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Before addressing the research and theory on goals and objectives, it is useful to consider the issue of terminology. The terms goals and objectives have been used by different people in different ways. For some, the term goal applies only to the overarching purpose of curriculum, and the term objective is reserved for day-to-day instructional targets. In the research and theoretical worlds, these terms tend to be used interchangeably for general and specific purposes. In this book, the terms will be used interchangeably. However, as the following discussion illustrates, the focus of this book is on day-to-day classroom instruction.

The importance of goals and objectives in education was established as far back as the first half of the last century by the educational philosopher and evaluation expert Ralph Tyler (1949a, 1949b). For Tyler, a well-constructed objective should contain a clear reference to a specific type of knowledge as well as reference to the behaviors that demonstrate proficiency relative to that knowledge. Prior to Tyler's recommendations, educators typically did not identify specific areas of information and skill as targets for student learning. Instead, broad topic areas such as “probability” or “World War II” represented the most specific level of curricular organization.

Where Tyler's insights into the nature of content and the nature of learning made it clear that educators must design specific objectives and identify the behaviors that demonstrate achievement of those objectives, David Krathwohl and David Payne (1971) made distinctions between three levels or types of objectives: global objectives, educational objectives, and instructional objectives. As described by Robert Marzano and John Kendall (2007), global objectives are the most general. They are broad, complex areas. For example, “Students will be able to apply basic properties of probability” would be considered a global objective.

Instructional objectives are the most specific of the three types of objectives. In Preparing Instructional Objectives, Robert Mager (1962) explained that a well-written instructional objective should include three elements:

1. Performance. An objective always says what a learner is expected to be able to do; the objective sometimes describes the product or result of the doing.

2. Conditions. An objective always describes the important conditions (if any) under which the performance is to occur.

3. Criterion. Whenever possible, an objective describes the criterion of acceptable performance by describing how well the learner must perform in order to be considered acceptable. (p. 21)

In the middle of the triad are educational objectives (Anderson et al., 2001). They articulate specific areas of knowledge, but don't identify the performance conditions and criteria for success as do instructional objectives. In Designing and Teaching Learning Goals and Objectives, we primarily address educational objectives, which we more commonly refer to as learning goals. How these goals can be addressed on the instructional level will also be examined in depth.

The importance of learning goals to the day-to-day execution of classroom activities is fairly obvious. Goals are the reason classroom activities are designed. Without clear goals, classroom activities are without direction. Researchers Joseph Krajcik, Katherine McNeill, and Brian Reiser (2007) explain that good teaching begins with clear learning goals from which teachers select appropriate instructional activities and assessments that help determine students' progress on the learning goals.

It is useful to keep in mind that goal setting is not unique to education. Indeed, it has its theoretical roots in organizational psychology. In their 1990 book A Theory of Goal Setting and Task Performance, Edwin Locke and Gary Latham provide an extensive history of goal-setting practice in the context of organizational theory. Although their research focus is exclusively on goal setting and performance in work settings, they note that much of the work-related goal theory can and should be extended to the field of education.

Table 1.1 (page 5) displays much of the research on which the recommendations in this book are based.

Dimensions of Learning Goals

From the research reported in table 1.1, one can conclude that two important characteristics of learning goals are goal specificity and goal difficulty. Goal specificity refers to the degree to which goals are defined in terms of clear and distinct outcomes. Goal difficulty refers to the degree to which goals provide a challenge to students.

Goal Specificity

Learning goals provide a set of shared expectations among students, teachers, administrators, and the general public. As discussed previously, they can range from the very specific (for example, “Students will be able to list the Great Lakes”) to the very general (“Students will be able to write a well-formed essay”). The research strongly implies that the more specific the goals are, the better they are. That is, goals that are specific in nature are more strongly related to student achievement than goals that are not. For example, Mark Tubbs (1986) examined goal specificity in a meta-analysis of 48 studies in mostly organizational settings. He found an overall effect size of .50 for goal specificity, which supports the notion that more specific goals lead to higher achievement (see table 1.1).

The terms meta-analysis and effect size might be familiar to some readers and unfamiliar to others. (These terms and their relationship are described in some depth in appendix B on page 119.) Briefly, meta-analysis is a research technique for quantitatively synthesizing a series of studies on the same topic. In this case, Tubbs (1986) synthesized the findings of forty-eight studies on goal specificity. Typically, meta-analytic studies report their findings in terms of effect sizes (see the ES column in table 1.1). An effect size tells you how many standard deviations larger (or smaller) the average score for a group of students who were exposed to a given strategy (in this case, highly specific goals) is than the average score for a group of students who were not exposed to a given strategy (in this case, nonspecific goals).

Table 1.1 Research Results for Goal Setting

In short, an effect size tells you how powerful a strategy is; the larger the effect size, the more the strategy will increase student learning. Effect sizes are typically small numbers. In fact, the average effect size of most classroom strategies is .4 (Hattie, 2009). However, small effect sizes can translate into big percentage gains. For example, a strategy with an effect size of .4 translates into a 16 percentile point gain. This means that a student scoring at the 50th percentile in a class that did not use that strategy would be predicted to rise to the 66th percentile after the strategy had been introduced. (See appendix B, page 119, for a detailed description of effect sizes and a chart that translates effect size numbers into percentile gains.)

One of the more useful aspects of effect sizes is that they can be transformed into an expected percentile point gain (or loss) for the strategy under investigation. The effect size reported by Tubbs (1986) of .50 is associated with a 19 percentile point gain. Thus, taking the findings at face value, one could infer that an average student in a group of students who were provided with specific learning goals would be at the 69th percentile of a group of students who were exposed to very general learning goals. Another way of saying this is that a student at the 50th percentile in a class that used nonspecific goals (an average student in that group) would be predicted to rise to the 69th percentile if he or she were provided very specific learning goals. In short, goal specificity is an important element to consider when trying to enhance student achievement.

In their 1990 meta-analysis of organizational studies, Locke and Latham found effect sizes that ranged from .42–.80 for specific instead of general goals (translating to a 16–29 percentile point gain). They argued that specific goals provide more concrete guidance for achievement that more general goals lack. A lack of concrete guidance creates ambiguity that students in school and laborers in the workforce simply have trouble translating into specific expected behaviors. Specific goals provide a clear direction for behavior and a clear indication of desired performance, and as such they serve as motivators.

More recently, Steve Graham and Dolores Perin (2007) conducted a meta-analysis of achievement in writing. They found five studies relating to goal specificity. Examples of goal specificity used in their study included a clearly established purpose in a writing assignment and the specification of product expectations. They found an average effect size of .70 for goal specificity, which translates to a 26 percentile point gain. Accordingly, Graham and Perin (2007) concluded that “assigning product goals had a strong impact on writing quality” (p. 464), but warned that although their conclusion was based on high-quality studies, their findings were drawn from only five studies and so should be interpreted cautiously.

Goal Difficulty

Students will perceive learning goals as more or less difficult depending on their current state of knowledge, their beliefs about what causes achievement, and their perceptions of their own abilities. Studies indicate that students are most motivated by goals they perceive as difficult but not too difficult. For example, Tubbs (1986) found an average effect size of .82 for difficult versus easy goals (translating to a 29 percentile point gain). The Locke and Latham (1990) meta-analysis found effect sizes of .52–.82 for difficult goals (a 20–29 percentile point gain), noting that “performance leveled off or decreased only when the limits of ability were reached or when commitment to a highly difficult goal lapsed” (p. 706). Goal difficulty may also moderate or change the effect of feedback on student achievement. For example, Avraham Kluger and Angelo DeNisi (1996) found that feedback as an instructional strategy is more effective when learning goals are at the right level of difficulty—challenging, but not too difficult.

Types of Learning Goals

In addition to their specificity and difficulty, learning goals vary in terms of their purposes and functions. Learning goals that emphasize mastery of content, or mastery goals, might enhance learning more than goals that specify attainment of a specific score, or performance goals. Noncognitive goals that involve students in cooperative tasks might have a unique effect of their own.

Mastery vs. Performance Goals

One well-investigated distinction regarding learning goals involves their overarching purpose; namely, mastery or performance. The first type, mastery goals, focuses on developing competence. The second type, performance goals, focuses on demonstrating competence by obtaining a specific score or grade (Kaplan, Middleton, Urdan, & Midgley, 2001).

This distinction between mastery goals and performance goals is subtle but profound in its implications. Performance goals will typically include a desired score or grade. For example, the following would be considered performance goals:

Students will obtain a grade of B or higher by the end of the grading period.

All students will be determined proficient or higher in reading by the end of the school year.

As these examples illustrate, performance goals don't describe content as much as they do a specific score or grade. Mastery goals, by definition, articulate the content that is to be learned. For example, the following are mastery goals:

Students will be able to use word segmentation and syllables to decode an unrecognized word.

Students will be able to compare ordinal numbers through the fifth position (that is, 1st, 2nd, 3rd, 4th, 5th).

Although each type of goal may be associated with increased student achievement, research indicates that mastery goals are typically associated with higher order learning and better retention than are performance goals, especially for more challenging content. For example, in his meta-analysis, Christopher Utman (1997) found an average effect size of .53 (a 20 percentile point gain) for mastery versus performance goals for grade school students completing a complex task. Research by Judith Meece (1991) revealed that teachers who used mastery goals in their classrooms promoted more meaningful learning, provided more developmentally appropriate instruction, and supported student autonomy more than did teachers with performance-oriented classrooms.

Noncognitive Goals

Much of the research on goals over the decades has focused on academic goals, sometimes referred to as cognitive goals. However, attention to noncognitive goals in education has increased in recent years. For example, a 2005 issue of Educational Assessment was devoted to noncognitive goals. In their introduction to the volume, editors Jamal Abedi and Harold F. O'Neil noted that “the affective (feeling) and psychomotor (doing) issues affect cognitive performance and are worthwhile domains of learning themselves” (p. 147). The remainder of the volume focused on the role of noncognitive goals such as motivation and affect in education.

Joseph Durlak and Roger Weissberg (2007) investigated the effects of after-school programs on noncognitive goals such as students' personal and social skills. They limited their analysis to programs that used “evidence-based” instructional strategies, which they defined as “well-sequenced” and “active.” Relative to well-sequenced, Durlak and Weissberg noted:

New skills cannot be acquired immediately. It takes time and effort to develop new behaviors and often more complicated skills must be broken down into smaller steps and sequentially mastered. Therefore, a coordinated sequence of activities is required that links the learning steps and provides youth with opportunities to connect these steps. Usually, this occurs through lesson plans or program manuals, particularly if programs use or adapt established criteria. (p. 10)

About active forms of learning, they noted:

Active forms of learning require youth to act on the material. That is, after youth receive some basic instruction they should then have the opportunity to practice new behaviors and receive feedback on their performance. This is accomplished through role playing and other types of behavioral rehearsal strategies, and the cycle of practice and feedback continues until mastery is achieved. These hands-on forms of learning are much preferred over exclusively didactic instruction, which rarely translates into behavioral change. (p. 10)

After examining ten studies that met their criterion of using evidence-based strategies, they concluded that after-school programs reduced problem behaviors and contributed significantly to student achievement and positive self-concept.

Durlak and Weissberg's study is noteworthy because it demonstrates that noncognitive goals can be a viable instructional focus. It is also noteworthy because it supports the linkage between noncognitive goals and achievement outcomes. Their meta-analysis found that effective after-school programs produced a positive impact on participating students' academic achievement with an effect size of .31, which translates to a 12 percentile point gain.

Jeff Valentine, David DuBois, and Harris Cooper (2004) conducted a meta-analysis that sheds light on the importance of noncognitive goals. They examined the effects of self-beliefs on student achievement. They synthesized the results of studies that measured student academic achievement and self-beliefs at an initial point and then again at a later point. They found that positive student self-beliefs had a small but significant influence (an effect size of .16 over 60 studies) on subsequent student achievement. Valentine et al. note that noncognitive goals that address students' self-beliefs are most effective when tailored to the content being taught. For example, noncognitive goals regarding beliefs about mathematics (let's say) have a stronger effect on achievement in mathematics than noncognitive goals regarding beliefs about academics in general.

Cooperative Learning and Noncognitive Goals

When considering noncognitive goals, one must consider cooperative learning as a necessary instructional component. Cooperative learning has a rich body of research in its own right. Cary Roseth, David Johnson, and Roger Johnson (2008) used a model based in social interdependence theory to investigate the relationship between cooperative goal structures and student achievement and peer relations in adolescent students, updating and elaborating on their earlier meta-analyses (Johnson & Johnson, 1989; Johnson, Maruyama, Johnson, Nelson, & Skon, 1981). They characterized cooperative goal structures as those involving “positive interdependence.” This means that they involve linked positive outcomes, mutually beneficial actions, and sharing of resources. Roseth, Johnson, and Johnson (2008) found that cooperative goal structures involving positive interdependence had a stronger relationship with achievement than did competitive or individualistic goal structures (average effect sizes .57 and .65, respectively). They concluded:

By implication, this study suggests that the more early adolescents' teachers structure students' academic goals cooperatively (as opposed to competitively or individualistically), (a) the more students will tend to achieve, (b) the more positive students' relationships will tend to be, and (c) the more higher levels of achievement will be associated with more positive peer relationships. (p. 237)

Taking this research at face value, it would be easy to conclude that cooperative goal structures are superior to other forms of goal structure regardless of the type of goal being addressed—cognitive or noncognitive. Witness the impressive results reported in table 1.2 (page 10) by David Johnson, Geoffrey Maruyama, Roger Johnson, Deborah Nelson, and Linda Skon (1981) for cooperative learning versus individual student competition (an effect size of .78 in favor of cooperative learning) and cooperative learning versus individual student tasks (an effect size of .78 in favor of cooperative learning). However, the actual practice of cooperative learning leans more toward a focus on noncognitive goals. This is demonstrated in the literature that describes how teachers might implement cooperative learning in their classrooms, such as Cooperation in the Classroom (Johnson, Johnson, & Holubec, 1998) and Learning Together and Alone: Cooperative, Competitive, and Individualistic Learning (Johnson & Johnson, 1999). In Learning Together and Alone, Roger Johnson and David Johnson make the following distinction:

A learning goal is a desired future state of demonstrating competence or mastery in the subject area being studied. The goal structure specifies the ways in which students will interact with each other and the teacher to achieve the goal. Students may interact to promote each other's success or obstruct each other's success. Students may also avoid interaction and thereby have no effect on the success or failure of others. Whenever people strive to achieve a goal, they may engage in cooperative, competitive, or individualistic efforts. (p. 3, emphasis in original)

This clears up much of the potential confusion regarding the literature on cooperative goal structures. Cooperative goals are not established in lieu of individual goals. Instead, cooperative goal structures are established to help students accomplish academic goals. Individual students are still held accountable for accomplishing academic goals, but those individual students do not have to work in isolation or in competition to accomplish those goals. Additionally, cooperative structures are particularly useful when focusing on noncognitive goals because cooperative learning skills are commonly the very targets of many noncognitive goals.

Another perspective on the power of cooperative goal structures to enhance noncognitive goals was highlighted by Marika Ginsburg-Block, Cynthia Rohrbeck, and John Fantuzzo (2006). They conducted a meta-analysis on the effects of peer-assisted learning on elementary school students' social skills, self-concept, and behavior. They found effect sizes of .52, .40, and .65 for noncognitive goals involving social skills, self-concept, and behavior, respectively (translating to 20, 16, and 24 percentile point gains). Table 1.2 reports much of the research on cooperative learning.

Table 1.2 Research Results for Cooperative Learning

Communicating Goals and Providing Feedback

If goals provide clear targets for learning, then feedback may be thought of as information that facilitates the process of reaching those targets. Researchers John Hattie and Helen Timperley (2007) claim that in educational settings “the main purpose of feedback is to reduce discrepancies between current understandings and performance and a goal” (p. 86). Their comprehensive review synthesized research on the power of feedback to improve student achievement. Noting that many of the individual findings included in feedback meta-analyses are negative (showing that feedback sometimes inhibits performance), Hattie and Timperley distinguished between the effects of feedback about the task, the process, self-regulation, and the self. Feedback regarding the task, process, and self-regulation is often effective, whereas feedback regarding the self (often delivered as praise) typically does not enhance learning and achievement. They concluded:

Learning can be enhanced to the degree that students share the challenging goals of learning, adopt self-assessment and evaluation strategies, and develop error detection procedures and heightened self-efficacy to tackle more challenging tasks leading to mastery and understanding of lessons. (p. 103).

Table 1.3 presents the findings regarding feedback for a number of meta-analytic studies. Based on the findings reported in the table, one can conclude that feedback should be an integral part of any teacher's arsenal of strategies. Within The Classroom Strategies Series, we highlight the research on feedback in the book Formative Assessment and Standards-Based Grading (Marzano, in press). Here we include the research on feedback because it has a symbiotic relationship with goals. Without effective goals, feedback is impossible. Without feedback, goals are rendered quite sterile.

Table 1.3 Research Results for Feedback

What can a teacher take away from the research? Certainly one generalization is that setting clear and specific goals for learning that are at just the right level of difficulty can greatly enhance student achievement.

Translating Research Into Classroom Practice

In subsequent chapters, we will translate the research presented in this chapter into a number of recommendations for designing learning goals and the tasks that determine accomplishment of those goals. As mentioned in the introduction, as you progress through the remaining chapters, you will encounter exercises that ask you to examine the content presented. Some of these exercises ask you to answer specific questions. Answer these questions in the space provided, and check your answers with those reported in the back of the book. Other exercises are more open-ended and ask you to generate applications of what you have read. Reproducible versions of the exercises are included at the end of each chapter, and reproducible answer sheets are included at the end of the book. Visit marzanoresearch.com/classroomstrategies to download all the exercises and answers in this book.