Alejandro Andrade, Joshua A. Danish, Adam V. Maltese
To understand ecosystem dynamics among competing populations, experts have focused on developing models of disturbances over time (Grotzer, Kamarainen, Tutwiler, Metcalf, & Dede, 2013). In ecosystems, a dynamic disturbance framework includes concepts such as interacting feedback loops among chained populations (Hokayem, Ma, & Jin, 2015). For instance, predation ecosystems show that while the numbers of predator and prey are continuously changing, the two populations constitute an unstable yet resilient equilibrium over time (Folke, 2006; Grotzer et al., 2013). However, understanding dynamic disturbances as an emergent property of predation ecosystems is hard because it requires an understanding of causal effects that take place over time, which manifest as cyclical, non-linear, bidirectional, and delayed effects (see Figure 1; Folke, 2006; Mitchell, 2009).
To help understand this complex aspect of ecosystems (i.e., disturbances and interactions), the proposed project builds upon theories of embodied cognition. Embodiment theory suggests that, in order to learn a concept meaningfully, this concept should be grounded in body-based experiences (e.g., Abrahamson & Sánchez-García, 2016; Lee, 2014; Nathan et al., 2014; Smith, King, & Hoyte, 2014). Body-based experiences provide support for the understanding of a concept via analogical mapping (Barsalou, 2008; Lakoff & Núñez, 2000), where simulated mental models of action support learning insofar as the dynamic aspects of the body-based experiences map onto the dynamic aspects of the to-be-learned concept. Although the exact cognitive mechanisms of embodiment are still unclear (Mahon & Caramazza, 2008), educational researchers are making use of sensing technologies, such as the Kinect, the Leap Motion sensor, or automated visual tracking, to design embodied activities in which learners make use of gestures or physical movements to interact with virtual objects.
Eliciting Congruent Gestures
While many studies have focused on spontaneous movements, fewer have examined how those movements solicited explicitly by the experimenter or the design of educational software might influence students’ explanations and learning (Lindgren, 2015; Nathan et al., 2014). We refer to these explicit movements, solicited by the pedagogical design, as elicited movements. The working hypothesis is that the increase of one’s movement repertoire would spur the learning of a concept by grounding that concept in elicited body-based experiences (Lindgren, 2015). Furthermore, the training experience would leave a historical trace in students’ cognition (Nathan & Walkington, 2017). During later performance or explanations of the physically-learned concept, this embodied impression will express in the form of gestures (Hostetter & Alibali, 2008; Nathan & Walkington, 2017). As these gestures are congruent with the dynamic aspects of a concept, but take place at a later time, we refer to them as spontaneous congruent gestures. Moreover, researchers suggest that production of spontaneous congruent gestures during later performance or explanations correlates with learning gains due to the tight coupling between gesture and conceptual understanding (Kang & Tversky, 2016; Nathan & Walkington, 2017).
The Embodied Simulation of Predation Dynamics (ESPD)
In our instructional design, called the Embodied Simulation of Predation Dynamics (ESPD), the student uses their hands to represent unstable equilibrium between two populations (e.g., foxes and rabbits) as a phase-shift sinusoidal cyclical pattern. The student sees a graphical change and is asked to shadow the movement of the graphs with their hands, and, in this way, has an embodied experience of unstable equilibrium between two populations (see Figure 2). In an environment such as this, how will we know if the embodiment is supporting learning?
Methods and Data Sources
Fifteen third and fourth graders (F = 8, M= 7, Avg. Age = 9.13, SD Age = 0.8) were individually interviewed by one of the researchers where they answered a pre-tutorial questionnaire, interacted with the simulation, and then answered a post-tutorial questionnaire. Interviews were videotaped and took thirty minutes in average. The learning space was created by two pedagogical moves: (a) eliciting a bimanual movement, and (b) an inquiry-based approach with predicting and reflection questions between tasks that help the student to reflect beyond superficial structural analogies (Nathan & Walkington, 2017). The tutorial protocol included nine tasks, dividing in three phases (Briefing, Training, and Demonstration). The pre- and post-tutorial questions were adapted from Hokayem, Ma, and Jin (2015) and students’ answers were scored using the Feedback Loop Reasoning Coding Scheme, also adapted from Hokayem et al. (2015). The coding scheme consists of seven levels (the higher the level the more in-depth the students’ understanding is), detailing the reasoning progression about feedback loops within a predator-prey ecosystem (see Figure 3). A Wilcox Sign test for repeated measures compared the pre- and post-tutorial median difference, and a Mann-Whitney U test compared between gesturing and non-gesturing groups median difference. To analyze students’ use of gestures during the interviews, multimodal transcripts (Norris, 2004) were created.
Finding 1: After experiencing an elicited movement of predation ecosystem dynamics with the ESPD, learners showed learning gains of unstable equilibrium
Feedback loop reasoning significantly increased from pre to post-tutorial scores (Mdn = 4 and 6, respectively), Z(15) = 2.779, p-value = .008, with a large effect size, r = .718.
Finding 2: Changes in UE understanding correlate with spontaneous use of congruent gestures
Students who gestured in their post-tutorial explanation had greater learning gains from pre to post-tutorial (Mdn = 1.5 point increase) than those who did not gesture (Mdn = 0 point increase), and this difference is statistically significant, Z(15) = 1.878, p-value = .033 one-sided alternative hypothesis, with a large effect size, r = 0.485.
Finding 3: Gestures play an important role in students’ explanations of cyclical, non-linear, bidirectional, delayed effects within predation ecosystems (See Figure 4)
Discussion and Significance
While this study was only exploratory in nature, the results are promising and suggest there is value in continuing to pursue these questions. First, we noted that students benefited from the instruction with the ESPD, as illustrated by the significant learning gains from pre- to post-tutorial explanations. Second, we found that students started to use gestures compatible with predator-prey dynamics to reorient their explanation of the ecosystem dynamics. Third, students who gestured in their post-tutorial explanations also had the greatest learning gains, compared to those students who did not gesture. These results suggest that using elicited gestures to support how students represent systems dynamics while learning about them may help students attend more productively to the patterns represented within the system. This has important consequences for instruction and assessment because, as noted by others (e.g., Alibali & Nathan, 2012; Goldin-Meadow, 2004; Goldin-Meadow & Alibali, 2013), gestures can be used to recognize the student’s disposition for learning a new concept. We are examining various novel ways in which our simulation can be combined with other simulations, such as the agent-based NetLogo simulation (Wilensky, 1999), to further challenge students into reasoning quantitatively about complex systems.
This work was funded by an Indiana University Proffitt Award (2015), by an NSF Data Consortium Fellowship (2016), and by the IU Center for Research in Learning Technologies (CRLT). We also thank the teachers and students who participated.
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