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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.

DNA le to, such as shown in Figure 5-30.

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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82 Limits of urbanization without an integration solution Policies of IT urbanization have been deployed principally in large enterprises throughout Europe23 Essentially they have consisted of incorporating the development model for computing applications into the rules of urbanism Norms and methods have been published to this end: it remains only for development teams to apply them, which does not always happen Business managers can sometimes, if not often, consider these norms as constraints that weigh down the development workload and cause schedules to drift It should be clearly understood that, viewed by the department heads, the urbanization of computing applications does not seem at first glance to be of much help The reactivity, flexibility and adaptability of urbanized applications is only observable over the long haul.

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

DNA le to, such as seen in Figure 5-31.

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.

What good does it do to spend more time and money, while the business side is often demanding short-term results IT urbanism rests in fact on the good will of both business and IT actors, or the sustained vision of certain key players in the enterprise There is no operational necessity to implementing it 83.

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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