Language Learning Strategy-Task Corollary: A Case of Jigsaw vs. Problem-solving Tasks Council for Innovative Research

This study aimed at investigating the relationship between language learning strategy use and performing jigsaw and problem-solving tasks. For this purpose, 53 students of a total 77 BA students majoring at ELT, English Literature, and English Translation were chosen through taking the Standardized Nelson English Proficiency Test. The homogeneous participants were randomly divided into two experimental groups: jigsaw and problem-solving. The jigsaw group practiced reading comprehension through jigsaw tasks while the problem-solving group did so through problem-solving tasks. Strategic behavior of each group was assessed through administrating the Strategy Inventory of Language Learning (SILL) prior to and after the treatment. The dataobtained undergone three main statistical analyses: independent sample t-test, paired samples t-test and MANOVA. Between group comparison indicated that the two groups were not much different regarding the strategy use implying that task type does not much affect strategy use. However, within group comparisons revealed that, although thestrategies as a unitary entity did not change as a result of the exposure to the task types, they showed changes in their use of various components of the strategies; metacognitive, social, and cognitive strategies were used most frequently by the members of both groups. Interestingly, the findings sustain the old but still attractive notion of Gestalt Psychology that „the whole, is other than sum of the parts‟ (Kurt Koffka)‟. between before Results that improved in terms of content, grammar, and organization of ideas. the two in affective strategies. task in the jigsaw group favored metacognitive, social, and cognitive strategies. study cognitive metacognitive strategies to do classroom use compensatory strategies strategy in a jigsaw task. on the subcategories of learning strategies students' strategic behavior changed after exposure to problem-solving tasks. metacognitive, social, and cognitive strategies were most frequently used by the learners. Results groups were similar in terms of the strategy types they used more frequently as also Smith (2003) reports in his study in which similar pattern of strategy use across different task types was reported.

D e c e m b e r 2 3 , 2 0 1 4 input as well as the opportunity for meaningful use of language. It is believed that learners' cognitive processing will be engaged by this combination of contextualized input and output helping learners to process and reshape the input. Therefore, tasks are considered as valuable activities which promote language teaching (Ellis, 2003). Similar to LLSs, tasks have also been classified differently depending on their nature in input processing. Pedagogically speaking, Richards (2001, p.162) suggests two broad categories of tasks including pedagogical and real world tasks. The former types "are based on second language acquisition theories and are designed to trigger second language learning processes and strategies. Real world tasks are designed to practice those activities that are found to be important in a needs analysis and that turn out to be important and useful in real life". In the same vein, Prabhu (1987) considers three types of pedagogical tasks: information-gap, reasoning-gap, and opinion-gap tasks. According to Willis (1996), there are six types of tasks: listing, ordering and sorting, comparing, problem-solving, sharing personal experiences, and creative tasks. Richards (2001) considers five task types: jigsaw, information-gap, problem-solving, decision-making, and opinionexchange tasks.Richards' classification seems to be more tangible pedagogically. However, two of them;jigsawand problem solving tasks are probably the most commonly used ones. The former, sometimes called split-information task (Willis&Willis, 2007), is an activity in which groups or individuals are expected to combine individual pieces of information, thereby they are involved in communication and collaborative work (Willis &Willis, 2007; Richards, 2001).In the latter category, however, learners are given a problem and a set of information. They have to communicate with each other in order to find a solution (Richards, 2001). Willis (1996) believes problem-solving tasks are challenging, engaging, and satisfying to solve. They require people's intellectual and reasoning power. Pica et al. (1993( , as cited in Smith, 2003 rank the task types based on their effect on second language learning: (1) jigsaw; (2) information-gap; (3) problem solving; (4) decision making; and (5) opinion exchange. Accordingly, jigsaw and problem solving tasks are considered as facilitative tasks for second language learning. Doughty and Pica (1986) compared a required information exchange task (jigsaw task) and an optional information exchange task in terms of the amount of modified interaction generated.Results indicated that more modification of interaction happened in doing the jigsaw task than did in doing the optional information exchange task. The researchers emphasized that a required information exchange task is necessary for the generation of conversational modifications of classroom interaction. Swain and Lapkin (2000) compared a jigsaw task and a dictogloss task in terms of the amount of focus on form. It was indicated that the students focused equally on form while doing both task types. What's more, the jigsaw task seemed to inspire greater linguistic creativity as the students used a greater range of vocabulary and language related episodes. Mengdus and Xialing (2010) used jigsaw technique to teach reading to a group of college students. They discovered that jigsaw technique is an effective way to promote students' participation and interest as well as a good technique to accomplish learning tasks in the EFL classroom. Kazemi (2012) compared the effect of traditional ways of teaching reading and using jigsaw to do so on students' reading comprehension achievement. The results indicated that jigsaw technique has a significant effect on students' reading achievement as the students' reading comprehension improved as a result of practicing jigsaw reading. Fredricks (1984) used a problem-solving task, a role play, and an authentic interaction in order to compare the language generated by the three tasks. The results showed that while doing the problem-solving task the students generated more communicative functions of language but narrower range of form and lexis. Poupore (2005) explored quality interaction and types of negotiation in problem-solving and jigsaw tasks. Based on the results of the study, Poupore considers problem-solving prediction tasks as being more beneficial than jigsaw tasks. He argues that the more open structure of the problem-solving tasks gives the learner more freedom to use a wider variety of language. Hussein et.al (2012) used a problem-solving approach to teach writing to a group of EFL students. The topic which was in form of a problem was discussed within group and even between groups before writing. Results indicated that the students writing improved in terms of content, grammar, and organization of ideas. Addressing tasks empirically is rationalized both theoretically and pedagogically. Oxford et al. (2004) hold that including a task in second language learning strategy assessment is necessary because in this case learners will focus on the strategies they use to do the particular task. Cohen and Macaro (2007) emphasize the need for assessing the strategic behavior of the learners in the context of specific tasks. However, there seems to be a dearth of research in this particular area.Moreover, research on language learning strategies has mainly focused on the strategies of good language learner, strategy instruction, and factors affecting language learning strategy use. Among these factors, task requirements , contrary to some sporadic studies, has still received little attention. Additionaly, probing learners' language learning strategy use in practicing reading comprehension through incorporating tasks in general and the jigsaw and problemsolving tasks in particular is wararnted. Sinceas the literature reviewed confirms, jigsaw and problem-solving tasks are valuable task types as they have required qualities for language leaning. They are both motivating and involve a great deal of negotiation of meaning. Therefore, using them in language classes will contribute to the students' progress in language learning; a claim requires more empirical supports. Rationalized in this way, this study tries to investigate the role of task types (jigsaw and problem solving) in LLS choice both individually and comparatively in a reading instruction class, which was realized in the following research questions addressed in the form of respective null hypotheses.

2.RESEARCH QUESTIONS
1. Does task type (jigsaw vs. problem-solving)have any statistically distinct effect on language learning strategy use both unitarily and componentially? 2. Does exposure to jigsaw tasks have any statistically distinct effect on language learning strategy use both unitarily and componentially? 3. Does exposure to problem-solving tasks have any statistically distinct effect on language learning strategy use both unitarily and componentially? Note: the research questions were addressed as respective null hypotheses. D e c e m b e r 2 3 , 2 0 1 4

Participants
Participants of the current study were 53 male and female students from Azad and ShahidBahonar universities of Kerman, Iran, majoring in English Literature, ELT, and English Translation. The mean age of the participants was 21, ranging from 18 to 36, who were selected through the Standardized Nelson English Proficiency Test (Version 300D) out of total population of 77 students. The students whose score fell within the range of one SD ± mean were selected as the participants of the study.

Instrumentation
The instruments used in this study were the Standardized Nelson English Proficiency Test, the Strategy Inventory for Language Learning(SILL) (Oxford,1990), and jigsaw and problem-solving tasks.The Standardized Nelson English Proficiency Test which was used to choose a homogeneous group of participantsconsists of 50 items including reading, grammar, vocabulary, and pronunciation items.The Strategy Inventory for Language Learningconsists of six categories: memory strategies (9 items), cognitive strategies (14 items), compensation strategies (6 items), metacognitive strategies (9items), affective strategies (6 items), and social strategies (6 items). These SILL 50 items are evaluated on a five-point Likert scale from 1 to 5. The tasks employedwere jigsaw and problem-solving reading comprehension tasks. The problemsolving tasks were developed based on suggestions made by Willis (1996) in the form of giving advice to other people, predicting the end of a story, or choosing the best course of action to do something. In order to make sure that the texts used by both groups enjoyed the same level of difficulty, the readability of the texts was estimated usingGunnig-Fog formula.

Procedure
To collect the required data, a group of homogeneous participants was divided into two groups which differed in terms of the treatment they received. One group practiced reading comprehension through jigsaw tasks and the other through problem-solving tasks. In order to elicit the strategic behavior of the participants, the SILL was administered twice, before and after treatment.

Reliability and Validity of SIIL and Nelson Test
Regardless of the already proven nature of the instruments in terms of reliability and validity, both of them were subjected to further validation process based on principal component analysis (i.e., varimaxrotation), indicating five factor loadings as the validity indices (Table 1) and KR-21 statistical formula showing the reliability ratios (Table 2).  Table 3 displays the descriptive statistics of the participants selected for the study: Based on the level of significance displayed in table 4, the distribution of the scores is normal. As sig=0.366>0.05 the variance of the scores in the three classes are the same.

4.2.2Testing Normality Assumptions
The data collected in this study enjoyed normal distributions. As displayed in table 6 the values of skewdness and kurtosis were lower than their critical values (c.r.). Besides, the components of the total pretest and posttest enjoyed normal distributions (Table 7), since they lie between ±1.96

Investigation of the First Research Question
The first research question addressing the task types (jigsaw vs. problem-solving) to find out if it has any statistically distinct effect on language learning strategy( LLS) types, was probed twice for investigating the effect of two different treatments on language learning strategies as a unitary construct first and then on its components.

Learning strategies as a unitary construct
An independent t-test was run to compare jigsaw and problem-solving groups on the posttest of LLS. As displayed in table 8, the jigsaw group (M = 171.42, SE = 3.81) showed a slightly higher mean score than the problem-solving group did (M = 169.08, SD = 3.66) on posttest of language learning strategies (LLS). The results of the independent t-test (t (48) = .44, P > .05, R = .064 representing a weak effect size) (Table.9) indicated that there was not any significant difference between the two groups" mean scores on the posttest of the LLS. The nullhypothesis was not rejected. It should be noted that the assumption of homogeneity of variances was met (Levene"s F = .051, P > .05). That is why the first row of table 9, i.e. "Equal variances not assumed" was reported.

Components of LLSs
A multivariate ANOVA (MANOVA) was run to investigate the effect of jigsaw and problem-solving methods on the components of LLS. Before reporting the main results, it should be noted that the assumption of homogeneity of variances as tested through the Levene"s F-valuesand the assumption of homogeneity of covarianceas tested through the Box"s testwere met. As displayed in table 10, the probabilities associated with the Levene"s F-values were all higher than .05. Thus, the assumption of homogeneity of variances was met.  (Table 11). Thus, the assumption of homogeneity of covariance matrices was also met.

Graph 2. Components of LLS
So, when language learning strategies are treated as discrete construct composed of various sub-strategies, different picture is revealed indicating that in certain areas (i.e. affective strategies), though not in majority of the cases, task type affects the use of language learning strategies.

Investigation of the Second Research Question
The second research question, concerned with the effect of exposure to jigsaw tasks on the use of language learning strategies by Iranian EFL learners, was addressed twice probing the effect of jigsaw tasks on language learning strategies as a unitary construct first and then on its components.

Learning strategies as a unitary construct (jigsaw group)
A paired-samples t-test was run to compare jigsaw group"s means on pretest and posttest of language learning strategies as a unitary construct. As displayed in The results of the paired-samples t-test (t (23) = 1.64, P > .05, R = .32 representing a moderate effect size) (table 15) indicated that there was not any statistically significant difference between the jigsaw group's means on the pretest and posttest of language learning strategies indicating that the hypothesis was not rejected. However, there is some moderate degree of difference between the two means.

Components of LLSs
A repeated measures ANOVA was run to compare jigsaw group"s means on pretest and posttest of component of LLS.
Based on the results displayed in tables 16 and 17, it can be concluded that there was a significant difference between the jigsaw group"s overall means on pretest (M = 32.47) and posttest (M = 34.10) (F (1, 23) = 2.87, P > .05, Partial η 2 = .11, representing a moderate to large effect size). Although the F-value of 2.87 was not significant, the effect size value of .11 indicated that the jigsaw group had a moderate to large improvement from the pretest to posttest. Then, the nullhypothesis was rejected.

Note. Each comparison was reported twice. The negative sign means that the first mean was smaller.
Based on the results displayed in tables 17 and 20, it can be concluded that there was significant and large interaction between time and components of language learning strategies (F (5, 19) = 2.87, P < .05, Partial η 2 = .43, representing a large effect size). Bar Graph 6 displays the jigsaw group"s means on components of pretests and posttests of language learning strategies. D e c e m b e r 2 3 , 2 0 1 4

Graph 4. Components of Pretests and Posttests of LLS (Jigsaw Group)
Therefore, when language learning strategies are treated as a unitary construct regardless of the components, the task type (i.e jigsaw) does not affectlearners' frequency of strategy use. However, when the components of language learning strategies are scrutinized, some changes in the strategic behavior of learners are revealed. It is notable that metacognitive, social, and cognitive strategies were used the most frequently by the learners.

Investigation of the third research Question
The third research question, concerned with the effect of exposure to problem-solving tasks on the use of language learning strategies by Iranian EFL learners, was addressed twice probing the effect of problem-solving tasks on language learning strategies as a unitary construct first and then on its components.

4.2.5.1Language learning strategies as a unitary construct (problem-solving group)
A paired-samples t-test was run to compare problem-solving group"s means on pretest and posttest of total language learning strategies. As displayed in table 21, the problem-solving group showed almost the same means on the posttest of language learning strategies (M = 169.08, SE = 3.66) than the pretest (M = 169.38, SD = 5.59) on posttest of language learning strategies. The results of the paired-samples t-test (t (25) = .058, P > .05, R = .012 representing a weak effect size) (table 22) indicated that there was not any significant difference between the problem-solving group"s mean scores on pretests and posttest of language learning strategies. The null-hypothesiswas not rejected.

Components of language learning strategies (problem-solving group)
A repeated measures ANOVA was run to compare problem-solving group"s means on pretest and posttest of component of Language Learning Strategies. Based on the results displayed in tables 23 and 24 it can be concluded that there was not any significant difference between the problem-solving group"s overall means on pretest (M = 32.62) and posttest (M = 33.80) (F (1, 25) = .030, P > .05, Partial η 2 = .001 representing a weak effect size). Then, the null-hypothesis was not rejected.

Note. Each comparison was reported twice. The negative sign means that the first mean was smaller.
Based on the results displayed in tables 23-28, it can be concluded that there was a non-significant but moderate to large interaction between time and Components of language learning strategies (F (5, 21) = .608, P > .05, Partial η 2 = .12 representing a moderate to large effect size). Bar graph 7 displays the problem-solving group"s means on components of pretests and posttests of language learning strategies. D e c e m b e r 2 3 , 2 0 1 4

Graph 6. Components of Pretests and Posttests of LLS (Problem-solving Group)
Thus, regarding language learning strategies as a unitary construct, exposure to problem-solving tasks does not affect learners' strategy use (i.e. the total number of strategies used is not changed). Nevertheless, when the components of language learning strategies are examined, it is indicated that the task type affects learners' use of different strategy types. The highest means belongs to metacognitive, social, and cognitive strategies.

5.Discussion and Conclusion
Approaching the LLSs as a unitary entity, the findings showed that the two groups were not significantly different indicating that the task types do not much influence the overall strategy use. This is in line with Oxford et al.'s (2004) study in which total reported mean frequency of strategy use did not differ significantly across three task conditions. The same result was reached by Sotudenama and Azimfar (2012) as well as Fazilatfar (2010).However, this result is inconsistent with findings of the study done by Khan (2010) who concluded that overall strategy use varies across different task types. It is also inconsistent with Junsheng's (2008) and Kaivanpanah, Yamouty, and Karami's (2012) findings.This was also partially true with respect to thecomponentsof LLSs. The only significant difference between the two groups was in affective strategies. This is in line with Khan's (2010) study which found that only a few strategies differed across task types. Moreover, the participants in the jigsaw group favored metacognitive, social, and cognitive strategies. This is in line with Junsheng's (2010) study in which learners employed cognitive and metacognitive strategies to do actual classroom tasks. Learners' infrequent use of compensatory strategies is inconsistent with Smith's (2003) research in which participants reported use of this strategy type in doing a jigsaw task. However, the analysis done on the subcategories of learning strategies showed that the students' strategic behavior changed after exposure to problem-solving tasks. It isnotable that metacognitive, social, and cognitive strategies were most frequently used by the learners. Results indicated that both groups were similar in terms of the strategy types they used more frequently as also Smith (2003) reports in his study in which similar pattern of strategy use across different task types was reported. It then may be safely concluded that, statistically speaking, task type does not play a significant role in LLS selection and use either as a unitary construct or when addressed componetially. However, componetially certain components appeared to have different pictures or realizations. This status quo may be attributed to the fact that in a jigsaw task a lot of negotiations happen which require the use of social strategies. The students may need to ask others for clarification, repetition, and other sorts of help. With regard to cognitive strategies, the students may have used skimming, scanning, and summarizing the information in the text in order to transfer the necessary information to other group members. As to the effects of problem solving tasks to LLS selection, metacognitive, social, and cognitive strategies were most frequently used. On the other hand, the problem-solving group outperformed on the posttest. This is justified on the nature of the task and the exposure of the members to more consciousness -raising statements in the questionnaire in the pretest stage.Generally, the insignificant results may imply that the target tasks are rested in the same psychological and underlying constructs in spite of their assumed format and structural differences. Perhaps if strategic behavior of learners is compared in doing non-communicative tasks with communicative ones more differences will be observed.