Our primary question was, could we forecast from teaching trailers the students who responded unusually well to specific professors' live lectures (i.e., relationship effects)?
But above all, the most important thing that reference class forecasting can teach is that we are naturally myopic, and consistently fail to account for dependence on people and technologies that live outside of the team.
Research on other constructs suggests that it should be possible to forecast relational teaching evaluations.
The goal of the current research was to test whether brief video trailers of professors' teaching can forecast students' evaluations of live lectures months later.
The goal of this study was to explore the viability of using video trailers of professors' teaching to forecast which students would respond unusually well to specific professors' teaching.
The accuracy of forecasting relational teaching evaluations was comparable to forecasting job performance from conscientiousness – one of the strongest predictors of job performance (Oh, Wang, & Mount, 2011).
Third, preparing carefully constructed trailers as in the present study might be too expensive to justify the cost-effectiveness of forecasting students' responses to teaching.
Threaded throughout the book are small, adept profiles both of world leaders and of less well known figures, like H. L. Williams, the godfather of offshore drilling; the Princeton mathematician John von Neumann, a pioneer of computer-driven weather forecasting; and Roger Revelle, who, while teaching at Harvard in the 1960s, educated Al Gore on global warming.
In multiple regression analyses, teaching evaluations and positive affect in response to the trailers forecasted 7% (R = .27) of the variance in live lectures.
We believe such forecasting would be useful, regardless of whether the reader is persuaded of the construct validity of students' evaluations of teaching.
