Letting Students Disagree: Innovations in Team-Based Learning Assessments

NTU’s Interdisciplinary Collaborative Core (ICC) curriculum features an innovative team teaching approach through CC0007 Science & Technology for Humanity, where interdisciplinary excellence is cultivated through thoughtful collaborative instruction. Led by a diverse group of academics including Dr Jennifer Cash (History), Dr Felix Lena Stephanie (Engineering Management), Dr Fannie Yifan Zhang (Biomedical Engineering), Dr Pritpal Singh (Biology), and Dr Jeremy Sng (Communication Studies), this interdisciplinary teaching team demonstrates how structured collaborative teaching can transform educational experiences. Their combined expertise creates a rich learning environment that transcends traditional disciplinary boundaries while offering students multiple perspectives on complex issues. 

In this post, we feature the teaching team of CC0007 Science & Technology for Humanity, who have developed an innovative adaptation of Team-Based Learning (TBL) that challenges traditional assumptions about team consensus while maintaining the pedagogical power of collaborative learning. Their approach particularly reimagines the Individual Readiness Assessment (IRA) and Team Readiness Assessment (TRA) components of TBL, creating a more authentic learning environment that better prepares students for real-world decision-making scenarios.

This innovation comes at a crucial time when higher education faces increasing pressure to bridge the gap between academic learning and workplace readiness. Traditional teaching methods often emphasise individual performance or forced consensus, neither of which fully reflects the complexity of real-world problem-solving environments.

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CC0007 Science and Technology for Humanity is a core course at Nanyang Technological University. It is designed to equip students with the critical thinking and collaborative skills needed to navigate contemporary challenges posed by scientific and technological innovations. The course aims to foster awareness and curiosity, pushing students to analyse the potential benefits and costs of technologies like Artificial Intelligence, Data Analytics, and Synthetic Biology from diverse viewpoints – specifically integrating scientific/technical, business, and humanistic/social scientific perspectives. 

A key intended learning outcome of CC0007 is the ability to collaborate effectively in cross-disciplinary teams to identify real-world challenges and propose balanced solutions. Achieving these ambitious goals, particularly fostering effective cross-disciplinary collaboration and analysis, required the teaching team to adapt traditional pedagogy.

A key innovation, featured in this post, is their unique approach to Team-Based Learning (TBL). This approach challenges traditional assumptions about team consensus. The team reimagined the Individual Readiness Assessment (IRA) and Team Readiness Assessment (TRA) components of TBL to foster a learning environment that mirrors the complexity of real-world problem-solving in teams. This better prepares students for future careers, moving beyond traditional methods that often over-emphasise either individual performance or forced consensus.

IRA/TRA Together

Although so-called team-based learning is expanding in many universities, and certainly at NTU, full-fledged TBL is difficult to implement. The full sequence requires pre-class readings, individual- and team-based readiness assessments, an appeals period, and a mini-lecture before proceeding on with any other class activities. While we have made a few other adjustments to the TBL sequence to fit our class, it is the adjustment to our readiness assessment that seems the most significant divergence from the model. Why have we made this modification?

TBL is time intensive. At the very least, it takes a significant amount of valuable class time to implement two cycles of readiness assessment. But CC0007 invests this time because it draws home the connection between the course ILOs and each day’s lesson: 

“While it may seem obvious that multiple minds produce better results when tackling weekly quizzes, I never truly realised how impactful collaboration is in such tasks. When we worked in groups, we were a lot more confident in our answers due to peer affirmation.” – So wrote one of our students in an end-of-term reflection essay, following it up with the table below, documenting the impact of the group working together on his quiz scores.

IRA vs TRA Comparison Table

Week no.	IRA	TRA	Difference(TRA-IRA)
2	33.33	44.66	11.33
4	26.33	35	8.67
5	43.33	42.66	-0.67
8	38.5	48	9.5
9	33.16	47.5	14.34
10	43.33	48	4.67
Average	36.33	44.30	7.97 ≃ 16%

Productive Disagreement in Interdisciplinary Contexts

Traditional TBL often pushes for team consensus. When readiness assessments are used, an individual phase (IRA) is followed by a team (TRA) phase in which students agree on a single answer. However, the CC0007 team recognised that real-world decision-making frequently involves navigating and proceeding despite differing, valid viewpoints. Their innovative approach maintains individual accountability while valuing productive disagreement as a learning opportunity.

Crucially, this approach acknowledges that disagreements often arise from the interdisciplinary nature of the course content itself. For example, students might disagree on interpreting a question’s language, identifying the core scientific principles involved, or deciding whether information from other domains (like business ethics or social impact studies learned in other courses) is relevant to a primarily technical problem. These are not mere procedural disagreements; they reflect the integration of technical, business, and social perspectives that the course deliberately cultivates. In a real-world team, similar disagreements are common when interpreting client needs, defining project goals across departments, or delineating organisational responsibilities.

The modified TBL allows these differing perspectives, potentially rooted in distinct disciplinary thinking, to be preserved through individual answers submitted during the TRA phase, even after team discussion. This preserves the richness of diverse viewpoints and allows instructors and students to revisit these differing rationales, for example, when exploring why a technically sound solution might face business hurdles or social opposition. In the class context, some answers are more (or less) correct than others, so the students also gain an additional tool in assessing each others’ judgment skills.

Preferring Consensus

Interestingly, although NTU students are renowned for their competitive nature, we see that most groups prefer to work towards agreement on their assessment activities. They usually pick the same answers, foregoing the chance to continue to disagree. Time and again, they use their end-of-term reflection essays to tell us about their experiences of coming to know each others’ strengths. As the same student who systematically documented his IRA/TRA improvement explained: 

“Due to the nature of the quizzes, there were times where we were individually confident in our answers. When that happens, usually mutual reassurance from the other members would reinforce our confidence, leading to a higher chance of achieving a better score than our IRA. Through TRA, I learned that learning is not just about getting the right answers. It is about growing through shared insight, trust, and collaborative efforts.”

It seems that students rarely focus on ostracising the students who are less confident, and pull together around their points of shared confidence. 

After the TRA: Integrating Perspectives through Mission-Based Learning

Beyond the initial IRA/TRA assessments, CC0007 carries TBL into scenario-based “missions” that replicate complex, interdisciplinary challenges. Students tackle it by implementing three things: the content knowledge they have built and tested in IRA/TRA; their own interdisciplinary perspectives; and steadily developing teamwork skills. 

Consider a mission focused on the ethical implementation of AI. Taken as an individual assignment, one student might focus on the algorithm’s efficiency (Technical perspective), another on its marketability and cost (Business perspective), and a third on potential biases affecting users (Social perspective). The modified TBL structure encourages these students to articulate and defend their viewpoints collaboratively. 

The team doesn’t need to force an artificial consensus; instead, they learn to build a solution that acknowledges and potentially integrates these diverse considerations. Teams embracing this productive disagreement often develop more nuanced and robust solutions, mirroring how complex real-world problems benefit from multiple expert perspectives rather than a single, potentially narrow, consensus view. This process makes the interdisciplinary and collaborative demands of the course explicit and experiential.

Pedagogical Implications: Authentic Learning for Complex Problems

This modification aligns with authentic learning by mirroring real-world professional environments where diverse teams must collaborate despite differing opinions. Students develop critical thinking and communication skills by defending their reasoning while respecting others’ perspectives. Reducing the pressure for artificial consensus may also lower extraneous cognitive load, allowing students to focus on understanding the complex, interdisciplinary content and engaging in deeper peer interactions.

Conclusion: Preparing Students for a Multifaceted World

CC0007’s innovative TBL adaptation demonstrates that embracing productive disagreement enhances learning, particularly for interdisciplinary subjects. By embedding this approach within content that inherently requires integrating technical, business, and social perspectives, the course provides a more authentic and effective preparation for the complexities of real-world science and technology challenges. This model offers valuable insights for aligning educational practices with the collaborative, multi-perspective realities students will face professionally. The ongoing success prompts further exploration, including refining metrics to assess the quality of discussion and disagreement, and investigating the scalability of this effective approach to other interdisciplinary courses and contexts.