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From Problems to Possibilities
From Problems to Possibilities
Throughout this OER experience, you explored several usability challenges commonly associated with microlearning and experienced two contrasting versions of the same learning module and reflected on how design choices influenced your learning experience.
Together, these activities highlight a central tension in modern learning technologies:
Current platforms are often very good at measuring what is easy to count; clicks, completions, streaks, and time spent, but much less effective at measuring meaningful learning, competency development, and real-world performance.
As learning technologies continue to evolve, researchers, educators, and industry leaders are beginning to ask a different question: What if future learning platforms measured what truly matters?
How do we know learning actually happened?
How do we know learning actually happened?
As microlearning expands across classrooms, universities, workplaces, professional certification programs, workforce reskilling initiatives, and self-directed learning environments, a new question is emerging: How do we know learning actually happened?
Completing a module does not necessarily mean someone understands the content. Maintaining a streak does not guarantee skill development. Spending more time on a platform does not always translate into improved performance.
As learning technologies become increasingly personalized and data-driven, educators, employers, platform designers, and learners must decide what evidence matters most.
Please share your perspective by completing the Google Form below.
A classroom teacher, corporate trainer, university instructor, employer, and learner may all define successful learning differently.
For some, success may be demonstrated through improved performance, competency, or skill transfer. For others, it may involve confidence, behaviour change, problem-solving, or the ability to apply learning in new contexts.
As organizations increasingly invest in upskilling, reskilling, and lifelong learning initiatives, future microlearning platforms may face growing pressure to demonstrate not only engagement, but also meaningful and measurable learning outcomes. The question may no longer be whether learners completed the content, but whether they can actually do something with what they learned.
Emerging Signals
Emerging Signals
What might microlearning measure next?
What might microlearning measure next?
If completion rates, streaks, clicks, and time-on-task tell us who participated, what might future systems use to determine whether learning actually occurred?
Researchers, educators, employers, and learning organizations are increasingly exploring new ways to measure understanding, growth, skill development, and performance. While these emerging signals are still evolving, they point toward a future where learning may be evaluated through evidence of application, competency, progress, and real-world impact rather than engagement alone.
Click each signal below to explore what it measures and why it may shape the future of microlearning.
Signal What It Helps Measure
Signal What It Helps Measure
AI Assessment
AI Assessment
Organizations such as Khan Academy (Khanmigo), Duolingo Max, ETS, and emerging AI tutoring platforms are exploring conversational feedback, coaching, scenario-based learning, and AI-supported assessment. These tools suggest a shift from measuring completion toward evaluating learner reasoning and understanding.
Understanding
Understanding
Traditional microlearning platforms often measure completion rather than comprehension. Emerging AI-powered assessment tools may enable more authentic demonstrations of learning through coaching conversations, scenario-based tasks, simulations, and real-time feedback. Rather than asking learners what they remember, AI systems may increasingly assess how learners apply knowledge, explain reasoning, and solve problems in context (Luckin et al., 2016; Holmes et al., 2022).
Why it matters: Future platforms may move beyond quizzes and completion rates toward richer evidence of understanding and application.
Adaptive Learning
Adaptive Learning
Future systems may personalize learning pathways, pacing, support, and review schedules based on learner performance and needs. Platforms such as Khan Academy (Khanmigo), ALEKS, and DreamBox Learning already adapt content and recommendations based on learner progress.
Growth
Growth
Adaptive learning systems adjust content, pacing, review schedules, and support based on learner performance. Rather than assuming all learners follow the same pathway, adaptive systems can identify strengths, gaps, misconceptions, and readiness for new learning. Research suggests personalized learning pathways can improve learner engagement and achievement by providing support at the point of need (Walkington, 2013; Pane et al., 2017).
Why it matters: Future microlearning experiences may be less about completing the same content and more about demonstrating individual growth over time.
Learning Analytics
Learning Analytics
Analytics may evolve beyond completion rates toward evidence of growth, skill development, and learning transfer. Learning management systems such as Canvas, D2L Brightspace, and Moodle increasingly provide dashboards and predictive analytics to help educators monitor learner progress.
Progress
Progress
Learning analytics combines learner interactions, assessment data, engagement patterns, and performance indicators to provide a more comprehensive picture of learning. As analytics tools become more sophisticated, they may help educators and organizations identify patterns of improvement, predict learning needs, and evaluate whether learning transfers beyond the platform itself (Siemens & Long, 2011; Ferguson, 2012).
Why it matters: Future systems may place less emphasis on clicks, streaks, and time-on-task and more emphasis on meaningful indicators of learner progress.
Competency Mapping
Competency Mapping
Learners may increasingly be recognized for demonstrated competencies and skills rather than completed courses or modules. Organizations such as Western Governors University (WGU), CompetencyWorks, and Credential Engine are helping shape competency-based models and alternative credentialing systems.
Skills
Skills
Competency-based approaches focus on demonstrated abilities rather than time spent learning. Learners progress when they can provide evidence that they have mastered specific skills, competencies, or outcomes. This approach is increasingly visible in professional certification, workforce development, higher education, and lifelong learning initiatives (Le et al., 2014; Gervais, 2016).
Why it matters: Future learners may be recognized for what they can do rather than what courses they have completed.
Performance Support
Performance Support
The most disruptive shift may be connecting learning directly to outcomes such as classroom performance, workplace productivity, career advancement, or behavioural change. Organizations such as LinkedIn Learning, WatSPEED, and the World Economic Forum’s Reskilling Revolution are increasingly focused on measuring the impact of learning investments.
Transfer
Transfer
Perhaps the most disruptive shift is connecting learning directly to real-world performance. In education, this may involve improved student outcomes, instructional practice, or classroom implementation. In workplace settings, it may involve productivity, innovation, leadership, or job performance. The ultimate goal is to determine whether learning creates meaningful change beyond the platform. Research on training transfer suggests that learning only has value when knowledge and skills are successfully applied in authentic contexts (Baldwin & Ford, 1988; Grossman & Salas, 2011).
Why it matters: Future microlearning systems may be judged not by engagement metrics, but by their ability to improve performance and behaviour in authentic contexts.
Who Gets to Decide What Counts as Learning?
Who Gets to Decide What Counts as Learning?
Future microlearning ventures may succeed not because they deliver more content, but because they can provide meaningful evidence of learning for multiple stakeholders simultaneously.
Stakeholder What Success Looks Like
Stakeholder What Success Looks Like
Learners
Learners
Growth, confidence, and meaningful progress toward goals
Educators
Educators
Understanding, application, and transfer of learning
Schools & Institutions
Schools & Institutions
Improved learning outcomes and achievement
Employers
Employers
Performance improvement and skill development
Governments & Workforce Agencies
Governments & Workforce Agencies
Workforce readiness and economic competitiveness
Platform Providers
Platform Providers
Engagement, retention, and demonstrated impact
Real-World Propsectives
Real-World Propsectives
Research can help us identify emerging trends, but the future of microlearning will also be shaped by the people designing, implementing, measuring, and investing in learning systems.
To complement the research presented in this section, we invited professionals working across education, workforce development, learning design, educational technology, and artificial intelligence to share their perspectives.
K - 12
K - 12
Leadership
Leadership
Sheldon Pereira
Deputy Head of School, SJK
“Give me something I can act on, like which students are stuck on the same underlying misconception, rather than another chart of who logged in.”
Emerging Trend
Schools are looking beyond engagement metrics toward learning insights that directly inform teaching and instructional decision-making.
Executive
Executive
Education
Education
Melanie Hains
Market Researcher, WatSPEED
“Bite sized learning formats that deliver the right content at the right time, but also stack into larger, recognized achievements like certificates.”
Emerging Trend
Microlearning will create greater value when short learning experiences connect to recognized credentials and long-term career pathways.
Industry
Industry
Professional
Professional
Microlearning User
Chemical Engineering
“Instead of taking courses in advance, I want knowledge distributed to me exactly when I need it.”
Emerging Trend
The future of learning is shifting from scheduled content delivery toward just-in-time performance support embedded within work.
Dive Deeper: Resources for Further Exploration
Dive Deeper: Resources for Further Exploration
Discussion
Discussion
Consider using one of the “Dive Deeper” resources or real-world protagonist perspectives to support your response to the discussion:
Emerging Questions
Emerging Questions
As microlearning evolves, several important questions remain unresolved:
How much learner data should platforms collect?
How can learning be measured without becoming surveillance?
How can AI-supported systems remain transparent and equitable?
Can microlearning support workforce reskilling at population scale?
How should demonstrated competencies be recognized across industries and institutions?
The future of microlearning may depend less on delivering content and more on answering these questions responsibly.
References
Association for Talent Development. (n.d.). Research reports. https://www.td.org/atd-research
Aurora Institute. (n.d.). CompetencyWorks. https://aurora-institute.org/competencyworks/
Credential Engine. (n.d.). Credential Engine. https://credentialengine.org/
Digital Promise. (n.d.). Learner variability project. https://learnervariabilityproject.org/
Educational Testing Service. (n.d.). Research. https://www.ets.org/research/
EDUCAUSE. (n.d.). Generative AI. https://library.educause.edu/topics/teaching-and-learning/generative-ai
EDUCAUSE. (n.d.). Learning analytics. https://library.educause.edu/topics/analytics-and-data/learning-analytics
Khan Academy. (n.d.). Harnessing AI so that all students benefit: A nonprofit approach for equal access. https://blog.khanacademy.org/harnessing-ai-so-that-all-students-benefit-a-nonprofit-approach-for-equal-access/
LinkedIn Learning. (2025). 2025 workplace learning report. https://business.linkedin.com/learn/resources/workplace-learning-report
Organisation for Economic Co-operation and Development. (n.d.). Future of education and skills 2030. https://www.oecd.org/education/2030-project/
Society for Learning Analytics Research. (n.d.). SoLAR. https://www.solaresearch.org/
University Technology Office, Arizona State University. (n.d.). Adaptive learning. https://eady.asu.edu/adaptive-learning/
WatSPEED. (n.d.). WatSPEED professional education. https://watspeed.uwaterloo.ca/
Western Governors University. (n.d.). Competency-based education and measuring learning. https://www.wgu.edu/about/story/measuring-learning.html
World Economic Forum. (2025). The future of jobs report 2025. https://www.weforum.org/reports/the-future-of-jobs-report-2025/
World Economic Forum. (n.d.). Reskilling revolution. https://www.weforum.org/initiatives/reskilling-revolution/
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