Ringenberg Ill-Defined Physics - Revision history

Michael-Ringenberg: /* References */

2009-05-15T19:16:52Z

References

← Older revision		Revision as of 19:16, 15 May 2009
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	==== References ====		==== References ====
	*Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.		*[Chi et al., 1981] Chi, M. T. H., Feltovich, P., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5:121-152.
	*Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). ~~Constraining Novices~~ to ~~Perform Expertlike Problem Analyses~~: Effects on ~~Schema Acquisition~~. Journal of the Learning Sciences, 2(3), 307-331.
	*Ge, X., ~~& Land~~, S. M. (~~2003~~). ~~Scaffolding students' problem-solving processes in an ill-structured task using question prompts~~ and ~~peer interactions. [Article]~~. ~~Etr\&D-Educational Technology~~ Research ~~and Development~~, ~~51(1), 21~~-38.		*[Dufresne et al., 1992] Dufresne, R. J., Gerace, W. J., Hardiman, P. T., and Mestre, J. P. (1992). Con- straining novices to perform expertlike problem analyses: Effects on schema acquisition. Journal of the Learning Sciences, 2(3):307-331.
	*Halloun, I. A.~~, &~~ Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043-1055.
	*Heller, P.~~, &~~ Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. ~~Part~~ 2: Designing problems and structuring groups. American Journal of Physics, 60(7), 637-644.		*[Felps et al., 2006] Felps, W., Mitchell, T. R., and Byington, E. (2006). How, when, and why bad apples spoil the barrel: Negative group members and dysfunctional groups. Research in Organizational Behavior, 27:175-222.
	*Larkin, J. H.~~, &~~ Reif, F. (1979). Understanding and ~~Teaching Problem~~-~~Solving~~ in ~~Physics~~. International Journal of Science Education, 1(2), 191-203.
	*Leonard, W. J., Dufresne, R. J., & Mestre, J. P. (1996). Using qualitative problem-solving strategies to highlight the role of conceptual knowledge in solving problems~~. [Article]~~. American Journal of Physics, 64(12), 1495-1503.		*[Halloun and Hestenes, 1985] Halloun, I. A. and Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11):1043-1055.
	*VanLehn, K., & Jones, R. M. (1993). Better learners use analogical problem solving sparingly. ~~Paper presented at the~~ Proceedings of the Tenth International Conference on Machine Learning, San Mateo, CA.
			*[Heller and Hollabaugh, 1992] Heller, P. and Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. part 2: Designing problems and structuring groups. American Journal of Physics, 60(7):637-644.

			*[Heller et al., 1992] Heller, P., Keith, R., and Anderson, S. (1992). Teaching problem solving through cooperative grouping. part 1: Group versus individual problem solving. American Journal of Physics, 60(7):627-636.

			*[Larkin and Reif, 1979] Larkin, J. H. and Reif, F. (1979). Understanding and teaching problem-solving in physics. International Journal of Science Education, 1(2):191-203.

			*[Leonard et al., 1996] Leonard, W. J., Dufresne, R. J., and Mestre, J. P. (1996). Using qualitative problem- solving strategies to highlight the role of conceptual knowledge in solving problems. American Journal of Physics, 64(12):1495-1503.

			*[Ringenberg and VanLehn, 2006] Ringenberg, M. A. and VanLehn, K. (2006). Scaffolding problem solving with annotated, worked-out examples to promote deep learning. In Ikeda, M., Ashley, K., and Chan, T.- W., editors, ITS 2006, LNCS 4053, pages 625-634, Taiwan. Springer-Verlag Berlin Heidelberg. Winner of Best Paper First Authored by a Student Award.

			*[Simon, 1973] Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4(3):181- 201.

			*[Singh and Rosengrant, 2003] Singh, C. and Rosengrant, D. (2003). Multiple-choice test of energy and momentum concepts. American Journal of Physics, 71(6):607-617.

			*[VanLehn and Jones, 1993] VanLehn, K. and Jones, R. M. (1993). Better learners use analogical problem solving sparingly. In Utgoff, P. E., editor, Proceedings of the Tenth International Conference on Machine Learning, pages 338-345, San Mateo, CA. Morgan Kaufmann.

			*[VanLehn et al., 2005] VanLehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., and Wintersgill, M. (2005). Andes physics tutoring system: Five years of evaluations. In McCalla, G., Looi, C. K., Bredeweg, B., and Breuker, J., editors, Artificial Intelligence in Education, pages 678-685, Amsterdam, Netherlands. IOS Pres.

	==== Connections ====		==== Connections ====

Michael-Ringenberg: /* Background and Significance */

2009-05-15T19:13:36Z

Background and Significance

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 === Background and Significance ===
-One of the great challenges in physics education is that traditional
+The goal of a physics course is not to teach how to solve highly constrained \physics problems" but to increase a student's understanding of the world around them and how it can be modeled using equations and formulas. Although students are often good at solving the highly constrained physics problems that are part of homework and exams, they often lack an understanding of the concepts and principles exemplified by those problems [Halloun and Hestenes, 1985].
-The homework and exam problems typically presented in a physics class
+One possible reason for this is that students develop adequate strategies for solving physics problems, but these strategies are highly procedural and do not rely on a conceptual understanding of the material.  There is evidence that students employ shallow problem solving methods. For example, when asked to sort problems, novices will rely on surface features of the problem like what objects (inclines, boxes, pucks, ice, etc.) are used and what quantities are specified (mass, velocity, coefficient of friction, etc.), whereas experts will match problems based on what principles are used to solve them [Chi et al., 1981].
-In contrast, experts in physics
+More evidence for shallow problem solving methods includes observed use of examples. When referring back to examples or previously solved problems, novices will focus on matching surface features and try to mimic all the steps presented in the example [VanLehn and Jones, 1993]. However, when supplied with a relevant example that matches the current problem based on the principles used and may or may not match surface features, students tend to rapidly develop better principle-based problem analysis strategies [Ringenberg and VanLehn, 2006].
-One problem with having students specify the principles used to solve a
+On the other hand, physics experts tend to solve these (and real-world) problems by first conceptualizing the problem, then forming a qualitative solution, and finally using relevant given quantities to derive a numeric solution [Larkin and Reif, 1979]. When students are required to perform more expert-like problem solving strategies, they can develop better conceptual knowledge. For example, when students are given the task of specifying which physics principles are needed to solve well-defined physics problems as part of their homework and exams (i.e. conceptualizing the problem), their understanding of these concepts improves more than just solving the problems [Leonard et al., 1996].
-One way to reduce the reliance on surface features is to remove them.  For
+Unfortunately, simply requiring students to perform more expert-like problem solving strategies may notbe enough. The Andes tutoring system requires students to draw diagrams and specify necessary variables which are steps that experts perform, and students do better on these skills but do not perform any better on "conceptual" skills like principle identification [VanLehn et al., 2005]. A shortcoming of just prescribing and enforcing a problem solving strategy is that students may not see the value of using it and just adapt their shallow methods to include these extra steps.
-This study aims a providing support for solving [[ill-defined problem]]s in the domain of physics in order to investigate their effects on conceptual understanding as compared to solving well-defined problems.
+Having physics students solve more ill-defined problems that are difficult to solve with shallow strategies seems like a viable method for encouraging more conceptual methods. According to [Simon, 1973], "ill-defined" problems covers a broad category of problem types, including problems that are missing necessary information, have multiple equally valid though not equivalent solutions, or have no definitive solution that experts can agree upon. Whereas experts often deal with these sorts of problems, students often have great difficulty solving these problems.
 === Glossary ===

Michael-Ringenberg: /* Annotated bibliography */

2009-05-15T18:44:35Z

Annotated bibliography

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	=== Further Information ===		=== Further Information ===
	==== Annotated bibliography ====		==== Annotated bibliography ====

			* Paper and poster presented at ITS 2008 Conference (young researcher's track).

	==== References ====		==== References ====

Michael-Ringenberg: /* Independent variables */

2008-10-08T12:53:20Z

Independent variables

← Older revision		Revision as of 12:53, 8 October 2008
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	For this study, the [[ill-defined problem]]s used lacked key information needed to solve the problem. The problem statements did not include the quantities needed to derive a numeric solution to the problem. Part of the task of solving these problems was to have the participants request the necessary quantities from the system. The system will provide hints and corrective feedback for this task. Once all of the necessary values are elicited by the participant, then the problem becomes a [[well-defined problem]]. The [[well-defined problem]]s used were identical to the [[ill-defined problem]]s except that all of the necessary information was given as part of the problem statement.		For this study, the [[ill-defined problem]]s used lacked key information needed to solve the problem. The problem statements did not include the quantities needed to derive a numeric solution to the problem. Part of the task of solving these problems was to have the participants request the necessary quantities from the system. The system will provide hints and corrective feedback for this task. Once all of the necessary values are elicited by the participant, then the problem becomes a [[well-defined problem]]. The [[well-defined problem]]s used were identical to the [[ill-defined problem]]s except that all of the necessary information was given as part of the problem statement.

			Figure 1. Example of one of the ill-defined problems. It is missing key information which would be required to produce a quantitative solution.
			{\| border="1" cellspacing="0" cellpadding="5" align="center"
			\|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress?
			\|}

			Figure 2. Example of the corresponding well-defined version of the problem in Figure 1. It includes all of the necessary and sufficient information needed to produce a quantitative solution to the problem.
			{\| border="1" cellspacing="0" cellpadding="5" align="center"
			\|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress?<br/>
			Height of rail: 0.5 m<br/>
			Regina's velocity when she leaves the rail: 0.45 m/s
			\|}

	=== Hypothesis ===		=== Hypothesis ===

Michael-Ringenberg at 14:58, 16 April 2008

2008-04-16T14:58:57Z

← Older revision		Revision as of 14:58, 16 April 2008
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	==== Future plans ====		==== Future plans ====

			[[Category:Study]]

Michael-Ringenberg: New page: == Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? == ''Michael Ringenberg and Kurt VanLehn'' === Summary Table === {| border="1" cellsp...

2008-04-16T14:57:40Z

New page: == Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? == ''Michael Ringenberg and Kurt VanLehn'' === Summary Table === {| border="1" cellsp...

New page

== Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? ==
''Michael Ringenberg and Kurt VanLehn''

=== Summary Table ===
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Michael Ringenberg (Pitt) & Kurt VanLehn (Pitt)
|-
| '''LearnLab Course''' || Physics
|-
| '''Number of Students''' || ''N'' = 40
|}

=== Abstract ===
Students who complete an introductory physics course often do not
have a good conceptual understanding of the principles taught. There
have been various attempts at increasing conceptual learning, often
with only modest improvements. One promising avenue is the use of
ill-defined problems. However, it can be very difficult for
students to solve these problems without proper support. If
ill-defined problem solving can be supported using intelligent
tutoring systems, then it will be possible to investigate the
potential of ill-defined problems and their influence on conceptual
learning.

=== Background and Significance ===
One of the great challenges in physics education is that traditional
physics teaching methods lead to shallow learning. Most physics
students, regardless of their grades in class, have a poor
understanding of the concepts being taught (Halloun & Hestenes, 1985).
One possible source of this discrepancy between conceptual
understanding and performance is that traditional teaching methods
rely heavily on the use of well-defined physics problems as both the
primary practice and primary assessment activity. While it is
important for students of physics to be able to solve these
well-defined problems, it is obviously not enough.

The homework and exam problems typically presented in a physics class
are so constrained that students do not have to do any conceptual
analysis of the problem in order to solve them. They tend to look at
the quantities supplied in a problem description, match them with
known equations, and simply use algebra to find the value of the
variable requested in the problem (Chi, Feltovich, & Glaser, 1981). Additionally,
successful novices will match surface features of the problem
(particular keywords, phrases, or quantities) to previously solved
problems or worked examples to decide which equation to use
(Vanlehn & Jones, 1993). These algebraic methods may be reliable methods
of solving straight-forward physics problems, but do not require any
conceptual knowledge of physics.

In contrast, experts in physics
solve problems by conceptualizing the problem first, forming a
qualitative solution, and then finally using the relevant given
quantities to arrive at the numeric solution (Larkin & Reif, 1979).
Performing more expert-like problem solving strategies can be
important for fostering this conceptual knowledge. When novices are
given the specific task of specifying which physics principles are
needed to solve a problem as part of homework and exams, which
requires them to perform an intermediate step in the expert
problem-solving strategy, their understanding of these concepts
improves more than just solving problems (Leonard, Dufresne, & Mestre, 1996).

One problem with having students specify the principles used to solve a
given homework problem is that students can still use surface features
of the problem to deduce the principles and without immediate corrective
feedback they could use naive problem solving strategies and then simply
report what principles they used by examining the equations used to solve
the problem.

One way to reduce the reliance on surface features is to remove them. For
example in the physics domain, key terms such as force, momentum, and energy
can be avoided and no given quantities could be specified in the problem statement.
As a consequence of this, they become [[ill-defined problem]]s in that key information is missing. Students typically have a difficult time solving [[ill-defined problem]]s, but
are able to if they have the support of well constructed pier groups (Heller & Hollabaugh, 1992)
or additional support (Ge & Land, 2003).

This study aims a providing support for solving [[ill-defined problem]]s in the domain of physics in order to investigate their effects on conceptual understanding as compared to solving well-defined problems.

=== Glossary ===

=== Research question ===
Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving?

=== Independent variables ===
* Type of problems solved: [[ill-defined problem]]s vs. [[well-defined problem]]s

For this study, the [[ill-defined problem]]s used lacked key information needed to solve the problem. The problem statements did not include the quantities needed to derive a numeric solution to the problem. Part of the task of solving these problems was to have the participants request the necessary quantities from the system. The system will provide hints and corrective feedback for this task. Once all of the necessary values are elicited by the participant, then the problem becomes a [[well-defined problem]]. The [[well-defined problem]]s used were identical to the [[ill-defined problem]]s except that all of the necessary information was given as part of the problem statement.

=== Hypothesis ===
If students are
required to figure out what information is needed to solve ill-defined
physics problems before solving them, then they will develop better
conceptual understanding than if they had been presented with the same
problems with all the necessary information provided.

=== Dependent variables ===
* [[Normal post-test]]
** Multiple-choice conceptual questions
* [[Transfer]]
** Judgement task
*** Problem matching task: participants are given a target problem statement and are asked which of two additional problem statements are solved most similarly to the target problem without solving any of the problems (Dufresne, Gerace, Hardiamnn, & Mestre, 1992).
* Performance on [[Andes]] problems
** Solution times
** Error rates
** Help requests

=== Expected Findings ===
Participants who solve the ill-defined versions of the problems will:
* Perform better on conceptual questions.
* Perform better on the problem matching task.
* Have faster solution times.
* Have lower error rates.
* Have fewer help requests.

=== Explanation ===
Because the experimental participants will be required to engage in more conceptual analysis of the problems, they will more deeply analyze and encode the [[knowledge component]]s used in the problems. This will lead to better performance on tasks that use this better encoding. It will also have effects on problem solving because with the conceptual analysis done before problem solving, there will be less floundering and help abuse during problem solving.

=== Further Information ===
==== Annotated bibliography ====

==== References ====
*Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
*Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). Constraining Novices to Perform Expertlike Problem Analyses: Effects on Schema Acquisition. Journal of the Learning Sciences, 2(3), 307-331.
*Ge, X., & Land, S. M. (2003). Scaffolding students' problem-solving processes in an ill-structured task using question prompts and peer interactions. [Article]. Etr\&D-Educational Technology Research and Development, 51(1), 21-38.
*Halloun, I. A., & Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043-1055.
*Heller, P., & Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. Part 2: Designing problems and structuring groups. American Journal of Physics, 60(7), 637-644.
*Larkin, J. H., & Reif, F. (1979). Understanding and Teaching Problem-Solving in Physics. International Journal of Science Education, 1(2), 191-203.
*Leonard, W. J., Dufresne, R. J., & Mestre, J. P. (1996). Using qualitative problem-solving strategies to highlight the role of conceptual knowledge in solving problems. [Article]. American Journal of Physics, 64(12), 1495-1503.
*VanLehn, K., & Jones, R. M. (1993). Better learners use analogical problem solving sparingly. Paper presented at the Proceedings of the Tenth International Conference on Machine Learning, San Mateo, CA.

==== Connections ====

==== Future plans ====