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Are We Preparing Preservice Educators for Using Technology to Personalize Learning?

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Pennsylvania Convention Center, 121BC

Lecture presentation
Listen and learn: Research paper
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Presenters

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Postdoctoral researcher
UNC at Chapel Hill
@LingZhang12
@LingZhang12
Ling Zhang received her doctoral degree in special education with a focus on Instructional Design, Technology, and Innovation (IDTI) from the University of Kansas (KU). She works as a postdoctoral fellow at the University of North Carolina at Chapel Hill. The end goal of Dr. Zhang’s scholarship is to create accessible, inclusive, and engaging learning experiences through innovative implementation of pedagogy and technology for diverse learners with and without disabilities.
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Assistant Professor
University of Wyoming
Richard Allend Carter, Jr. is an assistant professor in the Department of Counseling, Leadership, Advocacy, and Design (CLAD) at the University of Wyoming. His current research focuses on the implementation of self-regulation practices for students with disabilities in both fully online and blended learning environments. In addition, Carter is part of a team that is investigating the role of Voice User Interface in delivering instruction to multiple stakeholder groups in novel learning settings.
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Professional Learning Supervisor
Davis School District

Session description

This session will present a study that analyzed educator preparation standards in the US education system for the presence of teaching knowledge, skills and dispositions that may better respond to the need for implementing personalized learning across disciplines and for diverse student populations.

Framework

The theoretical frameworks guided our study. First, we explored the complexity of PL design and implementation using a theoretically guided model that highlights identifying learner characteristics, adapting instruction grounded in learning theories, and promoting desired educational outcomes for all learners as critical components of a PL design or implementation initiative (Bernacki et al., 2021).

Second, we took a holistic approach to analyze educator preparation standards that reflect expectations for PL design and implementation guided by complexity theory and its application in teacher education (Cochran-Smith et al., 2014; Sinnema et al., 2017). According to complexity theory, teacher knowledge, skills, and attitudes are interwoven to constitute effective teaching practices in an unpredictable and more complex way than the sum of individual parts (Martin & Dismuke, 2018). Explicit integration of various teaching knowledge, skills, and dispositions towards PL implementation provides insights into the complexity of preparing educators for PL implementation.

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Methods

This study utilized an alignment methodology to examine the congruence between educator preparation standards for intended teacher competency oriented towards PL conceptualizations and implementation. Studies applying alignment methodology explore the degree to which educational policies, standards, curriculum, and assessment are congruent with each other, thus providing data for educational stakeholders to move toward shared goals and achieve desired outcomes (DeLuca & Bellara, 2013; Martone & Sireci, 2009; Webb, 2007). For this study, we adapted Webb’s (2007) methodology, which has been widely used to investigate alignment between assessments and standards.

Data Source.
Data used for this study were collected from a large-scale education professional standards database. This database includes more than 5,000 professional standards in the U.S. education system, including Specialized Professional Associations (SPA) standards, accreditation standards, high-leverage practices for in-service general and special teachers, and teaching evaluation standards (Authors, 2022). As this study was focused on educator preparation, we only included the Interstate Teacher Assessment and Support Consortium (InTASC) and SPA standards from the database. Eighteen SPAs were included given that they are actively utilized for accreditation by the Council for the Accreditation of Educator Preparation (CAEP), which is an organization accrediting educator preparation programs (EPPs) in the United States. It is important to note that standards within each set are usually accompanied by an explanation and further delineated through components, indicators, or principles for implementation, all of which will be referred to as standard components to ensure consistency in nomenclature hereafter. For example, as the most comprehensive set of standards, InTASC contains 174 specific standard components within ten core standards.

Data Analysis
Two subject matter experts (SMEs) from the research team analyzed the categorical concurrence between standard components and core components regarding PL implementation. To extract relevant standards from the data pool for analysis, the two SMEs evaluated each standard component for the presence of keywords and terms reflecting PL. Guided by the taxonomy of PL critical components and design features proposed by Bernacki et al. (2021), SMEs created a list of keywords to extract standards. To ensure comprehensiveness, SMEs cross-analyzed keywords in PL definitions from frequently cited research papers, policy documents, and educational organization reports. These keywords fell under three broad categories: (a) learner characteristics; (b) flexible designs of instruction and learning environments adapted to learner characteristics; and (c) targeted learning outcomes promoted by PL designs. Further, SMEs conducted iterative, open-coding of included standards to allow themes within each PL-related category to emerge (Strauss & Corbin, 1998).

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Results

Of 126 educator preparation standards and 654 standard components, 72 standards and 193 components were included and coded for alignment to PL implementation. Overall, only one standard component for school counselor preparation explicitly used the phrase “personalized instruction.”

All included sets of standards contain components reflecting expectations for educators to tailor instruction and support individual learners’ needs. In total, 29.51% of standard components across all sets of standards contain phrases that reflect PL implementation. Most components focus on describing flexible instructional designs (n = 161; 83.42%) and learner characteristics (n = 131; 67.87%). Learning outcomes have a lower representation (n = 20; 10.36%) in components reflecting PL.

Analysis of references to flexible designs of instruction and learning environments in standards revealed four major themes centered around expectations for educators to 1) Design, 2) Adapt, 3) Assess, and 4) Access instruction to meet learner needs. Table 2 summarizes categorization results and specific instances. Eleven standard components highlight the use of technology for PL. Specifically, technology to support instructional design centered on advocacy to acquire necessary resources and collaboration across educational stakeholders to support implementation. These technologies aimed to reduce virtual barriers to instruction and leverage technology to provide accessible instruction for all students. Detailed information on technology use will be discussed in the presentation.

The analysis revealed that preparation standards use various terms to describe learner characteristics. During the open coding process, three subcategories emerged, including overarching/generic, context-, and performance/skill-related characteristics. Moreover, a small percentage of standard components (n = 20; 10.36%) were identified that incorporate elements specific to supporting students in achieving desired learning outcomes as part of educators’ efforts to recognize individual learner characteristics and adapt instruction.

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Importance

Building upon findings, we posit that there is a need for educator preparation programs to prepare pre-service teachers about varying forms and combinations of diversity (e.g., ethnicity, cultural diversity, disabilities) and their impact on educational access, pathways, and outcomes for diverse learners. There should be a multitude of complex variables involved in preparing educators for teaching in PL environments to support a wide range of learner variability. To our knowledge, this study served as an initial effort to explore whether and how PL is positioned in educator preparation standards that outline expectations of educator knowledge, skills, and dispositions across disciplines and for diverse learner populations. This study provided an understanding of coherence, or lack thereof, between educator preparation standards and current understandings of PL implementation.

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References

Authors. (2022). Blinded for review.

Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33(4), 1675-1716.

Cochran-Smith, M., & Lytle, S. L. (2009). Inquiry as stance: Practitioner research for the next generation. Teachers College Press.

Martin, S. D., & Dismuke, S. (2017). Investigating differences in teacher practices through a complexity theory lens: The influence of teacher education. Journal of Teacher Education, 69(1), 22–39. https://doi.org/10.1177/0022487117702573

Martone, A., & Sireci, S. G. (2009). Evaluating alignment between curriculum, assessment, and instruction. Review of Educational Research, 79(4), 1332-1361. https://doi.org/10.3102/0034654309341375

Sinnema, C., Meyer, F., & Aitken, G. (2017). Capturing the complex, situated, and active nature of teaching through inquiry-oriented standards for teaching. Journal of Teacher Education, 68(1), 9–27. https://doi.org/10.1177/0022487116668017

Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (2nd ed.). Sage.

Webb, N. L. (2007). Issues related to judging the alignment of curriculum standards and assessments. Applied Measurement in Education, 20(1), 7-25. http://dx.doi.org/10.1080/08957340709336728

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Session specifications

Topic:
Personalized learning
Grade level:
Community college/university
Audience:
Professional developers, Teachers, Teacher education/higher ed faculty
Attendee devices:
Devices useful
Attendee device specification:
Smartphone: Android, iOS, Windows
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
Subject area:
Inservice teacher education, Preservice teacher education
ISTE Standards:
For Educators:
Learner
  • Set professional learning goals to explore and apply pedagogical approaches made possible by technology and reflect on their effectiveness.
Designer
  • Use technology to create, adapt and personalize learning experiences that foster independent learning and accommodate learner differences and needs.