Product Data—Learning Outcomes
This is a very brief summary of the learning outcomes data from the college students. In relation to research question one, the data show that students in the Training condition scored significantly higher on the SRL quiz after training on the SRL processes and were also able to maintain their knowledge of the SRL processes ([t (32) = - 5.02, p < .05]). As for the second research question, preliminary results also indicate that there was no statistically significant difference between conditions for the matching task of the circulatory system [t (32) = .82, p < .05], however, there were significant differences between groups on the labeling task ([t (32) = 2.02, p < .05]) and multiple choice of the circulatory system [t (32) = 1.85, p < .05]. Participants in the training condition outperformed those in the control condition. We found the same pattern of statistically significant results on the three parts of the nervous system (p < .05).
Process Data—Concurrent Think-Aloud Processes
We have also transcribed, coded, re-coded the think-aloud data for several days of the experiment. Here is a brief summary of the process data. We calculated several independent t-tests on the means of the coded SRL processes used by the learners in each of the two conditions. Results indicate that learners in the MetaTutor SRL Training condition deployed significantly the training condition engaged in more activation of prior knowledge activation, recycled goals in the working memory (WM), monitored their emerging understanding by using (positive) judgments of learning, monitored their progress towards goals during the learning session, and used knowledge elaboration as a learning strategy.
In keeping with our goal of deriving instructional implications, it is important to highlight a few key observations. First, the variability reported in some key SRL process such as creating sub-goals, feeling of knowing, coordinating informational sources, summarizing, and taking notes is surprising since these were four of the thirteen SRL processes used in the SRL training regimen. The five processes are quite difficult to acquire and use immediately following the training regimen. As such, the adaptive version of MetaTutor should be able to detect, model, and provide extensive scaffolding regarding the use of the processes.
Second, the low raw frequencies observed is some SRL processes related to planning (planning, time and effort planning), monitoring processes (content evaluation, monitoring use of strategies, self-questioning), use of effective strategies (reading notes), and handling task difficulties and demands (help-seeking) has implications for the design of the adaptive version of MetaTutor. These processes have repeatedly shown to be associated with learning gains in several hypermedia learning studies [e.g., Azevedo, 2007, 2008, 2009]. For example, content evaluation is a key monitoring process that is used by learners (when using non-linear, multi-representational hypermedia learning environments) to compare current external representation of information (i.e., text, diagram, animation) with their current learning goal. This can lead to two situations—1) there is no discrepancy (content evaluation positive; CE+) and therefore the representation is ideal for the current goal, or 2) there is a discrepancy (CE-) and therefore there is a need to select or search the environment for an appropriate external representation. The issue of valence leads to a complex issue regarding adaptivity in MetaTutor since the system must be able to “map” each representation to the current goal and determine when and what type of scaffold to administer (e.g., do you think this diagram of the heart fits with your current goal), in which case the scaffold prompt is generic and is designed to raise a learner’s metacognitive awareness. The system can then wait for the learner’s response and instruct him/her to indicate (see the SRL palette in Figure 1; right-hand side of the screen) their content evaluation. This would then lead to various dialogue moves and corresponding feedback cycles necessary to search for and settle on an ideal representation [see 18]. A related issue, related to socio-cognitive models of SRL [Zimmerman, 2006] is the need for MetaTutor to continuously model these key (yet difficult processes to acquire and internalize) during key phases of learning with MetaTutor. For example, Mary the Monitor can model CE when the system recognized that the current content is complex (e.g., what is the difference between systemic circulation and pulmonary circulation) and the leaner needs to select several diagrams and sequence them in order to foster mental model development.
MetaTutor also traces learners’ navigational paths during each learning session (see Witherspoon et al., under review for a complete analysis). The work here it emphasizing the need to examine how various types of navigational paths are indicative (or not) of strategic behavior expected from self-regulating learners (Winne, 2005).
Figures 2a-2d illustrate the navigational paths of two learners from our dataset while they use MetaTutor to learn about the circulatory system. In figure 2a and 2b, the x-axis represents move x and the y-axis represents x+1. Figure 2a shows the path of a low-performer (i.e., small pretest-posttest learning gains) while Figure 2b illustrates the path of a high-performer. These figures highlight the qualitative differences between a low- and high-performer in terms of the linear vs. complex navigational paths and reading times. For example, the low-performer tended to progress linearly through the content until they got to a key page (e.g., page 16 on blood vessels) and decided to return to a previous page. In contrast, the high-performer’s path is more complicated and is more consistent with a strategic, self-regulated learner by the complexity shown in Figure 2b. This learner progresses linearly and at times makes strategic choices about returning to previously visited pages, and deployed twice as many SRL processes as the low-performing learner (i.e., 212 moves vs. 102 moves, respectively). This is symbolically illustrated in the Figure by the difference between the “space” between the dots—i.e., more space between dots = longer reading times.
The SRL processes deployed by these were hand-written on their navigational paths and presented in Figures 2c and 2d. In Figures 2c and 2d, the x-axis represents time (in minutes) within the learning session and the y-axis represents pages of content (and their corresponding titles). There are several key observations to highlight in terms of keeping with our goal of extracting information for the design of the adaptive MetaTutor. First, there is more complexity in the navigational paths and deployment of SRL processes as seen in the number of processes hand-written in the figures. Second, 70% of the low-performer’s SRL moves were coded as taking notes while the high-performer only used 39% of his processes for taking notes. Third, one can infer (from the “space” between moves) that the low-performer spent more time acquiring knowledge from the environment while the high-performer spent less time reading throughout the session. Fourth, the high-performer used a wider variety of SRL processes compared to the low-performer. A related issue is the non-strategic move by the low-performer to create a new sub-goal near the end of the learning session. However, the high-performer is more strategic in his self-regulatory behavior throughout the learning session. For example, he engages in what can best be characterized as “time-dependent SRL cycles”. These cycles involve creating sub-goals, previewing the content, acquiring knowledge from the multiple representations, taking notes, reading notes, evaluating content, activation prior knowledge, and periodically monitoring their understand of the topic.
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Azevedo, R., Witherspoon, A.M., Siler, E., Cox, M., Chauncey, A., Graesser, A., McNamara, D., Lintean, M., Cai, Z., & Rus, V. (August, 2009). The effectiveness of MetaTutor in training college students to deploy key self-regulatory processes during learning. Paper to be presented at the biennial meeting of the European Association for Research on Learning and Instruction, Amsterdam, The Netherlands.
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New AI-based hypermedia science learning environment with embedded animated pedagogical agents designed to trace, model, and foster students’ cognitive and metacognitive self-regulatory processes.