Re-Engineering the Educational System: Technology Transfer from a Behavioral Perspective

Abstract

Access to quality education is a fundamental human right, deeply connected to an individual's overall well-being and quality of life. This article explores how effectively transferring behavior-analytic principles can improve educational outcomes and re-engineer educational systems. Drawing on Dr. Henry S. Pennypacker's work on technology transfer and cultural concerns, it examines the research-to-practice gap in K–12 education. An iterative continuum for translating research into practice is proposed and applied to school improvement. The process of technology transfer in education is analyzed, highlighting successful examples and identifying barriers to widespread adoption of behavior-analytic techniques. Barriers include implementation fidelity, teacher training, resource allocation, and school culture. Deliberate strategies to advance behavior-analytic practices in education are shared, emphasizing the potential of behavior analysis to significantly improvement educational outcomes and broader societal progress.

Education is the most powerful weapon which you can use to change the world.

Nelson Mandela, South African civil rights activist

 July 16, 2003

Education is universally recognized as a fundamental human right, serving as a foundation for both individual and societal progress. This belief is enshrined in documents such as the Universal Declaration of Human Rights (United Nations General Assembly, 1949) and The Convention on the Rights of the Child (United Nations General Assembly, 1989). Both emphasize the right to education for all. By equipping individuals with knowledge and abilities, education enables each of us to be productive in life, participate in democracy, overcome discrimination, improve health, and often escape poverty. A well-educated populace is essential for economic growth, social cohesion, and sustainable development (Goczek et al., 2021; Little & Green, 2009). In essence, education is indispensable for creating a more just, equitable, and prosperous world.

However, the data paint a sobering picture of K–12 academic achievement in the United States. In 2022, half of all public-school students were behind grade level in at least one subject (Irwin et al., 2023). National Assessment of Educational Progress (NAEP) scores have plummeted to record lows, with a third of 4th and 8th-graders failing to achieve even basic reading proficiency (NAEP, 2022). More than 80% of public schools have reported increased behavioral and social-emotional issues with students since the pandemic (National Center for Education Statistics, 2022). It is alarming that 16 million students were chronically absent during the 2021–22 school year, which is double the prepandemic rate (Dee, 2024). This trend could result in an additional 2 million 8th–12th graders dropping out of school (Dorn et al., 2021). In addition, students from minority backgrounds, low-income neighborhoods, English language learners, and those in special education often bear the brunt of an ineffective system (Cohodes et al., 2022).

Developing countries face even more formidable obstacles. A 2023 UNESCO report indicated that worldwide, almost 250 million children and youth were out of school (UNESCO, 2023). Yet being in school does not guarantee learning of even basic skills. Throughout the world, the prevailing educational practices have left seven out of ten 10-year-olds unable to read and understand simple text (Hanemann, 2023). The COVID-19 pandemic has worsened these challenges, with 1.6 billion students affected by school closures at the peak of the crisis, leading to significant learning loss and widening disparities (UNESCO, 2021). In addition, many persons graduate from secondary education with insufficient literacy and numeracy skills—a phenomenon not exclusive to low-income countries (Hanemann, 2023).

Despite these challenges, "[w]e know how to build better schools" (Skinner, 1989, p. 96). The science, principles, strategies, and tactics offered by behavior analysis provide a robust framework for improving teaching practices and learning outcomes. Evidence-based educational tactics derived from applied behavior analysis (ABA) emphasize positive reinforcement, carefully constructed contingencies, individualized instruction, high rates of meaningful responding, and data-driven decision making, and can significantly enhance student outcomes in both special education and general education (Heward, 2003; Slocum et al., 2014; Twyman, 2014).

Henry S. Pennypacker (hereafter referred to as Hank) also believed we could do better in education. Using Hank’s work as inspiration, this article explores how a behavior-analytic lens and knowledge of technology transfer can drive societal improvement, particularly in education. It examines the challenges associated with transferring behavioral technologies into educational contexts (a la Pennypacker, 1986) and considers insights from Pennypacker and Perez's Engineering the Upswing (2022) for how to evoke behavior change. By integrating these approaches, educators can create more inclusive, supportive, and effective learning environments that promote academic achievement and social-emotional growth for all students.

Hank was not only passionate about education in a broad sense, he also worked directly applying behavior-analytic principles to improve teaching and learning. As a young professor at the University of Florida, Pennypacker designed courses that emphasized individualized instruction, precise performance criteria, and direct measurement. He developed the Personalized Learning Center, a multicourse instructional system that emphasized study objectives, frequent assessment, mastery criteria, and individualized tutoring (see St. Peter, 2023). This behaviorally based approach offerred a data-driven, personalized education model, one described in the 1971 article he co-authored with Jim Johnston, “A Behavioral Approach to College Teaching” (Johnston & Pennypacker, 1971). This publication continues to be cited as an early and influential application of behavioral principles in higher education, highlighting the value of precise measurement, individualized instruction, and direct feedback. Hank trained generations of future behavior analysts, many of whom went on to teach and apply behavioral principles in diverse educational contexts.

Hank’s role in advancing Precision Teaching (PT) is particularly notable. Collaborating with Ogden Lindsley in the growth of PT (Lindsley, 1992), Hank helped define a methodology that focused on the direct measurement of behavior, the daily recording of performance frequencies, and the use of Standard Celeration Charts (SCC, or “the chart”) for monitoring gains (Pennypacker & Ellis, n.d.). The SCC provides educators with a consistent visual tool for analyzing student progress and making informed instructional decisions (Evans et al., 2021). To promote its understanding and use, Hank coauthored the seminal handbook for “the chart” (Pennypacker et al., 1972), revised in 2003 (Pennypacker et al., 2003). Hank’s work championing and demystifying PT and the SCC continues to broadly influence educational practices, particularly in education (Barrett et al., 1986).

Later in life, Hank actively supported the P. K. Yonge Developmental Research School in Gainesville, Florida, a school dedicated to designing and testing best practices in education for a diverse student population. He often collaborated with educators, including his granddaughter, now the elementary school’s principal, to address student needs, refine behavior strategies, and select curricula (A. Pennypacker Hill, personal communication, November 14, 2023). He would have been proud that P. K. Yonge was recently named to the 2024 Advanced Placement Program (AP) School Honor Roll, earning both the AP Access Award and the AP Platinum distinction (Mainstreet Daily News, 2024). The P. K. Yonge program is described in chapter 5 of Engineering the Upswing (Pennypacker & Perez, 2022), which encapsulates Hank’s life-long views on education and what must be done to improve it. The chapter’s title, “The Science and Practices for Re-Engineering Our Educational System,” served as inspiration for the title of this article.

Behavior Analysis in Education

Behavior-analytic procedures have resulted in numerous positive benefits in educational settings, greatly influencing teaching practices and improved learning outcomes. Behavioral principles are rooted in approaches that are direct, observable, measurable, and socially valid, and that focus on meaningful improvement in behaviors relevant to individuals (Baer et al., 1968). Over the years, applied behavior analysis (ABA) has demonstrated effectiveness across a wide range of educational contexts and learner groups, including those previously considered unteachable. Decades of research have shown ABA to be a cornerstone of effective classroom management and instruction (Carr et al., 2023; Slocum et al., 2014; Twyman, 2014). Significant positive outcomes have included:

1. Enhanced Teaching Practices: Educators benefit from evidence-based strategies that are systematic and replicable, allowing for more effective instruction that meets the diverse needs of students (see Embry & Biglan, 2008; Greer, 2002; Hugh-Pennie et. al., 2022)

2. Development of Effective Learners: The application of behavior analysis helps in fostering essential learning skills, enabling students to become more competent and adaptable in various learning situations (see McGreevy et al., 2014; Tucci et al., 2004)

3. Improved Learning and Retention: Behavior-analytic techniques have been shown to enhance students' ability to learn new skills quickly and retain them over time, leading to better academic performance (see Evans et al., 2021; Lindsley, 1992; Street & Johnson, 2014).

4. Better Behavior Management: The applications of behavior analysis contribute to improved management of student behavior in the classroom, creating a more positive and productive learning environment (Erhard et al., 2022; Horner & Sugai, 2015; Pfiffner, 1985).

5. Scalable and Adaptable Approaches: The flexibility of behavior analysis allows it to be effectively applied across different educational settings and student populations, ensuring its relevance and impact in various contexts (Johnson, 2018; Vargas, 2013).

Behavior analysis has yielded a wealth of evidence-based strategies to enhance learning environments and provide teachers with a science-based, data-driven framework to understand and improve student behavior, giving educators the ability to create positive and supportive classrooms where students can reliably succeed. So, with all this knowledge and data, why aren’t we achieving better educational outcomes?

From Research to Practice

For Hank, that process could be distilled to a series of “what, so what, now what” questions based on quantitative measurement (Borton, 1970). Asking these three questions helped Hank follow the data. “What” identified the phenomenon, the independent variable and dependent variables in a testable scientific question (Johnston & Pennypacker, 2010). The “so what” tied in Hank’s concern for social validity and ensuring the practicality and meaningfulness of procedures. And “now what” drove his passion around technology transfer (Pennypacker, 1986) and the evolution of new, verified technologies and innovations from their research and development phase to practical application.

The continuum of research to practice is a broader concept that includes technology transfer as one of its key components. The continuum often encompasses the entire process from generating new knowledge to implementing it effectively at scale to achieve desired outcomes (Carayannis, 2020; Milat & Li, 2017; National Science Board, 2020). Although “research to practice” can be conceptualized in various ways (Milat & Li, 2017), for this article, we will consider it as an iterative continuum that encompasses:

In this conceptualization, the arrows represent the flexible, nonlinear nature of the continuum, indicating that steps may overlap, occur concurrently, or progress out of sequence depending on the specific context or needs of the research-to-practice process. Basic research involves the pursuit of fundamental knowledge in controlled settings without a direct application, serving as the foundation for future research. Translational research focuses on adapting theoretical findings into actionable approaches. It builds on basic research to begin to develop real-world applications—or vice versa, using applied insights to inform new basic research. Applied research directly addresses practical problems by using basic and translational findings, often on a small scale, to bridge the gap between theory and practice. Implementation focuses on integrating findings into real-world settings and systems, ensuring their adoption and effective use. Technology transfer involves ensuring findings are accessible, adaptable, and ready for implementation in real-world settings, by shifting knowledge, skills, and techniques, from development and testing to practical application. Dissemination involves distributing information and intervention materials to target audiences to foster knowledge adoption. Finally, scaling up expands the reach and impact of successful innovations, taking practices or technologies from pilot phases to broader contexts across multiple settings or populations.

Relevance for Education

Education offers a practical lens to further illustrate the components of this continuum and explore challenges in translating behavior-analytic research into widespread practice. Later in this article, real-life development and dissemination examples from Headsprout Early Reading (Layng et al., 2003) will illustrate each component, offering a more concrete understanding of the continuum in action.

• Basic Research: Research to increase fundamental understanding of a subject within the field of education, such as student learning processes, factors influencing motivation, or the effects of various teaching methods.

• Translational Research: Research on classroom management strategies focuses on adapting principles identified in basic research, such as reinforcement or motivation, into practical tools like token economies. It involves designing the strategy, piloting it in various contexts, and refining it based on feedback to ensure it can be broadly applied across diverse classrooms.

• Applied Research: Use research findings to address specific challenges and evaluate effectiveness in specific, real-world education settings. It is also used to develop and evaluate, in context, evidence-based interventions to improve educational practices and outcomes.

• Implementation: Deploy research-based innovations into a specific setting with a focus on the practical steps involved in adopting the new approach (e.g., teacher training, curriculum adoption, or support systems) to make it viable and sustainable.

• Technology Transfer: Technology Transfer: Develop educational products or services based on basic, translational, or applied research findings, such as creating online learning software, digital curriculum materials, or professional development models.

• Dissemination: Share information about effective educational practices through conferences, publications, professional development, and federal resources.

• Scaling Up: Expand successful educational programs or interventions to a larger population by replicating, franchising, or implementing policy changes.

The Research to Practice Gap

Throughout history, narrowing the gap between research-based knowledge and real-world applications has been a challenge. For instance, the ancient Greeks developed sophisticated mathematical theories, yet practical applications like engineering and architecture often lagged. Likewise, while early scientists tested their understanding of electricity, it took years to use this knowledge for lighting homes and powering machinery.1 Whether in education or any field, a significant challenge lies in closing the divide between generating new knowledge and effectively applying it in real-world settings (Morris et al., 2011).

Known as the research to practice gap (Carnine, 1997), this phenomenon refers to the challenge of translating research findings into practical applications in real-world settings. Overcoming it often requires addressing challenges related to each component of the continuum. Success often involves navigating cultural, legal, economic, and logistical challenges to ensure that the technology is adopted, accepted, and used effectively. It requires understanding the prevailing contingencies (that both support or hamper widespread effective use of new technology or their alternatives, see Layng et al., 2021), surveying market needs, adapting to meet needs, and educating potential users on how to utilize the new technology. By understanding these distinctions, we can better appreciate and respond to the complexities involved in bringing scientific discoveries to benefit society. Motivated by Hank’s work and publications, this article explores technology transfer and the key factors involved in the dissemination and scaling-up of educational innovations.

The disparity between behavioral science and its educational applications has long been a subject of concern among behavior analysts (Axelrod, 1996; Silvestri & Heward, 2015; Valentino & Juanico, 2020; see also Friman, 2021). Although behavioral principles are well-established in the research literature, their consistent and widespread application in classroom settings remains elusive (Bordieri et al., 2012; Hanley, 1970). Despite robust evidence supporting the efficacy of behaviorally based educational practices (Fredrick et al., 2000; Embry & Biglan, 2008), the translation of these principles into practical classroom interventions remains challenging. Two areas illustrate the state of technology transfer in education: classroom management and academic learning.

Classroom Management

In addressing classroom management and challenging behavior, ABA principles have proven remarkably effective (Hulac & Briesch, 2017; Landrum & Kauffman, 2013). The behavioral approach to classroom management has extensive empirical underpinnings, making it a leading paradigm in educational research and teacher preparation (Axelrod, 1996). However, the transfer of this research into widespread classroom practice has been inconsistent. Despite its demonstrated efficacy, only one model, School-Wide Positive Behavioral Interventions and Supports (SW-PBIS; Horner & Sugai, 2015; Horner et al., 2017), has achieved significant nationwide implementation and federal support.2

Student Learning.

Another example involves improving academic learning. Although ample research supports the use of ABA-based strategies to teach academic skills (Greer, 2002; Slocum et al., 2014; Twyman, 2014; Vargas, 2013), these strategies remain underutilized. Translating these methods into core curriculum subjects like math and reading requires a range of efforts, from adapting evidence-based techniques to fit traditional classroom settings to overcoming resistance from educators who may be unfamiliar with or skeptical of behavior-analytic approaches. In addition, challenges such as ensuring fidelity of implementation, supporting the consistent application of ABA techniques across various educators and settings, along with factors like teacher training, resource allocation, and school culture, have all been shown to hinder widespread adoption (Fullan, 2007). Effectively making research-based innovations in classrooms, schools, or districts, a reality is a complex undertaking.

Technology Transfer

Formally, technology transfer is the process of shifting scientific findings, research, innovations, or technologies from one context to another for the purpose of broader application, further development, or commercialization (Valentino & Juanico, 2020). It involves moving new knowledge, skills, processes, or technologies from research institutions, universities, or government laboratories into industry, private sector, or other organizations where they can be used effectively. Although implementation focuses on embedding innovations into systems, technology transfer is the process of ensuring innovations are viable and accessible, especially for larger implementation. The goal of technology transfer is to bridge the gap between discovery and practical application, ensuring that innovations are theoretically sound, doable, useful, and meaningful (Kremic, 2003).

Technology transfer has been a concern for behavioral scientists and behavior analysts for decades. As Hank noted: “A large body of literature exists on the topic of technology transfer, knowledge utilization, innovation diffusion-call it what you will. I have sampled just enough of this literature to be assured of one thing: It is woefully naive with respect to the contingencies that operate in the real world” (Pennypacker, 1986, p. 148). He reminded us that a significant challenge in technology transfer is the reluctance of institutions and potential adopters to embrace new technologies that do not align with existing metacontingencies—largely cultural or ceremonial practices that do not necessarily lead to meaningful behavior change (Pennypacker, 1986; Pennypacker & Perez, 2022; see also Glenn, 2023 and Glenn & Malott, 2004). This resistance can result in the systematic dismantling, dilution, or disregarding of innovative practices, rendering them ineffective. Educational innovations, no matter how effective, can be undermined if not carefully managed during the transfer process.

Technology Transfer in Education

The goals of technology transfer in education are to improve teaching and learning processes, enhance educational outcomes, and adapt to the evolving needs of students and educators. The transfer process involves developing, testing, modifying, and integrating new educational programs, methodologies, tools, and products into existing educational settings. Technology transfer can include adopting whole programs such as Direct Instruction (Becker & Carnine, 1980) or specific methodologies and practices such as those associated with Precision Teaching (Lindsley, 1992). It may involve digital technologies, including adaptive learning software (such as the online reading programs produced by Headsprout (Layng et al., 2003)), assessment (such as the online version of the Verbal Behavior Milestones Assessment and Placement Program [VB-MAPP; Sundberg, 2014; also see TherapyBrands, 2022]), curriculum and data collection (such as Catalyst, formerly of DataFinch (Peterson et al., 2024)), measurement and visual displays (such as Chartlytics, now known as CR PrecisionX (CentralReach, 2018)), or virtual reality tools for immersive learning experiences (such as BehaviorMe’s virtual reality training solution for autism service delivery professionals (Brandt et al., 2022)). In the end, technology transfer is successful when initiatives have expanded from pilot stages to broader implementation across schools, districts, states, or even countries.

Researchers have identified several barriers to successful technology transfer in education, which hinder the adoption of innovative, effective practices (see Baron, 1990; Chapman & Ainscow, 2019; Wahlgren & Aarkrog, 2021). These barriers include:

• Resistance to Change. Educators and institutions may resist adopting new technologies due to a lack of familiarity, perceived complexity, or concerns about disrupting established practices. This may be due to the often high response effort required to learn and integrate them, and are often coupled with weak or inconsistent reinforcement for attempting change.

• Resource Constraints. Schools, particularly in underfunded areas, may lack the necessary reinforcement or support systems (e.g., access to funding, infrastructure, qualified personnel) needed to adopt and support innovations.

• Alignment with Educational Goals. If new technologies, practices, or systems do not align well with existing practices, curricula, or educational standards, they may be avoided, thwarted, or rejected. This is more likely when resistance to changes offers a way to escape or avoid aversive stimuli.

• Equity and Accessibility. Ensuring that new tools and methods are accessible to all students, regardless of socioeconomic status, geographic location, or disability, is imperative but often difficult to achieve.

• Evaluation and Measurement. Determining the effectiveness of new approaches can be complicated and time-consuming, and both efforts to do so or the lack of doing so can prevent the adoption of evidence-based practices. In addition, efforts around data collection and measurement may be hindered by weak contingencies linking educator accountability to both performance metrics and student outcomes.

• Sustainability. Innovations may fail if there are insufficient contingencies to maintain ongoing support and reinforcement for their use over time, or if reinforcement contingencies for old practices re-emerge.

• Professional Development. Adequate and ongoing professional development, as well as sufficient reinforcement contingencies, for educators to effectively implement and utilize new innovations is often lacking.

• Collaboration and Partnerships. Effective collaboration between researchers, educators, policymakers, and industry requires myriad contingencies for both individual and group behavior. Too often these contingencies are overlooked or misunderstood, resulting in weak consequences for shared understanding, responsibility, and coordination among researchers, educators, policymakers, and industry stakeholders.

Behavior analysts have proposed various strategies to increase transfer of behavior-analytic technologies to educational settings. Heward (2008) outlined reasons why ABA is beneficial for education and provided a call to action for behavioral educators, including developing a technology of adoption, communicating our story in accessible language, maintaining realistic optimism, and reinforcing small changes towards global adoption of effective teaching practices. Horner (2008) encouraged us to step out of our research comfort zone and address issues related to large scale adoption, cautioning: "conducting science to validate the principles and practices of behavior analysis is necessary but insufficient for large-scale social adoption of behavioral technology" (n.p.). He then offered additional guidelines for increasing the utility of behavior analysis in education:

1. Build comprehensive behavioral interventions that produce change in highly valued outcomes.

2. Expand the unit of analysis to meet the level of societal significance.

3. Collect and use data for decision making.

4. Make behavioral principles accessible.

5. Implement behavioral technology with care and discipline.

6. Understand that scaling up effective practices differs from initial implementation in demonstration contexts.

Other proposed efforts to reduce the research to practice gap are broader, such as dissemination, collaboration, and active engagement with the broader educational community (see Axelrod, 1993; Carnine, 1997, 2000; Korthagen, 2007; Lerman, 2024). Suggestions in these domains include:

• Develop procedures that fit the ecology of the regular classroom and publish results in journals that teachers actually read (Hall, 1991).

• Participate more actively in the training of undergraduate regular education teachers (Hall, 1991).

• Collaborate more with educators interested in improving academic performance (Light-Shriner et al., 2023; Slim & Reuter-Yuill, 2021).

• Spend more time within a school's culture supporting and cultivating existing resources (Fantuzzo & Atkins, 1992).

• Strengthen ABA's presence at the national and political level to influence the research and teacher training agenda (Greer, 1982).

In chapter 5 of Engineering the Upswing, Pennypacker and Perez (2022) provided suggestions to increase the transfer of behavior-analytic technologies in education, including the importance of contextual adaptation, community involvement, and continuous improvement. They emphasize that educational strategies must be adaptable to diverse contexts, whether urban or rural, to increase the likelihood that interventions are relevant and effective in varied environments. They stressed the impact of community involvement (offering examples like the P. K. Yonge Developmental Research School, where Hank consulted) and integrating flexible, student-centered activities to promote collaboration and individualized learning. Also critical for technology transfer is continuous improvement, with regular reassessment and adaptation of strategies so they remain effective and responsive to evolving educational needs.

Re-Engineering Education through Behavioral Science

To have a genuine impact, educational practices must primarily target the behavior of the individual, at the individual level, as this is where change is most relevant. However, it is also essential to consider three levels of selection: phylogeny, ontogeny, and culture (Skinner, 1984). Although teaching’s primary focus should be on the individual’s learning history and progress (ontogeny)—such as using personalized instruction to build on a student’s prior knowledge, experiences, and interests (Murphy et al., 2014)—effective educational practices should also be informed by the genetic and evolutionary factors that influence behavior (phylogeny). For example, acknowledging that humans have evolved to learn through imitation and social interaction can guide the use of peer modeling and cooperative learning strategies in the classroom (Cosden & Haring, 1992). In addition, educational practices must align with the school’s (or community’s) cultural norms and values that shape and reinforce behavior at a broader societal level (culture). This could involve integrating culturally relevant materials and practices into the curriculum, ensuring that education resonates with students’ cultural backgrounds and helps them navigate and contribute to their communities (Twyman, 2021).

For educational practices to be effective and sustainable, they must ensure that the consequences at each level support the desired outcomes. Selection by consequences should be a unifying principle (Pennypacker & Perez, 2022; Skinner, 1984). Educational practices must account for three levels—individual/behavioral, ontogenetic/biological, and cultural—by aligning the consequences at each level with the intended educational goals. Integrating scientific evidence across these levels can effectively guide behavior change, leading to greater technology transfer and the union of educational best practices with broader societal objectives.

Examples of Successful Technology Transfer

MammaCare

A notable example of successful technology transfer in health care (education) is Hank’s own MammaCare, incorporating a technology for teaching breast self-examination (Pennypacker & Iwata, 2013). The project’s success was largely due to the direct involvement of its developers in managing the technology’s transfer to the marketplace, ensuring that the full benefits were realized without compromising quality. This model illustrates how educational and behavioral technologies can be effectively transferred to broader contexts, provided there is a commitment to maintaining their integrity and efficacy.

SW-PBIS

As noted earlier, SW-PBIS (Horner & Sugai, 2015) is a prime example of successful technology transfer in education. It is an evidence-based framework for school-wide behavior management that requires implementation fidelity and ongoing support to achieve optimal results (Fox et al., 2021). By focusing on prevention, intervention, and data-driven decision making, SW-PBIS has demonstrated its ability to improve school climate and student outcomes in diverse educational settings (Lewis et al., 2023). Like other successful technology transfers, the model's effectiveness is rooted in collaboration among stakeholders and a commitment to continuous improvement.

Headsprout

The Headsprout online reading programs (Layng et al., 2003) provide a model for successful technology transfer in early literacy education. As a company, Headsprout developed innovative, online reading programs grounded in the scientific principles of behavior and reading research (Layng et al., 2004a, b). Headsprout Early Reading and Headsprout Reading Comprehension have been highly successful in teaching foundational reading skills and essential comprehension strategies (Cullen et al., 2014; Huffstetter et al., 2010; Rigney et al., 2020; Storey et al., 2017; Twyman et al., 2011). These programs have reached over 4 million learners globally and have earned numerous national awards (see Novak et al., 2022). Successful technology transfer relied on thorough content analysis, rigorous instructional design, research-driven iterative development, ease of implementation, robust, reliable outcomes, and a clear contextual need for the products.

Returning to the research-to-practice continuum discussed earlier, the example of Headsprout Early Reading (HER) illustrates how an innovation can become a widely adopted educational tool through a scientific research and development process of analysis, development, transfer, and implementation. Launched in 2003, HER was designed as a supplemental program aimed at advancing nonreaders (typically ages 4 and up) to a mid-second grade reading level in approximately 30 hr of individualized, adaptive online instruction, supported by embedded data-driven assessment. Instructional sequences emphasized guided practice, reduced learner errors, mastery criteria, cumulative review, and application exercises. It was used across public schools, special education settings, learning centers, and homes, helping neurotypical children, individuals with special needs, and even adults achieve reading proficiency (Layng et al., 2003, 2004a, b; Rigney et al., 2020). Due to market forces the Headsprout programs were eventually acquired by larger education companies and discontinued in 2024 (Learning A–Z, n.d.). Extensive details on the research-based design, iterative development, implementation, and evaluation of the Headsprout products are documented in other publications; however, a curated selection of references is included in Table 1, exemplifying the research-to-practice continuum.

Table 1.

Continuum of Research to Practice: Headsprout Early Reading (HER)

Basic Research (Layng et al., 2003; Layng et al., 2004a, b)	HER began with a thorough content analysis, drawing on research in reading—focusing on phonemic awareness, phonics, vocabulary, fluency, and comprehension—as well as other areas such as early learning and stimulus control procedures. Specific teaching sequences (e.g., seeing a letter and saying a sound or blending two distinct sounds to form a new sound) were designed and rigorously tested within a strict scientific protocol. During this developmental testing phase, over 250 children were observed in an on-site learning laboratory, and Headsprout scientists analyzed more than 10 million interactions and made over 10,000 data-driven revisions to refine each learning routine. Adjustments and new sequences were continuously tested against existing ones; only the techniques yielding the highest learner success were retained.
Translational Research (Layng et al., 2003; Layng et al., 2004a, b)	Testing expanded to over 1,000 children using early versions of the program in their homes via the internet to confirm that outcomes were as robust as those achieved in the on-site lab. A key component of Headsprout was online delivery (novel at the time), which enabled continuous “behind the scenes” updates and improvements. The program adapted to individual learners by uploading data to Headsprout servers for analysis. Over 90% of learners achieved over 90% accuracy in each episode throughout the program.
Applied Research (Layng et al., 2003; Storey et al., 2020; Tyler et al., 2015; Twyman et al., 2011)	Testing focused effectiveness in real-world settings, confirming earlier outcome data, optimizing implementation strategies, and addressing diverse contexts or learner needs. For example, in one school evaluation, 16 young children started a summer program with below-grade-level reading scores. After completing HER, kindergarteners obtained mid-second-grade reading levels, whereas first graders advanced to mid-second- or early third-grade reading levels. During this time, further revisions to enhance program effectiveness were identified, and the development and testing of professional materials and additional resources began.
Implementation (Gillespie et al., 2023; Grindle et al., 2019; Nally et al., 2021; Watkins et al., 2016)	A key benefit of HER was its design, which minimized the need for extensive professional development or ongoing support. Instead, the program relied on carefully arranged contingencies to promote ease of use and integration into school and home routines. Field visits identified barriers to implementation, such as limited instructional time and internet connectivity, and helped establish best practices for success, including speaking out loud when prompted and using the program at least three times a week. These insights guided the development of professional materials and resources, such as a Teacher’s Guide and the Progress Map, to support effective use. The Implementation Science (Fixsen et al, 2015) framework helped inform and shaped efforts around HER’s acceptance and effective use.
Technology Transfer (Adlof et al., 2018; Denne et al., 2024; Layng et al., 2006; McWilliams et al., 2024; Twyman et al., 2005)	Fine-tuning HER for public release involved thousands of data-driven revisions, ensuring the software addressed the needs of diverse learners and contexts. This iterative process culminated in the development of a patented instructional methodology, “Generative Learning Technology,” in 2003. The patent underscores the program’s distinctive, research-based approach to teaching foundational reading skills online and exemplifies the successful transfer of scientific research into a scalable, practical educational tool for widespread use in schools and homes.
Dissemination (Clarfield & Stoner, 2005; Reitman, Resnick, Kaskel, & Alvear 2010; Morena, 2011; Whitcomb et al., 2011)	HER was broadly disseminated through presentations at over 50 conferences, at least 30 publications, and a dozen or more doctoral dissertations or masters theses (as estimated by the author) and in numerous demonstrations at professional development venues and trade shows. Independent researchers began publishing studies on HER’s impact, adding to its evidence base and credibility. In addition, word of mouth among educators, parents, and researchers contributed significantly to its rapid adoption.
Scaling Up (Grindle et al., 2021; Hansen et al., 2023)	HER was adopted in thousands of homes and hundreds of educational settings—from single classrooms to entire districts, such as Memphis City Public Schools (TN) and Biloxi Public Schools (MS)—and remained in use for decades in some locations. Efforts to increase widespread adoption were supported by a dedicated sales force and implementation specialists. HER’s alignment with the emerging national emphasis on evidence-based practices (EBP) and No Child Left Behind (U.S. Department of Education, 2002) policies further facilitated its widespread adoption. However, despite strong sales and positive outcomes, marketing challenges and competition ultimately led to its acquisition by a larger educational products company.

Opportunities for Behavior Analysis in Addressing National Challenges

Throughout his career, Hank identified several areas of national concern—education, urban violence, declining industrial productivity, climate change—as opportunities where behavior analysis can make a significant impact (Pennypacker, 1986; Pennypacker & Perez, 2022). These challenges have been resistant to traditional solutions, possibly making them ripe for the application of behavioral technologies. In addition, behavior analysts have the potential to contribute to a wide range of societal challenges. For instance, health care continues to be an area in need of behavioral technologies at individual and system levels, particularly in areas such as chronic disease management, promoting healthy behaviors, and improving patient adherence to treatment protocols (Greenwald et al., 2015). Techniques from behavior analysis could be instrumental in designing interventions that encourage individuals to adopt and maintain healthier lifestyles, potentially reducing the burden of chronic diseases on the health-care system (see Powell et al., 2020).

A few other examples include business, corporations, productivity, and workplace safety, where behavior analysts have applied behavioral and organizational management principles to improve business outcomes, reduce accidents, and improve overall efficiency (Geller, 2001; Wilder et al., 2009). In sustainability and environmental conservation, behavior analysis has addressed climate-related behaviors at individual and community levels by designing interventions that encourage recycling, energy conservation, and sustainable consumer practices (Lehman & Geller, 2004). In criminal justice and rehabilitation, behavior analysis can help reduce recidivism rates, improve rehabilitation programs, and support the reintegration of individuals into society through effective behavior change strategies (Lutzow, 2021). In addition, public policy and government initiatives offer opportunities for behavior analysts to influence the design and implementation of programs in areas such as public health, social welfare, or diversity and equity, potentially increasing programmatic desired outcomes (Napolitano et al., 2024; see also Critchfield, 2024).

The publication ABA from A to Z: Behavior Science Applied to 350 Domains of Socially Significant Behavior (Heward et al., 2022) is an excellent resource for identifying where behavior analysis has already made inroads and where opportunities for technology transfer may exist. ABA is defined by its focus on socially significant behavior and assumed relevance to improving it, yet as a field we have not yet fulfilled this promise of far-reaching social significance. The publication offers a comprehensive overview of domains that have been empirically studied, highlighting both the broad scope of behavior analysis while also hinting at areas for future research and dissemination. By effectively expanding behavioral technologies into areas of need, behavior analysis can address critical societal issues and strengthen the field’s relevance and value in solving real-world problems.

Re-Engineering Forward

Re-engineering the educational system requires a thorough understanding of behavior change principles and the complexities of technology transfer. By integrating evidence-based practices with successful technology transfer strategies, education and other domains can be re-engineered to better meet the evolving needs of society. Behavior analysts must play a proactive role in this transformation, not only developing effective technologies but also working to ensure those technologies are effectively implemented and sustained across various systems. Hank’s insights, drawn from his ABAI presidential address on “Technology Transfer” and Engineering the Upswing, offer a powerful framework for this effort, highlighting the importance of engineering social contingencies that foster cooperation, prosocial behavior, and social capital. To address the challenges of technology transfer, behavior analysts must be purposeful and actively engaged in the process, ensuring our technologies deliver maximum benefits to society. As Hank emphasized, this demands a hands-on approach, with behavior analysts engaging directly in the marketplace and leveraging the contingencies that drive it, ensuring that behavioral technologies not only succeed but also help create a more just and equitable society for all.

Funding

The author did not receive support from any organization nor was funding received to assist with the preparation of this article.

Data Availability

No datasets were generated or analysed for the current manuscript.

Declarations

Conflicts of Interest

The author is on the Board of Directors of the Cambridge Center for Behavioral Studies and receives no compensation as member of the Board of Directors. The author has no relevant financial or nonfinancial interests to disclose and no competing interests to declare that are relevant to the content of this article.

Preparation

During the preparation of this work the author used ChatGPT-4o to assist with grammar and punctuation checks, sentence revision, reframe thoughts, and on one occasion, for example generation (as noted in the article). After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.