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Showing posts with label Teacher Professional Development. Show all posts
Showing posts with label Teacher Professional Development. Show all posts

Custom eLearning ROI in Language Institutions — A Kirkpatrick-Aligned Approach

Custom eLearning, Kirkpatrick Model, Reflective Practice, ROI in ELT, Teacher Professional Development 0 comments

 

Visualizing ROI in language learning
AI-generated picture by Prof. Jonathan Acuña in January 2026

Introductory Note to the Reader

     This paper does not emerge from a supervisory or evaluative role. I am neither a teacher coach nor an academic manager; rather, I write as a reflective practitioner who has long been interested in how teachers are guided, supported, and evaluated throughout their professional journeys. As I revisit frameworks such as the Kirkpatrick Model and ROI methodologies, I do so with a personal question in mind: How do I wish I had been accompanied in my own process of learning how to teach a language?

     The reflections that inform this paper draw not only from academic literature but also from my own reflective journaling written in late 2025, particularly during November (insights on ROI application in EFL contexts) and December (critical reflections on ROI and the Kirkpatrick Model). These entries helped me reconcile institutional accountability with teacher dignity, growth, and professional meaning. What follows, then, is not a prescriptive model, but a reflective proposal, an attempt to think ethically, pedagogically, and pragmatically about professional development in ELT.


Custom eLearning ROI in Language Institutions — A Kirkpatrick-Aligned Approach


 

Abstract

Language institutions increasingly rely on professional development initiatives to improve teaching quality while simultaneously being asked to demonstrate a clear return on investment (ROI). In English Language Teaching (ELT), however, ROI extends beyond financial metrics to include teacher growth, classroom practice, student learning, and institutional sustainability. This paper explores how custom eLearning initiatives can be evaluated through a Kirkpatrick-aligned framework to make professional development outcomes visible and meaningful. Drawing on practitioner reflection, ELT pedagogy, and evaluation theory, the paper argues that when ROI is interpreted through teacher engagement, learning transfer, and institutional results, professional development becomes a strategic and ethical investment rather than a bureaucratic requirement. The discussion integrates reflective insights with established evaluation models to propose a context-sensitive approach to measuring impact in language institutions.

Keywords:
Teacher Professional Development, ROI in ELT, Kirkpatrick Model, Custom eLearning, Reflective Practice

 

 

Resumen

Las instituciones dedicadas a la enseñanza de lenguas dependen cada vez más de programas de desarrollo profesional para mejorar la calidad docente y, al mismo tiempo, demostrar un retorno de la inversión (ROI). No obstante, en el ámbito de la enseñanza del inglés como lengua extranjera (ELT), el ROI no puede limitarse a indicadores financieros, sino que debe incluir el crecimiento profesional del docente, la práctica en el aula, el aprendizaje del estudiantado y la sostenibilidad institucional. Este artículo analiza cómo los programas de eLearning personalizado pueden evaluarse mediante un enfoque alineado con el Modelo de Kirkpatrick, permitiendo visibilizar el impacto real del desarrollo profesional. A partir de la reflexión docente, la pedagogía del ELT y los modelos de evaluación, se propone una interpretación ética y contextualizada del ROI como inversión pedagógica y no meramente administrativa.

 

 

Resumo

As instituições de ensino de línguas investem cada vez mais em programas de desenvolvimento profissional docente e, simultaneamente, enfrentam a necessidade de demonstrar retorno sobre o investimento (ROI). No contexto do Ensino de Inglês como Língua Estrangeira (ELT), o ROI ultrapassa métricas financeiras e inclui o desenvolvimento profissional do professor, a prática pedagógica, os resultados dos alunos e a sustentabilidade institucional. Este artigo analisa como iniciativas de eLearning personalizado podem ser avaliadas por meio de uma abordagem alinhada ao Modelo de Kirkpatrick, tornando visíveis os impactos do desenvolvimento profissional. Com base na reflexão docente, na pedagogia do ELT e em modelos de avaliação, o texto defende uma compreensão ética e contextualizada do ROI como investimento pedagógico de longo prazo.

 

Introduction

What I have personally noticed nowadays is that many language institutions face a dual challenge: improving teacher performance while demonstrating measurable returns on professional development (PD) investments. After reading Upside Learning’s discussion (post) on custom vs. off-the-shelf eLearning highlights ROI in business contexts, but in ELT, and what I can see is that ROI also includes teacher growth, student outcomes, and institutional effectiveness. Aligning PD evaluation with Kirkpatrick’s four levels, Reaction, Learning, Behavior, Results, provides a structured way to measure the impact of custom eLearning in language institutions, transforming abstract investment into tangible evidence of professional and organizational value.

Level 1: Reaction – Teacher Engagement and Satisfaction

The first level of the Kirkpatrick level examines how teachers perceive in-house or external training. Custom eLearning, designed to reflect the institution’s methodology and classroom realities, tends to score higher in engagement and perceived relevance than off-the-shelf modules. Collecting surveys and feedback forms immediately after training provides data on:

a)

Enjoyment and usability of the module as of next class

b)

Perceived relevance to classroom practice and lesson planning

c)

Emotional engagement and motivation

High engagement at this stage correlates with willingness to apply the learning in practice, which is the first step toward measurable ROI.

Level 2: Learning – Knowledge, Skills, and Emotional Competence

At Level 2, ROI is assessed by evaluating what teachers actually learn:

a)

Pedagogical skills (e.g., implementing CLT or task-based learning)

b)

Assessment literacy

c)

Emotional competence for classroom management

Custom modules allow content to be contextualized to local learners, making skill acquisition more likely. Pre- and post-training assessments, along with reflective journals or quizzes, provide evidence that knowledge and skills have improved.

 Level 3: Behavior – Application in the Classroom

Level 3 examines whether teachers transfer learning to practice:

a)

Observation of lessons to track adoption of strategies

b)

Peer coaching feedback

c)

Self-reported changes in planning and delivery

Custom eLearning that mirrors classroom realities increases the likelihood that teachers will integrate new practices consistently. This level also begins to capture indirect ROI: fewer errors, improved classroom management, and stronger student engagement.

 Level 4: Results – Institutional Impact

The final level evaluates outcomes that matter to the institution:

a)

Student learning gains (e.g., CEFR progression, exam scores)

b)

Teacher retention and satisfaction rates

c)

Cost savings from reduced re-training or errors

d)

Improved institutional reputation and student enrollment

By mapping these results to specific KPIs, institutions can quantify ROI beyond finances, linking professional growth to student outcomes and institutional performance.

Strategic Considerations and Risks

a)

Data Collection Infrastructure

LMS, classroom observation tools, and surveys are essential for Level 2–4 measurement.

b)

Teacher Buy-In

Involving teachers in design ensures relevance and increases Level 1–2 engagement.

c)

Long-Term View

ROI in language institutions often emerges over semesters rather than weeks; patience and iteration are required.

d)

Ethical Practices

Transparency, confidentiality, and supportive coaching ensure that data collection is empowering rather than punitive.

Conclusion

By aligning custom eLearning with the Kirkpatrick Model, language institutions can systematically demonstrate ROI: from immediate teacher satisfaction to long-term institutional gains. Custom modules, when thoughtfully designed, provide a measurable pathway to enhance teacher performance, student outcomes, and organizational excellence. PD investments are no longer abstract expenditures; they become data-informed, strategically aligned assets that strengthen both human and institutional capital.

San José, Costa Rica

Sunday, January 4, 2026


📚 References

Acuña-Solano, J. (2025a, November). Reflective journals on ROI, Kirkpatrick Model. [Unpublished reflective journals].

Acuña-Solano, J. (2025b, December). Professional Development in EFL Contexts [Unpublished reflective journals].

Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels (3rd ed.). Berrett-Koehler.


Handout

Reader’s Comprehension & Reflection Worksheet (Kirkpatrick-Aligned) by Jonathan Acuña



Custom ELearning ROI in Language Institutions — a Kirkpatrick-Aligned Approach by Jonathan Acuña



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Sunday, January 04, 2026


Location: San José Province, Guadalupe, Costa Rica

From Reflection to Analytics: Integrating AI Tools into Reflective Practice and Teacher Growth in ELT

AI in ELT, Digital Pedagogy, Kirkpatrick Model, Learning Analytics, Reflective Practice, Teacher Professional Development 0 comments

AI-supported teacher professional development
AI-generated picture by Prof. Jonathan Acuña Solano in October 2025

🪶 Introductory Note to the Reader

     After having an enriching conversation with my colleague and partner, Jabib Haghiran, Head of Digital Platforms at the Centro Cultural Costarricense-Norteamericano, it suddenly occurred to me that AI voice recognition technologies and files stored in OneDrive accessed through Copilot could be leveraged in ways that go beyond administrative or operational use. These tools, if thoughtfully integrated, could support teacher coaches and academic coordinators in identifying instructional trends, “gray areas” in planning, and patterns in classroom interaction—whether in virtual or face-to-face modalities.

     The insights gathered from scholars such as Schön (1983), Farrell (2019), Healey (2018), Cutrim Schmid (2017), and Reeves and Lin (2020) strongly suggest that the next evolution in teacher professional development may not lie in choosing between reflection and technology but in integrating both. As this essay proposes, AI-powered reflection can help operationalize what we already know about reflective teaching, making it evidence-informed, iterative, and contextually adaptive. Perhaps this is the next natural step in the pursuit of continuous, meaningful professional development in English Language Teaching (ELT).

From Reflection to Analytics: Integrating AI Tools into Reflective Practice and Teacher Growth in ELT

 

🪶 Abstract

This essay explores how Artificial Intelligence (AI) can be integrated into reflective practice to enhance professional growth in English Language Teaching (ELT). Building upon Schön’s (1983) model of reflection and the evaluative framework of Kirkpatrick and Kirkpatrick (2006), it argues that AI-driven analytics—such as voice recognition, classroom data tracking, and automated feedback systems—can transform traditional reflection into a dynamic, data-informed process. Drawing on the work of Farrell (2019), Healey (2018), and Reeves and Lin (2020), the essay discusses how AI tools can help teachers and supervisors identify patterns in teaching behavior, support evidence-based decision-making, and design personalized development paths. It concludes by emphasizing the importance of ethical considerations, human mentorship, and emotional intelligence in ensuring that AI serves as a tool for empowerment rather than surveillance in both virtual and face-to-face teaching contexts.

🪶 Keywords:

Reflective Practice, AI in ELT, Teacher Professional Development, Digital Pedagogy, Learning Analytics, Kirkpatrick Model

 

 

🪶 Resumen

Este ensayo analiza cómo la inteligencia artificial (IA) puede integrarse en la práctica reflexiva para potenciar el desarrollo profesional en la enseñanza del inglés como lengua extranjera (ELT). Basándose en los modelos de reflexión de Schön (1983) y en el marco evaluativo de Kirkpatrick y Kirkpatrick (2006), se argumenta que las herramientas impulsadas por IA, como el reconocimiento de voz y los sistemas de retroalimentación automatizados, permiten transformar la reflexión tradicional en un proceso dinámico y basado en datos. A partir de los aportes de Farrell (2019), Healey (2018) y Reeves y Lin (2020), se propone que el uso ético y pedagógicamente informado de la IA puede fortalecer la toma de decisiones, la observación docente y la creación de trayectorias personalizadas de desarrollo profesional, sin perder de vista la dimensión humana del aprendizaje.

 

 

🪶 Resumo

Este ensaio examina como a inteligência artificial (IA) pode ser integrada à prática reflexiva para aprimorar o desenvolvimento profissional no ensino de inglês como língua estrangeira (ELT). Com base nos modelos de reflexão de Schön (1983) e no modelo avaliativo de Kirkpatrick e Kirkpatrick (2006), argumenta-se que o uso de ferramentas de IA, como o reconhecimento de voz e as análises automatizadas de desempenho docente, transforma a reflexão em um processo contínuo e fundamentado em evidências. A partir das contribuições de Farrell (2019), Healey (2018) e Reeves e Lin (2020), o texto destaca que a IA, quando aplicada com ética e sensibilidade humana, pode apoiar professores e mentores na identificação de padrões de ensino e na construção de percursos personalizados de crescimento profissional.

 

 

Introduction

Reflective practice has long been regarded as a cornerstone of professional growth in education (Schön, 1983). Within ELT, reflection enables teachers to examine their planning and classroom-delivery decisions, learning and teaching beliefs, and language instruction strategies critically (Farrell, 2019). However, as digital technologies evolve, new opportunities emerge for deepening and operationalizing reflective processes. Artificial intelligence, in particular, provides tools capable of analyzing performance data, tracking progress, and offering personalized feedback. This intersection of reflection, analytics, and digital pedagogy marks a paradigm shift in how professional development can be conceived and practiced.

Digital Reflection and the Evolution of Teacher Learning

The rise of online professional development environments has redefined the dynamics of reflection. Dr. Deborah Healey (2018) emphasizes that digital platforms expand teachers’ opportunities for collaboration, asynchronous feedback, and self-regulated learning. Through blogs, forums, and peer observation platforms, teachers can engage in multimodal reflection, combining written, visual, and interactive elements. Farrell (2019) argues that digital spaces facilitate “public reflection,” where teachers move beyond self-reflection toward collective sense-making.

In this context, teacher reflection on planning, lesson success, and student learning evidence is no longer confined to isolated reflective journaling. It becomes a socially constructed, data-supported dialogue that encourages awareness of professional identity and instructional choices that can positively impact how teachers perceive themselves in the act of teaching, planning, and ensuring student learning. As Cutrim Schmid (2017) points out, technology-enhanced teacher education supports meta-cognitive engagement while maintaining a balance between pedagogical reflection and technological fluency.

The Role of AI in Reflective Practice

AI can significantly enrich and boost reflective practice by automating data collection and analysis processes. Reeves and Lin (2020) argue that AI-powered tools can support professional learning analytics, identifying patterns in teacher behavior, engagement, and outcomes. For instance, platforms using speech recognition and classroom analytics can detect teacher-student interaction ratios, time spent on feedback, or even the emotional tone of communication. Nowadays, virtual EL teachers (and their supervisors), e.g., can use Zoom’s session audio, transform it into a text, feed it into a AI, and identify behavior patterns for both the instructor and the students.

Such AI-mediated reflection extends Schön’s (1983) notion of reflection-in-action by providing real-time insights. Teachers can review analytics dashboards, reflect on discrepancies between perceived and actual practice, and adjust future actions accordingly. These tools, when ethically implemented, complement rather than replace human judgment, turning reflective practice into an iterative, evidence-based process that can help teachers and supervisors decide on individual, perhaps tailor-made, PD paths.

AI-Supported Reflective Cycles in ELT

An AI-enhanced reflective cycle can be conceptualized in four stages:

1.    Experience Capture – Using AI-based observation tools (video, audio, and classroom analytics).

2.    Data Reflection – Reviewing generated data and identifying critical incidents.

3.    Collaborative Interpretation – Discussing insights with peers or mentors through digital communities.

4.    Action Planning – Integrating evidence-informed adjustments into future lessons.

As stated above, this process aligns with Kirkpatrick’s four levels of evaluation (reaction, learning, behavior, and results) by making teacher reflection both measurable and developmental. AI facilitates the transition from subjective recall to objective professional evidence, bridging intuition and data in pedagogical reflection.

Challenges and Ethical Considerations

While promising, AI-mediated teacher reflection requires careful ethical consideration. Issues of privacy, bias, and over-reliance on data must be addressed from the very beginning. Dr. Healey (2018) warns that digital analytics can depersonalize teacher learning if not accompanied by human mentorship. This process is not meant to replace teacher coaches; it is here to help both instructors and coaches to identify areas where instructors can work to algin to institutional processes and to guarantee that students’ CEFR exit profiles are thoroughly met. Therefore, institutions must ensure that AI serves as a supportive mirror, not a surveillance tool in brick-and-mortar and virtual teaching scenarios. Balanced frameworks should prioritize agency, confidentiality, and teacher empowerment (Cutrim Schmid, 2017).

Conclusion

AI-powered reflection represents an evolutionary step in ELT professional growth. By merging human insight with digital analytics, teachers gain access to a richer, more precise understanding of their planning and teaching practice. When integrated thoughtfully, AI can enhance Schön’s reflective cycle, foster continuous learning, and operationalize Kirkpatrick’s model within modern teacher development ecosystems with the presence of a teacher mentor or coach, as suggested by Dr. Healey. The future of reflective teaching lies in this synergy between empathy and evidence, a human-centered, data-informed approach to professional excellence.


📚 References

Cutrim Schmid, E. (2017). Teacher education in the digital age: The role of technology in supporting reflective practice. Routledge.

Farrell, T. S. C. (2019). Reflective practice in ELT: Perspectives, research, and practices. Equinox.

Healey, D. (2018). Digital literacy for language teachers: A framework for professional development. TESOL International Association.

Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels (3rd ed.). Berrett-Koehler.

Reeves, T. C., & Lin, L. (2020). The research we have is not the research we need: Using digital analytics to inform teacher learning. Educational Technology Research and Development, 68(3), 1285–1300. https://doi.org/10.1007/s11423-020-09747-3

Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.


Reader’s Comprehension and Reflection Worksheet

Reader’s Comprehension and Reflection Worksheet by Jonathan Acuña


Reflective Practice Series 3 - Training Handout

Reflective Practice Series 3 - Training Handout by Jonathan Acuña






Sunday, October 26, 2025



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