Constraints on the Calibration of Performance

We examined the effect of experimenter-controlled incentives and feedback on the calibration of performance. Subjects answered 36 reading comprehension and 8 mathematical multiple-choice questions and rated the accuracy of their responses. Perfect calibration was possible only when true and estimated test performance were approximately equal. Incentives for improved performance (i.e., doubling the credit people received for correct answers) adversely affected performance and calibration compared to the same incentives for improved calibration (i.e., doubling credit for minimizing the error between true and estimated performance). Feedback had no effect on performance or accuracy nor did it interact with the incentive variable. An examination of coefficient α suggested a strong response bias by individuals when calibrating their performance; individuals tended to rate their performance accuracy consistently regardless of item difficulty or whether they answered the item correctly. Educational implications were discussed.     

Using time dynamically in static intervals: Does it close the achievement gap?

A mixed-methods study was undertaken with 118 biology students in two urban high schools. A Student-Centered Adaptable Learning Environment (SCALE) was created to improve engagement from affective and cognitive perspectives using choice, creativity and technological allowances. Results demonstrated that fast and slow learners are generally separated by about 30 min in terms of inputting speeds, but can be as much as 65 min apart from one another. Given that traditional classrooms afford students only 45 min in which to learn, static time could have become a source of inequity in public schools. SCALE optimally allowed for the dynamic use of time in constrained periods, therefore reducing and even eliminating any negative relationships between speed of learning and resultant achievement gains in the block setting. Especially benefitting from their ability to maneuver were the slowest learners, who showed the largest achievement improvements in either time interval amongst ability groupings. As learning speed can be the most critical contributing component of resultant educational outcomes, providing students the ability to use time dynamically should be considered as a feasible solution to helping teachers reestablish equity in mixed-ability classrooms in public schools.     

University–industry collaboration: Patterns of growth for low- and middle-level performers

Following landmark legislation passed more than 20 years ago, university–industry relationships have now become central to understanding the changing role of research universities in American Society. The paper analyzes the development of university–industry partnerships during the 1990s. Past studies have used a broad array of measures of ties and a variety of research methodologies, but they have shared a focus on top collaborators or on samples of universities skewed toward the top. However, findings based on top collaborators may not be valid for other universities. Universities involved in mid- to low-levels of collaboration are qualitatively different in many ways from the more extensively studied set of top collaborators, suggesting that characteristics affecting university–industry ties may not be the same for these institutions. The paper shifts the focus away from top collaborators to this sizable and less studied majority. In general, we find that the same characteristics predicting high levels of involvement for the sample as a whole also predict high levels of involvement for the sub-sample of mid- and low-level collaborators. However, We find some particular characteristics of these institutions, such as land grant status, are also associated with stronger ties to industry, and that some characteristics of 25 top institutions do not predict the level of involvement of these lower-level collaborators. We will discuss whether the licensing of new technology is likely ever to become an important source of net revenues for current middle and low-level collaborators. Our findings raise doubts about whether many universities below the top 25 will earn substantial net revenues from licensing, though they do not dispute the potential service value of these ties. The study is based on examination of a wide range of potential influences on university–industry collaboration for institutions that are not currently among the most heavily involved in partnerships. These include status, other institutional characteristics (such as size and control), investment in science and engineering, and characteristics of offices or technology transfer.