Analogical Scaffolding in Collaborative Learning

Analogical Scaffolding in Collaborative Learning

Soniya Gadgil & Timothy Nokes

Summary Table

PIs	Soniya Gadgil (Pitt), Timothy Nokes (Pitt)
Other Contributers	Robert Shelby
Study Start Date	Sept. 1, 2008
Study End Date	Aug. 31, 2009
LearnLab Site	United States Naval Academy (USNA)
LearnLab Course	Physics
Number of Students	N = 72
Total Participant Hours	144 hrs.
DataShop	Anticipated

Abstract

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.

Background and Significance

Collaborative learning Much research on collaborative learning has been conducted over the past few decades. The idea that putting two heads together could be better than one seems intuitive, and research has shown that when students learn in groups of two or more, they show better learning gains (at the group level) than working alone. Much of the past research has focused on identifying conditions that underlie successful collaboration. For example, we know that presence of cognitive conflict is an important variable underlying collaboration. Schwartz, Neuman, and Biezuner (2000) showed that when students with misconceptions distinct from each others’ collaborated, they were more likely to learn compared to those with the same misconception, or without a misconception. Studies have also found that establishing common ground is an important factor in learning from collaboration (Clark, 2000). We also know that that scaffolding (or structuring) collaborative interaction is often critical for achieving effective learning gains (Palincsar & Brown, 1984; Hausmann, 2006; see Lin, 2001 for a review). For example, Hausmann (2006) conducted an experiment in which students solved a design problem in one of the three conditions: individually, in collaboration with a peer, and in collaboration with a peer but with specific instructions on conducting elaborative dialogues. Students in the elaborative dialogues condition outperformed the individuals and dyads who received no scaffolding. This is consistent with other results that show that providing scripted problem solving activities (e.g., one participant plays the role of the tutor vs. tutee and then switch) facilitate collaborative learning compared to an individual or unscripted conditions (McLaren, Walker, Koedinger, Rummel, Spada, & Kalchman, 2007). These results are typically explained in terms of the sense-making processes in which the structured collaborative environments provide the learner more opportunities to construct the relevant knowledge components.

Learning Mechanisms Underlying Collaboration Although much work has focused on improving learning through collaboration, little research has examined the cognitive processes underlying successful collaboration. Most of the prior has focused on the outcome or product of the group and less has been concerned with the underlying processes that give rise to the product. If we can uncover the cognitive processes underlying collaborative learning, it can further our understanding of how to improve collaborative learning environments. Hausmann, Chi, and Roy (2004) have identified three mechanisms in which collaboration can work. The first is “other directed explaining” and occurs when one partner explains to the other how to solve a problem. The second is explanation through “co-construction” in which both partners equally share the responsibility of sense-making. Collaborators extend each others’ ideas and jointly work towards a common goal. The third mechanism is “self-explanation” in which one partner is engaged in a knowledge-building activity for their own learning. Data from physics problem-solving by undergrads showed that all three mechanisms are at play in collaborative problem-solving. However, the former two are more beneficial to both partners while the third is only beneficial to the partner doing the self-explaining. In the current work we aim to build upon this research by examining dyads verbal protocols for how they engage in collaboration and the degree to which they use each of these mechanisms. In addition, we examine other cognitive factors that impact learning including error-correction (Ohlsson, 1996), constructing a joint mental model (Clark, 2000), and schema acquisition (Gick & Holyoak, 1983). In addition, the current work extends previous research by systematically investigating the degree to which analogical comparisons improve successful collaboration.

Schema Acquisition and Analogical Comparison A problem schema is a knowledge organization of the information associated with a particular problem category. Problem schemas typically include declarative knowledge of principles, concepts, and formulae, as well as the procedural knowledge for how to apply that knowledge to solve a problem. Schemas have been hypothesized as the underlying knowledge organization of expert knowledge (Chase & Simon, 1973; Chi et al., 1981; Larkin et al., 1980). One way in which schemas can be acquired is through analogical comparison (Gick & Holyoak, 1983). Analogical comparison operates through aligning and mapping two example problem representations to one another and then extracting their commonalities (Gentner, 1983; Gick & Holyoak, 1983; Hummel & Holyoak, 2003). This process discards the elements of the knowledge representation that do not overlap between two examples but preserves the common elements. The resulting knowledge organization typically consists of fewer superficial similarities (than the examples) but retains the deep causal structure of the problems. Research on analogy and schema learning has shown that the acquisition of schematic knowledge promotes flexible transfer to novel problems. Many researchers have found a positive relationship between the quality of the abstracted schema and transfer to a novel problem that is an instance of that schema (Catrambone & Holyoak, 1989; Gick & Holyoak, 1983; Novick & Holyoak, 1991). For example, Gick and Holyoak (1983) found that transfer of a solution procedure was greater when participants’ schemas contained more relevant structural features. Analogical comparison has also been shown to improve learning even when both examples are not initially well understood (Kurtz, Miao, & Gentner, 2001; Gentner Lowenstein, & Thompson, 2003). By comparing the commonalities between two examples, students could focus on the causal structure and improve their learning about the concept. Kurtz et al. (2001) showed that students who were learning about the concept of heat transfer learned more when comparing examples than when studying each example separately. Several factors have been shown to improve schema acquisition including: increasing the number of examples (Gick & Holyoak, 1983), increasing the variability of the examples (Chen, 1999; Paas & Merrienboer, 1994), using instructions that focus the learner on structural commonalities (Cummins, 1992; Gentner et al., 2003), focusing the learner on the subgoals of the problems (Catrambone, 1996, 1998), and using examples that minimize students cognitive load (Ward & Sweller, 1990). An ongoing project by Nokes and VanLehn in the Physics LearnLab explores how students’ learning and understanding of conceptual relations between principles and examples can be facilitated (Nokes & VanLehn, 2008). Students in this research, learned to solve problems on rotational kinematics in one of the three conditions: read worked examples, self-explain worked examples, and engage in analogical comparison of worked examples. Preliminary results showed that the groups that self-explained and engaged in analogical comparison outperformed the read-only control on the far transfer tests. Our current project builds upon these results by applying them in a collaborative setting. In summary, prior work has shown that analogical comparison can facilitate schema abstraction and transfer of that knowledge to new problems. However, this work has not examined whether analogical scaffolding can lead to effective collaboration. The current work examines how analogical comparison may help students collaborate effectively. We hypothesize that analogical prompts will facilitate not only analogical learning, but also other learning mechanisms such as explanation, co-construction, and error-correction.

Research Questions

• How can analogical comparison help students collaborate effectively?
• Can analogical comparison facilitate but also other learning mechanisms such as explanation, co-construction, and error-correction during collaboration?

Independent Variables

The only independent variable was Experimental Condition. There were three conditions: Compare, Non-compare, and Problem-solving.

Compare Condition: Participants in this condition first read through and explained two worked examples. The worked examples did not have explanations for the solution steps and students were encouraged to generate the explanations and justifications for each step of the problem. They then performed the analogical comparison task, in which they were told that their task was to explicitly compare each part of the solution procedure to one another noting the similarities and differences between the two (e.g., goals, concepts, and solution procedures). Prompts in the form of questions to guide them through this process were provided. After a fixed amount of time, they were given the model answers to the questions and asked to check them against their own answers.

Non-Compare Condition: Participants in this condition first read through a worked-out example. Similar to the non-compare condition, they were not given the explanations of the steps, and generated the explanations while working collaboratively. After reading through and explaining the first example they answered questions designed to act as prompts for the students to explain the worked example. These prompts were equivalent to the comparison prompts however they were only focused on a single problem (e.g., “what is the goal of this problem”). After a fixed amount of time, they were given the model answers to the questions and asked to check them against their own answers. They were then given a second worked example isomorphic to the first one. Again, students studied the example and generated explanations. They then answered questions based on the second worked example. After a fixed amount of time, they were provided answers to those questions.

Problem-Solving Condition: The problem-solving condition served as a control condition and collaborated to solve problems without any scaffolding. Students in This condition received the same worked examples as the two experimental groups, but without any prompts to guide them through the problem-solving process. They were given additional problems for practice, to equate the time on task with the other two conditions.

Hypothesis

The following hypotheses are tested in the experiment:

1. Analogical scaffolding will serve as a script to enhance learning via collaboration, therefore students in the compare condition will outperform students in the other two conditions. Students in the compare and non-compare conditions will both outperform students in the control condition.

2. Students learning gains will differ by the kinds of learning processes they engaged in. Specifically, students engaging in self-explaining, other-directed explaining, and co-construction will show differential learning gains. This is an exploratory hypothesis and will be tested by undertaking a fine-grained analysis of verbal protocols generated by students as they solve problems collaboratively.

Dependent variables

Normal post-test Near transfer, immediate: After training, students were given a post-test that assessed their learning on various measures. Specifically, 5 kinds of questions were included in the post-test. Robust learning Long-term retention: On the student’s regular mid-term exam, one problem was similar to the training. Since this exam occurred a week after the training, and the training took place in just under 2 hours, the student’s performance on this problem is considered a test of long-term retention. Near and far transfer: After training, students did their regular homework problems using Andes. Students did them whenever they wanted, but most completed them just before the exam. The homework problems were divided based on similarity to the training problems, and assistance scores were calculated. Accelerated future learning: The training was on electrical fields, and it was followed in the course by a unit on magnetic fields. Log data from the magnetic field homework was analyzed as a measure of acceleration of future learning.