Leading EdTech Change Across an Organization

A temperature probe can connect a preschool weather lesson to an AP Chemistry investigation. Making that connection happen across a school requires a deliberate learning sequence, practical teacher support and administrative decisions that give the work room to grow. Those conditions turn an educational technology purchase into a lasting change in practice.

When I initiated schoolwide science vertical teaming at an independent school in Southwest Florida, we brought our science classes into a shared conversation spanning K3, beginning with three-year-olds, through grade 12. We examined what students encountered in each course and what they carried into the next. My responsibilities included curriculum review, equipment planning, budgeting, faculty coaching and continued implementation support. Vernier integration became part of that larger effort to build a coherent science program.

The connections were concrete and deliberate. Young children compare warmer and cooler conditions through teacher-guided temperature measurements. Elementary students could begin recording units and describing patterns. Later investigations could develop control of variables, graph interpretation and quantitative reasoning. In advanced courses, students could use pressure sensors, pH probes and other advanced Vernier instrumentation to test models and support research. The expectations changed with the learners; the habit of using evidence to explain a phenomenon continued across the sequence.

My doctoral work in educational technology has helped me connect that practical leadership experience with formal change management. Looking back through Kotter’s organizational framework and Prosci’s ADKAR model clarifies two related responsibilities: establishing the conditions for change across an institution and supporting the people who will carry it into daily practice.

Kotter’s framework emphasizes a shared reason to act, a guiding coalition, a clear vision, removal of barriers, visible progress and practices that sustain the change (Kotter, n.d.). Our vertical team aligned with the coalition principle by bringing teachers across divisions into curriculum planning. The learning progression supplied a shared purpose, while equipment planning and ongoing support addressed practical barriers. These connections help explain why vertical teaming belongs inside an edtech implementation strategy.

They also offer a useful way to work with administration. A curriculum map gives leaders a concrete basis for considering a technology request. It shows where a skill begins, where students practice it again and which courses depend on that preparation. A temperature probe can then be considered in relation to several planned investigations, with age-appropriate expectations for each. That makes the investment easier to discuss in terms of curricular continuity and the students it will serve.

In my work, budget and purchasing decisions were connected to course needs, teacher preparation and continued support. An administrative proposal should make those connections visible. Alongside the equipment price, leaders need to understand the preparation time, compatible interfaces, consumables, storage, maintenance, and assistance required for classroom use. A clear plan also identifies what existing equipment can support and where an additional purchase would address a specific instructional need. This gives administrators a practical basis for setting priorities within the available budget.

Administrative sponsorship has an ongoing role in that process. Leaders can protect time for collaboration, clarify responsibility for shared equipment, and include implementation support in resource decisions. Those actions connect the vision to the working conditions teachers experience. Applying Kotter’s emphasis on removing barriers and sustaining change means treating these organizational arrangements as part of the implementation itself (Kotter, n.d.).

At the individual level, ADKAR identifies five elements of change: awareness, desire, knowledge, ability, and reinforcement (Prosci, n.d.). Applied to the vertical teaming work, awareness connects a teacher’s investigation to the larger learning sequence. Desire depends partly on whether the activity addresses something the teacher values and can realistically accomplish. Knowledge includes understanding the lesson and equipment setup. Ability becomes visible when the teacher can run and adapt the investigation in the classroom. Reinforcement comes through continued support, feedback, and opportunities to use the practice again.

That lens also helps administrators understand the support a teacher needs. Someone may understand the purpose and know the setup steps while still needing practice managing data collection with a full class. My role included coaching and lesson planning to support that transition. The framework in my case study pairs demonstrations and practical feedback with follow-up support so teachers can develop independence. ADKAR provides a language for distinguishing those needs and directing assistance accordingly (Prosci, n.d.).

Ertmer’s (1999) distinction between first-order and second-order barriers adds another useful perspective. First-order barriers concern external conditions such as access and institutional support; second-order barriers concern teachers’ beliefs and established approaches to teaching. For a science team, this means investigating both the practical obstacle and the instructional concern. A teacher may need reliable access to a probe, or may need to examine how automated collection affects students’ understanding of measurement. Those questions require different responses.

I approach the instructional question by keeping student reasoning visible. A Vernier graph needs a question that gives students a reason to interpret it. In a titration investigation, for example, students should connect features of the curve to the chemistry occurring in the solution. Teacher preparation therefore includes the explanation students should produce and the prompts that will reveal their understanding. This makes professional learning directly relevant to the teacher’s subject and classroom.

That approach is consistent with Darling-Hammond et al.’s (2017) review of 35 rigorous studies of effective professional development. The review identified features including content focus, active learning, collaboration, models of practice, coaching, feedback and sustained duration. In our science work, curriculum planning and practical feedback connected teacher development to actual investigations. The support framework in my portfolio extends that approach through preparation on classroom equipment, review of student materials, and revision after use. For administration, the implication is that professional learning needs time and resources across implementation.

Coaching research supports taking that commitment seriously. Kraft et al. (2018) synthesized 60 studies using causal research designs and found positive average effects on instruction and student achievement. They also found that larger programs faced challenges maintaining effectiveness. Much of the evidence came from literacy coaching in prekindergarten and elementary settings, so it cannot establish outcomes for science probeware. It does provide a reason to attend to the quality of coaching as an initiative expands. Adding participating teachers creates an additional support responsibility.

Evaluation gives teachers and administrators a shared basis for deciding what comes next. The framework accompanying my case study asks teams to examine student reasoning, teacher independence, and program use. Can students explain what a graph shows and apply the idea to another situation? Can the teacher recognize an implausible reading and adapt the investigation? Which courses use the equipment, and what scheduling or maintenance issues limit access? Evidence from those questions can guide revisions and future purchasing.

Broad program outcomes require careful interpretation. An examination pass rate can provide useful context, but it cannot isolate the contribution of a sensor or a particular support strategy. For an implementation decision, a student’s explanation, a teacher’s observed readiness or a documented access problem may offer more specific guidance. Working effectively with administration includes being clear about what the evidence supports and which questions still need investigation.

The continuity of the program also depends on shared materials and responsibilities. Teachers need usable directions, tested setups, and a way to pass improvements to colleagues. Administrators need enough visibility to support those routines through staffing and budget decisions. When equipment planning, curriculum review, and teacher development remain connected, the organization can carry the work forward as courses and personnel change.

My portfolio case study, Science Curriculum and Vernier Integration: K3 through Grade 12 Program Design and Teacher Support, includes the learning progression and adaptable planning resources behind this work. The lesson I carry forward is that sustainable edtech change requires a working partnership: teachers contribute their knowledge of learners and instruction, administrators shape the conditions for implementation, and curriculum leadership connects both to a clear educational purpose.

References

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute. https://learningpolicyinstitute.org/product/effective-teacher-professional-development-report

Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61. https://doi.org/10.1007/BF02299597

Kotter. (n.d.). The 8 steps for leading change. https://www.kotterinc.com/methodology/8-steps/

Kraft, M. A., Blazar, D., & Hogan, D. (2018). The effect of teacher coaching on instruction and achievement: A meta-analysis of the causal evidence. Review of Educational Research, 88(4), 547–588. https://doi.org/10.3102/0034654318759268

Prosci. (n.d.). The Prosci ADKAR model. https://www.prosci.com/methodology/adkar