1. Computer-Supported Learning Environments
Andy Carle
CS 260 – Fall 2006
11/19/2018

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
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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.
A: People need to *learn* new applications – they use existing mental models to generalize to new applications. As designers we should understand the process of constructing new mental models from existing ones.
A: Human knowledge is heavily layered. It can be very hard to understand users’ mental models by just observing them and trying to figure out what is going on.
A: People typically go through stages of understanding as they learn a new concept (concrete to abstract, novice to expert). This progression is very important for design.
A: Learning is an important part of knowledge work – when people work creatively, they are using existing knowledge to create new knowledge. They use mental models to anticipate what will happen, then act, and then analyze the results. This is a learning process.
A: In the learning sciences, there is a good understanding of how to measure human performance. We can adapt some of those ideas to other settings.
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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
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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
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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.
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7. Scaffolding and ZPD
Scaffolding produces a steady progression through the learner’s ZPD (Zone of Proximal Development)
ZPD
Inaccessible tasks
Solo tasks
Scaffolded learning
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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
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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
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Sharp et al.,

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 Patterns
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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.
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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
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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
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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
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15. Demo
PACT is available for download from
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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
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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.
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18. Patterns Built-In-Failure Test Tube Try it Yourself Larger than Life
Real World Experience
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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 a pen that turned it into a simple plotter – ideal for drawing math. shapes or seeing the trace of a simulation.
Original turtle (Irving) could go forwards, backwards, left, right, and could ring a bell.
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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.
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21. Logo
The “Microworlds” programming environment was created by Logo’s founders in It made better use of GUI features in Macs and PCs than Logo.
In 1998, Lego introduced Mindstorms which had a Logo programming language with a visual “brick-based” interface.
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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).
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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.
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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.
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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.
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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.
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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
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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 with a live student group, but also with a distributed group, as per the figure at right.
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29. DTVI
Sun Microsystems conducted a large study of distributed TVI in 1999.
More than 1100 students participated.
The study showed significant improvements in learning for TVI students, compared to students in the live lecture (about 0.3 sdev).
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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??).
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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 overlays PowerPoint lecture slides, so that note-taking and conversation are integrated.
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32. Livenotes Interface
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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.
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34. Livenotes Findings
But what was very common in undergraduate transcripts was student “dialog” with the PowerPoint slides:
Students often add their own bullets.
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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.
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36. Patterns and Purpose Invisible Teacher Active Student
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.
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37. Flashcards
Inexpensive and easy
Difficult to process
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38. Personal Response System
Completely anonymous response
Ensures near 100% participation
Allows recording of input, confidence levels, and instant summary of answers
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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.
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40. Inquiry cycles
Inquiry-based learning makes student’s meta-cognitive strategy explicit.
It also treats learning as a kind of scientific research.
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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)
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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.
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43. ThinkerTools
The tools include simulation (for doing experiments) and analysis, for interpreting the results.
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44. ThinkerTools
Students can modify the “laws of motion” in the system to see the results (e.g. F=a/m instead of ma).
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45. Agents: Inquiry Island
An evolution of the ThinkerTools project.
Inquiry Island includes a notebook, which structures students inquiry, and personified (software agent) advisers.
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46. Inquiry Island Task advisers: General purpose advisers:
Hypothesizer, investigator
General purpose advisers:
Inventor, collaborator, planner
System development advisers:
Modifier, Improver
Inquiry Island allows students to extend the inquiry scaffold using the last set of agents.
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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
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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
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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.
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50. UC-WISE Features Learning Management System Collaborative Tools
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
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51. Results
Early Warning patterns are easily instantiated using quizzes and brainstorms in UC-WISE
These activities have become the key to successful UC-WISE courses
Real time feedback affords TVI-like intervention by the lab TA
The courses are viewed as very time-intensive, but worthwhile
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