Improving the compositions of students with learning disabilities using a strategy involving product and process goal setting.

This study was conducted to determine if a planning and writing strategy would improve the essay writing of students with learning disabilities. Four participants were taught a strategy designed to facilitate the setting of product and process goals, generation and organization of notes, continued planning during writing, and evaluation of goal attainment. Training effects were investigated using a multiple probe design across subjects. Strategy instruction had a positive effect on students' essay writing performance and knowledge of the writing process, and effects were maintained over time.     

Gender Differences in College Mathematics Grades and SAT-M Scores: A Reanalysis of Wainer and Steinberg

Wainer and Steinberg (1992) showed that within broad categories of first-year college mathematics courses (e.g., calculus) men had substantially higher average scores on the mathematics section of the SAT (SAT-M) than women who earned the same letter grade. However, three aspects of their analyses may lead to unwarranted conclusions. First, they focused primarily on differences in SAT-M scores given course grades when the more important question for admissions officers is the difference in course grades given scores on the predictor. Second, they failed to account for differences among calculus courses (e.g., calculus for engineers versus calculus for liberal arts students). Most importantly, Wainer and Steinberg focused on the use of SAT-M as a single predictor. A reanalysis presented here indicated that a more appropriate composite indicator made up of both SAT-M and high school grade point average demonstrated minuscule gender differences for both calculus and precalculus courses.     

A pragmatic approach to courseware development in WIMP environments

Courseware development often involves a great deal of effort. One approach to minimising effort while maintaining flexibility is illustrated, using a tutoring system as an example. The system comprises two parts: a 'shell' which contains facilities useful for many different subjects, and an interpreter which generates questions which are subject specific.     

Categorical color coding by pigeons

Pigeons were trained on two independent tasks. One involved red and yellow hues, the other involved blue and green hues. For half of the birds, the two tasks were the same (i.e., both tasks were either matching-to-sample, or oddity-from-sample). For the remaining birds, the two tasks were different (i.e., one task was matching-to-sample; the other task was oddity-from-sample). Following acquisition, the pigeons were exposed to test trials on which either the correct or the incorrect comparison hue was replaced with one of the hues from the other task. On yellow-sample trials and on green-sample trials, the pigeons performed as if they had a common code for yellow and green. When there was one comparison available that was appropriate to the “yellow/green” code, performance remained high; but when either both comparisons or neither comparison was appropriate to the “yellow/green” code, performance dropped. The pigeons also tended to code red samples as green and to code blue samples as yellow. The results indicate that pigeons can categorically code colors under conditions that rule out a failure to discriminate among the colors.     

Intuitive statistical inference: How pigeons categorize binomial samples

Pigeons categorized binomial samples produced by two complementary random processes. Samples were 1, 2, 4, or 8 successively presented outcomes (vertical or horizontal lines) of, in effect, tossing one of two equally likely coins. One coin (A) was biased in favor of vertical, and the other coin (B) was similarly biased in favor of horizontal. Choosing red or green stimuli presented after each sample was reinforced with food if coin A or coin B had been tossed, respectively. In that sense, choosing red or green was logically analogous to inferring that the statistical evidence reflected tossing of coin A or coin B, respectively. The statistical diagnosticity of a sample, the relative likelihood of its having been produced by a particular coin, equaled, except for sampling fluctuation, the relative frequency of reinforcement of a particular color given that sample, and was experimentally varied by the bias on the coins and by sample size. All the variables that affect optimal, formal inference about binomial samples also affected intuitive inference. But inferences were very suboptimal: “undermatching” was obtained in part due to control of categorization by the sequential structure of binomial samples. These results reveal limitations of optimality theories for animal decision making in the face of uncertainty when observations in samples are presented successively. On the other hand, they are generally compatible with molecular analyses of instrumental learning which assign an important role to the local temporal organization of events preceding reinforcement. Most generally, they show that maladaptive control over intuitive statistical inference by a variable upon which optimal performance does not depend is neither a uniquely human phenomenon nor dependent upon linguistic strategies.