Research Post
In this study, we examined the teaching effectiveness of three strategies (trial-and-error, textbook, and combination) via a computer-based learning environment (CBLE) that teaches smart-home installation (SHiB). One hundred and twenty-five participants were randomly assigned to one of the strategies and tested with SHiB CBLE. Findings revealed that participants in the combination condition performed significantly better than those in the textbook (control) group with medium effect-size (g = 0.70). Senior participants in the trial-and-error group performed significantly better than those in the control condition with large effect-size (g = 0.89). Younger participants in the combination condition performed significantly better than those in the control condition with medium effect-size (g = 0.70). Results suggest that the teaching strategies had differential effects due to age groups.
Feb 15th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Adam White: Learning Expected Emphatic Traces for Deep RL
Feb 15th 2022
Research Post
Read this research paper, co-authored by Canada CIFAR AI Chair Kevin Leyton-Brown: The Perils of Learning Before Optimizing
Feb 14th 2022
Research Post
Read this research paper, co-authored by Amii Fellows and Canada CIFAR AI Chairs Osmar Zaïane,and Lili Mou, Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
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