Physics Project Descriptions
Do Reflective Dialogues that Explicitly Target the What? How? and Why (not)? Knowledge of Physics
- Primary Investigator: Sandy Katz
Cluster(s): Interactive Communications
Course(s): Physics
None
Project Runs: 2006-03-01 to 2007-09-01Most recent project report:
None
Most recent project poster:
None
The Effectiveness of Post-practice Reflective Dialogues in a First-year Physics Course
- Primary Investigator: Sandy Katz
Cluster(s): Interactive Communications
Course(s): Physics
Students in the sciences often have the delusion that they know all that there is to know about a problem that they have successfully solved. However, they may in fact have a poor understanding of the concepts associated with quantitative problems. They may also fail to recognize the general solution schemas that apply to sets of problems. In this study, we investigated the effectiveness of asking college students qualitative questions that focus on physics concepts immediately after they solve quantitative problems in the Andes physics tutoring system. We call these questions Reflection Questions (RQs) because they prompt students to think back on recently-solved problems. Another goal of this study was to determine which approach to reflection was most effective with respect to predicting gains from pre-test to post-test, on tests that measure both conceptual knowledge and quantitative problem-solving ability. This study was conducted in the Physics LearnLab during Fall Term 2005, at the U.S. Naval Academy.
Project Runs: 2005-01-01 to 2005-12-31Most recent project report:
(2006-11-30) katz-report1-11-29.doc
Most recent project poster:
(2006-03-16) katz-12-082.ppt
Elaborative and critical co-construction
- Primary Investigator: Robert Hausmann
Cluster(s): Interactive Communications
Course(s): Physics
Recent research on peer dialog suggests that some dialog patterns are more strongly correlated with learning than others. A peer dialog, which is a subordinate category of interactive communication, occurs when two novices work together to collaboratively learn a set of knowledge components, solve a problem, or both. Two types of peer dialog that have been shown to be correlated with learning are elaboration and constructive criticism. Elaboration can be defined as a conditionally relevant contribution that significantly develops another person’s idea. Constructive criticism is defined as either a request for justification or an evaluation of an idea. The primary goal for this project was to move beyond correlating dialog patterns with outcomes by experimentally manipulating peer dialogs.
Participants were asked to solve a design problem, which was to optimize the design of a pre-existing bridge structure. Participants iteratively edited their design, analyzed its cost and effectiveness, and discussed their analyses to formulate their next modification. This process continued for thirty minutes, after which a posttest measuring both shallow and deep knowledge was administered.
The results indicated that the critical dyads generated the same number of critical statements as control dyads; therefore, the critical condition was collapsed into the control condition. Alternatively, the elaborative condition generated better designs and learned more deep knowledge than the control condition. The elaborations led to shorter negotiations about what design modification to try next, so more designs were tried. These students thus sampled more of the underlying design space. This may also explain their increased learning because more appropriate learning events occurred. The problem-solving and learning outcomes also suggest that training individuals to elaborate may have been easier than asking them to produce evaluative statements.
Most recent project report:
(2006-11-21) hausmannabvisit2006a.doc
Most recent project poster:
(2006-03-15) hausmann co-construction pslcsitevisit05.ppt
The effect of generation and interaction on robust learning
- Primary Investigator: Robert Hausmann
- Co-PIs & Other Investigator(s): Kurt VanLehn
Cluster(s): Interactive Communications
Course(s): Physics
None
Project Runs: 2005-08-01 to 2008-08-31Most recent project report:
None
Most recent project poster:
None
Scaffolding Problem Solving with Annotated Worked-Out Examples to Promote Deep Learning
- Primary Investigator: Michael Ringenberg
Cluster(s): Interactive Communications
Course(s): Physics
Intelligent tutoring systems, like ANDES, use a strategy for helping students overcome impasses by providing hints that are often based on fundamental concepts of the target domain. While this has been an effective strategy, it can lead to help abuse, shallow learning, and confusion. An alternative to hint sequences would be to refer students to relevant worked-out examples that are annotated with the concept expressed in each line of the solution. This study compares these fundamental concepts hint sequences to the use of annotated, worked-out examples as forms of remediation in ANDES. It is expected that students who have access to the annotated, worked-out examples will have a better understanding of the problem structure (have a deeper understanding) and as a result perform better.
Project Runs: 2005-03-01 to 2005-08-31Most recent project report:
(2006-04-28) ringenberg-pslc-report.doc
Most recent project poster:
(2006-05-19) ringenberg - pslc final may2006.pdf
Investigating the robustness of vicarious learning: Sense Making with Deep level reasoning questions
- Primary Investigator: Scotty Craig
- Co-PIs & Other Investigator(s): Micki Chi, Kurt VanLehn
Cluster(s): Interactive Communications
Course(s): Chemistry, Physics
Craig and colleagues had participants watch information on computer hardware over a series of studies in an effort to determine ways improve learning while observing material. These studies pointed toward a deep level reasoning question effect for improving learning while observing (See Craig, at al. 2006; Gholson & Craig, in press). This effect states that if you insert a series of relevant deep level questions into observed material learning will be improved. A series of studies have shown that it is this series of deep-level questions that is important. They found that participants exposed to dialogs both increased deep level question asking and in another series of studies improved learning. However, this was only if deep-level questions were used. Further investigations found that simply observing a presentation with deep-level questions improves learning (regardless of monolog/dialog format) over various controls. However, it is not known why this method works over observing other methods of learning (e.g. observing lecture –like monologues or tutoring session by an ITS). It is also not known if this effect can be useful for learning outside the lab setting. This in vivo experiment presented identical core content on magnetism using the examples problems from the Andes tutoring system in three different ways. The material will be presented as a worked example. The content was divided into knowledge components. The knowledge components were preceded by a deep question (e.g. What are the implications of having the magnetic field close to an electrified wire?), a pause for learners to reflection on the material (i.e. a pause in the video) or a self explanation pause (e.g. Please begin your self-explanation). Andes transfer, and long term robust learning were measured. The learners’ interaction with Andes will be observed for differences on completion time, within task behavior, and the completion rates of the Andes homework.
Project Runs: 2005-10-01 to 2006-09-30Most recent project report:
(2006-11-29) project-report-deep-level questions (11-06).doc
Most recent project poster:
(2006-04-19) craig - project poster for nsf review visit 2006.ppt
Bridging Principles and Examples through Analogy and Explanation
- Primary Investigator: Timothy Nokes
- Co-PIs & Other Investigator(s): Kurt VanLehn
Cluster(s): Coordinative Learning, Fluency, Refinement
Course(s): Physics
None
Project Runs: 2007-01-01 to 2008-06-01Most recent project report:
None
Most recent project poster:
None
Learning from Problem Solving while Observing Worked Examples
- Primary Investigator: Scotty Craig
- Co-PIs & Other Investigator(s): Micki Chi, Kurt VanLehn, Soniya Gadgil
Cluster(s): Interactive Communications
Course(s): Physics
The current research proposal investigates why students learn from collaboratively observing. Previous laboratory research has shown that learners put into a collaboratively observing situation in which they can watch a video and can collaborate on the material with a partner learn significantly more than a learner that observes alone. We will test the robustness of this effect by seeing if it transfers into the Physics learnlab. Using worked examples of problem solving tasks in Andes, students will either collaboratively observe or observe individually information applying the principles of rotation either as a human-human tutoring session or from an expert solution. In order to help determine what is responsible for learning while collaboratively observing tutoring session, we will analyze an existing corpus of data from the Chi & Roy study (in press). These analyses will help to determine what features of the tutoring dialogue and collaborative dialogue are correlated to the observed learning.
Project Runs: 2006-09-01 to 2007-08-31Most recent project report:
(2006-11-16) project-report-observing.doc
Most recent project poster:
None
Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring
- Primary Investigator: Katherine Forbes-Riley
- Co-PIs & Other Investigator(s): Diane Litman
Cluster(s): Interactive Communications
Course(s): Physics
None
Project Runs: 2006-10-01 to 2007-05-31Most recent project report:
None
Most recent project poster:
None
Ill-structured physics problem solving
- Primary Investigator: Kurt VanLehn
- Co-PIs & Other Investigator(s): Michael Ringenberg
Cluster(s): Interactive Communications
Course(s): Physics
Ill-structured problems “are complex, ill defined, open ended and real world.” (Ge & Land, 2004, pg. 5). Nonetheless, students can and do learn from working on such problems, and dozens of studies have explored instructional methods for increasing such learning (see review below). One method is to view ill structured problem solving as 4 interwoven processes—problem representation, solution generation (brainstorming) and selection, solution justification, and evaluation of progress—and to provide prompts that scaffold each process. For instance “What information is missing?” is one of the prompts used to scaffold the problem representation phase. Such prompting seems to help students both “solve” the problems and more important, to learn effectively. Most research on ill-structured problems has been conducted in domains such as history(Wineburg, 1998), ethics (Ashley & Pinkus) or design (Ge & Land, 2003, 2004) where most problems are ill-structured.
Our question is whether scaffolded ill-defined problem solving will also help student learn physics, a task domain that is normally learned by solving well-defined problems. On the one hand, ill-defined problem solving seems to draw more on students’ background knowledge, to require a greater degree of abstraction and planning, to involve thinking more with physics concepts (e.g., forces) than symbols (F), and to invite practice of metacognitive and self-regulatory skills. Some ill-structured problems may be more intrinsically interesting than well-defined problems. On the other hand, ill-structured problems can be time consuming and frustrating than well-structured problems. The number of learning events per unit time may be lower with ill-structured problems than well-structured problems. Thus, we propose to compare scaffolded ill-structured problem solving with coached well-structured problem solving in the physics LearnLab.
Most recent project report:
None
Most recent project poster:
None
Analogical Scaffolding in Collaborative Learning
- Primary Investigator: Timothy Nokes
- Co-PIs & Other Investigator(s): Soniya Gadgil
Cluster(s): Coordinative Learning, Interactive Communications
Course(s): Physics
Past research has shown that collaboration can enhance learning in certain conditions. However, not much work has explored the cognitive mechanisms that underlie such learning. Chi,
Hausmann and Roy (2004) propose three mechanisms including: self-explaining, other-directed
explaining, and co-construction. In the current study, we will examine the use of these
mechanisms when participants learn from worked examples across different collaborative
contexts. We compare the effects of adding prompts that encourage analogical comparison to
prompts that focus on single examples (non-comparison) to a traditional instruction condition, as students learn to solve Physics problems in the domain of rotational kinematics. Students learning processes will be analyzed by examining their verbal protocols. Learning will be
assessed via robust measures such as long-term retention and transfer.
Most recent project report:
None
Most recent project poster:
None