This article is part of a series drawn from work in the Handbook of Reading Research: Volume III (Kamil, Mosenthal, Pearson, & Barr, 2000). In the coming months, Reading Online will publish additional chapter summaries from the book, prepared by the chapter authors.

College Studying

Sherrie L. Nist
Michele Simpson


It is well accepted both in theory and in practice that academically successful college students know how to study. Research suggests, however, that many students enter postsecondary institutions unprepared to meet the studying demands placed on them (e.g., Pressley, Yokoi, van Meter, Van Etten, & Freebern, 1997). This lack of preparation can be traced, in part, to the “hidden curriculum” at the secondary level (Mayer, 1996). That is, study strategies are “hidden” because teachers at all levels assume that their students already have a repertoire of studying behaviors when they enter the classroom. As a result of this lack of preparation, most colleges and universities offer courses or programs that teach students to be efficient and active learners (Maxwell, 1997).

We begin this discussion of college studying by examining models and taxonomies that have guided researchers as they have investigated studying. Using one model, we will then review the research factors related to studying at the college level: course characteristics, learner characteristics, and learning strategies. Finally, we conclude our discussion with implications for practice and offer suggestions for future research.



Theoretical Models | Factors Related to Studying | Learning Strategies | Implications | References



Theoretical Models

Since the early 1980s, researchers committed to helping students become successful, active learners have based their studies on a variety of interactive, theoretical models. Although diverse, these models share the common assumption that there are many variables that interact to affect students’ learning. Jenkins’ (1978) tetrahedral model of learning, for example, proposes that active learners consider the nature of the material to be learned and examine the task, determining the products (i.e., recognition or recall) and levels of thinking embodied in it. Moreover, active learners are aware of their own characteristics, especially their own strengths and weaknesses in terms of the specified tasks and texts. Using that information about themselves and the specified tasks and texts, active learners then determine the appropriate strategies to employ.

Gradually, researchers refined the tetrahedral model and created others. Of these models, Thomas and Rohwer’s (1986) component and process model perhaps best captures the factors affecting college students’ studying. Their model proposes that the experiences, abilities, and volition-related characteristics of students and the characteristics of a course (i.e., materials, tasks factors, course conditions) are filtered by students’ perceptions or beliefs. These components interact to affect students’ study activities and their subsequent performance. What particularly distinguishes this model is the research that Thomas and Rohwer (1987) have conducted in classroom settings. They have used their model to investigate study practices in social studies courses ranging from the junior high school to the college level, finding that students’ study activities are influenced by a relatively large number of course features or characteristics.

We can draw four important generalizations about studying from these theoretical models. First, the models imply that there are no generic best strategies or methods of studying. Rather, strategies are considered appropriate when they match the demands of the texts and tasks and the beliefs and background knowledge of the learner. Second, studying involves more than knowledge of the possible strategies. Students must understand the what, when, how, and why of strategies if they are to apply them to their own tasks and texts (Paris, Lipson, & Wixson, 1983; Pressley, 1995). Third, there is a core of essential cognitive and metacognitive processes that cut across domains. These processes include selecting, transforming, organizing, elaborating, monitoring, planning, and evaluating (Mayer, 1996; Weinstein & Mayer, 1986). Fourth, and most important, these models imply that active learning takes a long time to foster and develop (Butler & Winne, 1995; Pressley).

As we have suggested, studying and active learning involve considerably more than students employing strategies. In the sections that follow, we will briefly address these other factors -- course characteristics, learner characteristics, and belief systems -- before discussing learning strategies.

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Factors Related to Effective Studying and Active Learning

Course Characteristics

The first factor that influences studying at the college level is a student’s ability to understand the situation or context (Alexander, 1995; Garner, 1990). Thomas and Rohwer (1986) describe context as the characteristics of a course, or the external factors that influence reading and studying (Thomas, Bol, & Warkentin, 1991). These external factors include the texts that are assigned and the academic tasks that are either tacitly or explicitly communicated by the professor. First, we will discuss the role of text. Then, we will describe the role of the academic task, which is beginning to make its way into the literature.

Text. Early research examining text was mainly concerned with text structure. In the mid- to late 1980s, research on text characteristics, which was primarily quantitative, focused on how text aids and organization influenced text comprehension. This research had a profound influence on publishers of college textbooks, although some research suggested that text aids promoted passive processing (Schallert, Alexander, & Goetz, 1985). Publishers began inserting numerous aids into their textbooks, many of which had been scientifically researched, to help students better understand and organize text information.

Currently, text research, as it is related to college studying, seems to be heading in two directions. First, there has been a recent emphasis on how students approach text in a variety of domains, particularly in history and science (Carson, Chase, & Gibson, 1993; Donald, 1994; Simpson & Nist, 1997, online document; Voss & Silfies, 1996; Wineburg, 1991), as well as on students’ beliefs about these texts. That is, rather than simply focusing on the differences between narrative and expository texts, researchers are examining differences between expository texts from a variety of disciplines using more qualitative methodology (e.g., Simpson & Nist; Simpson, Nist, & Sharman, 1991). Such studies represent a step forward and have important implications for college readers because they indicate that text, like strategies, does not operate independently of other factors (Alexander, 1992; Simpson & Nist).

A second direction that currently is drawing interest examines lecture notes as texts, particularly how students attempt to organize and study these texts as part of test preparation (Kiewra, 1989; Kiewra, Benton, Kim, & Risch, 1995; King, 1991, 1992; van Meter, Yokoi, & Pressley, 1994). The results from these studies, which are helping to create a theory of note-taking beyond the encoding-storage perspective, strongly support the notion that college students should use generative strategies to interact with lecture notes (Wittrock, 1990).

Academic tasks. The second course-specific characteristic is academic tasks, the products students are asked to formulate (e.g., tests or papers) and the operations or thinking processes they should use to do so (Doyle, 1983). To be successful in their studying, students must understand the characteristics and nuances of academic tasks and then adjust their strategies accordingly. Because many college students do not select strategies that match the varying task demands, texts, or domains, they demonstrate what has been termed “transfer appropriate processing deficiencies” (Pressley et al., 1997, p. 8).

Current research on academic tasks has focused on two areas. Some researchers have investigated how tasks vary across domains (Burrell, Tao, Simpson, & Mendez-Berrueta, 1996; Chase, Gibson, & Carson, 1994; Donald, 1994; Schellings, Van Hout-Wolters, & Vermunt, 1996a, 1996b, online document; Simpson & Nist, 1997). In general, the findings from these studies suggest that academic tasks are not only specific to a domain, but also to a professor and a setting. Moreover, the findings indicate that students and professors frequently have different perceptions of what is considered the essential thinking processes in a particular domain (Donald).

Other studies have investigated academic tasks using case study methodology to describe the patterns of students’ interpretation of academic tasks, their choice of strategies, and their subsequent academic performance. For example, Simpson and Nist (1997) concluded that students who earned high grades in a history course either understood the professor’s tasks initially or were flexible enough to modify their task perceptions and strategies.

Characteristics of the Learner

In their model, Thomas and Rohwer (1986) also consider the characteristics that college students bring to each learning environment. Among the characteristics that are important to active learning are students’ prior knowledge, metacognitive abilities, motivational levels, and interest in what they are reading or studying.

Prior knowledge. Alexander (1996) divides the research that has examined the role of knowledge in comprehension and learning into two periods: first generation of knowledge, which lays the groundwork, and second generation of knowledge, which examines knowledge as it relates to social and cultural contexts. Most closely related to our focus is the role that domain knowledge plays in college students’ ability to understand and learn through text or lecture, as well as the interaction between domain and strategy knowledge. Domain knowledge is defined as the knowledge learners possess about a specific field of study (Alexander & Judy, 1988). As such, it involves declarative, procedural, and conditional knowledge (Paris et al., 1983). Alexander (1992) aptly points out that researchers have not been quick to understand the relationship between domain knowledge and strategy research as a way of building a more complete model of learning.

Current researchers appear to find Alexander’s ideas compelling, and are focusing on the more complex questions regarding domain knowledge, strategy selection, and other factors that may influence performance. What seems to be emerging is the degree to which both domain knowledge and topic knowledge influence not only strategy choice, but also the performance level of students (Alexander, Kulikowich, & Schulze, 1994).

Other research suggests that some knowledge that college students bring to learning situations is highly resistant to change, even when they have read information that challenges or contradicts it (e.g., Alvermann & Hynd, 1989; Marshall, 1989), and that prior knowledge can inhibit comprehension (Pace, Marshall, Horowitz, Lipson, & Lucido, 1989). The results of this and similar research once again indicate the interaction of many variables in learning and studying processes.

Metacognitive ability. In 1984, Baker and Brown concluded that even mature readers have limited metacognitive skills and that college students often fail to monitor their comprehension. These two conclusions still hold true. College students, who may or may not be mature readers, not only have persistent problems in monitoring text reading (Bielaczyc, Pirolli, & Brown, 1991; Maki & Berry, 1984; Pressley, 1995), but also when it comes to test preparation and subsequent predictions concerning how well they have performed on tests for which they have studied (e.g., Pressley, Snyder, Levin, Murray, & Ghatala, 1987; Nist, Simpson, Olejnik, & Mealey, 1991). Intervention research has also confirmed the idea that there is a big payoff in training students to monitor their learning (Dunlosky & Nelson, 1994; Nelson & Narens, 1990; Pressley et al.; Shenkman & Cukras, 1986; Thiede & Dunlosky, 1994).

Currently, research seems to be heading in two directions. First, metacognition is being studied with respect to how it relates to self-regulated learning (SRL), rather than in isolation. For example, Winne (1996) suggests that cognitive and metacognitive tasks are highly interrelated, yet admits that there is a “relatively small population of (empirical) studies that directly examine how metacognition is used within SRL” (p. 346). The second area of research focuses on how to measure metacognition (Dennison, 1997). Several instruments have subscales, which tap both cognitive and metacognitive abilities. The best known of these instruments are perhaps the Learning and Study Strategies Inventory (LASSI) (Weinstein, Palmer, & Schulte, 1987) and the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, & McKeachie, 1991).

Motivation. The role that motivation plays in strategic learning and self-regulation is portrayed well in the reciprocal empowerment model (McCombs, 1994). In this model, skill, will, and social support are deemed essential if maximum motivation is to occur, but the will component is the center if a student is to become truly self-regulated. This model addresses the importance of “hot” cognition, or the blending of motivation with cognition (e.g., Winne, 1996). Likewise, Mills (1991) claims that students must gain control over their own thinking processes rather than be controlled by external standards; it is only then that they will be open to learning new strategies. Moreover, if students believe that strategies are useful in meeting their goals, they will have higher levels of motivation and put forth greater effort (Schunk & Swartz, 1991).

In a somewhat different perspective, Paris and Turner (1994) present what they refer to as “situated motivation,” the idea that students’ motivation is dynamic, and thus changes as the situation changes. It should be noted that much of the research that examines the interaction between college students’ strategy use and motivation is correlational (e.g., Pintrich & Garcia, 1991; Pintrich & Schrauben, 1992).

Interest. Hidi’s (1990) synthesis concludes that interest is key in determining how students process information, and that interesting information seems to be processed differently from uninteresting information. Other research with college students has found that interest plays a role in how they respond to text, but that domain knowledge (Garner, Alexander, Gillingham, Kulikowich, & Brown, 1991) and the nature of the text (Schallert, Meyer, & Fowler, 1995) also make a difference. Garner et al. found that interestingness had a particular effect on recall if the participants had little domain knowledge about the topic. Similarly, Schallert et al. found that when students responded to text, whether related or unrelated to their major areas of study, they had significantly more interest and involvement in readings for which they had the most domain knowledge. However, the considerateness of the text also played a role.

In a review exploring the intersection of the importance of text information and interest, Alexander and Jetton (1996) concluded the following:

  1. Discrepancies in findings may be more a result of how researchers have defined these constructs than a reflection of any real discrepancies
  2. There were no developmental studies that examined both importance and interest
  3. Researchers need to investigate the role of importance and interest using nonlinear texts as well as in authentic classroom situations
  4. Research should expand into other domains and topics

Students’ Beliefs as Filters

What college students believe about learning influences how they interpret the task, how they interact with text, and ultimately the strategies they select. Because student beliefs are so important, Thomas and Rohwer (1987) suggest that they serve as the “filter” through which students decipher and interpret the other components of their model. College students’ beliefs about knowledge, or epistemologies, and how those beliefs influence learning receive considerable attention from researchers.

Perry (1968, online abstract; 1970) was the first to discuss epistemologies in an academic setting, and much of the subsequent work is rooted in his findings. The most current line of research defines epistemological beliefs that may influence students’ performance on academic tasks. Schommer (1990, 1993, 1994) and her colleagues (Schommer & Hutter, 1995; Schommer & Walker, 1995) characterize epistemologies as individuals’ beliefs about the nature of knowledge and learning. These include a student’s belief about the certainty of knowledge, the organization of knowledge, and the control of knowledge acquisition (Schoenfeld, 1988; Schommer, 1994; Schommer, Calvert, Gariglietti, & Bajaj, 1997; Schommer & Hutter, 1995). Schommer (1990, 1993) found significant relationships between certain scales on the epistemological questionnaire she developed and student performance. In her later studies, she concluded that beliefs about knowledge may also influence students’ reports of their own strategy use.

A slightly different approach, the reflective judgment model (King & Kitchener, 1994; Kitchener, King, Wood, & Davidson, 1989), posits that epistemological beliefs are developmental and assumes that individuals progress through seven stages, without skipping any. Although these approaches to thinking about epistemologies differ in some respects, all the models assume that, with experience, individuals move from naive to mature beliefs.

Some epistemological research focuses on how beliefs influence factors such as motivation, strategy use, and performance. For example, Ryan (1984) found that epistemological beliefs (whether students were dualists or relativists as defined by Perry’s theory) influenced how students monitored their reading and learning. Moreover, Simpson and Nist (1997) and Simpson, Hynd, Nist, and Burrell (1997) concluded that students’ beliefs about knowledge in general -- and, more specifically, their beliefs about what history is -- strongly influenced the strategies they selected and their interpretation of the task in a college history class.

Another area currently of interest is the controversy regarding whether beliefs are domain specific or whether college students have the same underlying beliefs across all domains (Hofer & Pintrich, 1997). Most of the research indicates that epistemological beliefs are developmental, suggesting that at a particular point in time, a student’s beliefs would be at the same stage across all domains (e.g., Schommer & Walker, 1995). Yet, as noted by Hofer and Pintrich, the issue of domain specificity as related to epistemological beliefs has received little attention.

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Learning Strategies

Learning strategies are the “behaviors of a learner that are intended to influence how the learner processes information” (Mayer, 1988, p. 11). Although learning strategies have been part of an assumed or hidden curriculum, researchers have acknowledged their importance and many studies investigating their efficacy have been conducted.

Most of the studies done during the 1960s, 1970s, and early 1980s were experimental or correlational and attempted to isolate a superior study strategy system or to determine which strategy was more effective in a particular situation. These early studies offered an equivocal array of findings for practitioners and researchers. For example, the conclusions from the numerous studies on note taking and outlining indicated that these strategies were no more effective than passive techniques such as rereading and memorizing (Brown, 1982). Literature reviews such as that of Anderson and Armbruster (1984) concluded that “empirical research fails to confirm the purported benefits of the popular strategies” and “the effort to find the one superior method has not been successful” (p. 665).

The strategy research studies conducted during the mid-1980s and 1990s shifted focus in several significant ways. Rather than attempting to identify a superior strategy, most of the researchers during this period investigated whether the performance of college students could be altered if they received an instructional intervention. These later studies were particularly noteworthy in that the interventions were intensive and provided participants with conditional and procedural knowledge, employing what Brown, Campione, and Day (1981) characterized as informed training.

During this period, however, the most significant change in the research on studying occurred with the emergence of the “cognitive constructivist vision of learning” (Mayer, 1996, p. 364). These studies typically occurred in more authentic contexts and viewed the learner as an active participant and “sense maker.” The researchers, although diverse in their approaches, agreed that learning strategies embodied the essential cognitive and metacognitive processes necessary for college students to make meaning or sense of the world of academia (e.g., Mayer, 1996; McKeachie, Pintrich, Smith, & Lin, 1986; Thomas & Rohwer, 1986; Weinstein, 1994).

Although these methodological and philosophical changes had an impact on investigations focusing on studying at the college level, there were still a considerable number of atheoretical studies being conducted that repeated the questions and mirrored the methodologies of the past. Hence, it is not surprising that meta-analyses of the more recent literature on studying have yielded conclusions and cautious recommendations similar to those of Anderson and Armbruster in 1984 (e.g., Hadwin & Winne, 1996). Although the results of more recent meta-analyses are also equivocal, we believe that important generalizations about studying and active learning have emerged from the large body of research studies. Three generalizations seem particularly relevant:

  1. Quality strategy instruction can promote active learning
  2. Research-validated strategies are small in number
  3. Students’ cognitive and metacognitive processing is important

Quality Strategy Instruction Can Promote Active Learning

To develop active learners who have a repertoire of strategies, a substantial amount of time must be committed to instruction (Garner, 1990; Paris, 1988; Pressley, 1995). Such instruction should not only be intensive, but should also be of significant duration (Nist & Simpson, 1990).

In addition, strategy instruction should include not only the declarative knowledge about a strategy, but also the procedural and conditional knowledge (Butler & Winne, 1995; Garner, 1990; Paris, 1988; Pressley, 1995). For students to gain conditional knowledge, it is critical that they practice strategies with authentic and challenging texts and tasks (Butler & Winne; Garner; Simpson et al., 1997; Paris & Byrnes, 1989; Pressley).

Moreover, instruction should occur within a specific context and specific domain (Alexander, 1996; Garner, 1990; Mayer, 1996; Perkins & Salomon, 1989; Pressley, 1995). As Garner pointed out, “One thing that we already know about strategy use is that it is embedded. It does not occur in a vacuum” (p. 523). Finally, effective strategy instruction should be explicit and direct (Garner; Pressley; Winograd & Hare, 1988). Students should also receive specific feedback from the instructor on their practice attempts because such process checks are critical to the development of active learners (Butler & Winne, 1995).

Research-Validated Strategies Are Small in Number

Most researchers and practitioners would agree that it is best to teach students a limited number of validated strategies (Levin, 1986; McKeachie et al., 1986; Pressley, 1995). In fact, that recommendation is easy to adopt, given that there are a limited number of research-validated strategies appropriate for college students. The four strategies we will examine here have been validated in several studies conducted, in most situations, by a variety of researchers rather than just one. In addition, these studies have included explicit instruction, using high school or college students as their participants. However, one caveat should be noted: Their selection in no way implies that they are useful for all students, domains, or tasks. Research that has investigated these variables in a consistent manner simply does not exist.

Question generation and answer explanation. When students generate questions about what they have read, they are actively processing text information and monitoring their understanding of that information. As a result, their text comprehension improves (Graesser & McMahen, 1993; King, 1990, 1995; Palincsar & Brown, 1984; Rosenshine, Meister, & Chapman, 1994; Spires & Donley, 1998). To train students how to create task-appropriate questions that elicit higher levels of thinking, several methods have been used, the most popular being generic question stems (King, 1989, 1992) and reciprocal teaching (e.g., King, 1990; King & Rosenshine, 1993; Palinscar & Brown). The findings from these and other studies suggest that the question answering is equal in importance to the question asking because students are encouraged to clarify concepts, create alternative examples, or relate ideas to their partner’s prior knowledge to answer the question from their partner (King, 1995).

Text summarization. Writer-based summaries are external products that students create for themselves to reduce and organize information for their subsequent study and review. According to Wittrock’s (1990) model of generative comprehension, for a summary to be effective, students must use their own words to form connections across the concepts and relate the concepts to their prior knowledge and experiences. Such a definition of summarization implies that it is not a strategy quickly mastered (Brown et al., 1981; Pressley et al., 1997). Most of the more recent studies have found that writer-based summaries not only improve students’ comprehension, but also help them monitor their understanding (Hare & Borchardt, 1984; King, 1992; O’Donnell & Dansereau, 1992; Wittrock).

Summarization as a study strategy has taken many forms. For example, Nist and Simpson (1988) incorporated many of Wittrock’s (1990) principles of summarization into a text-marking strategy called annotation, training students to write brief summaries in the margins of their texts. In other studies, students learned rules for summarizing in an attempt to make explicit the steps that expert readers use when they read and study text (Day, 1980; Hare & Borchardt, 1984). In general, the research has concluded that students’ test performance and summary-writing abilities improve when they are taught to summarize and annotate (Harris, 1991; Hynd, Simpson, & Chase, 1990).

Student-generated elaborations. When students generate elaborations, they create examples or analogies, draw inferences, and explain the relationships between two or more concepts (Gagne, Weidemann, Bell, & Anders, 1984). Recent studies have demonstrated consistently that students can be trained to create elaborations, and that self-generated elaborations can significantly affect students’ performance on both recall and recognition measures (Pressley, McDaniel, Turnure, Wood, & Ahmad, 1987; Simpson, Olejnik, Tam, & Supattathum, 1994; Woloshyn, Willoughby, Wood, & Pressley, 1990). For example, researchers, including Pressley and his colleagues, have conducted many studies on elaborative interrogation (e.g., Kaspar & Wood, 1993; Pressley et al., 1987; Pressley, Symons, McDaniel, Snyder, & Turnure, 1988; Woloshyn et al.), which involves students in making connections between ideas they have read and their prior knowledge by asking and answering questions. The findings suggest that the quality of the generated elaborations does not affect students’ understanding when the targeted topic domain is one for which they have some prior knowledge. In a different type of elaboration study, Simpson et al. (1994) trained students to generate their own elaborations and then to recite them orally. Similar to the findings from the elaborative interrogation studies, the students who produced oral elaborations performed significantly better than their counterparts on immediate recall and recognition measures.

Organizing strategies. Several researchers have sought to validate the effectiveness of strategies that assist students in visually organizing and representing important relationships among ideas present in written or oral text (Bernard & Naidu, 1992; Briscoe & LeMaster, 1991; Kiewra, 1994; Lambiote, Peale, & Dansereau, 1992; McCagg & Dansereau, 1991). Although there are variations in these organizing strategies, the two basic types are concept maps and network representations.

Concept maps, often researched in the sciences, generally depict a hierarchical or linear relationship and can be created to represent complex interrelationships among ideas. When researchers have provided training in how to map, they have found that students who studied the maps performed better on dependent measures than their counterparts (Bernard & Naidu, 1992; Lipson, 1995). Mapping appears to be especially effective in situations where students must read and study complex expository text and then demonstrate their understanding on measures requiring higher levels of thinking, such as synthesis and application (Bernard & Naidu; Briscoe & LeMaster, 1991). The studies seem to suggest that mapping most benefits students who are persistent in using the strategy and who have high content knowledge in a particular domain (Hadwin & Winne, 1996).

Another type of organizing strategy, NAIT (node acquisition and integration technique), differs from concept maps in that students link key ideas with a canonical set of labels or links (Lambiote, Dansereau, Cross, & Reynolds, 1989). Gradually, NAIT evolved into a strategy that was renamed the knowledge map, or k-map. Research indicates that students using k-maps perform better than their counterparts using alternative methods. In general, however, the findings from studies of these organizing strategies have not been as compelling as the findings from studies of other strategies, such as self-questioning and elaborating.

Students’ Cognitive and Metacognitive Processing Is Important

The third generalization that emerges from the extant literature reaffirms the importance of students’ cognitive and metacognitive processing. In a quest to determine a superior strategy or to train students to use a specific strategy, researchers targeted their efforts solely on the strategy itself, thus overlooking processes underlining that strategy. More recently, however, researchers have focused their investigations more on processes (e.g., Mayer, 1996; Pintrich, Smith, Garcia, & McKeachie, 1993), believing that the cognitive and metacognitive processes that students enact as they read and study are what make a difference in students’ learning. Although there are some slight differences in the terminology, the processes typically include selecting and transforming ideas, organizing, elaborating, monitoring, planning, and evaluating (Hadwin & Winne, 1996; Mayer; Pintrich et al.; Weinstein, 1994).

The cognitive and metacognitive processes essential to active learning have been studied in many ways. However, most of these studies have used quantitative methodologies, and, in particular, correlational designs that have attempted to determine what relationships exist between students’ self-reported cognitive and metacognitive processes and their performance in a particular domain or their overall achievement. For example, Pintrich and Garcia (1991) concluded that students who were engaged in deeper levels of processing, such as elaboration and organization, were more likely to do well in terms of grades on assignments or exams, as well as overall course grades.

The renewed emphasis on process rather than strategies has significant implications for program evaluation efforts and for studies of strategy transfer. That is, students could be employing certain cognitive or metacognitive processes as they read and study, but not using the specific strategy that embodies these processes. Practitioners and researchers may be overlooking the most important data when they ask students in interviews or in questionnaires to list or check the strategies they are currently using. The irony of this oversight is that we want students to focus on the processes they are using when they study, not just the strategies.

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Implications of the Current Research

In this section, we examine the implications of the existing research and theory. We describe four components of effective college studying programs and courses, comparing and contrasting, when appropriate, the “ideal” to the status quo. We then offer suggestions for future research.

Components of Effective Courses and Programs

Based on our review of the literature, we recommend that programs and courses do the following.

Reflect students’ academic tasks. Although it may seem obvious, our first recommendation is that programs and courses should reflect the academic tasks and texts that professors assign. However, our 20 years of field experience have taught us that many college studying programs still use a generic model, which relies upon commercial materials to dictate what students will be taught. A more powerful approach is for instructors to begin curriculum development with an explicit understanding of the tasks and texts expected of students at their institutions. The objectives of the studying course should reflect these tasks.

When instructors emphasize the teaching of processes and strategies within a specific domain, the approach is usually classified as the embedded approach. Perkins and Salomon (1989) describe the embedded approach as one that “calls for the intimate intermingling of generality and context-specificity in instruction” (p. 24). Although there are many embedded approaches that acknowledge college students’ tasks and texts, the two most prevalent are “Learning to Learn” courses and paired courses. Learning to Learn courses are designed to teach students a variety of study strategies that they then apply to their own tasks and texts. In paired courses, also known as supplemental instruction (Martin & Arendale, 1994, online abstract), an instructor “pairs” strategy instruction to a particular high-risk college course.

Encourage students to decipher academic tasks and become aware of personal epistemologies. To succeed, students must understand their professors’ objectives and goals, build an awareness of how professors think about their domain, and learn how to organize that information. The paired course approach is an ideal situation for teaching students how to interpret academic tasks because the study-strategy instructor and the students are both placed in a specific context. However, the embedded model presents only one learning context to students. Instructors wanting to build task awareness might consider using scenarios or case studies to sensitize students to the nuances of tasks and the many ways in which professors directly and indirectly communicate tasks.

In addition to learning to decipher academic tasks, it is important for students to become aware of their personal epistemologies about learning and to realize that their professors may have differing belief systems. General or domain-specific beliefs about learning are important because they affect students’ choices regarding how they read, process, and choose to study (Gibbs, 1990; Schommer, 1993).

Emphasize a variety of validated strategies and processes. Because the extant literature suggests that there is no superior study strategy or study system, it seems reasonable that students be taught a repertoire of strategies, some general cognitive and metacognitive strategies and some domain specific (i.e., problem solving steps for mathematics). More important than the decision of which research-validated strategies to teach is the commitment to make sure that students know how to select, transform, organize, elaborate, plan, monitor, and evaluate, and that they understand the pivotal role that motivation plays in active learning.

Encourage strategy transfer and modification. The literature suggests that students do not automatically or immediately transfer strategies in a flexible manner (Garner, 1990; Pressley, 1995). For transfer to occur, students must understand strategies and be able to discuss “knowingly” the domains and tasks for which they are appropriate (Butler & Winne, 1995; Campione, Shapiro, & Brown, 1995). In addition, students must understand the advantages of a particular strategy, especially if they are expected to abandon their usual approaches, which may be more comfortable and accessible (Pressley; Winne, 1995). They must learn how they can modify a strategy to fit situations slightly different from those in which they originally learned it (Pressley, Harris, & Marks, 1992). As students analyze academic tasks and try out strategies, instructors should gently nudge them toward the belief that learning is not always quick and easy. Finally, to promote flexible strategy employment, instructors should arrange opportunities for students to reflect and evaluate (Campione et al.).

Future Research Directions

As we conducted this review, we perceived gaps that suggested future directions that the research on studying might take. First and foremost, researchers need to focus research studies on the interactive nature of studying. In the past, most studies have concentrated on how strategy use interacts with one other variable such as domain knowledge or text type. As outlined in the Rohwer and Thomas (1986) model, researchers need to know more about how factors such as text, academic task, and students’ beliefs affect and interact with strategy use.

Second, further research is needed on the processes that underlie studying rather than on specific strategies. Some researchers have done an admirable job of beginning to identify the processes (Mayer, 1988, 1996; Weinstein & Mayer, 1986), but, clearly, researchers do not yet understand how these processes interact.

Third, additional studies are needed that focus on course characteristics, concentrating on the role that both academic tasks and texts play in student learning. That is, it is important to determine how academic tasks are communicated in college classrooms and how students go about interpreting those tasks within varying contexts. In terms of text, one of the major gaps in the research is how students deal with the problem of multiple texts.

Fourth, studies that examine the role of student beliefs as they relate to strategy selection, motivation, task interpretation, and academic performance are lacking. Our experiences suggest that most students have little idea about their own beliefs concerning learning and knowing, let alone how these beliefs influence their academic performance. Moreover, research findings indicate that professors’ beliefs and students’ beliefs are generally disparate (Wineburg, 1991). Additional studies examining these issues within classroom settings across a variety of domains are needed.

Fifth, program evaluation studies on Learning to Learn or study strategy courses are virtually nonexistent, except for a few presented at conferences or published in tertiary journals. We believe that this lack of evaluation has occurred for several reasons. Good evaluation studies are challenging to design and difficult to get published, particularly in the better research journals. We applaud the efforts of researchers such as Dubois, Staley, and Dennison (1998) and Weinstein, Hanson, Powdril, Roska, Dierking, Husman, and McCann (1997), who have designed and reported long-term evaluation studies, but such investigations are rare -- or perhaps just rarely reported. Further, good evaluation studies involve the researchers in a long-term commitment because data on students’ academic achievement must be collected for at least two years after they have completed such a program or course. Finally, program and course evaluations can be politically charged. Because pressure can be exerted administratively to indicate that such programs and courses are worthy, many program evaluation studies are designed using instruments and variables guaranteed to show growth. Given these reasons for the scarcity of evaluations in the literature, well-planned studies are important if studying how-to courses or Learning to Learn programs at the college level are to survive.

Our final recommendation involves the type of methodology used in future studies. Researchers need to understand the shortcomings of their instrumentation in terms of reliability and validity. To this end, all instruments should be piloted on groups similar to those used in the actual study. In addition, a variety of instruments and data collection approaches should be used. Researchers should also explore alternative methodologies, including qualitative methods. Those interested in quantitative research should move beyond correlational studies and work toward building models of studying. Further, considerably more research needs to be long term. It is only through long-term studies that researchers can understand strategy transfer and make better sense of the factors that play a role in students’ studying behaviors.

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About the Authors

portrait of Sherrie Nist  

Sherrie Nist (e-mail snist@arches.uga.edu) received her Ph.D. in reading education from the University of Florida in 1982. Since that time, she has taught at the University of Georgia, Athens, USA, in the Division of Academic Assistance, which she currently directs. Her research interests focus on the factors that influence how college students learn and study, particularly on how students transition from learning in high school to learning in college. She has published more than 75 articles and has coauthored several textbooks on college studying. She lives in Athens with her husband and their Jack Russell terrier.

portrait of Michele Simpson

 

Michele Simpson (e-mail msimpson@arches.uga.edu) received her doctorate in secondary education/reading from Arizona State University after teaching in the public schools for 11 years. She currently is a professor in the Division of Academic Assistance at the University of Georgia, where she teaches undergraduate and graduate students. Her research interests include cognitive and metacognitive strategies that promote active learning and students’ beliefs and actions that facilitate, as well as compromise, their academic performance in large classroom settings.

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Citation: Nist, S.L., & Simpson, M. (2002, April). College studying. Reading Online, 5(8). Available: http://www.readingonline.org/articles/art_index.asp?HREF=handbook/nist/index.html




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