Failed action limitation paradigm
When imitating an adult’s behavior, infants will imitate what was intended rather than what was actually done.10 But what exactly does this mean? American psychologist Andrew Meltzoff wanted to know what age children started to assess intentionality.19
In a well-known and often cited study from 1995, Meltzoff showed 18-month-old children an adult performing unsuccessful acts.19 For example, the adult held a tube as pictured in the top row. Despite trying to pull off the cap on one side of the tube, the adult’s hand kept slipping. The adult demonstrated this failed action a few times, before the children were allowed to try. If children understood the adult was trying to pull off the cap, they would try to do that too. Indeed, 60% of 18-month-olds attempted the failed action.
However, when the adult was replaced with a machine (as pictured in the bottom row), only 10% of the children attempted the failed action.19 This difference suggests that children are much more likely to make goal-to-action interpretations when observing an adult, suggesting they can assess an actor’s credibility.10 Clearly, children as young as 18 months are aware of the differences between humans and machines, such that intentionality can be ascribed to human behaviors, but not to machines.19 This also suggests that theory of mind can begin developing from an early age.13
Gamification techniques have recently become a popular educational strategy.20 An example is awarding virtual badges after watching certain video lessons or completing a set of exercises. Applying learning analytics to gamified environments has given educators useful information about student motivation, but they do not tell us much about intentionality.
A team of Spanish researchers set out to analyze student intentionality through badge achievement on the popular online platform, Khan Academy.20 The researchers utilized two different types of badges: topic badges and repetitive badges. Topic badges required students to reach a proficient level in a set of exercises to earn the badge, while repetitive badges were awarded to students who kept solving exercises after they had already achieved proficiency.
Intentionality for topic badges was calculated using an algorithm which assessed the maximum number of badges that students could have received, given the number of exercises that they mastered. It did this by dividing the number of earned badges by the maximum number possible in that set.20 On the other hand, intentionality for repetitive badges was calculated through the percentage of badges that were earned once mastery had already been achieved.
After assessing 291 university students, the researchers found that they did not show much interest in earning badges, despite having a large percentage of students who spent at least 60 minutes on exercises.20 However, there was notably more interest in repetitive badges than new topic badges, with 39.52% of users earning repetitive badges intentionally. While students generally showed higher motivation for earning repetitive badges, repetitive badges may have been easier to earn, relative to mastering a new topic.
Knowing which badges students are most motivated by can be useful for educators who are structuring the learning process in a virtual environment.20 The researchers suggest that other platforms should implement badges to motivate intentional learning. More specifically, topic badges should be awarded when proficiency is reached on a set of predefined exercises. When it comes to repetitive badges, platform designers should carefully consider the utility and effectiveness of offering repetitive badges, if students have already mastered the topic. Perhaps motivation reallocated toward new material could bolster students’ learning.