The Role of Technologies in Facilitating Collaborative Engagement
Suparna Sinha, Karlyn Adams, Toni Kempler Rogat, Cindy E. Hmelo-Silver
Rutgers University, 10 Seminary Place, New Brunswick, NJ
Abstract: Computer-supported inquiry learning has the potential to foster productive
engagement with the task and also enhance students' motivation. This may occur because
students have the opportunity to collaborate around on authentic problems, often situated in
media-rich environments. However, we have limited understanding of the quality of
engagement fostered in these contexts and how technology might support high quality
engagement. This study explores technological affordances on influencing students’
engagement technology-rich curriculum unit on aquatic ecosystems.
Previous research has identified design features of technologies that foster self-regulation and high quality
engagement (Azevedo, 2005; Gresalfi, et al., 2009). Current research suggests that students can be engaged if
given opportunities to work in computer-supported, inquiry based learning environments (Järvela & Salovaara,
2004; Veermans & Järvela, 2004). However, we have limited understanding of the specific affordances for
students’ engagement in these technology-enhanced settings, as well as the range in quality of engagement
fostered in these contexts. Our primary research interest here is to compare the extent to which computer
technologies with varying features promote two forms of engagement- task and conceptual-to-consequential
(Gresalfi, et al., 2009), in middle school science students. Facilitating high quality engagement is critical given
benefits of engagement for learning outcomes.
We define high quality task engagement (TE) as attempts to plan, monitor the plan’s enactment, and move
beyond focusing on superficial features. Planning is more than superficial when it moves toward the task
solution and problem solving. Conceptual-to-consequential (CC) engagement characterizes attempts at content
connections on a continuum that range from simple knowledge telling (low), to content connections (moderate),
to connections to prior knowledge, everyday experiences or the larger problem (i.e., consequential engagement).
Over the course of a 6-week 7th grade science unit, two four-student groups were videotaped as they
investigated possible causes of fish death in a pond. Students used Net Logo simulations (Wilensky & Reisman,
2006) to explore aquatic ecosystem processes (see Figure 1) to uncover the cause of lack of oxygen for the fish,
and the Ecological Modeling Toolkit (EMT; Vattam et al, 2011) to model their evolving understanding of the
problem (see Figure 2). We predicted that the simulations would foster moderate CC engagement because we
anticipated students would engage in interpret the simulations without making connections to the larger
problem. In comparison, we predicted EMT would foster high quality CC engagement given the opportunities to
make sense of data gathered from multiple sources that they would then integrate into their evolving
explanatory models. Two full-lesson observations per group were selected for analysis; as students worked with
simulations and then as they revised their models using EMT. Videos were segmented at five-minute intervals
and coded as high, medium or low engagement based on our definitions of TE and CC engagement; codes were
accompanied with justification.
Figure 1. Aquatic ecosystem simulation
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Figure 2. EMT model
In contrast to our predictions, groups demonstrated higher quality TE and CC engagement using the simulations.
For example, high levels of CC engagement were observed when students set up parameters (i.e., high sunlight,
low nutrients) to recreate the problem scenario for understanding fish death. Students discussed relationships
among components (content connections) and considered how these factors may have led to the conditions that
caused fish death (CC engagement). Equally surprising, groups focused on specific, but individual, aspects of
the larger problem when working with the EMT software. This included defining the components they
considered in their model (e.g. algae, sunlight). However, groups did not go beyond to make connections among
these concepts (Figure 2). For instance, both groups discussed that high temperature is responsible for the algal
bloom, but neither connected it to lack of oxygen. Moreover, observed TE was moderate in quality, given that
planning during model creation focused on the physical layout, rather than on solving the larger problem.
There is a general concern that schools do not give students opportunities to engage with curricular content in
conceptually and consequentially meaningful ways (Gresalfi et al., 2009). If students have not been prepared to
think about conceptual connections between varying contexts, it is not likely that they will transfer what they
have learned beyond the school setting. This study is a step towards developing models of how high quality
collaborative engagement is mediated by specific technological affordances and how that affects learning and
transfer. Future work needs to generalize these findings beyond this case study and examine the relation
between conceptual and consequential engagement on one hand, and learning and transfer on the other hand.
Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-
regulated learning. Educational Psychologist, 4, 199- 209.
Blumenfeld, P. C., Kempler, T. M., & Krajcik, J. S. (2006). Motivation and Cognitive Engagement in Learning
Environments. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 475-488).
New York: Cambridge University Press.
Gresalfi, M., Barab, S., Siyahhan, S., & Christensen, T. (2009). Virtual worlds, conceptual understanding, and
me: designing for consequential engagement. On the Horizon, 17, 21-34.
Järvela, S., & Volet, S. (2004). Motivation in real-life, dynamic and interactive learning environments:
Stretching constructs and methodologies. European Psychologist, 9, 193-197.
Vattam, S., Goel, A., Rugaber, S., Hmelo-Silver, C., Jordan, R., Gray, S., et al. (2011). Understanding complex
natural systems by articulating structure-behavior-function models. Educational Technology & Society,
Veermans, M. & Järvelä, S. (2004) Generalized achievement goals and situational coping in inquiry learning.
Instructional Science, 32, 269–291.
Wilensky, U. & Reisman, K. (2006). Thinking like a wolf, a sheep or firefly: Learning biology through
constructing and testing computational theories – an embodied modeling approach. Cognition and
Instruction, 24, 171-209.
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