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  • Editor’s Note
  • George Anthony Peffer

While assessment efforts rightly focus on student learning, such efforts sometimes place their leaders so solidly within the “trees” of syllabi, classrooms, and assignments as to hinder appreciation of how these more formal indicators fit within a broader view of student development. For those who seek to effectively “close the loop” by using assessment data to inform improvements in teaching methods, failure to view their work through the lens of the “forest” can generate considerable frustration when genuinely better teaching still fails to result in appreciably better learning outcomes. The array of ancillary factors that affect learning, if not properly accounted for, can erode the trust of faculty members in processes they might already approach with at least a modicum of suspicion—faculty members who serve as the bedrock of both assessment and its application.

The articles contained in JAIE 4.2 illustrate the transformational potential of successfully merged “tree” and “forest” thinking. Dale Carpenter, Renee Corbin, and Nancy Luke demonstrate how support initiatives in operational areas like advising, organizational climate, and evaluation methods can help to yield better learning outcomes. Gary Blau, Corrine Snell, Deborah Campbell, Krupa Viswanathan, Lynne Andersson, and Andrea Lopez present a most promising means of measuring the impact of pre-professional engagement on student success in the classroom and advancement toward graduation. Shaikha Jabor Al-Thani, Ali Abdelmoneim Khaled Daoud, Adel Cherif, and Dalal Moukarzel offer an impressive example of how [End Page vii] depth of institutional commitment can affect the quality and applicability of assessment efforts. Shani Carter offers a comprehensive strategy for combining “tree” and “forest” perspectives in the assessment of doctoral programs. Together, these authors create an authentically optimistic guide for effectively addressing the complexities of assessment. We offer them as inspirational as well as advisory models. [End Page viii]

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