Software Can Help Learners Become Action Researchers Investigating the Four Challenges of Learning in VS .NET

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Software Can Help Learners Become Action Researchers Investigating the Four Challenges of Learning
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We believe that appropriately designed software can assist students to overcome four key challenges entailed by SRL theory. Each of these challenges can be addressed through opportunities for prompts, assessments, and feedback. Students can use software tools which enable these opportunities as instruments for researching their learning processes.
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Calibrating prior knowledge
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If learners are asked what they know, they typically err in their estimates; that is, they are miscalibrated about knowledge. In general, the correlation between estimated knowledge and actual knowledge hovers at about 0.3 (Dunning, Heath, & Suls, 2004). Learners with more knowledge, as measured by tests or other objective means, commonly are underconfident about what they know. In contrast, learners with less knowledge tend to be overconfident 179
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John C. Nesbit and Philip H. Winne (Hacker, Bol, Horgan, & Rakow, 2000). Weak calibration of knowledge interferes with productive SRL. Specifically, learners who overestimate their knowledge are led to believe their studying tactics have greater effectiveness than they do, and thus bypass adapting how they learn. Learners who underestimate what they know may be inclined to switch from effective study tactics when they don t need to, and replace these with less effective tactics.
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Calibrating study tactics and learning strategies
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As with knowledge, learners err in recalling how they study. This area of research is quite new owing to only recently having tools (such as the gStudy software we describe later) to record how learners study. In two experiments, learners were observed to be moderately inaccurate in recalling how they had studied (Winne & Jamieson-Noel, 2002; Winne, Jamieson-Noel, & Muis, under review). Typically, correlations between recollections of the frequency with which study tactics were used and traces of study tactics actually used range from near 0.0 to approximately 0.5. When learners are poorly calibrated about how they study, they are in a weak position with respect to knowing what to change. They may unknowingly choose to repeat study tactics that are less effective.
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Underdeveloped study tactics and learning strategies
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It is easy to demonstrate that learners are not as well equipped with study tactics as they might be. Almost all of the hundreds of studies in which some learners have been taught study tactics or learning strategies while others have not, reported differences in achievement favoring students specifically taught how to learn (Pressley & Harris, 2006). Regrettably, too few learners have access to explicit instruction about these tools for learning (Rosenshine, 1997).
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Tracking progress in knowledge and study tactics
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Like forming sentences in conversation or tying one s shoes, studying is often on autopilot and much of this activity goes unnoticed. In part, this can explain why unproductive SRL may persist. If learners don t attend to enough of what they do as they learn, they not only are miscalibrated but occupy a weak position for modulating learning on the fly. When they are challenged by compelling evidence that something is off track, particulars of what was done while studying are inaccessible. We label this the tracking problem. Without accurately tracking study tactics, it is very difficult to make effective repairs. The same is true of knowledge. Although learners 180
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Tools for Learning in an Information Society are generally less accurate in self-monitoring individual test item performance than overall test performance, high performing students are more precise than others in tracking performance at the item level. As Neitfeld, Cao, and Osborne (2005, p. 24) put it, poor students understand they are poor students but may not know where to target their efforts to improve, whereas better students may tend to be more strategic and aware of where they need to expend their efforts toward improvement.
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Software can be designed so that, as learners work with it, their interactions are recorded. Each record may identify the information on which the learner acted, the learner s action, the information context in which the action was performed, and the time at which the action was performed. These data trace the cognitive operations with which learners process information and mediate instructional designs (Winne & Perry, 2000). With a log of trace data, one can reconstruct a time-marked description of the observable actions that reflect how a learner studied or solved a problem. This includes the fine-grained activities that constitute learning, expressions of motivation, and the strategic patterns of these that constitute active and self-regulated learning. Referring to activity reports generated by software that analyses trace data, researchers can investigate how learners study and to what effects. Using activity reports, learners can answer a multitude of questions such as: How often do I make notes What do my searches reveal about my interests Is there a regular pattern to my studying How long did it take me to construct a concept map Did I forget the vocabulary words I missed on the test, or did I not study them Researchers can use activity reports to answer similar questions relevant to instructional theories. Quantitative and qualitative research methods can be applied to characterize patterns of study activities at individual and aggregate levels (Nesbit & Hadwin, 2006; Winne, Gupta, & Nesbit, 1994). It was the potency of log data for researching SRL, and the promise of software tools to assist students in meeting the four challenges of learning, which motivated our participation in the Learning Kit Project.
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