36 research outputs found
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of the
most effective educational interventions: personalized instruction.
Reinforcement learning could be a key tool to reduce the development cost and
improve the effectiveness of, intelligent tutoring software that aims to
provide the right support, at the right time, to a student. Here we illustrate
that deep reinforcement learning can be used to provide adaptive pedagogical
support to students learning about the concept of volume in a narrative
storyline software. Using explainable artificial intelligence tools, we also
extracted interpretable insights about the pedagogical policy learned, and we
demonstrate that the resulting policy had similar performance in a different
student population. Most importantly, in both studies the
reinforcement-learning narrative system had the largest benefit for those
students with the lowest initial pretest scores, suggesting the opportunity for
AI to adapt and provide support for those most in need.Comment: 23 pages. Under revie
Comparing Smartphone Speech Recognition and Touchscreen Typing for Composition and Transcription
International audienceRuan et al. found transcribing short phrases with speech recognition nearly 200% faster than typing on a smartphone. We extend this comparison to a novel composition task, using a protocol that enables a controlled comparison with transcription. Results show that both composing and transcribing with speech is faster than typing. But, the magnitude of this difference is lower with composition, and speech has a lower error rate than keyboard during composition, but not during transcription. When transcribing, speech outperformed typing in most NASA-TLX measures, but when composing, there were no significant differences between typing and speech for any measure except physical demand
Pedagogical Agents for Fostering Question-Asking Skills in Children
Question asking is an important tool for constructing academic knowledge, and
a self-reinforcing driver of curiosity. However, research has found that
question asking is infrequent in the classroom and children's questions are
often superficial, lacking deep reasoning. In this work, we developed a
pedagogical agent that encourages children to ask divergent-thinking questions,
a more complex form of questions that is associated with curiosity. We
conducted a study with 95 fifth grade students, who interacted with an agent
that encourages either convergent-thinking or divergent-thinking questions.
Results showed that both interventions increased the number of
divergent-thinking questions and the fluency of question asking, while they did
not significantly alter children's perception of curiosity despite their high
intrinsic motivation scores. In addition, children's curiosity trait has a
mediating effect on question asking under the divergent-thinking agent,
suggesting that question-asking interventions must be personalized to each
student based on their tendency to be curious.Comment: Accepted at CHI 202
Representation Discovery for MDPs Using Bisimulation Metrics
We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approach leverages bisimulation metrics, which have been used in prior work to generate features to represent the state space of MDPs. We address a drawback of this approach, which is the expensive computation of the bisimulation metrics. We propose an algorithm to generate an iteratively improving sequence of state space partitions. Partial metric computations guide the representation search and provide much lower space and computational complexity, while maintaining strong convergence properties. We provide theoretical results guaranteeing convergence as well as experimental illustrations of the accuracy and savings (in time and memory usage) of the new algorithm, compared to traditional bisimulation metric computation
Representation Discovery for MDPs Using Bisimulation Metrics
We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approach leverages bisimulation metrics, which have been used in prior work to generate features to represent the state space of MDPs.We address a drawback of this approach, which is the expensive computation of the bisimulation metrics. We propose an algorithm to generate an iteratively improving sequence of state space partitions. Partial metric computations guide the representation search and provide much lower space and computational complexity, while maintaining strong convergence properties. We provide theoretical results guaranteeing convergence as well as experimental illustrations of the accuracy and savings (in time and memory usage) of the new algorithm, compared to traditional bisimulation metric computation
A proactive approach to ending the use of university debit cards for indoor tanning
Recently, a review of the top 125 colleges ranked by US News and World Report found that 14.4% of universities have a campus debit card that can be used to purchase tanning services. We sought to dissolve the affiliations between universities and tanning salons
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10.1145/3313831.3376173CHI '20: CHI Conference on Human Factors in Computing System