¶ … emerged from the various sources above about how often a review of data should take place? In principle, review of data should occur continuously and, more importantly, at intervals contemporaneous with ongoing lesson delivery to confer the maximum benefit to learners. By contrast, retrospective review of the data completely excludes current learners from the benefits of even the most prescient data analysis and any corresponding changes to the educational program or curriculum inspired by those analyses. In theory, the more frequently and regularly data review is conducted, the better for all stakeholders. Ideally, data review should occur on a day-by-day, hour-by-hour, or even minute-to-minute to provide maximum benefit. Realistically, periodic data review at practical intervals allows educators to respond to the implications of those data while those responses can still benefit current learners and without over-burdening the institution or the system's resources.

In the Canadian Report of Data Use PDF included in this assignment, there are four Lessons to Learn and some "Implications for Educational Practice" from the study report. How do they compare/contrast to the goals of data collection that the Texas Turnaround PDF (Austin ISD program) describe as the best use of data?

The Texas Turnaround Austin ISD Program approach seems to be a departure from the more general consensus emphasizing continuing data analysis at the shortest possible regular intervals (such as the day-by-day, hour-by-hour, or even minute-to-minute concept advocated by the 2011 Region XIII Texas Turnaround Framework). Instead, the Texas Turnaround Austin ISD Program merely recommends a summertime Campus Leadership Team retreat to analyze data and to complete a focused data analysis. Moreover, that approach incorporates data review together with a needs assessment, student level review, and the development of a school improvement...

Specifically, waiting until summertime to conduct the data analysis would almost certainly contradict the lesson about not flying blind through large amounts of data (Lesson 1), simply because the irregularity of summertime data review would almost guarantee the inundation of reviewers with large amounts of data to be reviewed in a very limited timeframe. Similarly, according to the Canadian report, in order for data analysis to generate meaningful results, teacher need training, guidance, and practice in drawing relevant conclusions from raw data (Lesson 3). In that regard, it would seem that the Texas Turnaround Austin ISD Program approach of conducting data analysis during the summertime retreat is not at all conducive to providing the necessary training and guidance referenced in the Canadian report.
Predictably, these major conceptual differences in the two approaches are also reflected in the relative applicability of the implications for educational practice outlined by the Canadian Report. Specifically, the Canadian report advocates the development of data-analysis-oriented learning communities of educators; it emphasizes the responsibility of educational leaders to guide the data analysis process; and it refers to the importance of creating a "data-based decision making culture" among educators. All of these recommendations presume that data analysis will be much more thoroughly and regularly incorporated into the teaching environment than the summertime retreat approach to data analysis and results application.

3. In the Texas School Turnaround PDF presentation, we see that goals for the AU campuses in Austin ISD were important aspects of the reform process. What does the work of Mike Schmoker say about goals and do you feel this aligns with the Texas Turnaround center…