one another and students will be lost if they do not understand early material • Your students may not realize that they are falling behind or that they have misconceptions, but you are in a better position to recognize it. Students may waste time and effort if they have fallen behind or have misunderstood, but time is short. If your students fall behind or miss early material it will be difficult for them to catch up and succeed. • Therefore, give them early warning when you see that they are not coping with the amount of work, or they have misunderstood some topic. Advice is best if it points a path to success, not just pointing out the roadblock. The earlier you give the advice, the better chance for success in the student. This can take many forms. If your course has special pitfalls for the student, you can publish these on your course FAQ. • It helps if you give frequent short exams and quickly return the marked papers. Some universities require exams in every course every Friday, for example. from: Bergin et al., Feedback Patternshttp://www.pedagogicalpatterns.org/current/feedback.pdf

11. Problems in Practice • Pedagogical patterns have a tendency to be too abstract to be useful. • Difficult to apply to a new context • Pattern-informed environments rarely reveal clues about the underlying patterns to the untrained observer • Collaboration between content experts and pedagogical specialists is rare • Individuals that can fill both roles are even more scarce.

12. Pattern Annotated Course Tool • Research project intended to bridge the gap between pedagogical patterns in theory and in practice • Visual editor in which expert course designers can create representations of their own courses, complete with references to pedagogical patterns • Novice instructors can see patterns instantiated in a context that they can relate to directly

13. Learning Theory in PACT (1/2) • Make Thinking Visible • Enable virtual navigation for exploring complex (physical) systems • Model scientific thinking • Provide knowledge representation tools • Help Students Learn From Each Other • Encourage learners to learn from others • Scaffold the process of generating explanations

14. Learning Theory in PACT (2/2) • Promote Autonomous Life Long Learning • Encourage reflection • Engage learners as critics • Make Theory Accessible • Connect to personally relevant examples • Provide students with templates to help reasoning • Reduce complexity to help learners recognize salient information

15. Demo • PACT is available for download from http://www.cs.berkeley.edu/~acarle/PACT/

16. Constructionist Learning Systems • Microworlds • Logo, Microworlds, Boxer • Group Learning Systems • TVI, DTVI, Livenotes • Peer Instruction Systems • Flashcards, PRS • Integrated Learning Environments • WISE, UC-WISE • Inquiry Based Systems • Thinker Tools, Inquiry Island

17. Microworlds • Give students a sandbox in which they can explore and test their mental models • Provide far more functionality than would be obviously useful to beginners • Usually with no explicit scaffolding to keep them away from advanced features • Microworlds encourage less structured exploration by learners. • The learner’s discoveries should be driven more by their own goals, leading to better learning. • The structure of the Microworld should ensure that they make the right inferences.

18. Patterns • Built-In-Failure • Test Tube • Try it Yourself • Larger than Life • Real World Experience

19. Logo • The Logo project began in 1967 at MIT. • Seymour Papert had studied with Piaget in Geneva. He arrived at MIT in the mid-60s. • Logo often involved control of a physical robot called a turtle. • The turtle was equipped with apen that turned it into a simpleplotter – ideal for drawing math.shapes or seeing the trace of asimulation. • Original turtle (Irving) could go forwards, backwards, left, right,and could ring a bell.

20. Logo • Early deployments of Logo in the 1970s happened in NYC and Dallas. • In 1980, Papert published “Mindstorms” outlining a constructionist curriculum that leveraged Logo. • Logo for Lego began in the mid-1980s under Mitch Resnick at MIT.

21. Logo • The “Microworlds” programming environment was created by Logo’s founders in 1993. It made better use of GUI features in Macs and PCs than Logo. • In 1998, Lego introducedMindstorms which had a Logo programming language with a visual “brick-based” interface.

22. Logo • Logo was widely deployed in schools in the 1990s. • Logo is primarily a programming environment, and assignments need to be programmed in Logo. • Unfortunately, curricula were not always carefully planned, nor were teachers well-prepared to use the new technology. • This led to a reaction against Logo from some educators in the US. It remains very strong overseas (e.g. England, South America).

23. Uses of Logo • Logo is designed to create “Microworlds” that students can explore. • The Microworld allows exploration and is “safe,” like a sandbox. • Children “discover” new principles by exploring a Microworld. • e.g. they may repeat some physics experiments to learn one of Newton’s laws.

24. Boxer • Boxer is a system developed at Berkeley by Andy diSessa (one of the creators of Logo). • Boxer uses geometry (nested boxes) to represent nested procedure calls. • It has a faster learning curve in most cases than pure Logo.

25. Strengths of Logo • Very versatile. • Can create animations and simulations quickly. • Avoids irrelevant detail. • Tries to create “experiences” for students (from simulations). • Provides immediate feedback – students can change parameters and see the results right away. • Representations are rather abstract – which helps knowledge transfer.

26. Weaknesses of Logo • Someone else has to program the simulations etc – their design may make the “principle” hard to discover. Usability becomes an issue. • The “experience” with Logo/Mindstorms is not real-world, which can weaken motivation and learning. • The “discovery” model de-emphasizes the role of peers and teachers. • It does not address meta-cognition.

27. Group Learning Systems • Students tend to synthesize material more thoroughly when they feel that they are creating a social artifact • Strong mental associations are constructed between abstract course contents and concrete concepts, such as other people or a particular conversation • Patterns: • Invisible Teacher • Groups Work • Study Groups

28. TVI • TVI (Tutored Video Instruction) was invented by James Gibbons, a Stanford EE Prof, in 1972. • Students view a recorded lecture in small groups (5-7) with a Tutor. They can pause, replay, and talk over the video. • The method works witha live student group, butalso with a distributedgroup, as per the figureat right.

29. DTVI • Sun Microsystems conducted a large study of distributed TVI in 1999. • More than 1100 students participated. • The study showed significant improvementsin learning for TVIstudents, compared tostudents in the livelecture (about 0.3 sdev).

30. DTVI • The DTVI study produced a wealth of interesting results: • Active participation was high (more than 50% of students participated in > 50% of discussions). • Amount of discussion in the group correlated with outcomes (exam scores). • Salience of discussion did not significantly correlate with outcome (any conversation is helpful??).

31. Livenotes • TVI requires a small-group environment (small tutoring rooms). • Livenotes attempts to recreate the small-group experience in a large lecture classroom. • Students work in small virtual groups, sharing a common workspace with wireless Tablet-PCs. • The workspace overlaysPowerPoint lecture slides,so that note-taking andconversation are integrated.

32. Livenotes Interface

33. Livenotes Findings • The dialog between students happens spontaneously in graduate courses – where student discussion is common anyway. • It was much less common in undergraduate courses. • Students have different models of the lecture – something to be “captured” vs. some that is collaboratively created.

34. Livenotes Findings • But what was very common in undergraduate transcripts was student “dialog” with the PowerPoint slides: • Students oftenadd their ownbullets.

35. Peer Instruction Systems • Peer instruction (Mazur) is a pattern that encourages all these steps: • Students are given a multi-choice question • They write down an individual answer • The class “votes” their answer • Students discuss in small groups, then answer again. • Another vote is taken • The instructor explains the right answer.

36. Patterns and Purpose • Invisible Teacher • Other students are able to recognize misconceptions in an individual that an instructor may not be able to anticipate • Active Student • Students that know they will need to prove their understanding to a peer tend to engage in the learning process more actively • Own Words and Early Warning • Students often under-appreciate the basic concepts of a course while focusing on the details of particular methods. By having students address non-trivial questions in their own words with their fellow students one can expose this underlying lack of understanding.

37. Flashcards • Inexpensiveand easy • Difficultto process

38. Personal Response System • Completely anonymous response • Ensures near 100% participation • Allows recording of input, confidence levels, and instant summary of answers

39. Inquiry-Based Systems • A development of Piaget based on similarities between child learning and the scientific method. • In this approach, learners construct explicit theories of how things behave, and then test them through experiment. • The “ThinkerTools” system (White 1993) realized this approach for “force and motion” studies.

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

41. 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)

42. 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.

43. ThinkerTools • The tools include simulation (for doing experiments) and analysis, for interpreting the results.

44. ThinkerTools • Students can modify the “laws of motion” in the system to see the results (e.g. F=a/m instead of ma).

45. Agents: Inquiry Island • An evolution of the ThinkerTools project. • Inquiry Island includes anotebook, which structuresstudents inquiry, and personified (software agent) advisers.

46. Inquiry Island • Task advisers: • Hypothesizer, investigator • General purpose advisers: • Inventor, collaborator, planner • System development advisers: • Modifier, Improver • Inquiry Island allows studentsto extend the inquiry scaffoldusing the last set of agents.

47. Integrated Learning Environments • Web-Based Inquiry Science Environment (WISE) • UC Berkeley TELS group • Middle School ~ High School science classes • UC-WISE • TELS group + CS Division • UC Berkeley & Merced lower division CS courses • Sakai • Multiple institutions • Called bSpace in the UC system

48. “The WISE Way” • Simple authoring environment to encourage iteration and experimentation by the teacher • Inquiry-driven learning environment in which students learn about a topic while constantly having their understanding checked • A gateway to peer instruction, group learning, and various microworlds

49. UC-WISE Goals • to provide technology and curricula for laboratory-based higher education courses that incorporate online facilities for collaboration, inquiry learning, and assessment, and to investigate the most effective ways of integrating this technology into our courses • to allow instructors to customize courses, prototype new course elements, and collect review comments from experienced course developers.

50. UC-WISE Features • Learning Management System • Cohesive collection of lessons, tasks, assignments, assessments, and related info • Collaborative Tools • Brainstorms, discussion forums, collaborative reviews • Inquiry-Based Tools • Web-Scheme, Eclipse exercises, Web-Java • Meta-Cognitive Tools • Quick quizzes, “extra brain,” peer assessment