Computer-Supported Learning Environments

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Computer-Supported Learning Environments Andy Carle acarle@cs.berkeley.edu CS 260 – Fall 2006 11/19/2018

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 11/19/2018

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. 11/19/2018

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 11/19/2018

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 11/19/2018

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. 11/19/2018

Scaffolding and ZPD Scaffolding produces a steady progression through the learner’s ZPD (Zone of Proximal Development) ZPD Inaccessible tasks Solo tasks Scaffolded learning 11/19/2018

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 11/19/2018 Alexander, Ishikawa, & Silverstein, 1977.

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 11/19/2018 Sharp et al., 2000. http://www.pedagogicalpatterns.org/

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 http://www.pedagogicalpatterns.org/current/feedback.pdf 11/19/2018

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. 11/19/2018

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 11/19/2018

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 11/19/2018

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 11/19/2018

Demo PACT is available for download from http://www.cs.berkeley.edu/~acarle/PACT/ 11/19/2018

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 11/19/2018

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. 11/19/2018

Patterns Built-In-Failure Test Tube Try it Yourself Larger than Life Real World Experience 11/19/2018

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. 11/19/2018

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. 11/19/2018

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 introduced Mindstorms which had a Logo programming language with a visual “brick-based” interface. 11/19/2018

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). 11/19/2018

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. 11/19/2018

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. 11/19/2018

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. 11/19/2018

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. 11/19/2018

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 11/19/2018

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. 11/19/2018

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). 11/19/2018

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??). 11/19/2018

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. 11/19/2018

Livenotes Interface 11/19/2018

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. 11/19/2018

Livenotes Findings But what was very common in undergraduate transcripts was student “dialog” with the PowerPoint slides: Students often add their own bullets. 11/19/2018

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. 11/19/2018

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. 11/19/2018

Flashcards Inexpensive and easy Difficult to process 11/19/2018

Personal Response System Completely anonymous response Ensures near 100% participation Allows recording of input, confidence levels, and instant summary of answers 11/19/2018

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. 11/19/2018

Inquiry cycles Inquiry-based learning makes student’s meta-cognitive strategy explicit. It also treats learning as a kind of scientific research. 11/19/2018

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) 11/19/2018

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. 11/19/2018

ThinkerTools The tools include simulation (for doing experiments) and analysis, for interpreting the results. 11/19/2018

ThinkerTools Students can modify the “laws of motion” in the system to see the results (e.g. F=a/m instead of ma). 11/19/2018

Agents: Inquiry Island An evolution of the ThinkerTools project. Inquiry Island includes a notebook, which structures students inquiry, and personified (software agent) advisers. 11/19/2018

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. 11/19/2018

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 11/19/2018

“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 11/19/2018

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. 11/19/2018

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 11/19/2018

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 11/19/2018