Technology-Based Personalization
Subtitle: Instructional Reform in Five Public Schools
By: David Nitkin
Publisher: [publisher not identified]
Published: 2018
Place of publication: [New York, N.Y.?]
Language: eng
Description: This dissertation addresses the question: How does an attempt to redesign instructional delivery using technology-based personalization affect the technical core of teaching, learning, and student outcomes? In recent years, many prominent educators, business leaders, and philanthropists have suggested that schools be redesigned to personalize students’ learning experiences using technology. However, the justification for these reforms remains largely theoretical. Empirical research on technology-based personalization is sparse, and what little research does exist focuses predominantly on macro effects rather than the specific school-level, class-level, student-level, and lesson-level mechanisms that contribute to overall student achievement. The absence of research that pushes inside the “black box” of implementation is particularly problematic given a century of failed attempts to reform the technical core of instructional delivery, with symbolic reforms typically withering in the face of institutional resistance.

This study attempts to address that gap by examining the implementation of an innovative model for using technology-based personalization to deliver middle school math instruction. I draw upon theoretical tools from institutional theory, instructional improvement, and the history of educational reform to deepen our understanding of how technology-based personalization affects the role of students and teachers, the logistics of content delivery, and students’ learning outcomes. Unlike previous studies in K-12 settings, which typically use summative assessments and virtual control groups to estimate aggregate effects on student learning, this study examines the relationships among a diverse set of lesson-level variables, including instructional modality, instructional content, group size and composition, teacher characteristics, student characteristics, and learning outcomes. In doing so, this study contributes to our understanding of the on-the-ground processes and mechanisms by which technology- based personalization affects (or does not affect) student learning.

Although the instructional model documented in this case study will remain anonymous, it is well known and respected among educators and philanthropists, and regarded as one of the most prominent and archetypical examples of technology-based personalization currently active in American schools. Using multiple methods, including novel applications of hierarchical linear modeling, cluster analysis, and heatmap data visualization, I explore: (a) the degree to which ground-level implementation of technology-based personalization represents an authentic departure from the traditional technology of schooling, and (b) the relationships among various elements of the model and student learning outcomes. I draw on longitudinal data from a full year of implementation in five schools, including the daily lesson assignments and assessment scores of 1,238 unique students supervised by 48 teachers. This study supports four main findings: (a) the program succeeds in altering the technical core of instruction in several fundamental ways; (b) policy and logistical constraints limit the program’s ability to reform the technical core of instruction to the degree that it aspires; (c) students who enter the program as already higher-performing are more successful on daily exit slips than students who enter the program with lower performance; and (d) the quantitative methods used in this paper represent useful and replicable tools for exploring the data produced by technology-based and personalized models.
Notes: Department: Education Policy.

Thesis advisor: Douglas Ready.

Thesis (Ph.D.)--Columbia University, 2018.