Academic Procrastination in Two Settings: Motivation Correlates,Behavioral Patterns,and Negative Impact of Procrastination in Canada and Singapore

Two studies are reported examining academic procrastination and motivation in 1,145 university students from Canada and Singapore. In Study 1, relationships between procrastination and motivation variables were found to be similar across contexts, with self‐efficacy for self‐regulated learning most strongly associated with procrastination in both contexts. In Study 2, patterns of procrastinating behavior and the negative impact of procrastination were examined and compared in Canadian and Singaporean undergraduates. Participants in both contexts reported writing to be the academic task most prone to procrastination. More Singaporeans than Canadians were classified as negative procrastinators (i.e. rated procrastination as a negative influence on academic functioning). In both contexts, negative procrastinators spent more time procrastinating than neutral procrastinators and displayed lower self‐efficacy for self‐regulated learning. On décrit deux recherches portant sur la motivation et la procrastination universitaire (tendance à remettre au lendemain) de 1145 étudiants du Canada et de Singapour. Dans la première étude, les relations entre la motivation et la procrastination sont apparues être analogues dans les deux pays, la variable la plus fortement associéà la procrastination étant l'auto‐efficience pour l'apprentissage autorégulé. Dans la deuxième étude, les schémas comportementaux de procrastination et son impact négatif ont été analysés et comparés chez les étudiants de 1° cycle canadiens et singapouriens. Les sujets des deux pays ont mentionné la rédaction comme étant la tâche universitaire la plus soumise à la procrastination. Davantage de Singapouriens que de Canadiens été classés en procrastinateurs négatifs (c'est‐à‐dire que la procrastination est considérée comme étant un handicap pour les études). Dans les deux cas, les procrastinateurs négatifs gaspillaient plus de temps que les procrastinateurs neutres et manifestaient une moindre auto‐efficience pour l'apprentissage autorégulé.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.