Innovation on the rise, math scores take a dive: Unraveling the potential and perils of teaching innovation in schools. Guest blog by Saloni Gupta

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This is the 13th in this year’s series of posts by PhD students on the job market.

In today's job market, the demand for higher-order skills like critical thinking, problem-solving, teamwork, and creativity is growing  (Deming, 2022). Take the case of innovation, defined as the ability to conceive and effectively implement novel ideas, equips individuals to address challenges around them and enables their active engagement in a globally competitive economy. Innovation is pertinent on a global scale, spanning advanced economies to resource-constrained settings in developing countries, where even modest innovations can significantly enhance people's lives and stimulate economic progress (The World Bank, 2010). Despite its importance, there's a surprising lack of systematic efforts to teach innovation in schools, even though research strongly emphasizes its early-stage exposure (Bell et al., 2019; Bloom et al., 2019).

The importance of innovation as a higher-order skill raises a few fundamental questions: can it be taught, or is it an innate, unteachable ability? What does teaching innovation look like in school settings? How does it interact with existing school curricula in settings where rote memorization and standardized testing are dominant pedagogies?

I answer these questions in my job market paper by evaluating a government-led initiative in Telangana, India, that focuses on teaching students in rural India the art of observation of the world around them and the ability to craft innovative solutions for the problems they identify. This initiative, conducted in collaboration with a non-profit organization, involved a cluster randomized assignment of 80 schools, with a total of 6,224 Grade 8 students, into either the treatment or comparison group.

In the treatment schools, students received systematic instruction in developing innovative solutions, drawing from Stanford's design thinking pedagogy. In contrast, students in the comparison group were tasked with creating innovative solutions without any formal training, relying solely on their innate innovative abilities. This approach allows me to clearly measure the actual impact of teaching innovation beyond students' innate innovative talents.

Two different ways to measure the ability for innovation

While the ability to teach innovation is complex, a bigger challenge is that we still do not have the technology to measure an individual's ability to innovate. I confront this challenge by employing two distinct measures of innovation. These include a novel innovation scale that I developed with input from 57 inventors to measure the innovation quality of students' solutions, and a lab-in-the-field game borrowed from experimental economics.

The first measure of innovation, a new scale called Innovation-S, evaluates the quality of frugal innovations. It was developed with input from experienced inventors, with its predictive validity established through the success of their innovations. I developed this scale with input from 58 scientists, high-impact patent recipients, and founders of both for-profit and social enterprises. I established its predictive validity, by examining the scale's correlation with success indicators, specifically the number of users reached and the funding received for innovations created by these experts. The scale was used by a separate set of experienced inventors who used it to evaluate students’ ideas in the treatment and comparison groups.

The second measure, a lab-in-the-field game adapted from the influential study by Ederer and Manso (2013), measures innovation as the ability to experiment and discover more valuable solutions. In this game played on an offline app, players must realize that simply tinkering with existing solutions results in limited profit potential. To achieve higher profits, they must explore new strategies that deviate from the familiar ones. This game assesses the differences in the treated students' ability compared to the comparison group in their ability not only to experiment by moving away from existing solutions but also to identify those higher-profit alternatives.

Main results: Innovation is teachable, but at the expense of academic motivation.

For both of these measures, I find that the treated students consistently perform higher on the innovation outcomes (in the range of 0.08-0.20SD), suggesting that teaching innovation is possible, and it is not an inherent skill that can't be taught (Figure 1).

 

Figure 1. Preview of main results

Figure 1. Preview of main results

However, I also found a trade-off with academic learning even though the intervention was scheduled after school hours. Treated students reported a reduced enthusiasm for all academic subjects except science. Especially, in the case of mathematics, the students' increased ability to innovate came at the cost of a 0.13 standard deviation decrease in scores (Figure 1) and a significant 0.30 standard deviation decline in enthusiasm (Figure 2). On the other hand, in the case of science, student performance (Figure 1) and enthusiasm (Figure 2) remained steady, most likely due to some alignment with the intervention itself.

There is a universal decrease in enthusiasm for math in the treatment group, and this decrease is not linked to whether students' math scores improved or declined. Even among the subset of treated students who showed an improvement in math scores from baseline to endline, there was a reported decline in enthusiasm similar in magnitude to students whose performance decreased. In contrast, the enthusiasm for the innovation intervention remains consistently positive across the entire treatment group. A sentiment analysis using GPT-4 on mandatory feedback from all students revealed that 97% of them reported a positive experience with the innovation program.

This trade-off raises the question of whether we can effectively teach innovation alongside traditional academic subjects without inadvertently competing for students' attention and motivation in subjects crucial for their prospects. The disparity may not be in these skills conflicting with one another but rather in the pedagogical approaches employed. While conventional academic subjects are typically taught through traditional lecture-based methods and memorizing abstract concepts, the innovation program adopted a self-directed learning pedagogy where students collaborate in teams to create tangible, real-world solutions. To determine if applying a similar interactive approach to teaching academic subjects could mitigate the decline in enthusiasm and learning, I am evaluating the program in its second year.

 

Figure 2 Declined enthusiasm for all academic subjects except physical sciences

Figure 2. Declined enthusiasm for all academic subjects except physical sciences

The ability for innovation is uncorrelated with academic and cognitive outcomes

A final finding of this paper suggests that the capacity for innovation, as measured by the innovation lab-in-the-field game, is distinct from students' performance in math, science, and cognitive assessments. Although there is a notable correlation among math, science, and cognitive ability scores, ranging from 0.35 to 0.47, and a similarly high correlation (0.67) between the ability to experiment strategically and discover valuable solutions, the ability to excel in academic and cognitive outcomes and the capacity for strategic experimentation leading to innovative solutions do not exhibit a significant correlation, with coefficients ranging from -0.07 to 0.03. This suggests that students' innovative potential is not inherently linked to their performance in academic and cognitive assessments, highlighting the distinct nature of innovation as a skill.

Implications for future research and policy

In conclusion, the evolving labor market's demand for higher-order skills is undeniable, making their incorporation into education systems imperative. The case of innovation, a cornerstone of economic growth, showcases the need for systematic interventions of teaching it in school settings. This study demonstrates that innovation can indeed be taught, debunking the notion of an innate ability. However, the trade-off with traditional academic subjects unveils a critical concern. The next question we must address is not whether innovation can be taught but how it can be effectively integrated into existing pedagogy. This pursuit promises to shape the future of education and workforce development, aligning them with the ever-changing landscape of the labor market.

 

Saloni Gupta, Ph.D. Candidate, Economics and Education, Teachers College, Columbia University.

Join the Conversation

Richard Collins
November 28, 2023

I am not surprised by the outcomes, after reading what they were forced to do. You should have facilitated their own explorations and ideas, not made them do things you or others imposed. I feel sorry for the students. No one likes to be forced to act out one person's idea of innovation. Freedom, not force. Then you measured what you wanted to measure, not what they could have learned. You did not innovate, you used every tool you learned in your classes. GPT will almost always answer to flatter and agree. I follow innovation globally, What you forced them to do is at the far end of what not to do. Your paper is all "I". Global open collaboration is "we" and "Where does it lead us?", "How many can this help?" You gave them trivial problems when groups that size can solve global scale problems. The difficulty has to match the power of the groups' ambitions, not bore them. Your span of attention was not great enough to follow that many with true care for them individually.