relationships among student characteristics, study activities, and achievement as a function of...

C O N T E M P O R A R Y E D U C A T I O N A L P S Y C H O L O G Y 12, 344-364 (1987)

Relationships among Student Characteristics, Study Activities, and Achievement as a Function of

Course Characteristics

JOHN W. THOMAS Far West LaboratoJT"

AND

LORRAINE IVENTOSCH AND WILLIAM D. ROHWER, JR. University of California, Berkeley

A battery of instruments were administered to 1240 junior high, senior high, and college students to assess their academic aptitude, self-efficacy, achievement orientation, and course-specific study actities. Also assessed were characteristics of the 22 social science courses within which these students were enrolled. The results revealed (a) an increase in demand for information capacity between junior and senior high school and an increase in demand on integration activities between junior high school and college; (b) significant positive correlations between academic achievement and both academic aptitude and self-efficacy ratings; (c) an interaction, across grade levels, between achievement orientation scales and achievement; (d) different patterns of relationships between the student characteristic measures and study scales across grade levels; and (f) some indication that grade-related differences in course features may account for these grade-related differences in the pattern of correlations between student characteristics and study activities. © 1987 Academic Press, Inc.

Studying is one of the few areas of learning and cognition in which research on individual differences has played a central rather than a pe- ripheral role. For the most part, however, this research has not produced general principles useful for understanding the patterns of studying and the relative effectiveness of different study methods across different academic tasks and settings. Addressing questions of why students engage in one kind of study activity or another or why one method is associated with achievement in one situation but not in another may require the simultaneous consideration of student and contextual factors (Anderson & Armbruster, 1984; Brown, Bransford, Ferrara, & Campione, 1983; Entwistle, 1985; Rohwer, 1984).

There are at least three student factors that appear to be related to

Reprint requests may be sent to the Autonomous Learning Project, Far West Laboratory, 1855 Folsom St., San Francisco, CA 94103. This research was supported by a grant from the National Institute of Child Health and Human Development (HD 17984-02).

344

0361-476X/87 $3.00 Copyright © 1987 by Academic Press, Inc. All rights of reproduction in any form reserved.

STUDENT CHARACTERISTIC DIFFERENCES IN STUDY ACTIVITIES 345

studying and achievement: academic aptitude, achievement orientation, and self-efficacy. In each case, research to date provides some evidence, albeit indirect, that the effect of these factors on the study-achievement relationship is mediated by characteristics of the study environment.

The relationship between academic aptitude and the kinds of performance indices that contribute to academic achievement has been well documented. Students who score high on indices of academic aptitude perform and achieve better on a variety of cognitive, academic tasks throughout the age range of schooling (Snow, 1977, 1982). However, research on aptitude-treatment interactions (ATIs) has revealed that the relationship between general aptitude and academic achievement may not be equivalent in all contexts. Cronbach and Snow (1977) and Clark (1982), in their reviews of ATI research, report that academic aptitude tends to interact with a classroom context variable referred to as "latitude of self-direction" by Cronbach and Snow and as "information load" by Clark. These two factors involve the amount of structure and elaboration provided by the instructor and the resultant burden placed on students to generate structure and process information on their own. Clark, for example, classified treatments as being low in information load when they included such features as teacher-provided structure, didactic teaching, redundancy of content coverage, teacher-provided syntheses, and mathemagenic aids. Across a number of studies, low aptitude students learned best in courses classified as low in information load or high in structure; high aptitude students achieved best under conditions of higher loads and/or greater responsibility for information processing.

Research on the relationship between academic aptitude and study activities has tended to take one of two general forms: studies of the relationship between academic aptitude and proficiency at particular, iso- lated study "skills;" or correlational analyses that examine the relationship between academic aptitude and gross measures of study processes aggregated across course or subject-matter contexts. Nevertheless, this research has revealed some popular notions about the relationship between academic aptitude and study practices. These generalizations provide a useful starting point for examining contextual differences in the apti tude-study activity relationship.

Compared to their low aptitude peers, high aptitude students have been found to be more sensitive to task demands, to have more effective study methods, to use these methods more skillfully, to have a larger repertoire of such methods, to use these methods more flexibly across tasks, and to employ study strategies more spontaneously (Belmont, Butterfield, & Feretti, 1982; Bransford, Stein, Shelton, & Owings, 1981; Brown & Cam- pione, 1978; Cronbach & Snow, 1977; Mayer, 1980; Rigney, Munro, & Crook, 1979). With respect to the relationship between academic aptitude

346 THOMAS, IVENTOSCH, AND ROHWER

and particular scales of study activity inventories, high aptitude students, as compared to low aptitude students, report using study strategies classified as more "diligent" and to engage in more active transformation or reorganization of learning material (Goldman & Warren, 1973; Rut- kowsky and Domino, 1975; Schmeck, 1983; Schmeck & Grove, 1979).

In light of the definition of academic aptitude as the ability to learn from incomplete instruction (Resnick & Glaser, 1976; Snow, 1982), an inference from existing research is that the relationship between academic aptitude and processing activities, and hence between academic aptitude and achievement, may vary systematically with features of academic courses (structure, latitude, load). However, no such analysis of this interaction is available at the study-activity level.

Self-efficacy, in the academic context, is defined as the extent to which students believe they can control the outcomes of their learning. The construct of self-efficacy (or personal efficacy, self-concept of academic ability) combines notions of perceived competence or self-worth (Co- vington, 1984) with those of locus of control and the perceived contin- gency between actions and outcomes (Bandura, 1977; Weiner, 1976). At the one end of the self-efficacy dimension are students who, because of the attributions and assessments they have made about their own abilities, believe that they can control their level of achievement in subsequent situations by means of strategic effort. At the other end of the dimension are students who believe that they have little or no ability to control their level of achievement.

Brookover and his colleagues conducted a large-scale investigation of the relationship between self-efficacy and achievement (Brookover, Er- ickson, & Joiner, 1967). The relationship between self-efficacy, as measured by the Self Concept of Academic Ability Test (SCAAT), and academic achievement ranged between r = .48 and r =- .57 over the 6 years of the study. In one study from this project, Brookover, Paterson, and Thomas (1962) found the correlat ion be tween SCAAT scores and achievement to be .57 for all seventh graders in a district. For this same sample, the correlation between IQ and SCAAT was found to be fairly low (r = . 17) when achievement was partialled out. Brookover and his colleagues interpreted this as evidence that the SCAAT measures some- thing different than IQ.

Evidence of the influence of context factors on self-efficacy judgments is mostly indirect. Course conditions that may be associated with low- ered self-efficacy ratings on the part of students include the perceived difficulty of learning materials, the absence of learning aids (Covington, 1984; Schunk, 1984), competitive classroom goal structures, and norma- tive grading standards (Ames, 1984). One inference that can be drawn from these studies is that self-efficacy exhibits a greater effect on


achievement under these conditions (competitive climate, norm-refer- enced grading, difficult materials, challenging tasks) because, in these circumstances, students with a low sense of self-efficacy would be less apt than their more self-confident peers to exert the study effort neces- sary for minimum achievement or to engage in productive and appro- priate study methods. This inference has not yet been investigated in a systematic fashion.

Evidence for the relationship between self-efficacy and engagement in particular study activities is also indirect. Laboratory research has revealed that students with a low sense of personal efficacy tend to (a) avoid intellectually challenging activities to a greater extent (Bandura, 1982; Weiner, 1976); (b) exhibit less intense and persistent effort on learning tasks (Bandura, 1982; Butkowsky & Willows, 1980; Weiner, 1976); (c) generate lower quality learning strategies on memory tasks (Kurtz & Borkowski, 1984); and (d) engage in less self-monitoring behavior during learning (Diener & Dweck, 1978; Kuhl, 1985; Pearl, Bryan, & Herzog, 1983) than students with a higher sense of self-efficacy.

Evidence for the relationship between self-efficacy and particular study activities is limited. Griffin (1978) found scores on the SCAAT to correlate significantly (r = .42) with the "Work Methods" scale of a popular index of study habits (Brown & Holtzman, 1953). The Work Methods scale measures how effectively a student organizes study materials.

Achievement orientation, in the present analysis, refers to the extent to which students prefer to learn by methods that involve conformance with versus independence from instructors' methods. This construct is defined by two scales of the California Psychological Inventory (Gough, 1957); the Ac (Achievement via Conformance) and the Ai (Achievement via In- dependence) scales. Gough (1964) reported a correlation between the Ac scale and a GPA of .44 in early cross-validation studies. This correlation was higher than that found for IQ for this same sample (r = .26).

Domino (1968, 1971, 1975) found achievement orientation to interact with teaching style and/or learning environment. Students who exhibit a preference for a highly structured, conforming learning environment (high Ac scores) tend to do better in compatible courses, whereas students who prefer an independent learning setting (high Ai scores) do better in learning environments that reward independent behavior.

Peterson (1977) replicated Domino's (1971) study with ninth-grade students. One teacher taught different sections of a social science course using teaching methods characterized as being either high or low in structure. High structure was defined, for example, by the use of instructor,provided objectives, reviews, transition signals, selection aids, summaries, and integration aids. Students' achievement orientations


were assessed using the Ac and Ai scales of the CPI. Peterson found significant at tr ibute-treatment interactions for performance on both essay and multiple-choice tests. Students rated high on Ac did better under high structure conditions. Students rated high on Ai did best under low structure conditions.

Research on learning orientation to date has focused neither on the relationship between the preferred style of learning and students' study activities nor on how this relationship varies with features of the learning context. An unpublished study by Covington and Jacoby (1972), however, suggests that students rated high on Ac may differ from those rated high on Ai in their work habits, at least under conditions of high student latitude. Covington and Jacoby found high Ac students to show greater reliance on course materials and more dependence on the instructor t h a n high Ai students. In addition, high Ai students were less homogeneous as a group in their work habits than were students rated high on Ac. Schmeck & Ribich (1978) found high positive correlations, for both Ac and Ai, with a study-process scale reported to measure the disposition to process information in a deep rather than a superficial fashion.

The research reported here was intended to test three classes of hypotheses: (1) there should be a positive relationship between three cate- gories of student characteristics (academic aptitude, self-efficacy, and achievement orientation) and academic achievement throughout the period of adolescence; (2) in light of the increase in cognitive processing demands associated with courses across the period from junior high school to college (Strage, Tyler, Rohwer, & Thomas, 1987), the magnitude of relationships between student characteristics and achievement should increase with grade level; and (3) relationships between student characteristics and engagement in different classes of study activities should vary with grade level in ways that reflect differences in the pattern of demands and supports associated with these levels. Additional data concerning the differential demands of academic courses as a function of grade level and the relationships between variations in these demands and the character, duration, and effectiveness of students' study activities are reported in companion papers in this volume (Christopoulos, Rohwer, & Thomas, 1987; Curley, TrumbuU Estrin, Thomas, & Rohwer, 1987; Jensen Delucchi, Rohwer, & Thomas, 1987; Strage et al., 1987).

METHOD

Participants

The present investigation was conducted in the Fall of 1985 and the Spring of 1986. Students and their instructors from two universities, four senior high schools, and three junior high schools participated in the


study. A total of 1240 of the 1586 students enrolled in 22 different courses was surveyed. At the college level, 2 courses were sampled, 1 in Amer- ican history and 1 in European history (two instructors and 284 student participants). At the senior high school level, participants consisted of 536 students from 11 American history or government courses (nine instructors). At the junior high school level, there were 9 American history courses (eight instructors) and 420 students. The senior high schools sampled were known feeder schools for the universities included in the study. Wherever possible, a feeder relationship was maintained between the participating senior and junior high schools as well. Where the relationship could not be maintained, an attempt was made to substitute a junior high school which matched the senior high school demographic- ally.

Instruments

Information pertaining to four major classes of variables was used to address the hypotheses outlined above: (1) information regarding course characteristics; (2) information regarding students' study activities; (3) indices of enduring characteristics of students (academic ability, self-efficacy, and achievement orientation), and (4) indices of students' course- level, academic achievement.

Course characteristics. For each course, information regarding course characteristics and instructional practices was derived from two sources: classroom observations and document analyses. Observational data were collected using observation logs supplemented by audio tapes. These sources plus all assigned readings, handouts, and tests associated with the particular instructional unit were analyzed to yield 80 distinct indices. These indices combined to yield scores for each course on 28 course features, each classified on two dimensions: (1) demands versus supports and compensations, and (2) cognitive versus self-management. For example, the course feature referred to as "Extent of Exam Preparation," which was indexed by the number of minutes instructors devoted to helping students prepare for the examination during the last class day before the test, was classified as a cognitive compensation.

Intercorrelations among feature scores were then computed in order to identify potential "factors" that would have construct value for subsequent analyses. This process resulted in seven composite demand features: demand for (1) Information Capacity, (2) Reading Ability, (3) Ver- batim Information, (4) Comprehended Information, (5) Integrated Infor- mation, (6) Retrieval Ability, and (7) Coping with Stress. In addition seven composite factors were identified that were hypothesized to support or compensate for students' study activities. The five composite


support features were: (1) Selection Supports, (2) Comprehension Sup- ports, (3) Memory Supports, (4) Integration Supports, and (5) Time and Effort Supports. The two composite compensation features were: (1) instructor-provided, Exam-Performance Assistance and (2) Integrated Pre- sentations. A companion paper in this volume (Strage et al., 1987) provides a detailed description of these composite features.

Study activities. A locally developed instrument, The Study Activity Survey (SAS), was administered to all participating students in order to assess their degree of engagement in different kinds of study activities. This inventory is divided into two forms, Form R which assesses students' routine study activities and Form T which surveys the activities students engage in while preparing for a test. The items on the SAS reflect the kinds of study activities students might engage in in order to (1) select important information, (2) comprehend lectures and study material, (3) commit that material to memory, (4) integrate material within and across sources of information, and (5) manage their study behavior across tasks and time. Additionally, items ask students to evaluate their own cognitive and self-management ability. Both forms of the instrument were used to assess in-class as well as out-of-class activities. All items ask students what they did when they studied "for this course."

The 76 study activity items from Form R and the 93 items from Form T were alloted to 15 scales, 12 of which concern cognitive activities and three of which concern self-management activities. Among the cognitive scales there are four selective allocation scales (Focus on Test Relevance, Hyperprocessing, Selective Notetaking, and Pre-Reading Preparation), one nonselective allocation scale (Uniform Processing), three generative processing scales (Generation of Verbatim Information, Generation of Interpreted Information, and Generation of Constructed Information), two nongenerative processing scales (Duplicative Processing and Recep- tive Processing), one Cognitive Monitoring scale, and one Self-Evalua- tion of Cognitive Ability scale. The three self-management scales consist of one Self-Evaluation scale, one general scale (Assiduous Resource Management), and one specific activity scale (Means of Resource Man- agement). Table 1 provides a brief description of these scales. A detailed account of the development of the SAS can be found in a companion paper in this volume by Christopoulos et al. (1987).

Student characteristics. Three instruments were used to assess cognitive, personality, and motivational characteristics of students. Academic ability was measured using a shortened form of the Concept Mastery Test (Terman, 1973). Achievement orientation was measured using the Cali- fornia Psychological Inventory (Gough, 1957), shortened and altered ac- cording to standard conventions. Finally, self-efficacy was assessed by means of an abbreviated form (Covington & Omelich, 1979) of the Self-


TABLE 1 CHARACTERIZATION OF COGNITIVE AND SELF-MANAGEMENT SCALES OF THE STUDY

ACTIVITY SURVEY: FORMS R (ROUTINE) AND T (TEST PREPARATION)

Form Scale Characterization

R,T

R

I. Cognitive scales Uniform Processing

Hyperprocessing

R, T Focus on Test Relevance

R,T

R R

R,T

T

R,T

R,T

R,T

R,T

R,T

Selective Notetaking

Pre-Reading Preparation Receptive Processing

Duplicative Processing

Generation of Verbatim Information

Generation of Interpreted Information

Generation of Constructed Information

Cognitive Monitoring

Self-Evaluation of Cognitive Ability II. Self-management

scales Assiduous Resource

Management

R, T Means of Resource Management

R, T Self-Evaluation of Management Ability

Nonselective processing of all information at hand, voluntarily, intensely, or earnestly.

Self-initiation of extra processing of information that is anticipated to present comprehension or memory difficulties.

Self-initiated investigation, identification, and allocation of processing to information that is likely to be important for a test.

Purposeful recording of information selected on the criteria of difficulty or substantive relevance.

Planning for selection prior to reading. Reception of information given in texts or by

instructor without implication of further processing. Unaltered reencoding or mental recycling of

previously encountered information. Elaborating or transforming mental modality of target

information to enhance memorability. Explicating, investigating, or inquiring into the

meaning of target information to enhance comprehension or memory.

Elaborating, reorganizing, contrasting, integrating, or summarizing newly encountered, previously recorded or encoded information.

Active checking of one's own state of comprehension or memory for information.

Characterizes self as able and knowledgeable in dealing with challenging cognitive tasks.

Voluntary, intense, or earnest preparation for or application of one's energy to the task or activity at hand.

Using specific procedures for managing time and effort in contrast to worrying about managing them.

Characterizes self as able to and knowledgeable about how to deal effectively with challenging resource management tasks.

Concept o f Academic Ability Test (SCAAT) d e v e l o p e d b y B r o o k o v e r et al., (1967).

A c a d e m i c achievement . A c h i e v e m e n t in p a r t i c u l a r c o u r s e s w a s in-

d e x e d in t e r m s o f t w o o f t h e g r a d e s s t u d e n t s r e c e i v e d : t e s t g r a d e a n d

t e r m g r a d e . Tes t g r a d e w a s t h e g r a d e s t u d e n t s r e c e i v e d in t h e e x a m c o v -


ering the instructional unit observed in the study. Term grade was either the instructor's overall grade for the marking period or, in the case of the college sample, the grade for the course.

Procedure

All data were collected within one marking period for each course. Before the beginning of the marking period, project staff visited each participating course and briefly described the study. For all courses, observations of two routine classes were conducted at least 2 weeks prior to the end-of-marking-period exam. During this period, Form R of the Study Activity Survey, the Self-Concept of Ability Test, and the California Psy- chological Inventory were administered. The third observation was conducted on the last class day before the exam. Form T of the Study Ac- tivity Survey and the Concept Mastery Test were administered during the class meeting immediately following the exam. Test and final course grades were collected as soon as they were available.

RESULTS

Data of relevance to three questions are presented: (1) What differences are there across grade levels in the pattern of demands and supports associated with typical (social science) courses? (2) How do relationships between student characteristics and course-level achievement differ as a function of grade level? (3) In what ways do relationships between student characteristics and students' engagement in different types of study activities vary as a function of grade level and, by inference, as a function of the demands characteristic of courses at these levels?

Grade-Level Differences in Course Characteristics

Courses at each grade level were assessed on a number of course features. These features were combined to yield 7 composite demand features and 7 composite support or compensation features. Table 2 presents the mean standardized scores for courses for each of these 14 composite features, aggregated by grade level. Using courses as the unit of analysis, analysis of variance procedures were conducted in order to assess differences, between grade levels, in the amount of demands and supports associated with courses at those levels. Results from these analyses are displayed in Table 2 as well. Between junior high school and senior high school, the demand on information capacity (information load and amount of details) increases significantly. Demand differences also emerged between college and junior high level courses. College courses require significantly less verbatim reproduction of information and more integration of information than do courses at the junior high level. In the case of integration demands, the results also show a significant quadratic trend, with the demand for integration decreasing between junior high

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school and college and increasing between senior high school and college.

The amount of instructor-provided supports and compensations for integration demands reveals a similar pattern across grade levels, with supports increasing significantly between senior high level courses and college courses and the magnitude of integration compensations differing among levels. The incidence of text and instructor-provided supports and compensations for integration demands (e.g., information presented in integrated fashion) decreases significantly between junior-high school and senior-high courses and increases significantly between senior-high- school and college courses. However, the extent to which instructors provide compensations for exam performance (e.g., giving students practice test questions in handout form) decreases significantly between senior-high-school and college courses.

Grade-Level Differences in the Relationship between Student Characteristics and Achievement

Correlations between the Concept Mastery Test and academic achievement, whether measured by test grade or term grade, were significant and positive at all grade levels. Figure ! displays the pattern of these correlations across grade level for test-grade achievement. The magnitude of these correlations rises from the junior high school level (r = .20) to the senior high school (r = .33) and college (r = .32) levels.

Correlations between the SCAAT and achievement show a pattern similar to the one found for academic aptitude (JHS, r = .37; SHS, r = .46; C, r = .30), although in the case of the SCAAT, the magnitude of the correlations is higher at the junior high and high school levels. Figure 2 displays these results.

Figure 3 displays the correlations between achievement (test grade) and both Ac and Ai. Note that the correlations between achievement and the two scales of achievement orientation interact with grade level. Whereas the correlation between Ac and achievement is higher than that

0 . 5

O ~ 0.4 ca

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Junior High Senior High College Grade Level

FIG. 1. Correlations between academic aptitude (Concept Mastery Test) and test grade as a function of grade level.


~ ~ 0.4 o . -

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FIG. 2. Correlations between self-efficacy (Self-Concept of Academic Ability Test) and test grade as a function of grade level.

between Ai and achievement at the junior high school level (r = .26 for Ac; r = . 13 for Ai), at the college level, the opposite pattern is evident (r = .11 for Ac; r = .30 for Ai). At the senior high level, the two correlations are of similar magnitude (r = .28 for Ac; r = .31 for Ai). All correlations are significant with the exception of that between Ac and achievement at the college level.

Multiple regression analyses were performed to predict achievement using all student characteristic indices as well as routine and test preparation study-activity clusters. These analyses can be used to estimate the relative importance of academic ability, self-efficacy, and achievement orientation in accounting for achievement variance, as well as the independent contribution of these indices. Table 3 displays the result of these analyses. Stepwise multiple regression analyses, performed separately for Routine and Test-Preparation study-activity scales, indicated that a significant portion of the achievement variance at all educational levels was accounted for by the SCAAT. The Concept Mastery Test also accounted for significant variance in term grade in all analyses except that conducted with test-preparation scales at the senior high school level.

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FIG. 3. Correlations between achievement orientation (Ac and Ai scales of the California Psychological Inventory) and test grade as a function of grade level.

356 THOMAS, I V E N T O S C H , AND R O H W E R

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Although the SCAAT accounted for the major share of variance at all educational levels with respect to other indices, the independent contri- butions of both the Concept Mastery Test and the two self-evaluation scales of the Study Activity Survey indicates that the CMT and these self- evaluation scales contribute unique variance to the prediction.

Grade-Level Differences in the Relationship between Student Characteristics and Study Activities

Table 4 presents correlations between the Concept Mastery Test and both routine and test preparation study-activity scales. Academic aptitude does not show a consistent relationship with any study-activity scale across grade levels. However, the pattern of relationships between the CMT and study-activity scales is somewhat consistent with grade-level differences in course demands. For example, at the junior high school level, the CMT correlates positively with just one scale, a measure of diligent effort (Assiduous Resource Management). At the senior high school level, on the other hand, able students tend to engage in Uniform Processing, to avoid Duplicative Processing, and to Focus on Test Rele- vance in their studying, a finding that is consistent with the increase in information load (Demand for Information Capacity) associated with senior high versus junior high school courses. At the college level, where courses are characterized by relatively high information loads and greater

T A B L E 4

CORRELATIONS BETWEEN ACADEMIC APTITUDE (CONCEPT MASTERY TEST) AND BOTH

ROUTINE (R) AND TEST PREPARATION (7) STUDY ACTIVITY SCALES AS A FUNCTION OF

GRADE LEVEL

Form R Form T

Study activity scales J H S S H S C o l l e g e J H S S H S College

Uniform Processing 0 .09 0 . 1 2 " 0 .29* 0 .09 0 . 1 2 " 0 .12

Assiduous Resource Mgt 0 . 1 4 " 0 .05 0 .14 0 .04 0 .10 0 .09

Cognitive Monitoring 0.03 0 .00 0 .17 - 0 .03 0 . 1 8 " 0 . 1 9 '

Duplicative Processing - 0 .04 - 0 . 1 3 ' 0 .08 0 .08 - 0 . 1 9 " - 0 . 1 9 "

Focus on Test Relevance 0.01 0 . 1 4 " 0 .02 0 .09 0 .23* 0 .09

Constructive Processing 0.06 0 .07 0 .13 - 0 . 1 4 " - 0 . 1 3 ' 0 .08

Interpretive Processing 0.03 0 .08 0 . 1 9 " - 0.11 - 0 .03 0 .00

Hyperprocessing 0 .09 0.11 0 .28* N A N A N A

Verbatim Processing N A N A N A - 0 .05 - 0 .08 - 0 .07

Means for Resource Mgt - 0 . 0 9 - 0 . 0 5 - 0 . 1 0 - 0 . 1 7 " - 0 . 1 4 ' - 0 . 1 0

Pre-reading Preparation 0.11 - 0.11 0 .05 N A N A N A

Receptive Processing - 0 .02 - 0 .09 - 0 . 1 9 " N A N A N A

Selective Notetaking - 0 .15* - 0 . 1 4 " - 0 .03 - 0.03 0 .04 0 .12

N 367 433 185 375 455 180

* p < .01.


demands for integration, academic aptitude relates positively to presumably productive cognitive processing activities (Interpretive Processing, Uniform Processing, Hyperprocessing, and the Avoidance of Receptive Processing), but these relationships are confined to routine study contexts.

In contrast to findings from laboratory studies, the expected positive relationship between academic aptitude and engagement in constructive processing activities (the use of elaborative, reorganizing, or integrative study methods) did not emerge. In fact, this relationship was found to be negative for test-preparation activities for both the junior high and senior high samples. Given the low level of net integrative demands found at these levels, this result may reflect adaptive rather than maladaptive behavior on the part of able students.

Table 5 presents correlations between the SCAAT and SAS scales. In general, for routine studying, self-concept of academic ability is related to seemingly productive, generative (Generation of Constructed Informa- tion) and selective allocation (Hyperprocessing and Focus on Test Rele- vance) activities and is negatively related to seemingly unproductive activities (Receptive Processing) at all grade levels. With regard to test preparation, a grade-level effect is somewhat more noticeable in that self- efficacy is positively related to Uniform (nonselective) Processing, diligent course work (Assiduous Resource Management), and generative

TABLE 5 CORRELATIONS BETWEEN SELF-EFFICACY (SELF-CONCEPT OF ACADEMIC ABILITY) AND BOTH ROUTINE (R) AND TEST PREPARATION (T) STUDY ACTIVITY SCALES AS A FUNCTION

OF GRADE LEVEL

Form R Form T

Study activity scales JHS SHS College JHS SHS College

Uniform Processing 0.27* 0.34* 0.33* 0.29* 0.23* 0.12 Assiduous Resource Mgt 0.32* 0.23* 0.23* 0.20* 0.13" -0 .11 Cognitive Monitoring 0.12" 0.17" 0.08 0.16" 0.30* 0.19" Duplicative Processing 0.17" 0.11" 0.03 0,02 0.01 -0 .14 Focus on Test Relevance 0.22* 0.32* 0.26* 0.22* 0.26* 0.10 Constructive Processing 0.13" 0,23* 0.22* -0 .05 0,02 0.15 Interpretive Processing 0.15" 0.24* 0.17 0.01 0.15" 0.03 Hyperprocessing 0.32* 0.36* 0.36* NA NA NA Verbatim Processing NA NA NA - 0.03 0.04 - 0.04 Means for Resource Mgt 0.00 0.10 -0 .02 -0 .14" 0.03 -0 .04 Pre-reading Preparation - 0.03 - 0.07 0,08 NA NA NA Receptive Processing - 0.16" - 0.25* - 0.22* NA NA NA Selective Notetaking 0,04 -0 .01 0.02 0.08 0.15" -0 .01 N 373 504 285 361 415 175

* p < .01.

STUD E N T CHARACTERISTIC D I F F E R E N C E S IN STUDY ACTIVITIES 359

study activities (Constructed Processing) at the secondary level, but not at the college level, where self-efficacy relates only to Cognitive Moni- toring activity. Despite the tendency for self-efficacious students to exhibit diligent study and strategic processing behaviors in their routine study activities, these students may study for tests in ways that are shaped by the particular demands of their courses. At the college level, the surfeit of supports and compensations for integrated processing may serve to reduce the magnitude of relationships between self-efficacy and engagement in generative study activities.

Table 6 presents correlations between routine study-activity scales and both achievement orientation subscales, Ac and Ai. Both scales show significant positive correlations with activities that might be described as diligent processing activities (Uniform Processing and Hyperprocessing). However, for the most part, the two achievement orientation scales show different patterns of correlations with other study activity scales. Stu- dents with a preference for achievement via conformance tend to engage in diligent self-management behavior (Assiduous Resource Manage- ment), to focus on course requirements (Focus on Test Relevance), and to avoid desultory processing activities (Receptive Processing). Aside from engagement in Uniform Processing, the routine study behavior ex- hibited by students preferring achievement via independence can be characterized by nonengagement in presumably productive activities such as Selective Notetaking and Pre-Reading Preparation, although the pattern varies with grade level.

TABLE 6 CORRELATIONS BETWEEN ACHIEVEMENT ORIENTATION (Ac, AI) AND ROUTINE STUDY

ACTIVITY SCALES AS A FUNCTION OF GRADE LEVEL

Ae Ai


Uniform Processing 0.24* 0.17" 0.25* 0.12" 0.19" 0.23* Assiduous Resource Mgt 0.47* 0.37* 0.45* 0.13" 0.04 0.09 Cognitive Monitoring 0.22* 0.12" 0.01 -0 .04 0.00 0.02 Duplicative Processing 0.16" 0.12" 0.10 - 0.09 - 0.07 - 0.11 Focus on Test Relevance 0.21" 0.22* 0.22* 0.03 0.12" 0.05 Constructive Processing 0.14" 0.14" 0.21" 0.01 0.06 0.04 Interpretive Processing 0.15" 0.13* 0.11 0.03 0.07 0.02 Hyperprocessing 0.34* 0.29* 0.29* 0.07 0.14" 0.19" Means for Resource Mgt 0.05 0.07 0.07 -0 .15" -0 .07 -0 .30* Pre-reading Preparation 0.04 0.05 0.14" - 0.04 - 0.16" - 0.05 Receptive Processing - 0.17" - 0.22* - 0.17" - 0.14" - 0.08 - 0.08 Selective Notetaking 0.05 - 0.02 0.23* - 0.05 - 0.08 - 0.14"

N 384 439 284 372 432 284

* p < .01.


Table 7 presents correlations between test-preparation study-activity scales and both Ac and Ai. Only the senior high level shows anything resembling a common pattern of study activities for the two scales. Both the Ac and Ai scales show a significant positive relationship with Uni- form Processing, Cognitive Monitoring, and Focus on Test Relevance at the senior high level. Again, there is some indication that, overall, students scoring high in Ac tend to engage in activities that conform more to the demands of schooling (Focus on Test Relevance, Selective Note- taking, Assiduous Resource Management, and Uniform Processing), whereas students scoring high in Ai tend to be distinctive in their avoidance of the kinds of study activities assessed by the Study Activity Survey, especially at the junior high and college levels.

DISCUSSION

The principal inference from the results reported here is that the character of studying at all educational levels sampled is affected by enduring characteristics of students as well as by the pattern of demands and supports characteristic of the courses at these levels. Grade-related differences in course features appear to account for the discontinuities between grade levels in relationships between student characteristic variables and both study activities and academic achievement. However, this general conclusion must be regarded as speculative at this point for at least two reasons. First, despite the attempt to ensure comparability between samples at each grade level, there is no doubt a restriction of range at the college level with respect to the individual measures. The restric-

TABLE 7 CORRELATIONS BETWEEN ACHIEVEMENT ORIENTATION (Ac, AI) AND TEST PREPARATION

STUDY ACTIVITY SCALES AS A FUNCTION OF GRADE LEVEL

Ac Ai


Uniform Processing 0.26* 0.22* 0.15 0.06 0.15" 0.02 Assiduous Resource Mgt 0.17" 0.18" 0.13 0.01 0.03 -0 .03 Cognitive Monitoring 0.17" 0.26* 0.35* 0.01 0.16" 0.02 Duplicative Processing 0.03 -0 .04 0.08 - 0 . 16" -0 .21" -0 .28* Focus on Test Relevance 0.27* 0.22* 0.27* 0.01 0.11" -0 .05 Constructive Processing - 0.11 0.02 0.12 - 0.12* - 0.09 - 0.06 Interpretive Processing 0.01 0.08 0.13 - 0.08 - 0.01 - 0.10 Verbatim Processing - 0.07 0.01 0.02 - 0.09 - 0.06 - 0.15 Means for Resource Mgt -0 .11 0.04 0.08 -0 .17" - 0 . 1 3 ' -0 .20* Selective Notetaking 0.02 0.17" 0.23* -0 .07 0.06 -0 .02 N 362 392 178 351 386 178

* p < .01.


tive selection process that occurs at the college level may have served to reduce variability in scores on such indices as the CMT, which would tend to reduce the magnitude of the correlations between such indices and measures of study activity and achievement at this level. Second, in the present study, grade-related differences in course features and relationships between student characteristic variables and study activities were reported in separate analyses. Confirmation of the mediating effect of course features on the relationship between student characteristics and study activities must await further investigation in which the independent and interactive effects of these factors can be assessed in a single analysis.

Nonetheless, there seem to be a few findings worth noting. First, it is clear that achievement, whether measured by test grade or by course grade, is highly related to self-efficacy. Students who have confidence in their ability to achieve in a particular course are the ones who do best. Although one may expect that an objective measure of academic ability would be a better predictor of achievement, SCAAT was the leading predictor of achievement at all grades. Of the 14 stepwise multiple regres- sions that were run split by course, the SCAAT was the leading predictor in 8 of those analyses, compared to 3 instances where the Concept Mas- tery Test accounted for the most variance in achievement.

We had hypothesized that self-efficacy would affect academic achievement by means of the quality of effort expended in studying. Some support for this hypothesis is evident in the pattern of correlations between the SCAAT and the more generative and selective versus less generative and selective study-activity scales. Students with a high self-concept of academic ability tend to engage in more generative (e.g., Generation of Constructed Information) and selective (e.g., Hyperprocessing) activities than their less self-efficacious peers, especially in routine studying.

A second finding that appears relatively robust is the differential relationship between the Ac and Ai scales of the California Psychological Inventory and the scales of the SAS. Achievement via Conformance identifies those aspects of motivation which facilitate achievement in contexts where conforming behavior such as a high degree of self-disci- pline, efficiency, acceptance of regulations, and responsibility are re- warded. Students who embody these characteristics are more prone to adopt routine generative and selective study activities at all grade levels. Students who prefer to achieve via independence show a very different pattern of studying, characterized, in their test preparation activities, by an avoidance of high effort methods (Duplicative Processing, Generation of Constructive Information, and Means for Resource Management). They also exhibit less consistency in their study activities across grade levels than was found for students scoring high in Ac. At least two things


are puzzling about these results. First, the pattern of correlations for Ac and study activities closely resembles the pattern for the SCAAT and study-activity scales. One possible interpretation is that students who have a high self-concept of their academic ability are likely to report engaging in activities that conform to what is expected of one who studies dili- gently. However, the correlation between SCAAT and Ac ranged from a high of .44 for the junior high school level to a low of .20 at the college level. A second puzzling outcome was that the interaction between achievement orientation scales and test grade evident between the junior high school and college level (Fig. 3) is not readily interpretable with reference to differences in study activities between the two achievement orientation groups from one grade level to the other. Future analyses will attempt to compare courses within grade levels on features that represent the conformance-independence dimension more precisely and to examine the relationship in these contexts between the Ac and Ai scales and both study-activity engagement and the study-achievement relationship.

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