Sometimes, nurturing and educating a child can seem more like an enigma or a theoretical ideal than an achievable goal. This is felt on an individual level, when thousands of books, magazine, and commentators float around offering contradictory advice, but it could also seem that way on a societal level. It’s a common fact that the socioeconomic status of a child is the top predictor in his or her educational outcome. Coupled with how intrinsic inequality in SES is in our society, it could seem impossible to fix educational deficiencies on a societal level.
However, despite these massive and seemingly overwhelming correlations, there are many simple actions that can be done on a personal parent to child level that have huge impacts on life outcomes. A post-WWII study in Britain, tracking some 14,000 babies born in 1946, unveiled several basic actions that led to disproportionately successful outcomes for their recipients. These include talking and listening to kids, teaching letters and numbers, reading to kids, and maintaining a regular bedtime.
Perhaps some of the given suggestions, such as taking children on excursions outside, could only be achieved through more active parenting. However, many of these can be supplanted with external help. A chatbot (such as the one I am developing) can remind children of bedtimes easily using pre-existing notification concepts. A little more development, and it can incorporate teaching of letters and read stories. Using contextual AI, it has a good shot of talking and listening in a roughly similar manner to an adult.
Much of this sentiment is echoed in the development of the new mentor robot Moxie. Already, it functions much like an actual human both physically and in social interactions. However, if we focus all our attention on visibly sci-fi concepts such as these, we risk losing the idea of new technology allowing us to reduce inequities and improve educational outcomes for everybody through increased accessibility. Indeed, first developed as a specialist solution for kids with special needs, Moxie is now on the general market for a price of $1699, on the market for kids whose parents can afford to spend that kind of money on unproven technologies that have a history of failing.
It is commonly thought that the personality of a teacher has a great impact on his or her ability to teach, for good reason. Personality inevitably affects everyday interactions between teacher and student. Just as with every other job or action, certain personality traits would work better for a teacher than others.
One attempt to quantify personality is the Big Five personality domains for teachers: openness to experiences, conscientiousness, extraversion, agreeableness, and emotional stability. Openness deals with an appreciation for new things: novel ideas, untried experiences, curiosity. Conscientiousness measures self-discipline and control of impulses. Extraversion is what you might expect: the inclination to interact with the external, social world. Agreeableness measures how concerned one is with the opinions of others. Emotional stability is how well one reacts to stress and other negative emotions.
A 2019 study published in Educational Psychology Review analyzed the correlation between these domains and two educational outcomes, one of them being teacher effectiveness (Kim). It found that every domain except agreeableness is positively associated with teacher effectiveness. Teachers that were more open to new experiences, more conscientious, more extroverted, and more emotionally stable tended to be better teachers.
This tendency also might extend to learning assistants such as chatbots. A 2018 study found that the personality of a chatbot has a “significant positive effect on the user experience of chatbot interfaces” (Smestad). However, this use of personality measures the level of personality (as in, does the bot have personality) instead of the type of personality. Regardless, this could still line up with the previously mentioned findings in human teachers since it is usually considered that extroverted, curious people can appear more “personable”. A 2018 article differentiates user preference based on the purpose of the chat bot. It found that people prefer “slow types” of personality, submission and compliance, for bots based around counseling like most education related bots would be (Kang). This especially ties into the positive relationship between agreeableness and teaching effectiveness.
Advanced chatbots, using conversational, contextual AI, have versatile and promising applications. Chatbots have many advantages inherent within an automated platform, including promptness of response, scalability and accessibility. Recent advances have also increased the amount of personalization a chatbot can offer. This is especially evident when compared to existing communication methods such as mass emails and push notifications sent out to every user of an app.
This has been proven in traditionally human fields such as mental care. A company called Woebot created a chatbot that provides “continuous emotional support” to its users. It forges a personalized connection to the user to glean useful information for human specialists and to help deal with symptoms of stress and anxiety. This allows for human-like support that isn’t limited by doctor availability or cost.
Higher education institutions have also begun to use chat bots. Georgia State University rolled out a bot that helped students with enrolling and getting to college, decreasing the amount of admitted students who didn’t show up by 19%. Response levels from students were much higher with the bot than with email reminders. Chatbot platform Acquire identifies several different functions a chatbot can perform in education: providing information about school, administrative support, offering reminders and assistance, tutoring, and engaging students.
A chatbot built for younger students would most likely focus on the third, fourth, and fifth functions due to the nature of elementary schooling. As mentioned in a previous post, simple nudges and reminders are especially essential for younger children. In addition, children are less likely to be able to navigate information sources on their own. Communication-based chatbots are naturally easier interfaces for anyone, especially children, to use. Being able to answer basic questions and guide exploration would be a major benefit in and of itself in the education of young children.
Of course, a potential chatbot would have to be tailored for younger children specifically. It has to use grade-appropriate wording, while ideally selectively using new vocabulary to promote linguistic growth. There’s also a higher barrier to reach in terms of chatbot personality: a child would have little intrinsic motivation to keep on talking to a chatbot if it doesn’t act like a human. These are challenges that must be addressed in any chat bot dedicated towards childhood education.
A child’s attention span — how long he or she can spend on a task before getting distracted — increases with age. In a 1990 study of young children in play, researchers found that a child’s duration of “focused attention” on the toys correlated with the age of the child. Older children focused more on problem solving and were less distracted by other physical movements. This means that the increase in attention span was not only due to the intrinsic development of the older child, but also the increased complexity of his activities.
In addition, the conditions in which these activities are presented also impact attention span. When frequently asked to “stay on task”, preschoolers paid less attention to distractions and more attention on the task itself. Simply repeating instructions to focus has a great effect on a child’s attention span, and can be implemented in any classroom or environment.
Children’s attention spans are also dependent upon how big each activity is. Sites such as parents.com claim that breaking a task into small pieces can keep children engaged more. This is corroborated in a 2010 study, which showed that young children allocated attention similarly to adults with small arrays of information. This means that children don’t have smaller attention spans because of inefficiencies in their memory allocation, but simply because they don’t have as much working memory to work with.
When dealing with younger children, it’s important to note the “why” behind general principles of education. It’s easy to assume that the reason of breaking up activities for children is simply to make each activity fit into smaller attention spans. However, this would miss activities that are short in duration, but are too complex to keep a child’s attention regardless. These nuances must be kept in consideration.
In a 2013 study at Stanford University, 48 infants of diverse economic socioeconomic status (SES) were tracked from 18 – 24 months of age and measured in language proficiency. Researchers found that at 18 months, children of higher SES were already significantly better off in vocabulary and language processing efficiency, and by 24 months, there developed “a 6-month gap between SES groups in processing skills critical to language development” (Fernald 2013).
This could easily be linked to the 1995 Hart and Risley Study, which found that children from professional families heard a significantly greater quantity of words per hour on average (2153 words) than those from working class (1251) or welfare-recipient (616) families, leading to the former having larger vocabularies. (Hart 1995). They reach the stark conclusion that the “most important aspect of children’s language experience is quantity”, though they note that children in professional families were encouraged more and discouraged less than their counterparts in less well-off families.
This gap is not just in vocabulary. According to an article published in Psychophysiology, “SES disparities in neurocognitive functioning have been shown across the domains of language, EF, memory, and social-emotional processing on both the behavioral and neurobiological levels” (Ursache 2017). They note a couple possibilities why these disparities could exist. As previously mentioned, language stimulation is a major distinction between households of different SES. In addition, the stresses of poverty could lead to “inconsistent, unpredictable, and non responsive parenting behaviors”, harming emotional development, and also lead to less time and energy being spent on supportive parenting.
Even though educational systems cannot solve the root issue of socioeconomic disparity, our preliminary understanding still points towards certain behaviors that would help solve the problem. In the classroom, educators can introduce more new words in their day to day speech as well as help parents to do the same (Colker 2014). Attention needs to be paid towards consistency and responsiveness in educator-child interactions, especially if these qualities are lacking in those of the parent and child.