In a study conducted with middle school students, we examined the rela-
tion between students’ affect and their scores on a computer math activity
(Linnenbrink & Pintrich, 2003, study 1). In particular, middle school stu-
dents worked in groups to learn how to solve number sequences. They then
completed a similar series of math problems on the computer for 15 minutes.
Immediately following the completion of the math problems, they reported
on their current affect using single item indicators (sad–happy, tense–calm,
tired–excited). Finally, after completing a series of word-recognition tasks,
students were asked to report on their effort regulation and cognitive regula-
tion during the computer math task.
Interestingly, the three indicators of affect (sad–happy, tense–calm, and
tired–excited) were unrelated to students’ scores on the math exam. However,
affect was significantly related to students’ effort and cognitive regulation
during the math exam. For effort regulation, students who reported being
more excited than tired reported higher levels of persistence even when they
did not want to work on the task (b= .22,p< .001). For cognitive regulation,
students who reported feeling more happy than sad (b= .13,p< .05) and
more excited than tired (b= .16,p< .01) also reported that they planned,
monitored, and checked their work as they completed the number sequences
on the computer. What is interesting about these findings is that both valence
(sad–happy) and arousal (tired–excited) were predictors of students’ cogni-
tive regulation while only arousal (tired–excited) significantly predicted effort
regulation. This may mean that arousal is important in terms of motivation
to engage in the task while both valence and arousal are important in terms of
the quality of engagement (e.g., using higher level strategies). It is somewhat
surprising, however, that the other measure of arousal, calm–tense, was unre-
lated to either type of regulation.
When interpreting these results, it is important to keep in mind that there
were several limitations in the methodology used in this study. First, the af-
fect measure was designed to assess students’ affect while working on the
computer math activity, but their affect may have changed as they completed
the computer math test as a result of how well they perceived they were doing
on the math problems. Second, the use of self-reported affect and self-
reported regulation leaves one open to the possibility of a method bias, where
shared variance may have more to do with similarities in measurement than
with similarities in the underlying constructs (Winne & Perry, 2000). Third,
the use of bipolar affect measures may be problematic if both ends of
the scale (e.g., sad and happy) relate in the same way to the outcome. For
instance, if both sadness and happiness are negative predictors of math per-
formance, the use of a bipolar measure would not be able to detect a signifi-
cant relation and would instead suggest that sad–happy and math perform-
ance were unrelated.
74 LINNENBRINK AND PINTRICH