McLaren et al - Conceptual Learning in Chemistry

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Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support

Bruce M. McLaren, Nikol Rummel, Andreas Harrer, Hans Spada, Niels Pinkwart


PI: Bruce M. McLaren

Co-PIs: Nikol Rummel, Andreas Harrer, Hans Spada, Niels Pinkwart

Others who have contributed 160 hours or more:

  • Dimitra Tsovaltzi, University of Saarland, Germany, experimental design and execution
  • Isabel Braun, Freiburg University, Germany, experimental design and execution
  • Oliver Scheuer, University of Saarland, Germany, data mining and programming
  • Roger Miller, University of Saarland, Germany, programming

The goal of this one-year project is to build/integrate technology and perform small-scale lab tests in preparation for in vivo studies in later years.


Chemistry students, like students in physics, mathematics, and other technical disciplines, often learn to solve problems algorithmically, applying well-practiced procedures to textbook problems. But often these students do not understand the underlying conceptual aspects of the problems they solve algorithmically. An important setting for promoting conceptual knowledge in chemistry is the laboratory, where students must apply not only pre-defined problem solving procedures, but must also plan experiments, hypothesize outcomes, conduct and monitor experiments, and evaluate outcomes. In the PSLC Chemistry LearnLab, the Virtual Laboratory (VLab) is the online software environment used to simulate a real chemistry laboratory and assist students in their conceptual understanding of chemistry. However, the VLab on its own is not enough. We propose to further assist chemistry students in gaining conceptual knowledge, first, through having pairs of students collaborate on problems, assisted by computer-mediated collaboration scripts that extend the VLab and, later, through dynamic adaptation of those collaboration scripts.
In this one-year project, we conducted a pilot study (STUDY 1) comparing how singles and dyads solve chemistry problems with the VLab with and without scripts. We used the results to inform the design of a computer-mediated collaborative environment around the VLab, using a collaborative software tool called FreeStyler. In a subsequent small-scale study (STUDY 2) we compared an adaptive and a non-adaptive version of the system. The adaptation was realized by a human wizard sending feedback to the students following a predefined model. A qualitative data analysis of this study revealed a tendency for the dyads that received the adaptive feedback to improve collaborative skills and be more motivated than the non-adative dyads.
The one-year PSLC project will provide the foundation for an externally-funded project, still conducted within the PSLC Chemistry LearnLab, in which we will perform full-scale in vivo studies to test the hypotheses that (1) collaboration, supported by collaboration scripts, can promote the creation and strengthening of conceptual chemistry knowledge components and (2) that dynamic adaptation of the collaboration scripts can further improve that learning.


See Scripted Collaborative Problem Solving Glossary

Research Questions

Does collaboration – and in particular adaptive scripted collaboration – improve students’ robust learning, and in particular conceptual learning, in the domain of chemistry?

Does the adaptive script approach improve students’ collaboration, and does this result in more robust learning of chemistry content?


These research questions led us to the following two hypotheses:

Computer-mediated collaboration, facilitated by collaboration scripts and added to experimental exercises within the stoichiometry course, can promote the creation and strengthening of conceptual stoichiometry knowledge components.
Computer-mediated collaboration, facilitated by adaptive collaboration scripts and added to experimental exercises within the stoichiometry course, can promote the creation and strengthening of conceptual stoichiometry knowledge components.

Background and Significance

A central issue in chemistry education is teaching students to problem solve conceptually rather than simply apply mathematical equations. Research in chemistry education has shown that students tend to learn and solve problems “algorithmically” but often do not grasp the deeper conceptual aspects of chemistry and reasoning necessary to be more creative and flexible problem solvers (Gabel & Bunce, 1994; Bodner & Herron, 2002). Dave Yaron, the chair of the Pittsburgh Science of Learning Center (PSLC)’s Chemistry LearnLab, has expressed a similar view in observing his students, saying, “many students learn the mathematical tools necessary to solve chemistry problems but don’t know when to appropriately apply those tools” (Personal communication between McLaren and Yaron February 27, 2006; also discussed in Yaron et al, 2003). The phenomenon that learners have problems transferring instructed procedures to new problems due to a lack of conceptual understanding has been observed and investigated also in other domains, for example, math (Singley & Anderson, 1989).

The difficulty chemistry students have can be viewed as a transfer problem, an important area of investigation in the PSLC’s emerging theory of robust learning. In particular, while chemistry students often have success on problems that are very similar to ones illustrated in a textbook or demonstrated in a classroom, they tend to struggle with problems that could be solved with similar techniques but are not obviously of the same type (e.g., the source and target problems don’t share surface features). This difficulty is due to students lacking the conceptual knowledge of chemistry to recognize similar core problems that come in “different clothes.”

There is some descriptive evidence in chemistry education research indicating that collaborative activities can improve conceptual learning in chemistry (e.g. Towns & Grant, 1998; Fasching & Erickson, 1985). Other studies, while not focused specifically on conceptual versus algorithmic learning, have demonstrated increased performance as well as motivational benefits of collaborative learning in chemistry (Ross & Fulton, 1994). On the other hand, none of these collaborative learning studies in chemistry was a randomized controlled experiment. In general, there is a lack of controlled experimentation on the potential benefits of collaborative learning in chemistry. However, such evidence exists in math (Berg, 1993, 1994), physics (Hausmann, Chi & Roy, 2004; Ploetzner, Fehse, Kneser, & Spada, 1999), or scientific experimentation (Teasley, 1995). Research in collaborative learning has shown promise in helping students to more deeply process information and thus improve their conceptual learning. A few different mechanisms are accountable for the benefits of collaborative activities, like giving explanations to the partner, receiving help from the partner after making a mistake or asking for help, and co-constructing or jointly negotiating knowledge (Hausmann, Chi, & Roy, 2004; Ploetzner, Dillenbourg, Preier, & Traum, 1999; Webb, 1989; Webb, Trooper, & Fall, 1995). In sum, results from this research lead us to the assumption that it would be worthwhile investigating the advantages of collaborative activities on the acquisition of robust, transferable conceptual knowledge in controlled experimental studies in chemistry.

In this project, we will test the hypothesis that a computer-supported collaborative learning system can help students improve their conceptual understanding of chemistry. Our goal is to help students actively process the material they encounter, moving them away from the mechanical, algorithmic approach taken by many chemistry students. In terms of the PSLC theoretical framework, we assume that the collaborative situation creates additional learning events through the above cited mechanisms of receiving help, giving explanations, and co-constructing knowledge. In addition, the collaborative setting may increase the likelihood that students capitalize on the learning events offered by the domain setting (i.e. the chemistry learning environment). That is, collaborative interactions can increase the likelihood of particular path choices in the learning event space that benefit learning. To test our hypothesis, we will develop collaborative extensions to the Virtual Lab (VLab) and compare individual learning in the course with scaffolded collaborative learning.

We believe that it might be best to scaffold collaboration in an adaptive fashion, emphasizing and fading structured support for collaboration according to the particular needs of the specific collaborators. Past studies suggest that different students, under different circumstances, may benefit from different types of collaboration support; thus, a collaborative learning system that can adapt its support might prove quite powerful. One study that we are aware of, in which adaptive, strategic “prompts” in a collaborative system were shown to lead to productive collaboration and support for learning, is the work of Gweon, Rosé, Carey, & Zaiss (2006). While interest in adaptive collaborative learning systems is on the rise in the computer-supported collaborative learning (CSCL) community (Soller, Jermann, Muehlenbrock, & Martinez, 2005), little progress has yet been made on the implementation of adaptive support.

This PSLC project is only the first year of what we intend to be a four-year project. Our goal in the first year will be to

  • analyze problem solving in the stoichiomery course, both individual and collaborative,
  • perform small-scale (i.e., small N) lab studies to experiment with the general hypothesis that collaboration support, in the form of collaboration scripts, can enhance conceptual learning of stoichiometry,
  • develop a prototype of an online collaboration system that integrates the VLab, an online simulation of chemistry experimentation and an integral part of the PSLC Chemistry LearnLab (Yaron et al, 2003), with Cool Modes, a software tool that facilitates computer-mediated collaboration and simulation (Pinkwart, 2003).

The technical work will then set the stage for the adaptive collaboration effort we propose in the second phase of the project.

The second phase of the project, after the initial year of PSLC seed funding, will focus on full-scale in vivo experimentation, first testing collaboration scripts and, later, adaptive collaboration scripts. The second phase of the project will also involve continued technical development to support dynamic adaptation of collaborative support, continuing work on the bootstrapping technique, described above, and investigating the use of cognitive tutoring techniques to support collaboration.

Independent Variables

  • scripted (paper-based script consisting of the steps "Orientation", "Experimentation", "Drawing a Conclusion" and "Making an Evaluation") vs unscripted
  • singles vs dyads (students sitting next to each other)
  • control: unscripted singles

  • non-adaptive scripted dyads (computer-implemented script consisting of the steps "Plan & Design", "Test", and "Interpret & Conclude") vs adaptive scripted dyads (non-adaptive script + adaptive support provided by a human wizard using a flowchart to recognize situations of bad collaboration and bad script practice)
  • control: non-adaptive scripted dyads

Figure 1. Screenshot of the computer-based CoChemEx script. The tabs at the top show the different script phases. Human-triggered prompts ("Message from Wizard" in the middle of the screenshot) are only seen by students in the "adaptive" condition. In the "non-adaptive" condition, students receive no prompts.


Dependent Variables

  • Learning
  • Problem solving performance and efficiency
    • Average problem solving time
    • Average number of problems solved
    • Average number of VLab actions performed (per action type)
  • Learning
  • Problem solving behavior (measured by counting the number of occurrences of behavior classes)
    • good and bad script practice (behavior according to the script)
    • good and bad collaboration practice
    • progress during a session with respect to script and collaboration practice (improved, deteriorated, stable)


Due to the small sample size of both studies we cannot report meaningful statistical results. Our findings are therefore preliminary and of anecdotal nature.


The pre-posttest data (Table 1) reveal no substantial differences in the gain scores between the four conditions. The scripted dyad condition performed the poorest in the pre-post test analysis; it was the only group that scored lower on average on the posttest than the pretest. Moreover, in the interviews after the problem solving the scripted dyads unanimously expressed the view that the script was not helpful. However, our results also indicate that, in spite of the perceived constraints of the script, it was nonetheless helpful: An additional log file analysis revealed that the scripted conditions were more efficient in solving problems because they performed far fewer “mix solution” actions in the VLab. That is, they took less steps to achieve similar results. At least anecdotally, collaboration was also helpful, as the scripted and unscripted dyads conditions solved 12 problems in total, while the singles solved only 8 problems (Table 2).

Table 1: Pre-Posttest results

Condition N Pretest (SD) Posttest (SD) Gain (SD)
Scripted dyads 8 4.44 (0.82) 4.31 (0.88) -0.13 (0.52)
Scripted singles 4 3.88 (1.11) 4.38 (1.25) 0.50 (1.48)
Unscripted dyads 8 3.56 (0.62) 4.06 (1.37) 0.50 (1.49)
Unscripted singles 4 4.38 (0.63) 4.38 (0.63) 0.00 (1.08)

Table 2: Problem solving performance

Condition N Avg. time problem type 1 (DNA) Avg. time problem type 2 (Oracle) # problems solved type 1 (DNA) # problems solved type 2 (Oracle)
Scripted dyads 4 19min 43min 3 2
Scripted singles 4 20min 39min 3 1
Unscripted dyads 4 18min 27min 4 3
Unscripted singles 4 21min 36min 3 2


The test results showed a tendency of better conceptual understanding in the adaptive condition. With a highest possible score of 6 points, the adaptive condition mean was M=4.6 (SD 1.63) whereas the non-adaptive condition scored in average M=3.5 (SD 2.81). A process analysis (Table 3) based on video recordings showed a big difference in terms of “good script practice” and “good collaborative practice” in favor of the adaptive dyads. “Bad collaborative practice” is also considerably less in the adaptive condition. Looking at tendencies during sessions we see that students’ collaboration and scripting practice improved for all of the three dyads in the adaptive conditions whereas it remained stable (1 dyad) or even deteriorated (2 dyads) in the non-adaptive condition.

Table 3: Process analysis results

Analysis category Avg. counts adaptive(SD) Avg. counts non-adaptive(SD)
Good scripting practice 6.33 (2.51) 5.00 (2.64)
Bad scripting practice 4.33 (3.21) 7.33 (2.30)
Good collab. practice 5.66 (1.15) 3.00 (1.00)
Bad collab. practice 2.00 (1.00) 1.66 (1.15)



Scripted dyads might have been overloaded by the simultaneous demands of getting acquainted with a computer-based learning environment, collaborating with a partner, attending to a script, and solving a task (Rummel & Spada, 2005b; Rummel, Spada, & Hauser, 2006). The fact that they had to work with a paper-based script, which involved a lot of reading and monitoring of their own activities, increased this effect. As a consequence, we simplified the task in STUDY 2 by using a less complicated script and introducing individual (non-collaborative) script phases.


Our qualitative data analysis suggests that adaptive feedback can improve collaborative skills and might have positive motivational effects. Given the small N, larger scaled studies are needed to get results which are statistically more meaningful.

Connections to Other PSLC Studies

Annotated Bibliography

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