In recent years, educational innovation has been inspired by practices designed initially for industry, from Agile to the most recent InnerSource. Educators are increasingly reimagining how students can learn through collaboration, iteration, and transparency. However, as universities experiment with such open, collaborative models, the question of how to fairly evaluate both individual and collective contributions becomes more complex.

One powerful yet underexplored approach to evaluation in higher education is the adoption of OKRs (Objectives and Key Results), a goal-setting and performance framework widely used in industry to align teams, measure progress, and drive accountability. When thoughtfully adapted to academia, OKRs can complement methods and practices like Agile and InnerSource, enabling student-centred, transparent, and continuous evaluation across disciplines.

Why OKRs Make Sense in Education

OKRs encourage alignment between individual goals, team objectives, and institutional learning outcomes. In a traditional educational model, evaluation is often backwards-looking — grades reflect outcomes rather than growth. OKRs, in contrast, are forward-looking and iterative, focusing on measurable progress and learning impact.

Incorporating OKRs into university courses, particularly those leveraging InnerSource practices or project-based learning, offers several key advantages:

1. Clarity and Purpose.
2. OKRs help students understand why they are doing something, not just what they are doing. This builds intrinsic motivation and a sense of ownership over learning outcomes.
3. Continuous Feedback Loop.
4. Like Agile sprints or InnerSource peer reviews, OKRs emphasise iteration. Regular check-ins (e.g., every 2–3 weeks) enable professors and teams to realign objectives and assess progress dynamically rather than relying on static midterms or finals.
5. Balance Between Autonomy and Accountability.
6. Students define their own objectives within the scope of the course’s goals. Professors act as mentors, guiding the alignment between personal learning trajectories and team deliverables. This balance promotes self-management and collaboration — two critical professional competencies.
7. Measurable Soft Skills Development.
8. Traditional grading struggles to capture growth in communication, teamwork, or leadership. OKRs, especially when integrated into InnerSource-style collaborative environments, make these “soft” skills visible and measurable through concrete key results (e.g., “Lead two peer review sessions and provide feedback on at least three pull requests”).

Implementing OKRs in Academic Contexts

1. Individual OKRs: Driving Personal Growth

Each student defines personal learning objectives aligned with the course’s overall goals. For instance, in a Software Engineering course using InnerSource repositories:

• Objective: Improve collaborative coding and code review skills.
• Key Results:
  • Submit at least three pull requests reviewed and approved by peers.
  • Lead one peer review discussion with constructive feedback.
  • Resolve at least two issues raised by teammates.

These key results can be automatically tracked through version control analytics or peer-review metrics, creating transparent, data-driven evidence of contribution.

2. Team or Group OKRs: Structuring Collaborative Work

In disciplines involving projects (Engineering, Design, Business, etc.), teams can define shared OKRs that reflect both product outcomes and process maturity.

• Objective: Deliver a functional prototype that meets stakeholder requirements.
• Key Results:
  • Complete all core features by Sprint 4.
  • Conduct usability testing with at least 10 participants.
  • Document and present results to the class and external mentors.

Here, the emphasis shifts from individual grading to collective accountability, mirroring professional project environments. Each team’s OKRs can be reviewed in structured checkpoints, ensuring transparency and consistent evaluation standards.

3. Cross-Disciplinary OKRs: Enabling Interconnected Learning

Just as InnerSource envisions a unified educational “mega-project,” OKRs can operate at higher levels linking objectives across multiple courses or faculties. For instance, a capstone project combining Computer Science, Business, and Design could establish overarching OKRs that align individual contributions with interdisciplinary outcomes.

• Objective: Create a market-ready digital product prototype integrating technical, business, and design perspectives.
• Key Results:
  • Deploy a functional MVP using InnerSource collaboration.
  • Develop a viable business model validated through market testing.
  • Present interdisciplinary findings at the university’s innovation fair.

Such alignment fosters systems thinking and helps students appreciate how diverse disciplines contribute to holistic innovation.

Evaluating Students with OKRs

Evaluation using OKRs shifts from grading what students deliver to assessing how they progress toward meaningful objectives. The approach combines quantitative and qualitative metrics:

• Quantitative:
  • Repository activity (commits, reviews, issues resolved).
  • Objective completion rates.
  • Peer review participation.
• Qualitative:
  • Reflection reports discussing learning outcomes.
  • Peer and mentor feedback on collaboration and leadership.
  • Demonstrated alignment with course-level goals.
  • We can evaluate communication, collaboration, teamwork, and organisational skills.

Professors can evaluate students in three complementary dimensions:

1. Individual learning growth (via personal OKRs).
2. Team performance and contribution (via group OKRs).
3. Interdisciplinary integration (for cross-course projects).

This approach enables a 360-degree evaluation system—one that values both autonomy and collaboration, as well as process and outcomes.

Challenges and Considerations

Adapting OKRs for education comes with challenges similar to implementing InnerSource:

• Training and Calibration: Students and professors need orientation on setting realistic, measurable OKRs.
• Consistency: Overly vague or ambitious OKRs may lead to frustration or uneven evaluation.
• Tooling: Integrating OKR tracking with existing platforms (LMS, GitHub, or Project Management tools) ensures data consistency.
• Equity: Clear guidelines are needed to balance recognition of individual effort within team outcomes.

However, these challenges can be mitigated through structured onboarding, template OKRs, and regular coaching sessions, which echo the phased approach already proven effective in InnerSource adoption.

The Synergy Between InnerSource and OKRs

While InnerSource provides the collaborative infrastructure, OKRs provide the evaluative compass. Together, they create a powerful feedback loop:

InnerSource Focus	OKR Contribution
Collaborative learning and contribution	Clear objectives and measurable results
Peer review and mentorship	Structured accountability and recognition
Iterative improvement cycles	Regular goal-setting and progress check-ins
Transparent repositories	Transparent evaluation metrics

This synergy bridges the gap between learning and assessment, transforming evaluation into an ongoing, participatory process rather than a one-time event.

My Thoughts: From Evaluation to Empowerment

The adoption of OKRs in higher education represents a shift from grading performance to cultivating purpose-driven learning. When integrated with InnerSource-inspired collaboration, OKRs can transform large-scale courses into dynamic ecosystems of continuous growth, peer learning, and innovation.

Students are no longer passive recipients of assessment but active co-creators of their educational journey, capable of setting goals, measuring impact, and reflecting on progress, skills essential for thriving in the modern, agile, and interdisciplinary workplace.

As universities navigate the evolving landscape of education, combining InnerSource practices with OKR-based evaluation offers a promising pathway toward scalable, fair, and future-ready learning.

Acknowledgements: I would like to sincerely thank Sara Santos for her careful review and insightful feedback, which helped refine and strengthen this work.

#OKRs #InnerSource #CollaborativeLearning #HigherEducation #AgileEducation #SoftSkills #EvaluationInnovation

After sharing a short article titled “Unlocking Innovation with InnerSource: Why Organisations Should Embrace” and a paper I co-authored that was presented at the HICSS conference titled “Agile and InnerSource: A Match made in Heaven and in Hell!“, my friend Nuno Seixas challenged me to share my thoughts on scaling collaborative learning with InnerSource in courses with over 250 students. I gladly accepted this challenge, and here are my insights.

Additionally, before starting, I would like to thank my colleagues Clare Dillon and Daniel Izquierdo Cortázar for their helpful review and feedback.

Scaling collaborative learning in large educational environments presents unique challenges, especially in courses with many students [1]. The InnerSource practices, inspired by open-source software methods, offer an effective strategy to address these challenges. InnerSource applies open-source principles such as transparent collaboration, shared development, and community engagement within an organisation or closed group. Based on my discussions with several professors and students, this approach is already being partially implemented in specific courses, such as Software Engineering, with promising outcomes.

In the current social media-driven world, there are noticeable gaps in communication and teamwork skills among students [2]. InnerSource can significantly narrow this gap by fostering a collaborative learning environment where students develop their soft skills, particularly in communication, teamwork, and problem-solving. These skills are highly valuable and essential for successful integration into professional organisational environments [3].

InnerSource versus Open-Source: Which is suitable for Education?

A key question when discussing InnerSource in education is: Why not opt for open source instead? Many educational initiatives encourage students to work on real-world projects, making their contributions visible to recruiters and aligning with open research and open science trends. While InnerSource and open source share benefits such as collaboration, transparency, and practical experience, there are situations where InnerSource (initially closed) proves to be more appropriate in educational settings.

• Project Completion and Later Open-Sourcing: Some projects require a closed environment initially, before opening up once they reach maturity.
• Private Practice Environment: Students may prefer a closed setting to experiment without exposing early-stage experiments or mistakes publicly.
• Research Contexts: Proprietary or confidential research projects require controlled internal collaboration spaces.

These scenarios demonstrate that InnerSource serves as a safe, structured first step before transitioning students to open-source workflows.

How InnerSource Works in Education

InnerSource in educational settings involves creating shared repositories for larger, common projects accessible across all course disciplines and student groups. These repositories centralise materials such as documentation, code, assignments, and student projects, promoting transparency and collaboration. Students from various academic years are encouraged to participate by submitting pull requests, refining peers’ code, and engaging constructively in peer reviews. Professors and teaching assistants supervise this collaborative workflow, ensuring high standards of quality and consistency while helping students develop essential technical skills and critical soft skills needed for modern workplaces. Expanding this model further, courses could integrate InnerSource practices into a broader, unified educational framework, imagining the entire degree as one interconnected “mega-project”. Each course would contribute distinctly defined components, collectively forming a holistic, collaborative learning experience. Although ambitious, such integration could significantly improve interdisciplinary collaboration and student motivation [4].

Benefits of Adopting InnerSource in Large Courses

InnerSource offers several significant advantages for collaborative learning in large educational environments:

• Increased Student Engagement and Participation: Students are more likely to engage with course material when they work on real-world projects. The collaborative structure of InnerSource mirrors professional environments, fostering intrinsic motivation and investment in the learning process [5].
• Realistic Simulation of Industry Workflows: By adopting InnerSource practices, students follow processes similar to those used in real software development, such as shared repositories, code reviews, and issue tracking. This realistic simulation helps them become familiar with industry-standard tools and workflows, better preparing them for professional environments after graduation.
• Improved Motivation and Accountability: InnerSource introduces structured mechanisms for recognition (e.g., contribution histories, visible pull requests, and peer feedback), which can boost motivation. Knowing that their work is visible to peers creates a natural sense of accountability and encourages students to produce higher-quality contributions.
• Development of Soft Skills: Working within InnerSource practices helps students enhance key soft skills such as communication, collaboration, and problem-solving abilities that are invaluable in modern workplace settings and often overlooked in traditional academic courses.
• Iterative Feedback: Peer reviews and continuous feedback loops allow students to refine their work over time. This not only enhances technical skills but also improves their ability to give and receive constructive feedback, which is an essential professional competency.
• Enhanced Transparency and Consistency: A shared repository increases transparency and ensures consistency in course content, assignments, and evaluations. Everyone works from a common source of truth. That said, it’s essential to recognise that some students and professors may still prefer to keep detailed evaluations or grading discussions private.
• Reduced Workload for Professors and Teaching Assistants: As students begin to mentor and support one another, the overall burden on faculty diminishes. While this model may require some upfront planning and setup, it can significantly ease the workload in the long run by leveraging structured peer mentoring.

Structured Approach to Implementation

Given the scale of implementing InnerSource in large courses, a step-by-step approach is essential. Breaking it down into manageable phases allows for smoother adoption and effective scaling.

Phase 1: Training and Onboarding

• Provide initial training sessions for students, professors, teaching assistants, and mentors (senior students) on the tools used for collaboration (GitHub, Slack, SonarQube, among others).
• Set up shared repositories for course projects and assignments.

Benefit: This initial phase equips all participants with the tools and knowledge needed to start working collaboratively.

Phase 2: Small-Scale Collaborative Projects

• Introduce smaller collaborative projects where students work together in teams. Begin integrating peer review and feedback loops.

Benefit: This offers a low-pressure introduction to collaboration, helping students build confidence and familiarity with the process.

Phase 3: Peer Mentoring and Iterative Reviews

• Develop a system for peer mentoring, where students give feedback on each other’s work, using a structured peer review process.

Benefit: Peer mentoring helps lessen the workload for professors and teaching assistants while enhancing students’ communication and review skills.

Phase 4: Larger, Cross-Disciplinary Projects

• Scale up to larger projects that span multiple courses, encouraging interdisciplinary collaboration. This step mimics the complexity of real-world projects, where different teams work on various components.

Benefit: Students gain experience in complex, interdisciplinary teamwork, reflecting industry-level project structures.

Phase 5: Evaluation and Recognition Systems

• Create automated systems to track and assess individual contributions to the project (e.g., GitHub analytics, automated code reviews). Implement a structured recognition system for students who contribute significantly.

Benefit: This phase boosts motivation and accountability by clearly recognising individual efforts while ensuring fair, data-driven assessments.

Addressing Main Challenges

While the benefits of InnerSource are clear, several challenges must be addressed for successful implementation in academic settings:

Time and Workload for Professors and Teaching Assistants

• Challenge: Implementing InnerSource demands significant time from instructors and TAS, especially when evaluating individual contributions across large, shared repositories.
• Action: Automate parts of the assessment process using analytics tools like GitHub Insights or SonarQube, which can track contributions and support data-driven evaluation of student work. Structured onboarding and gradual adoption of InnerSource practices can help reduce workload.

Resistance to Collaborative Workflows

• Challenge: Students may be reluctant to adopt collaborative workflows, particularly if unfamiliar with peer review or open contribution models.
• Action: Provide clear guidelines, initial training sessions, and continuous support to help students transition to collaborative work. Regular development sprints, iterative submissions, and ongoing feedback can boost acceptance and foster a culture of open collaboration.

Managing Tool Access and Security

• Challenge: Managing access to tools (e.g., GitHub, Slack) becomes more complex in large courses, especially when students need access to specific projects or repositories.
• Action: Use integrated platforms that simplify access management and align permissions with project roles. Assign mentors to oversee project flow, ensure security, and help students use the tools effectively.

Maintaining Consistency and Quality

• Challenge: Ensuring consistent code quality across contributions can be challenging when multiple students work on the same repository.
• Action: Set clear coding standards and employ automated tools like SonarQube to maintain quality. Routine peer and instructor reviews reinforce best practices and encourage meaningful participation.

Fair Assessment of Individual Contributions

• Challenge: Accurately evaluating each student’s input within collaborative projects is a common issue.
• Action: Use clear metrics and regular assessment checkpoints supported by analytics from version control systems and peer reviews. Implement recognition systems to reward significant contributions, effective code reviews, or valuable suggestions.

InnerSource can replicate real-world development environments in academic courses. Students may work in specialised teams (e.g., backend, frontend, testing, among others) and collaborate to integrate their work into a larger product. Submitting and resolving issues, maintaining documentation, and participating in regular sprint cycles develop technical communication skills, promote proactive behaviour, and improve team dynamics. A structured recognition system for high-quality contributions further enhances engagement and motivation.

My Thoughts on the Future of Education

The future of education is collaborative, interdisciplinary, and closely connected to real-world challenges through strong industry partnerships. To prepare students for a swiftly changing world, institutions need to go beyond traditional lectures and encourage interactive, digitally integrated learning environments. Professors and teaching assistants will become mentors and facilitators, helping students develop critical thinking, creativity, and adaptable problem-solving skills while fostering genuine teamwork. Students, in turn, must adopt self-directed exploration, collaboration, and ongoing learning as essential 21st-century skills.

One practical approach to this transformation is implementing InnerSource practices, inspired by open-source software development. By working on shared projects, whether within a single course or strategically across multiple courses, students gain not only technical knowledge but also critical soft skills such as communication, coordination, and peer review. InnerSource offers a structured, safe environment for collaborative learning, which can later expand into open-source participation, allowing students to engage with global communities, showcase their work, and build professional portfolios.

Scaling these initiatives, especially in large courses, requires careful planning, clear guidelines, effective communication, and collaborative tools to monitor progress and ensure quality. Pilot programmes or integration with existing innovation projects can quickly demonstrate value without overwhelming participants.

As AI, remote collaboration, and rapid technological change continue to reshape how we work and learn, adopting innovative educational models like InnerSource is no longer optional; it is essential. A collaborative mindset is not just a bonus; it is a fundamental competency for the modern workforce. By adopting and integrating methods beyond Agile, such as InnerSource, institutions can expand collaborative learning, develop industry-relevant skills, and better prepare students to succeed in interconnected, agile, and dynamic environments. Now is the moment to lead this change, empowering students, professors, teaching assistants, and institutions to co-create the future of learning and work.

References:

[1] P. Dillenbourg, Collaborative Learning: Cognitive and Computational Approaches. Elsevier Science Ltd, 1999.

[2] OECD, Skills for Social Progress: The Power of Social and Emotional Skills. OECD Publishing, 2020.

[3] World Economic Forum, The Future of Jobs Report 2020. World Economic Forum, 2020.

[4] D. W. Johnson and R. T. Johnson, “An educational psychology success story: Social interdependence theory and cooperative learning,” Educational Researcher, vol. 38, no. 5, pp. 365-379, 2009.

[5] M. Prince, “Does active learning work? A review of the research,” Journal of Engineering Education, vol. 93, no. 3, pp. 223-231, 2004.