Relations between Motivational-Regulation Strategies and Language-Learning Strategies

Motivational regulation has been regarded as an important aspect of SRL, which plays an important role in academic learning. Motivational regulation serves to increase students’ level of cognitive engagement (Wolters, 1999). The relations between motivational regulation strategies and indicators of cognitive engagement were explored in prior research. Sansone, Wiebe, and Morgan (1999) found that college students who employed a strategy for making the task more situationally interesting tended to copy more letter arrays over a longer period of time than students who did not employ this strategy. Similarly, older students’ use of self-provided rewards has also been linked to greater persistence (Jackson & Molloy, 1983, 1985). Zimmerman and Martinez-Pons (1990) found that self-consequating but not environmental structuring was related positively to teacher ratings of students’ self-regulation in the classroom. In addition, Wolters (1998) found that students who reported using regulation strategies based on intrinsic forms of motivation (e.g., mastery self-talk, interest enhancement) tended to report greater use of strategies for elaboration, critical thinking, and metacognition. However, students’ reported use of regulation strategies related to extrinsic forms of motivation (e.g., performance self-talk, self-consequating) was not related to these cognitive outcomes. Wolters (1999) found that students who reported using the motivational-regulation strategies more frequently tended to report using the cognitive and metacognitive learning strategies more frequently as well. McCann (1999) found that volitional control (motivation and emotion control) was significantly predictive of all of the learning strategies measured by the MSLQ, including rehearsal, elaboration, organization, critical thinking, metacognition, time management, peer learning, and help-seeking.

Overall, there is some indication that students’ use of motivational regulation strategies is tied to greater effort and persistence and greater cognitive and metacognitive strategy use. However, the research in this area is incomplete. The motivational regulation strategies examined are limited and it is important to include more motivational regulation strategies in the study about the role of motivational regulation. Therefore, the current study builds on these past studies by exploring the relations between students’ use of motivational regulation strategies and indicators of cognitive engagement. The examination of the relations between motivational regulation and language-learning strategies in the present study, on the one hand, is an attempt to examine the role and effectiveness of motivational regulation for FL learning process. On the other hand, it provides more evidence about the relations between motivational regulation and cognitive engagement.

Results

Pearson product-moment correlations were performed to examine the relations between Chinese college students’ use of motivational regulation strategies and language learning strategies. Overall, the results indicated moderate to strong relations between students’ motivational regulation and their use of language learning strategies. Each of the motivational regulation strategies was significantly related to all the language learning strategies, including metacognitive strategy, practice strategy and memory strategy. Further, all the significant correlations among the motivational-regulation strategies and language-learning strategies were positive, indicating that students who used the motivational-regulation strategies more frequently tended to use the language-learning strategies more frequently as well.

Next, multiple regression analyses were performed to further examine the relations among Chinese college students’ use of motivational- regulation strategies and language-learning strategies. To run the multiple regression analyses, the eight motivational regulation strategies were used to predict the three language-learning strategies. The results of multiple regression analyses are displayed in Table 5.3. For these analyses, the eight motivational-regulation strategies were entered as a group in one step.

Table 5.3 Results of multiple regression analyses predicting language learning strategies using motivational-regulation strategies

Dependent variables

Metacognitive

strategy

Practice

strategy

Memory

strategy

в

Sig.

в

Sig.

в

Sig.

Predictors

Interest

enhancement

Performance

self-talk

Mastery self-talk

Self-reward

Negative-based

incentive

Task-value

enhancement

Volitional control

Self-efficacy

enhancement

.154

.058

.337

.142

-.049

.145

.018

-.018

.000

.221

.000

.000

.280

.001

.672

.712

.367

.088

.027

.123

-.051

.221

  • -.067
  • -.154

.000

.086

.613

.005

.297

.000

.137

.004

.329

.084

.065

.102

.046

.061

.002

.027

.000

.096

.223

.018

.343

.201

.956

.600

R

R2

R2 (adjusted) Д8544)

Sig.

.598

.357

.348

37.773

.000

.498

.248

.237

22.466

.000

.521

.272

.261

25.389

.000

Hence, the R2 results from these analyses provide information regarding the amount of variance explained by the eight motivational-regulation strategies as a group, whereas the individual standardized regression coefficients indicate the variance explained by the individual motivational- regulation strategies after accounting for the other motivational-regulation strategies in the equation. Overall, the motivational-regulation strategies explained a significant portion of the variance in all the three types of language-learning strategies. Together the eight motivational-regulation strategies explained 35.7 % of the variance in students’ use of metacognitive strategies (Д8544) = 37.773, p = .000). The amount of variance in practice strategy explained by the eight motivational-regulation strategies was 24.8 % (Д8544) = 22.466, p = .000). Of the variance in students’ memory strategy 27.2 % was explained by the eight motivational regulation strategies as a group (Д8544) = 25.389, p = .000).

With regard to individual predictors, interest enhancement and selfreward were significant positive predictors for all the three types of language learning strategies. Hence, students who tried to maintain or increase their effort and persistence at English-learning tasks by increasing the interest or situational enjoyment of the tasks or providing themselves with external rewards tended to use metacognitive and cognitive strategies more frequently. Furthermore, among the eight motivational-regulation strategies, interest enhancement was the strongest predictor for practice strategy = .367, p = .000) and memory strategy (в = .329, p = .000), and the second strongest predictor for metacognitive strategy (fi = .154, p = .000). Mastery self-talk tended to be a positive individual predictor for all the three language learning strategies, but only for metacognitive strategy (в = .337, p = .000) was the strength of this relation statistically significant. Meanwhile, mastery self-talk was the strongest predictor for metacognitive strategy. Therefore, students who bolstered their willingness to complete a task by focusing on their desire to learn as much as they could tended to report planning, monitoring, and reflecting on their English-learning activities more frequently than students who did not sustain their motivation in this way. Task-value enhancement was a significant positive predictor for metacognitive strategy (в = .145, p = .001) and practice strategy (в = .221, p = .000). On average, students who emphasized the value of the English course and the importance of English for their future reported greater use of metacognitive strategy and cognitive strategy based on functional and formal practice. Self-efficacy enhancement only significantly predicted the use of practice strategy (в = -.154,

p = .004), but it was a negative predictor for practice strategy. Hence, students who reported enhancing their efficacy on English learning as a means of maintaining their effort and persistence at English-learning tasks tended to use the cognitive strategy based on functional and formal practice less frequently. Performance self-talk, negative-based incentive and volitional control did not individually account for a significant portion of the variance in any of the language learning strategies.

Discussion

From the perspective of SRL, students who actively regulate their motivation should be more likely to use cognitive and metacognitive learning strategies than students who fail to regulate their motivation and give up working on tasks more readily. Consistent with this expectation and prior studies (McCann, 1999; Wolters, 1999), the results of the current study provide evidence that motivational regulation is positively associated with students’ use of cognitive and metacognitive strategies. First, results from Pearson product-moment correlation analyses indicated positive relations between students’ motivational regulation and their use of metacognitive and cognitive language-learning strategies. Furthermore, the multiple regression analyses indicated that, as a group, the motivational-regulation strategies predicted students’ use of metacognitive and cognitive languagelearning strategies. Therefore, these findings support the belief that students who actively regulate their motivation show more adaptive cognitive and metacognitive learning strategy use than students who do not regulate their level of motivation. The strong predictive nature of motivational regulation on language-learning strategy use may be due to the fact that both motivational-regulation strategy use and language-learning strategy use indicate purposive action by students and represent a self-regulatory aspect of the goal-striving process. This finding is supportive of the assertion that motivational regulation aids in the maintenance of goal-directed activity. It suggests an increased probability of learning-strategy engagement in connection with motivational regulation.

Results also provide insight into the relative importance of different motivational-regulation strategies for Chinese college students. Each of the motivational-regulation strategies based on intrinsic forms of motivation (i.e., mastery self-talk, interest enhancement, task-value enhancement, and self-efficacy enhancement) could predict some of the language-learning strategies. The findings are consistent with the results of Wolters (1998) that intrinsic-regulation strategies could predict students’ use of cognitive and metacognitive strategies. Of the four strategies based on intrinsic forms of motivation, notably only interest enhancement could predict all the three types of language-learning strategies. This result is not difficult to understand. Interest is the best teacher. With interest, students are willing to make greater efforts for or persist in learning. Therefore, they are also more likely to display greater cognitive engagement. The result about interest enhancement in the present study is inconsistent with the findings of Wolters (1999), which found that interest enhancement could not predict any of the six cognitive and metacognitive learning strategies examined. However, the result is consistent with Qu’s (2004) finding that Chinese middle school students’ use of interest enhancement was most closely related to cognitive and metacognitive strategies by predicting all the seven types of strategies assessed. These findings indicate that there may be cultural differences in the effectiveness of motivational-regulation strategies. In addition, mastery self-talk was the significant predictor of metacognitive language-learning strategies. This finding is in consistency with that of Wolters (1999), in which mastery self-talk was found to significantly predict the use of planning and monitoring strategies. Task-value enhancement was an individual significant predictor for the use of both metacognitive language-learning strategy and practice strategy. The findings fit with previous studies as they have found a positive relation between intrinsic motivation and the adoption of mastery goals and greater cognitive and metacognitive strategy use (e.g., Ames, 1992; Deci & Ryan, 1985; Dweck & Leggett, 1988; Pintrich & Garcia, 1991). Another motivational-regulation strategy based on intrinsic motivation, self-efficacy enhancement, was a significant individual predictor only for practice strategy, but a negative predictor. These results are somewhat surprising given that previous research has shown that students with high self-efficacy were more likely to use various cognitive and self-regulatory or metacognitive learning strategies (e.g., Pintrich & DeGroot, 1990; Wolters & Pintrich, 1998). However, such a result is also understandable. The students that use the strategy of self-efficacy enhancement frequently can increase their confidence in their ability to learn English well but at the same time may see no need to practice more in learning English to increase their English proficiency since they believe they have the ability to do it well. More research is needed to examine the relations between selfefficacy enhancement and students’ use of cognitive and metacognitive strategies since previous research has not specifically examined the role of self-efficacy enhancement.

Among the three motivational-regulation strategies related to extrinsic motivations (i.e., performance self-talk, self-reward, negative-based incentive), only self-reward could predict the use of language learning strategies of metacognitive strategy and practice strategy. Thus, students’ regulation of their extrinsic motivation was related less positively to the cognitive engagement than students’ regulation of their intrinsic motivation. These results fit with previous findings concerning students’ goal orientation (Ames, 1992; Dweck & Leggett, 1988; Pintrich & Garcia, 1991). Prior research also found that students who reported using a self-reward strategy tended to report the use of some cognitive and metacognitive learning strategies (Wolters, 1999). The strategy of performance self-talk in the present study could not predict any language-learning strategy. This result is inconsistent with the findings of Wolters (1999) who found that performance self-talk was a significant predictor for the strategy of rehearsal and regulation, and also inconsistent with those of earlier research on goal orientations showing that students with a greater performance-goal orientation report greater use of low-level strategies (Anderman & Maehr, 1994; Midgley, Kaplan, & Middleton, 2001). Negative-based incentive could not predict language-learning strategies. This finding indicates that this type of strategy may help promote students’ efforts to work on the tasks but not necessarily increase students’ cognitive engagement. This type of strategy has been included in AVSI but its importance to academic learning has not been studied separately. Therefore, more research should be done to examine the relations of this type of strategy with cognitive engagement. In general, these findings about motivational-regulation strategies related to extrinsic motivations and language-learning strategies suggest that students who make efforts to sustain or boost their motivation by appealing to extrinsic motivation may succeed in raising their motivation, but may not necessarily exhibit a concurrently higher level of cognitive engagement.

Volitional control in the present study includes the strategies of environmental control and emotional control. It was significantly correlated with language-learning strategies, but could not predict any of the language learning-strategies after accounting for the other motivational- regulation strategies. This result is consistent with the finding of Wolters (1999) that environmental control cannot predict any of the cognitive and metacognitive strategies examined. This finding indicates that volitional control can help students sustain or increase their motivation to learn but may not necessarily increase students’ cognitive engagement concurrently.

 
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