Evaluating Educational Innovations through Data

📊 Only 5 percent of genAI pilots deliver fast revenue gains. The other 95 percent do not move the P&L. The question is not does AI work, it is are we setting it up to work. 🧩 MIT’s new analysis shows heavy investment with light returns, especially when projects stay at the demo stage. The winners embed AI into real workflows, adapt systems over time, and measure business outcomes, not novelty. 🎓 For education and EdTech this matters even more. If revenue led use cases struggle to show quick wins, learning led use cases will need patient design, teacher training, strong data governance, and clear guardrails. Quick demos do not equal durable classroom impact. 👩🏫 As EdTech Specialist & AI Lead, I focus on long term value. I am building AI literacy pathways for staff and students, running practical PD tied to lessons, and aligning tools with GDPR and the EU AI Act. We track time saved, feedback quality, and student outcomes, not hype. 💡 Short term metrics can underprice long term transformation. The real gains show up in better feedback loops, improved planning, and consistent assessment, plus safer data practices that unlock responsible innovation. That takes strategy, not just spend. 💬 How are you balancing quick wins with long term AI investment in your school or organisation. Which 2 or 3 metrics prove value in the first 12 months without chasing vanity numbers. Share your approach below!

This document is a comprehensive reference for professionals and researchers engaged in instructional design, learning technologies, and applied research in education and training. It introduces design and development research (D&DR) as a distinct empirical methodology focused on creating and studying instructional and non-instructional products, tools, and models. The authors move beyond theory to show how design processes themselves can be the object of research—merging innovation, testing, and validation into a structured scientific inquiry. It is especially relevant for scholars who aim to build knowledge directly from practice, rather than from abstract theorizing. The content is structured into a clear, methodical progression that includes: – A foundational overview of D&DR, its scientific rationale, and its distinction from traditional design activities – Practical guidance for identifying and framing research problems grounded in workplace settings, emerging technologies, and theoretical gaps – Detailed methodology chapters covering validity, causality, research design strategies, and the use of mixed or multiple methods – Applied research designs for both product and tool development as well as model creation, validation, and real-world use – Strategies for participant selection, data collection tools, ethical considerations, and technology-enhanced data gathering – Case-based illustrations of tool development, model testing, and evaluation studies in natural work environments – A forward-looking analysis of the future of D&DR and its potential to expand the theoretical base of instructional design This book is not a general methods manual—it is a specialized guide for those conducting empirical research on how instructional solutions are designed, implemented, and improved. It provides both conceptual clarity and operational tools for generating design knowledge that is valid, contextual, and usable. For educational researchers, instructional designers, and doctoral students aiming to contribute to the evidence base of their field, it is both foundational and forward-thinking.

Does following conventional wisdom on innovation culture position innovative initiatives for failure? Focusing on culture and experimentation, conventional wisdom deprives innovators of "innovation fuel": data. Much discussion centers on validating ideas and finding their market fit post-conception. Many articles advise on culture and fostering creativity rather than understanding the muse for innovation and the elements that drive blockbuster solutions. The greatest muse for creativity, data, is the reason why we experiment and a factor for assessing an innovation initiative's capacity to succeed. Ultimately, it is success that most informs culture. Long-term, culture is a byproduct of success, and attempting to model successful behavior in a "fake it till you make it" manner is notably hollow. A more effective approach to innovation starts with the one thing built off the innate human desire to innovate: data reflecting real-world conditions where a business operates. A data journey begins by understanding these factors: Changes in Market Behaviors: Identifying shifts in consumer or industry behavior, use of technology, and PESTEL factors reveal new opportunities for innovation. Market Demand, Technical Feasibility, and Company Capabilities: Using data to assess the viability and potential demand for new ideas where they intersect with the company's capabilities. Experiment Development: Understanding the dynamics that exert the greatest influence on the success vs failure of an initiative. Interesting papers (hyperlink in the comments): Intrinsic Motivation and Curiosity: Humans have an innate desire for data to bridge knowledge gaps. This drive is part of our intrinsic motivation to explore and learn, as seen in the "information-gap" hypothesis. This intrinsic motivation is powered by our brain's dopamine system, which enhances our capacity to explore and innovate (Loewenstein, 1994; Deci and Ryan, 1985) (Frontiers). Neuroscience of Exploration: Studies show that the dopamine systems in our brain play a role in our exploratory behavior and intrinsic motivation. This system helps us seek out new information and patterns, supporting the idea that data that drives insights fosters creativity and innovation (Frontiers) (Frontiers) and (Nature) (Nature). Action and Job Satisfaction: Using data to solve problems significantly improves job satisfaction and employee retention. Data-driven approaches connect employees to company success, making employees feel valued and happy in their jobs, which in turn enhances retention and productivity (Sciendo). By prioritizing the aggregation of data, we empower innovators to see opportunities, make decisions, advocate for their projects and foster action, and reliably achieve success; ultimately fostering a genuine innovation culture. https://lnkd.in/g8SFTqbW #innovationmanagement

Measuring Research and Innovation Outputs Research and Innovation are key drivers of progress in academia, leading to new discoveries, technologies, and ways of thinking that can have a profound impact on the world. However, measuring the research and innovation capacity and output of a university can be a complex challenge. What metrics should be used, and how can universities effectively track and assess their research and innovative activities? One important factor to consider is research productivity. The number and quality of publications, patents, and other intellectual property generated by a university's faculty can be a strong indicator of innovative thinking and problem-solving. Citation impact, or how frequently a university's research is referenced by others in the field, is another useful metric. Universities can also track the commercialization of their innovations, such as the number of startup companies spun out or licensing deals made. Beyond traditional research outputs, universities should also look at more holistic measures. This could include the number of interdisciplinary collaborations, number and quality of doctoral programs, number and quality of international conferences, number and quality of international academic partnerships, joint publications, quality of research labs, amount of internal funding, the diversity of research topics and methodologies, the speed of knowledge transfer to real-world applications, and the university's ability to attract top talent and external funding (from industry and research funding agencies) for innovative initiatives. Student-led projects, hackathons, and entrepreneurship programs are other important indicators of a culture of innovation. In addition to academic impact through publications and citations, the social, economic, health, environmental, and quality of life impact should also be measured. Qualitative assessments can supplement quantitative metrics. Interviews, case studies, and peer reviews can provide valuable insights into the quality, creativity, and impact of a university's innovations. Gathering feedback from industry partners, community stakeholders, and other external collaborators can also shed light on the university's ability to drive meaningful change. Ultimately, a multifaceted approach is needed to accurately gauge a university's research and innovative capacity. By tracking a balanced set of quantitative and qualitative measures, institutions can identify their strengths, pinpoint areas for improvement, and ensure they are delivering on their mission to advance knowledge and positively transform society. At ADU, Research and Innovation is led by my esteemed colleague Professor Montasir Qasymeh and all the above measures are taken into account when measuring our research and innovation outputs. Please provide your views if I have missed any important measures. #Research #Innovation #ADU Hamad Odhabi Khulud Abdallah Abu Dhabi University

Updated and latest Monitoring and Evaluation (M&E) methods and techniques: Quantitative Methods: 1. Data Analytics: Using statistical software (e.g., R, Python) for data analysis. 2. Machine Learning: Applying algorithms for predictive modeling. 3. Big Data Analysis: Handling large datasets for insights. 4. Survey Methods: Online surveys, mobile-based surveys. 5. GIS Mapping: Geospatial analysis for spatial planning. Qualitative Methods: 1. Participatory Rural Appraisal (PRA) 2. Focus Group Discussions (FGDs) 3. Key Informant Interviews (KIIs) 4. Case Studies 5. Narrative Analysis Mixed-Methods Approaches: 1. Integrating quantitative and qualitative data 2. Triangulation: Combining multiple methods for validation 3. Meta-Analysis: Synthesizing findings from multiple studies Real-Time Monitoring: 1. Mobile-based data collection 2. Remote sensing and satellite imaging 3. Social media monitoring 4. Sentinel Site Surveillance Impact Evaluation Methods: 1. Randomized Controlled Trials (RCTs) 2. Quasi-Experimental Designs (QEDs) 3. Counterfactual Analysis 4. Propensity Score Matching (PSM) Participatory and Collaborative M&E: 1. Participatory M&E (PM&E) 2. Collaborative, Learning, and Adapting (CLA) approach 3. Empowerment Evaluation 4. Community-Based M&E Technology-Enabled M&E: 1. Mobile apps for data collection (e.g., ODK, SurveyCTO) 2. Online M&E platforms (e.g., DevInfo, TolaData) 3. Data visualization tools (e.g., Tableau, Power BI) 4. Artificial Intelligence (AI) for data analysis Other Innovative Methods: 1. Theory of Change (ToC) approach 2. Outcome Mapping 3. Most Significant Change (MSC) technique 4. Social Network Analysis (SNA) Stay updated on the latest M&E methods and techniques through: 1. American Evaluation Association (AEA) 2. International Development Evaluation Association (IDEAS) 3. Evaluation Capacity Development (ECD) Group 4. BetterEvaluation website 5. M&E journals and publications (e.g., Journal of MultiDisciplinary Evaluation)

Unpacking the impact of digital technologies in Education This report presents a literature review that analyses the impact of digital technologies in compulsory education. While EU policy recognizes the importance of digital technologies in enabling quality and inclusive education, robust evidence on the impact of these technologies is limited especially due to its dependency from the context of use. To address this challenge, the literature review presented here, analyses the focus, methodologies, and results of 92 papers. The report concludes by proposing an assessment framework that emphasizes self-reflection tools, as they are essential for promoting the digital transformation of schools. The literature review on the impact of digital technologies in education revealed several key findings: - Digital technologies influence various aspects of education, including teaching, learning, school operations, and communication. - Factors like digital competencies, teacher characteristics, infrastructure, and socioeconomic background influence the effectiveness of digital technologies. - The impact of digital tools on learning outcomes is context-dependent and influenced by multiple factors. - Existing evidence on the impact of digital tools in education lacks robustness and consistency. The assessment framework proposed in the report offers a structured approach to evaluating the effectiveness of digital technologies in education: 1. Identify contextual factors influencing technology impact. 2. Map stakeholders and their characteristics. 3. Assess integration into learning processes and practices. 4. Utilize self-reflection tools like the Theory of Change. 5. Provide evaluation criteria aligned with the framework. 6. Adapt existing tools for technology assessment. 7. Consider digital competence frameworks for organizational maturity. Implications and recommendations for policymakers and educators based on the report findings include: - Recognizing the contextual nature of technology use. - Focusing on creating rich learning environments. - Adopting a systems approach to studying technology impact. - Ensuring quality implementation and professional development. - Developing policies for monitoring and evaluation. - Encouraging further research on technology impact. By following these recommendations, stakeholders can leverage digital technologies effectively to improve teaching and learning outcomes in educational settings. https://lnkd.in/eBEN5XQg

Data-driven learning and evaluation published in AG-CONNECT, edited by Tanja de Vrede As technology and education become increasingly intertwined, a new way of interactive and data-driven learning emerges. By integrating statistical analyses and digital tools into teaching methods, educators and trainers can not only directly respond to the learning needs of participants but also gather valuable insights into trends and patterns in the learning process. Digital tools, such as group support systems, create a dynamic and interactive learning environment where participants actively contribute via laptops or smartphones. This input can be aggregated, analyzed, and discussed, providing both qualitative insights and statistical trends. The method is flexible and applicable at all levels – from primary education to PhD research. Structured techniques such as ThinkLets make sessions more efficient, whether for SWOT analyses, business cases, or retrospectives. Another powerful feature is automatic reporting and benchmarking, which helps identify strengths and weaknesses, while supporting a continuous cycle of knowledge building and quality improvement. In short: this approach enables more interactive education, more efficient knowledge transfer, and sustainable learning. Article is written with prof.dr. Yuri Bobbert of the Antwerp Management School