Patterns of self-regulatory strategy use among low-achieving and high-achieving university students.

Gifted students or high achieving students are often described as bright, curious, intelligent, motivated, and driven to accomplishment (Davis & Rimm, 2004). These students are also often described as possessing an arsenal of adaptive self-regulatory methods that help them succeed in academic settings (Risemberg & Zimmerman, 1992; Schunk, 1998). Low achievers and underachievers are often described as being more anxious and less self-efficacious (VanZile-Tamsen & Livingston, 1999). These students are believed to set lower academic goals, lack persistence, and engage in maladaptive academic behaviors (Borkowski & Thorpe, 1994). Do all high achievers always set high academic goals, create a plan for managing their daily tasks, manage their school projects effectively, and monitor their progress? Do all low achievers engage in maladaptive study behaviors, fail to set realistic goals, use ineffective study methods, and use little self-regulation in general? Recent research on academic self-regulation or self-regulated learning describes group differences as less clear-cut, and more researchers have come to believe that complex patterns of inter- and intra-individual differences in self-regulated learning appear to exist among different populations of students, such as low-achieving and high-achieving college students (e.g., Pintrich & Schunk, 2002).

In recent years, academic self-regulation has emerged as a key variable in explaining academic and even professional success (Bandura, 1997; Dai, Moon, & Feldhusen, 1998; Nist, Simpson, & Olejnik, 1991; Schunk & Zimmerman, 1998; Zimmerman, 1998). Self-regulated learning is an emerging area of research on student performance and achievement in classroom settings. Researchers have found that there is a strong relationship between low and high achievers' use of study skills and learning strategies and their academic achievement (Schunk & Zimmerman, 1994, 1998; Zimmerman, 1989).

Many researchers both in the fields of educational psychology and gifted education have turned to the self-regulatory models in attempts to explain the dilemma of academic problems experienced by low-achieving students and underachieving students, particular by gifted underachievers (Borkowski & Thorpe, 1994; Reis, Hebert, Diaz, Maxfield, & Ratley, 1995; Reis & McCoach, 2000). Research shows that a major cause of students' academic learning difficulties is their insufficient ability to self-regulate their learning and academic behaviors effectively (Schunk & Zimmerman, 1994, 1998).

Significant inroads have been made in research on individual differences, with particular emphasis on the importance of self-regulated learning variables in explaining these differences. Research has consistently shown that, while self-regulation can help high achievers reach their potential (Risemberg & Zimmerman, 1992), it makes a difference between failure and success for low achievers (Borkowski & Thorpe, 1994), and serves as an important internal mediator for gifted underachievers (Reis & McCoach, 2000). However, a growing need exists for research employing methodologies that capture the complexities of students' self-regulation of their academic behavior. Even though quantitative research conducted within a hypothetico-deductive paradigm has provided a fairly thorough understanding of group differences in achievement, motivation, and cognition, the use of more inductive and comprehensive approaches is warranted (Van Etten, Pressley, & Freebern, 1999). More recently, a growing number of researchers have suggested that more qualitative studies need to be conducted within the inductive paradigm to provide an in-depth view of the phenomena of academic self-regulation among different student populations and to study within-group variability (Perry, 2002). It appears that the use of mixed-method approaches may be particularly suited for capturing these complexities (Creswell, 1994). The present mixed-method study extends the work in this area by providing a comprehensive perspective on the patterns of self-regulatory strategy use among low- and high-achieving university students. The research reviewed is in the area of academic self-regulation, self-regulated learning strategies and group differences in serf-regulatory strategy use among low- and high-achieving students.

Background of the Study

According to Zimmerman's social cognitive perspective (1989), the construct of academic self-regulation refers to "the degree that individuals are metacognitively, motivationally, and behaviorally active participants in their own learning process" (p. 329). Self-regulated learners are self-starters who efficiently manage their own learning experiences and processes in many different ways (Schunk & Zimmerman, 1998). In reviewing research on giftedness and self-regulation, Risemberg and Zimmerman (1992) explained that, with respect to learning processes and responses, these students set goals for themselves, use a large repertoire of learning strategies to achieve their goals, and monitor their performance closely. In terms of their motivational characteristics, these learners are self-efficacious about their learning capabilities, inquisitive about their intellectual interests, persistent in the face of difficulties, and dedicated to the attainment of knowledge and skill. Many researchers concur with Risemberg and Zimmerman's proposition that the value of self-regulation in school is readily apparent. Students who can initiate learning tasks, set their own goals and select appropriate strategies for their completion, and then monitor and evaluate their own progress would be more likely to achieve at higher levels than their peers who are dependent on their teacher.

Further, in advocating the triarchic theory of intelligence as a basis for gifted education, Sternberg and Grigorenko (2002) explained that, besides intellectual superiority, the key features of giftedness are metacomponents. In linking Sternberg's ideas to self-regulation, Risemberg and Zimmerman (1992) explained that the abilities to select strategies that will achieve desired goals, to regulate one's thoughts and motivation, and to monitor one's progress as to ascertain that goals are being achieved comprise some of the very same abilities characteristic of the metacognitive component of self-regulated learning. Sternberg's conception of successful intelligence, viewed as a form of developing expertise and defined as the ongoing process of the acquisition and consolidation of a set of skills needed for a high level of mastery in one or more domains of life performance, may provide a link to academic self-regulatory processes which are viewed as flexible, dynamic, modifiable, and trainable (Pintrich & Maehr, 2002). Despite a long-standing controversy about the importance of standardized tests of intelligence and aptitude, it is reasonable to assume that individuals who actively develop and use adaptive self-regulatory skills are more likely to achieve at higher levels in academic and professional settings.

A major component of academic self-regulation is self-regulated learning (SRL) strategies defined by Zimmerman (1989) as "actions and processes directed at acquiring information or skills that involve agency, purpose, and instrumentality perceptions by the learners" (p. 329). Zimmerman and Martinez-Pons (1986, 1988), using a structured interview protocol with high-school students, found evidence of 14 types of SRL strategies including, but not limited to, such methods as planning and monitoring, organizing and transforming information, self-consequating, seeking information, seeking help, structuring their environment, and using rehearsal and memory aids. Students' use of these strategies was highly correlated with their achievement and with teachers' ratings of their self-regulation in a class setting. In fact, students' reports of their use of these self-regulated learning strategies predicted their achievement track in school with 93% accuracy, and 13 out of the 14 strategies discriminated significantly between students from the upper achievement track and students from lower tracks. The self-regulated strategies as described by Zimmerman (1989) within the social cognitive framework (Bandura, 1997), encompass three classes of strategies that all students use to improve self-regulation of their (a) personal functioning, (b) academic behavioral performance, and (c) learning environment. Further, Entwhistle and Ramsden (1983) distinguished between two types of learning strategies that correspond to two types of goal orientations: deep processing strategies, which include discriminating important from unimportant information, trying to fit new information into existing schemata, and monitoring comprehension; and surface processing strategies, which simply include reading passages repeatedly, memorizing new vocabulary, and rehearsing information. Whereas students who primarily use deep processing strategies demonstrate task, or learning goal orientation, students who resort to the use of surface processing strategies exhibit ego, or performance goal orientation (Nicholls, 1984).

Researchers have found that differences exist in academic self-regulation and motivation among low- and high-achieving students. In terms of self-regulatory strategy use, high-achieving students actively develop, modify, and transfer strategies to new contexts (Alexander, Cart, & Schwanenflugel, 1995; Bouffard-Bouchard, Parent, & Larevee, 1993; Hannah & Shore, 1995; Zimmerman & Martinez-Pons, 1990), and employ more advanced, effective, and efficient strategies than low-achieving students (Borkowski & Thorpe, 1994; Risemberg & Zimmerman, 1992; Zimmerman & Martinez-Pons, 1990). In terms of motivational components, these students are more goal-oriented (Dai, Moon, & Feldhusen, 1998), and have higher levels of self-efficacy perceptions for learning and for self-regulatory strategy use (Schunk, 1998). Gifted students outperform their counterparts on intrinsic motivation, challenge seeking, persistence, and independent mastery (Dai et al.). In contrast, underachieving and low-achieving students appear to be on the opposite end of the continuum. In view of Krouse and Krouse (1981), the major cause of underachievement is the inability of students to use self-control strategies effectively. It is reasonable to assume that many students who find themselves on academic probation in colleges and universities are students who lack self-regulation. Some of these students who are very bright seem incapable of adapting to a new more challenging postsecondary environment with increased emphasis on independence and autonomy, and they may become low achievers in college.

In addition, Borkowski and Thorpe (1994) reviewed a large body of research indicating that underachievers and low achievers possess many motivational characteristics that exacerbate their problems with learning, referred to as internal mediators by Reis and McCoach (2000). For example, they are more anxious, prone to fear of failure, have lower academic goals, tend to give up more easily in the face of difficulties than high achievers, and are less self-efficacious about their performance. These self-regulatory deficiencies may exert negative causal effects on underachievers' personality and emotional development (Reis & McCoach).

The study described in this article, grounded in a synthesis of research in related fields of giftedness and academic self-regulation, attempts to offer a comprehensive perspective and deeper insights into the nature and individual patterns of self-regulatory strategy use and motivation for using self-regulated learning strategies among low-achieving and high-achieving college students. As a consequence, the present study addressed two research questions. First, what self-regulated learning strategies and methods are reported by the students in both groups (i.e., low- and high-achieving students)? And second, are there identifiable patterns of differences in self-regulatory strategy use among low- and high-achieving students?

Research Methods

Sample

The sample included two groups of undergraduate students (N = 180) from a large research university in the northeast: low-achieving students and high-achieving students. The low-achieving group (n = 49) was comprised of students who participated in a university intervention program for students who were at-risk academically and who agreed to complete the survey. These students failed to meet the university's minimum academic standards during the semester prior to enrollment in the program. Freshmen and sophomores need to maintain a GPA of at least 2.0, whereas juniors and seniors must maintain a GPA above 2.20. In addition, a sample of students drawn from a larger pool of students who participated in a university program for honors scholars comprised the high-achieving group (n = 131). Students need at least a 3.4 grade point average, and must be in the top 8% of their graduating high school class with a minimum of 1320 on the SAT test to be considered for the Honors Program. Low-achieving and high-achieving students represented a variety of academic levels and majors. The majority of students across both groups were European American. The demographic and academic characteristics of the entire sample are presented in greater detail in Table 1.

Procedure

Several data collection procedures were used in this study in order to ensure the highest response rate and to obtain accurate data. These strategies included mailed surveys, distribution of the surveys through the personnel working with the students in their respective programs, distribution of surveys in class, direct phone calls, and e-mail messages to students. The first author conducted the initial mailing, followed by a follow-up mailing to non-respondents three weeks later. A cover letter and a postage-paid return envelope were sent along with a questionnaire. The same researcher also called the students and/or sent e-mail messages to encourage the students to complete the survey. After the study was approved, the administration of the Honors Scholars Program and the Scholastic Probation Program allowed the researcher to have access to the mailing lists to enable the researcher to contact the students. In addition, personnel working with the students in these programs facilitated the distribution and return of the surveys. The surveys also were distributed in class by the second author. Students were offered incentives to participate in the study: (a) The respondents' names were entered in a random drawing of gift certificates from the campus bookstore, and (b) students who filled out the surveys in class were given extra credit. Students were assured of the anonymity and were told that only the investigator would have access to the data.

Measures

A new instrument entitled Learning Strategies and Study Skills Survey (LSSS) was developed for a larger study in order to assess students' self-reported academic behaviors across college learning contexts and to evaluate the predictive validity of several self-regulatory factors in explaining students academic achievement in college (Ruban & Reis, 1999). The survey included both close-ended and open-ended items. Students were first asked to respond to 58 items on a 5-point scale (1 = not at all typical of me, 5 = very typical ()['me). A leading statement was used, "As a student, I...." Examples of the items included, "Review material for exams and tests" and "Memorize formulas, names, and dates." Students' cumulative grade point average was used as a dependent variable. The validity and reliability of the LSSS instrument, the results of the scales, and the results of the hierarchical multiple regression and discriminant function analyses were reported in Ruban (2000) and Ruban, McCoach, McGuire, and Reis (2003).

The present study reports the results of the questions relating to students responses to an open-ended question related to their self-regulatory strategy use. Specifically, students were asked to list and describe any special ways of studying or creative approaches that were useful in their current academic work (such as figuring out how to study difficult material more efficiently, finding a good way to memorize important information, etc.). This question was designed to elicit information about college students' use of self-regulated learning strategies across academic contexts. In addition, students were asked to indicate how often they developed these special ways of studying. Students were asked to place a check mark next to one of the following options: "I do it rarely," "I do it sometimes," "I do it most of the time," or "I can't think of any." By eliciting responses to an open-ended question from a large sample of students, the researchers were hoping to obtain a fairly comprehensive and representative picture of self-regulatory strategy use among low-achieving and high-achieving students at a research university.

Data Analyses

In this mixed-methods survey study the data was collected from two samples of students using an open-ended question on a written survey and analyzed using both qualitative and quantitative methods. Qualitative data analysis techniques included the use of a coding paradigm described by Strauss and Corbin (1990), as well as coding suggested by the same researchers, including three levels: open coding, axial coding, and selective coding. The initial type of coding, known as open coding, involved unrestricted coding of all students' responses across both groups. In open coding, data were analyzed and coded. As the researchers verified codes and determined relationships among and between codes, a determination was made about the relationship of a code to a category. After initial categories were determined, axial coding enabled the researchers to specify relationships among the many categories that emerged in open coding, and, ultimately, resulted in the conceptualization of the categories selected as the "core," which accounted for most of the variation in a pattern of behavior. In the final stage of coding, selective coding, the relationships among categories were examined to determine the saturation of categories in the identification of the core category. Both researchers independently coded all students' responses (N = 180) to the open-ended question. Cohen's kappa, an index of interrater reliability that corrects for chance agreement, indicated fairly high agreement for the coding of the strategies (k = .80).

In addition to describing and listing categories of learning strategies using Strauss and Corbin's (1990) method, the qualitative data were further examined using quantitative methods to add breadth and precision to the findings, as well as to describe patterns of group differences in the students' use of self-regulated learning strategies in their academic work. The data obtained from the axial and selective coding of the open-ended question were summarized, percentages of students using a particular strategy in each of the two groups were presented, and the data was rank ordered to explicate what kinds of categories of SRL strategies were used by students in the two groups most frequently, and to what kinds of strategies low-achieving and high-achieving students ascribed more importance.

Results

The results are divided into two sections. The first section presents the summary of the salient themes and categories derived from the students' responses describing their use of the SRL strategies. The second section describes the patterns in the SRL strategy use across low achievers and high achievers highlighting group differences as well as within-group heterogeneity in students' responses.

Coding and Classification of the Students' Responses into Salient Themes and Categories

Students' responses to the first open-ended question were classified into salient themes and categories. Category "Other" was used when students mentioned nonstrategic behaviors. Because researchers in the present study did not use the structured interview protocol developed initially by Zimmerman and Martinez-Pons (1986, 1988), but rather, solicited students' responses to an open-ended question about their strategy use, the results are not directly comparable with the ones obtained by those researchers. In the present study, as a result of the open, axial, and selective coding, as well as through the discussion between the researchers with respect to the relationship between and among the codes, it became apparent that some of the categories that emerged from the analysis were more differentiated than the original 14 categories proposed by Zimmerman and Martinez-Pons (1988). In addition, several new categories and a new theme emerged. In total, seven themes comprised of a total of 25 individual strategies were described. Themes, categories, and examples of the SRL strategies that emerged from the analysis are summarized in Table 2.

Patterns of Group Differences in Self-Regulatory Strategy Use among Low-Achieving and High-Achieving Students

Table 3 reports the number and proportion of students who use a particular SRL strategy in both groups of students (i.e., low achievers and high achievers). For each of the strategies, the table provides the number of students who reported using a particular strategy, along with the corresponding percentage in the parenthesis representing a proportion of students using a particular SRL strategy within each group. Table 3 is informative with regard to the differences in the patterns of strategy use among the students in both groups, as well as the kinds of strategies that were reported by students most often. To put the examination of the group differences in the self-regulatory strategy use into a broader perspective, the researchers examined group differences for the strategies that were reported by students most often. With that purpose in mind, the researchers first calculated the total number of students reporting the use of a particular strategy for each of the strategies listed in Table 3. Specifically, the column entitled "Total" refers to the number of students across both groups who reported using a particular strategy (e.g., this number was obtained by summing across each row of data). For instance, a total of 17 students across both groups reported using a strategy of keeping records of their work and monitoring their time and effort. The "Totals" column for each row was created to provide a more common denominator for comparing the frequency with which students in both groups reported strategy use. This method of data reporting, which is frequently employed in the self-regulatory research (see e.g., Wolters, 1998) was used to ensure the integrity of data reporting and interpretation.

Based on the descending numerical values in the "Total" column, five strategies that were used by most students were selected. These "five top" strategies listed in Table 4 are ranked in the descending order in terms of the total number of students who reported using a particular strategy. For example, the highest number of students in = 50) across both groups reported creating flashcards for studying material; 48 students reported condensing their notes. Furthermore, each strategy was further differentiated or ranked in terms of the proportions of students who reported using a particular strategy within each group, and rankings representing strategy importance were assigned based on the proportions of students who used a particular strategy within each group. For instance, whereas for low achievers creating flashcards was the most frequently reported strategy, high achievers favored the strategy of condensing and re-organizing their notes over the other categories of strategies. With regard to the total number of different strategies reported by each group, high achievers reported using the larger number of strategies (n = 25), as compared to low achievers (n = 19). Overall, the examination of the data presented in Tables 3 and 4 reveals interesting differences and similarities in the self-reported SRL strategy use among low-achieving and high-achieving college students.

Discussion and Conclusions

The research described in the present study was conducted within a larger framework of theories of intelligence and giftedness and academic self-regulation. We emphasize a link between broader theories of intelligence and giftedness, such as those of Sternberg and Renzulli, and the contemporary emphasis on academic self-regulation and motivation. For example, Renzulli's (1986) widely popular three-ring conception of giftedness includes above average ability, creativity, and task commitment that could be considered as a refined form of motivation. Sternberg's (1985, 1997) theory of successful intelligence (also known as triarchic theory), which has been gaining increasing acceptance in the field of gifted education, emphasizes the importance of self-regulatory processes as well. For example, the componential component of his triarchic theory emphasizes the importance of analytical abilities, which are needed to analyze and evaluate options available to an individual. Among such abilities are identifying the existence of the problem, creating a strategy for solving the problem, adapting the strategy to a particular situation, and monitoring one's solution process. In fact, these are the same abilities that are associated with the metacognitive component of self-regulated learning (Alexander et al., 1995; Risemberg & Zimmerman, 1992). The link between gifted education and academic self-regulation has implications for practice that are discussed later.

The purpose of the present study was to examine patterns of self-regulated learning (SRL) strategy use among college students representing different achievement levels (i.e., low achievers and high achievers). We wanted to examine self-regulated learning among low achievers and high achievers from different perspectives. Specifically, we wanted to examine what kinds of SRL strategies university students use in their academic work and whether identifiable patterns of self-regulatory strategy use by low achievers and high achievers could be distinguished from students' responses to the open ended question about their academic behaviors. An interesting multifaceted pattern of self-regulatory strategy use that has emerged from the findings is discussed within the framework of the relationship between students' strategy use, their mental learning models, and their goal orientations, with implications for gifted education.

The first research question explored the problem of what kinds of self-regulated learning strategies students in different achievement groups report using in their academic work at a university. In general, several of the salient themes, or general themes of categories of strategies reported by the students in this study were similar to those reported by Zimmerman and Martinez-Pons (1986, 1988), such as self-evaluating, organizing and transforming material, and reviewing records. One of the most provocative findings in the present study was that in cases where the categories of strategies were more differentiated, our study found strategies representing both deep processing and surface processing, thus extending the work of Dweck (1985), Pintrich and Maehr (2002), and Wolters (1998) who have emphasized the relationship between students' goals or motivational orientations and their use of different kinds of self-regulated learning strategies. For instance, when memorizing and rehearsing material, many students reported memorizing material routinely and inefficiently, or simply reviewing notes and texts. In contrast, other self-directed students whose goal was to learn for meaning and not to just memorize information for exams, used mnemonics and visual cues, as well as often devising their own creative methods for retaining the material and integrating it with their knowledge and understanding. The first example represents a type of strategy referred to as surface processing (Entwistle and Ramsden, 1983), and corresponds to an intake of knowledge learning orientation or mental model (Vermetten, Vermunt, & Lodewijks, 1999) on the part of the students who use such strategies. In contrast, the type of adaptive strategy deployed by students in the second example is characterized as deep processing strategies, and links to the construction of knowledge learning orientation, which these students consciously chose to adopt in their own learning. The examination of the students' responses showed that, whereas most high achievers reported using more advanced, deep processing strategies, many low achievers resorted to the use of simpler surface processing strategies. This finding may provide support to the hypothesis that many low achievers are individuals who lack self-regulation (Borkowski & Thorpe, 1994) and who are unable for different reasons to use self-control effectively (Schunk & Zimmerman, 1994).

Recently, Zimmerman (1998) provided anecdotal evidence that certain kinds of learning strategies used by secondary-school students also are employed in such diverse fields as professional writing, music, and sports. In order to describe the phenomenon of the generalizability of the SRL strategies across settings, Renzulli's (Renzulli & Reis, 1994) idea of the distinction between "school-house" and "creative-productive" giftedness was applied. It is logical to assume that the use of "reproductive," or surface-processing strategies underlying the school-house learning orientation, allows students to be good lesson learners, whose mental orientation is "intake of knowledge," or reproduction of knowledge (Vygotsky, 1936/1991). These students tend to be surface processors of knowledge who demonstrate performance-goal orientation (Nicholls, 1984). On the other hand, "constructive," or deep processing learning strategies underlying the creative-productive learning orientation, are used by students who have devised their own system of studying that is effective for them, and which may later enable them to become creative producers of knowledge. These students are sell-starters who tend to integrate knowledge, make connections among main ideas and concepts, and use these methods for doing creative class projects. These students tend to be deep processors of knowledge who transform, synthesize, and integrate knowledge and who may strive to produce new knowledge. They possess a construction of new knowledge (Vygotsky, 1936/1991) learning orientation, and they tend to use "meaning-directed" mental models (Vermetten et al., 1999). Future studies should explore further integration of these ideas. The differentiation of the strategies with links to different levels of processing knowledge and diametrically opposite kinds of learning orientations provides a logical link to the discussion of patterns of group differences in self-regulatory strategy use, which will be discussed next.

The second research question examined group differences in self-regulatory strategy use among low-achieving and high-achieving students. The five top strategies presented in Table 3 highlight differences in the patterns of strategy use among low achievers and high achievers, and reveal provocative findings with regard to the kinds of strategies that were reported most often by students.

High achievers reported strategies related to condensing and reorganizing notes and using various mnemonic devices and visual cues, which reinforces the idea that many high achievers are deep processors of material. Low achievers primarily reviewed notes, created flashcards and engaged in routine memorization of the material, which provides support for the idea that low achievers tend to engage in low-level strategies. Why would this be the case? This situation could be related to several reasons. High achievers may have had more opportunities earlier in their school careers to learn and use SRL strategies and study skills, some of which they may have learned in enrichment programs, when they were enrolled in advanced courses in high school, or during their work on advanced projects in an area of their interest and motivation. High achievers also may have internalized many of the learning strategies to the extent that they almost became second nature. One could speculate that because high achievers have greater facility with learning strategies, they have more cognitive capacity to deal with complex issues and to learn material in a more meaningful way. In other words, high achievers appear to exhibit an "enhancement model" of learning, whereas low achievers tend to demonstrate a "survival model" of learning. However, even though it is important to study group differences, recent research shows that group differences now appear to be less clear-cut as they were conceptualized previously (e.g., Perry, 2002). In the present study, students in the low-achieving group had a wide range in terms of their high-school rank (See Table 1). It is possible that some of them were unidentified gifted underachievers, who were unable to use self-control effectively when they found themselves in a challenging postsecondary environment, thus exhibiting a paradox of failure (Perry, Hladkyi, Pekrun, & Clifton, 2001). The finding that patterns of underachievement might have a negative spillover from secondary to postsecondary settings provides implications for practice.

An important finding that emerged from this study relates to the pattern of differences in the level of complexity of learning strategies used by low-achieving and high-achieving students. Students' written comments on the survey regarding the special study methods they devised on their own to help them with their academic work were provocative. Many high achievers reported using fairly complex, sophisticated kinds of strategies, which allowed them to study more efficiently and process material at a deeper and more meaningful level, such as connecting main ideas and concepts using concept maps, seeing patterns and the big picture in a large amount of written material, using word associations, creating stories based on the material, and so on. We can speculate that one possible underlying reason for the differences in self-regulated learning among low and high achievers may not be related as much to how much time they spend studying, but to how effectively they study and what kinds of learning strategies they use in their academic work (King, 1992; Risemberg & Zimmerman, 1992). This assumption was partly supported by written comments that some students wrote on the survey. For example, some high achievers reported using a variety of strategies, which could be qualified as highly creative and complex. For example, one high achiever noted, "For my chemistry class, I visualize the processes and then create color-coded diagrams and concept maps explicating relationships among the concepts and processes." On the other hand some low achievers reported using lower level strategies, as evidenced in one student's comment, "I just reread my notes and textbook repeatedly till I get it." The results from the present study are supported by previous research linking more advanced study strategies to higher levels of achievement (Pressley & McGormick, 1995; Schunk & Zimmerman, 1998). These findings indicate the importance of teaching college students, particularly those who are struggling, effective study methods and learning strategies to help them succeed academically.

Limitations

Several limitations pertaining to this study fall under the themes of (a) sample and design of the study; (b) instrumentation; and (c) generalization of the findings. First, because of the realities of data collection in real school settings, it was not possible to obtain equal sample sizes for both groups. As explained in the Results section, to provide a common denominator for the group comparisons in SRL strategy use, these comparisons were made based on the total number of students across both groups reporting the use of self-regulated learning strategies in their academic work. Next, in this cross-sectional study, students represented a variety of academic levels. However, the distribution of students across academic levels was unequal, partly as a function of the nature of these groups. Specifically, students who find themselves on academic probation at a university are primarily in the lower-academic division (i.e., freshman and sophomores), whereas the honors students comprising the high-achieving group represented all academic levels. This limitation may be mitigated by the fact that academic level was not a variable of interest in this study. More broadly, we did not design the study with controls for aptitude and academic level in mind. Rather, we were trying to study the real groups of students as they exist at a university: high achievers in the honors program and low achievers on academic probation. We were interested in the question: Which characteristics that students bring to college may help understand their academic sell-regulatory behaviors and academic achievement in college?

In terms of instrumentation and methodology, the results in the present study are based on brief written responses to an open-ended question as part of a larger survey provided by a fairly large sample of students. In-depth interviews with a much smaller group of students may have provided a different perspective on the results. However, the strength of approach used in the current study is that more diverse kinds of opinions were included in the results, which may have helped obtain a comprehensive picture of self-regulatory strategy use among low and high achievers in a university setting. In addition, the results reported in this article pertain to a sample of students from a large research university. Finally, this study's findings may not generalize to students at other universities, or students in four-year or two-year colleges.

Implications

Many researchers have argued about the importance of self-regulated learning at all academic levels. According to Pintrich (1995), this construct "offers an optimistic perspective on college learning and teaching" (p. 11). This perspective includes several assumptions about learning and teaching that have important implications for students in K-12 and college settings, school teachers and college faculty: students can learn how to become self-regulated; self-regulation is controllable; self-regulated learning is appropriate for both K-12 and college contexts; and self-regulated learning is teachable. It seems clear that students of various achievement levels who develop their own unique set of strategies involving both self-regulation and metacognition strategies will emerge as more effective learners in challenging academic settings. The use of these strategies also may enhance student motivation in these settings, particularly in areas in which students have personal interests and task commitment (Renzulli & Reis, 1985, 1997). It also appears that some of the participants in this study developed learning strategies as a function of their environment, providing support for the idea that underachievement may be a learned reaction to a largely unchallenging academic program for talented students (Reis & McCoach, 2000), and reinforcing the need for academic challenge in the form of enrichment, projects of choice, and acceleration options.

Previous research has emphasized the critical importance of self-directed learning skills for student achievement. The use of learning strategies enables students to actively process information, thereby influencing their mastery of the material and subsequent academic achievement (Schunk & Zimmerman, 1994, 1998). It is commonly agreed that self-regulated learning strategies, which were previously believed to be used only by the best and brightest students, can be taught to students of different ability and achievement levels (Risemberg & Zimmerman, 1992). Research shows that instruction in the use of SRL strategies has a positive impact on the academic achievement of all students (VanZile-Tamsen & Livingston, 1999). In underscoring the importance of instruction in self-regulated learning and cognitive strategies, Schunk and Zimmerman (1994) explained that academic self-regulation is acquired in stages. In particular, self-regulatory processes and skills are not acquired overnight but rather become further developed through repeated instruction and practice. One practical implication that can be drawn from this research is that it is important to teach students how to become self-regulated and self-directed learners early on, starting from elementary school. Because academic self-regulation and self-regulatory strategy use is an incremental process, the earlier students get exposed to various study methods and learning strategies, the greater the chance that they will acquire a large arsenal of self-regulatory methods that they can use in different academic settings requiring an increasing degree of self-directedness and autonomy on the part of the students.

Furthermore, both Vermetten et al. (1999) and Trigwell, Prosser, and Lyons (1997) demonstrated a relationship between teachers' instructional approaches and student learning. In particular, students who adopted significantly deeper approaches to learning than their peers were more often taught by teachers who adopted approaches to teaching that were more oriented towards students and to changing their conceptions. Teachers who described their approach as having a focus on transmitting knowledge more often had students who reported surface approaches to learning. Renzulli (1986) and Renzulli and Reis (1985, 1997) have advocated the use of enrichment opportunities that focus on authentic projects, in which students use the methods of practicing professionals, and have found that students who participate in these types of self-selected opportunities develop task commitment as well as self-regulation and an understanding of how they like to learn (Renzulli & Reis, 1994). On the postsecondary level, instructors providing students with a certain degree of choice in course projects may foster students' self-directed learning behaviors.

Of particular interest in this study is the apparent link between broader theories of intelligence and giftedness and the contemporary emphasis on academic self-regulation. Risemberg and Zimmerman (1992) recommended including an instrument measuring self-regulation into a battery of diagnostic tests, which would help differentiate gifted from nongifted students. More recently, Sternberg and Grigorenko (2002) recommended using Sternberg Triarchic Abilities Test (Sternberg, 1993) as part of a comprehensive identification and assessment system for gifted students, in order to "cast a broader net" and identify more gifted students with diverse abilities. Perhaps academic self-regulation may assume greater importance in making high-stakes decisions relating to selecting students to gifted programs, in differentiating high achievers from low achievers, predicting students' academic success in colleges and designing interventions tailored to the individual needs of the students representing different achievement levels.

In conclusion, while making generalizations and noting patterns may provide some insights into the complex nature of the multi-faceted phenomena such as academic self-regulation and giftedness, it should be noted that between-group generalizations should not mask the richness of the individual variation within groups, whether we are studying high-achieving or low-achieving students. According to Perry (2002), the present trend has moved away from studying serf-regulation as an aptitude, which traditionally resides in the internal characteristics that remain stable across situations and can be aggregated and generalized for certain groups. In contrast, current research efforts reflect a growing interest among researchers "in finding ways to study this phenomenon in real contexts and in real time, in events rather than aptitudes" (Perry, p. 1). Future research should continue examining complex patterns of interaction between the individual and the context within the framework of individual differences. This would provide a foundation for designing effective differentiated interventions aimed at enhancing cognitive and motivational strategy use among students representing different achievement levels, and would nurture meaning-oriented learning orientation in students.

Manuscript submitted July 19, 2004.

Revision accepted February 25, 2005.

REFERENCES

Alexander, J. M., Carr, M., & Schwanenflugel, P. J. (1995). Development of metacognition in gifted children: Directions for future research. Developmental Review, 15, 1-37.

Bandura. A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Borkowski, J. G., & Thorpe, P. K. (1994). Serf-regulation and motivation: A life-span perspective. In D. H. Schunk & B. J. Zimmerman (Eds.). Self-regulation of learning and performance: Issues and educational implications (pp. 45-74). Hillsdale, NJ: Erlbaum.

Bouffard-Bouchard, T., Parent, S., & Larevee, S. (1993). Self-regulation on a concept formation task among average and gilled students. Journal of Experimental and Child Psychology, 56, 115-134.

Creswell, J. W. (1994). Research design: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage.

Dai. D. Y., Moon, S. M., & Feldhusen, J. F. (1998). Achievement motivation and gifted students: A social cognitive perspective. Educational Psychologist, 33, 45-63.

Davis, G., & Rimm, S. (2004). Education of gifted and talented students (6th ed.). Boston: Allyn & Bacon.

Dweck, C. S. (1985). Intrinsic motivation, perceived control, and self-evaluation maintenance: An achievement goal analysis. In C. Ames & R. Ames (Eds.), Research on motivation in education: Vol. 2. The classroom milieu (pp. 289 305). New York: Academic Press.

Entwistle, N. J., & Ramsden, P. (1983). Understanding human learning. London: Croom Helm.

Hannah, C. L., & Shore, B. M. (1995). Metacognition and high intellectual ability: Insights from the study of learning-disabled gifted students. Gifted Child Quartetly 39, 95-109.

King, A. (1992). Comparison of self-questioning, summarizing, and note taking review as strategies for learning from lecture. American Educational Research Journal, 29, 303-323.

Krouse, J. H., & Krouse, H. J. (1981). Toward a multimodal theory of academic achievement. Educational Psychologist, 16, 151-164.

Nicholls, J. G. (1984). Achievement motivation, conceptions of ability, subjective experience. task choice, and performance. Psychological Review, 91,328-346.

Nist, S. L., Simpson, M. L., & Olejnik, S. (1991). The relation between self-selected study processes and test performance. American Educational Research Journal. 28, 849-874.

Perry, N. E. (2002). Introduction: Using qualitative methods to enrich understanding of self-regulated learning. Educational Psychologist, 37, 1-4.

Perry, R. P., Hladkyi, S., Pekrun, R. H., & Clifton, R. A. (2001, April). Self-regulation in college students' scholastic attainment: A three-year longitudinal study of control and action control. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.

Pintrich, P. R. (1995). Understanding self-regulated learning. In R. J. Menges & M. D. Svinicki (Eds.), New directions for teaching and learning (Vol. 63. pp. 3-12). San Francisco: Jossey-Bass.

Pintrich, P. R., & Maehr, M. L. (2002). Advances in motivation and achievement: Vol. 12. New directions in measures and methods. New York: JAI.

Pintrich, P. R., & Schunk. D. H. (2002). Motivation in education: Theory research, and applications. Upper Saddle River. N J: Prentice Hall.

Pressley, M., & McGormick, C. B. (1995). Advanced educational psychology for educators, researchers, and policymakers. New York: Harper Collins.

Reis, S. M., Hebert, T. P., Diaz, E. I., Maxfield, L. R., & Ratley, M. E. (1995). Case studies of talented students who achieve and underachieve in an urban high school (Research Monograph No. 95120). Storrs. CT: The National Research Center on the Gifted and Talented.

Reis, S. M., & McCoach. D. B. (2000). The underachievement of gifted students: What do we know and where do we go? Gifted Child Quarterly, 44. 152-170.

Renzulli, J. S. (1986). The three ring conception of giftedness: A developmental model for creative productivity. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (pp. 53-92). New York: Cambridge University Press.

Renzulli, J. S., & Reis, S. M. (1985). The Schoolwide Enrichment Model: A comprehensive plan for educational excellence. Mansfield Center, CT: Creative Learning Press.

Renzulli, J. S.. & Reis, S. M. (1994). Research related to the Schoolwide Enrichment Triad Model. Gifted Child Quarterly, 38, 7-20.

Renzulli. J. S., & Reis. S. M. (1997). The Schoolwide Enrichment Model: A comprehensive plan for educational excellence (2nd ed.). Mansfield Center, CT: Creative Learning Press.

Risemberg. R., & Zimmerman. B. J. (1992). Self-regulated learning in gifted students. Roeper Review, 15, 98-101.

Ruban, L. M. (2000). Patterns of self-regulated learning and academic achievement. Unpublished doctoral dissertation. University of Connecticut, Storrs.

Ruban. L. M.. McCoach. D. B., McGuire, J. M., & Reis, S. M. (2003). The differential impact of academic self-regulatory methods on academic achievement among university students with and without learning disabilities. Journal of Learning Disabilities, 36. 270-287.

Ruban, L. M., & Reis, S. M. (1999). Learning Strategies and Study Skills Survey (LSSS). Unpublished instrument. University of Connecticut at Storrs.

Schunk, D. H. (1998. November). Motivation and self-regulation in gifted learners. Invited address presented at the annual meeting of the National Association for Gifted Children, Louisville, KY.

Schunk, D. H.. & Zimmerman, B. J. (Eds.). (1994). Self-regulation of learning and performance: Issues and educational implications (pp. 75-99). Hillsdale, NJ: Erlbaum.

Schunk, D. H., & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: From teaching to self-reflective practice. New York: Guilford.

Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press.

Sternberg. R. J. (1993). [Sternberg Triarchic Abilities Test]. Unpublished test.

Sternberg, R. J. (1997). Beyond IQ: A triarchic theory of human intelligence. New York: Plume.

Sternberg, R. J., & Grigorenko, E. L. (2002). The theory of successful intelligence as a basis for gifted education. Gifted Child Quarterly. 46. 265-277.

Strauss, A. L., & Corbin, J. (1990). Basics of qualitative research. Newbury Park, CA: Sage Publications.

Trigwell, K., Prosser, M., & Lyons, F. (1997, August). Defining good teaching: Relations between teachers' approaches go teaching and student learning. Paper presented at the Conference of the European Association of Research on Learning and Instruction, Athens, Greece.

Van Ellen, S., Pressley, M., & Freebern, G. (1999, August). College seniors' theory of their academic motivation. Paper presented at the annual meeting of the American Psychological Association, Boston. MA.

VanZile-Tamsen, C., & Livingston. J. A. (1999). The differential impact of motivation on the serf-regulated strategy use of high- and low-achieving college students. Journal of College Student Development. 40. 54-60.

Vermetten, Y. J., Vermunt. J. D., & Lodewijks, H. G. (1999). A longitudinal perspective on learning strategies in higher education: Different viewpoints towards development. British Journal of Educational Psychology, 69, 221-242.

Vygotsky. L. S. (1936/1991). Voobrazheniye i tvorehestvo v detskov vozraste [Imagination and creativity in childhood] (3rd ed.). Moscow: Prosvesuheniye.

Wolters, C. A. (1998). Self-regulated learning and college students' regulation of motivation. Journal of Educational Psychology, 90, 224 235.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 329-339.

Zimmerman, B. J. (1998). Academic studying and the development of personal skill: A self-regulatory perspective. Educational Psychologist, 33, 73-86.

Zimmerman, B. J., & Martinez-Pons, M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal. 23, 614-628.

Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80. 284-290.

Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82. 51-59.

Lilia M. Ruban, Ph.D., is an assistant professor of curriculum and instruction in the gifted and talented program area and researcher at the Urban Talent Research Institute at the University of Houston. She specializes in research about students with exceptionalities, including gifted and talented students and students with both learning disabilities and giftedness, academic self-regulation, motivation, and self-efficacy. E-mail: [e-mail]

Sally M. Reis, Ph.D., is a professor and the department head of educational psychology at the University of Connecticut where she also serves as principal investigator of the National Research Center on the Gifted and Talented. She specializes in research about talented girls and women, talented students with learning disabilities, and curricula for talented students. E-mail: [e-mail]

Table 1

Demographic and Academic
Characteristics of the Samples

                                          Group

                         Low achievers (a)    High achievers (b)

Gender
  Male                         45.1                  27.6
  Female                       54.9                  72.4
Age                         20.5 (.88)           20.0 (1.12)
Ethnicity
  Caucasian                    62.7                  80.9
  Asian                         9.9                   7.8
  Hispanic                      7.8                   2.1
  Black                        11.8                   1.4
Academic level
  First year                   33.3                  26.5
  Second year                  49.0                  31.0
  Third year                   14.7                  24.8
  Fourth year                   2.9                  17.7
High school (HS) rank         69 (15)               95 (4)
  HS rank range                40-95                77-99
Cumulative GPA (CGPA)       1.73 (.47)            3.62 (.33)
  CGPA range                 1.00-2.20            3.40-4.00

Note. The numbers for the variables of gender, ethnicity, age, and
academic level represent percentages. The numbers for the variables
of high-school rank and cumulative GPA represent means. Numbers
in the parentheses are standard deviations.

N = 180. (a) n = 49. (b) n = 131.

Table 2

Summary of the Self-Regulated Learning Strategies
Reported by Low- and High-Achieving Students

SRL strategies                 Definitions and examples

1. Self-evaluating             Statements indicating student-initiated
                               efforts to gauge the degree of the
                               effort required for completing academic
                               task(s).

   1.1 Assessing task          e.g., "in studying for a course, I must
       demands                 first know what I am trying to get out
                               of a certain task."

   1.2 Differentiating         e.g., "My basic method is to first
       effort                  approach a course by determining (a)
                               what the teacher wants me to do to
                               get a good grade; and (b) what I
                               want to get out of the class, then
                               I prioritize."

   1.3 Foreshadowing           e.g., "I think of possible essay
                               topics and write them out."

2. Managing time and           Statements indicating students' setting
   redistributing workload     of educational goals and planning for
                               sequencing, allocating time, and
                               completing activities; break study
                               material into manageable parts.

   2.1 Planning time           e.g., "I start studying two weeks in
                               advance so I have enough time to
                               prepare for an exam."

   2.2 Chunking material &     e.g., "I study small pieces of material
       study time              at a time;" "I break up my study
                               periods."

3. Organizing & transforming   Statements indicating student-initiated
   material                    overt or covert re-arrangement of
                               instructional materials to improve
                               learning.

   3.1 Condensing &            e.g., "I go through my history notes
       reorganizing material   and condense them down by re-writing
                               them and leaving out less important
                               parts."

   3.2 Using a system in       e.g., "I take notes in the same format
       note taking             with dashes and wavy and straight lines
                               in sort of an outline form."

   3.3. Highlighting & color   e.g., "I use highlighters, asterisks
        coding                 and arrows to mark important points
                               in text."

   3.4 Outlining Material      e.g., "Oftentimes, I outline all
                               relevant information for test into
                               one comprehensive whole (book,
                               notes, outside reading)."

   3.5 Creating concept maps   e.g., "Memorizing is something I
                               find fairly difficult. I often use
                               association, mnemonics, and visual
                               charts in my studying."

   3.6 Keeping records &       e.g., "I make sure that understand what
       monitoring              I am writing in my notebook; if not,
                               I check on it later."

4. Structuring environment     Statements indicating student efforts
                               to organize the learning context to
                               facilitate their learning.

   4.1 Structuring physical    e.g., "I once studied in a closet for
       environment             5 hours ... I got an A. I have to find
                               somewhere where I feel comfortable to
                               study."

   4.2 Enhancing learning      e.g., "I listen to a piece of music,
                               always harmonic sequence."

5. Memorizing, rehearsing,     Statements indicating student-initiated
   & retaining material        efforts to memorize material by using
                               memory techniques; doing practice
                               problems; or rehearsing material
                               individually or in a group.

   5.1 Memorizing routinely    e.g., "When memorizing or learning
                               important information, I write it
                               down."

   5.2 Doing practice          e.g., "I use learning goals in the book
       problems                combined with practice problems."

   5.3 Using mnemonics &       e.g., "I use a lot of mnemonic
       visual cues             devices;" "I make up stories and
                               metaphors."

   5.4 Learning for meaning    e.g., "I try to make connections/see
                               how everything works to make sense
                               between present and past knowledge."

   5.5 Practicing material     e.g., "Sometimes I study in a group."
       with others

6. Reviewing records &         Statements indicating student-initiated
   clustering material         efforts to revise or review relevant
                               study materials (notes, texts, study
                               guides, course packets, etc.); student
                               efforts to cluster the material from
                               different sources relating to a certain
                               problem/question under study.

   6.1 Reviewing records       e.g., "I review my notes every day
                               after class for basic subjects." "I
                               review old exams for applications of
                               formulas."

   6.2 Clustering material     e.g., "[I use] Clustering-using notes,
                               textbooks, and lecture."

7. Utilizing support           Statements indicating student-initiated
   networks                    efforts to solicit help from other
                               people or sources, or offer assistance
                               to others.

   7.1 Seeking social          e.g., "I ask the professor for
       assistance              assistance if I don't understand
                               something."

   7.2 Offering social         e.g., "Sometimes, I get a study group
       assistance              together and discuss the material. It
                               works best if I do the teaching!"

8. Non-Strategic behavior      Statements indicating that learning
                               behaviors were initiated by another
                               person, will power statements, or
                               non-scorable responses.

   8.1 Vague, non-scorable,    e.g., "I rely on the teacher to tell me
       reactive responses      what to do." "Work off my foot until I
                               am the best I can be. I will settle for
                               nothing short of best."

Note. N = 180. SRL Strategies = Self-regulated learning strategies.

Coding and Classification of the Students' Responses:
Self-Regulatory Strategy Use among
Low-Achieving and High-Achieving Students

                                                     Group

                                            Low        High
Broad categories & specific              achievers   achievers   Total
SRL strategies                              (a)         (b)       (1)

1. Self-Evaluating
   1.1 Assessing task demands               --        5 (4%)       5
   1.2. Differentiating effort            2 (4%)     17 (13%)     19
   1.3. Foreshadowing & taking              --       11 (8%)      11
        initiative

2. Managing time & redistributing
   workload
   2.1. Planning time                     9 (18%)     5 (4%)      14
   2.2. Chunking material & study time    2 (4%)      6 (5%)       8

3. Organizing & transforming material
   3.1. Condensing & reorganizing
        material
   3.1.1. Creating flashcards            18 (36%)    32 (24%)     50
   3.1.2. Condensing notes                6 (12%)    42 (32%)     48
   3.2. Using a system in note taking       --       10 (7%)      10
   3.3. Highlighting & color coding       5 (10%)    11 (8%)      16
   3.4. Outlining material & writing      4 (8%)     16 (12%)     20
        summaries
   3.5. Creating concept maps &           1 (2%)      5 (4%)       6
        diagrams
   3.6. Keeping records & monitoring      1 (2%)     16 (12%)     17

4. Structuring environment
   4.1. Structuring physical                --        5 (4%)       5
        environment
   4.2. Enhancing learning                1 (2%)      8 (%)        9

5. Memorizing, rehearsing & retaining
   material
   5.1. Memorizing routinely              7 (14%)    23 (17%)     30
   5.2. Doing practice problems           1 (2%)      3 (2%)       4
   5.3. Using mnemonics & visual cues     1 (2%)     31 (24%)     32
   5.4. learning for meaning              1 (2%)     11 (8%)      12
   5.5. Practicing/rehearsing material      --        5 (4%)       5
        with others

6. Reviewing records & clustering
   material
   6.1. Reviewing records
   6.1.1. Reviewing notes                12 (24%)    15 (11%)     27
   6.1.2. Reviewing texts                 6 (12%)    19 (15%)     25
   6.1.3. Reviewing study guides/old      1 (2%)      2 (<2%)      3
          tests
   6.2. Clustering material               1 (2%)     11 (8%)      12

7. Utilizing support networks
   7.1. Seeking social assistance         7 (14%)     8 (6%)      15
   7.2. Offering social assistance          --        2 (<2%)      2

9. Non-Strategic behavior
   Vague, non-scorable, reactive          1 (2%)      5 (4%)       6
     responses
   Other                                    --        5 (4%)       5

Note. (1) "Total" refers to the total number of students
using a particular strategy across both groups.

SRL Strategies = Self-regulated learning strategies.

N = 180. (a) n = 49. (b) n = 131.

Table 4

The "Top Five" SRL Strategies Ranked in
Terms of the Proportions of Students Using
a Particular Strategy Within Each Group

                                                 Group

                                        Low        High
                                     achievers   achievers   Total
Self-Regulated Learning Strategies      (a)         (b)       (1)

1. Creating flashcards                   I          II        50
2. Condensing notes                     IV           I        48
3. Using mnemonics & visual cues         V          III       32
4. Memorizing material routinely        III         IV        30
5. Reviewing notes                      II           V        27

Note. (1) "Total" refers to the total number of students using a
particular strategy across both groups. These numbers were taken
from Table 3.

N = 180. (a) n = 49. (b) n = 131.