Download - Attributions, group size, and exposure time as predictors of elementary children's performance on a microcomputer task

Pergamon Computers in Human Behavior, Vol. 12, No. 1, pp. 145-157, 1996

Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved

0747-5632/96 $15.00 + .00

0747-5632(95) 00025-9

Attributions, Group Size, and Exposure Time as Predictors of Elementary Children's

Performance on a Microcomputer Task

Lois J. Baron and Miranda D'Amico

Department of Education, Concordia University

Mary Elizabeth Sissons and Patricia L. Peters

Concordia University

Abstract- In order to assess the relationship of group size, exposure time, and attributions on children's performance on a microcomputer task, fifth-and sixth-grade children were exposed to either a drill-and-practice program (n -~ 254), or a tutorial program (n = 239), in groups of one, two, or four, for one, two, or three half-hour sessions. The ability of attributions about performance, exposure time, and group size to predict actual performance was investigated. Hierarchical regression indicated that exposure time was a significant predictor of performance on posttests for the drill- and-practice program, but not for the tutorial program. Group size did not predict performance on posttests. Attributions to uncontrollable causes (ability and task difficulty) contributed to the prediction of performance on both tasks. Attributions to effort and luck were not significant predictors of performance. Interactions between group size and attributions, and exposure time and attributions, did not contribute to the prediction of performance. It is suggested that program developers focus on designing software which gives children appropriate effort feedback in order to enhance motivation to learn.

Requests for reprints should be addressed to Dr. Lois J. Baron, Department of Education, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec, Canada H3G 1M8.

145

146 Baron, D'Amico, Sissons, and Peters

In our achievement-oriented society, great emphasis is placed on ways in which children are motivated to work in school, in preparation for later life. Although the majority of elementary students are generally perceived to be highly motivated to learn, some students are more motivated to achieve than are others (Rogers, 1990). The level of achievement motivation is a function of the individual's disposition to avoid failure or strive for success, as well as of task variables (Atkinson, 1958, 1964). Achievement motivation is in turn positively correlated with a child's academic self-concept (Shavelson & Bolus, 1982; Wylie, 1979), and academic self-concept and feelings of efficacy can be predicted by how successful one has been in the past (Calsyn & Kenny, 1977).

The influence of past experience on academic self-concept, and hence, achievement motivation, is evidenced in the types of attributions the child makes in explaining past success or failure. According to Weiner (1972, 1974), the four major perceived causes of success or failure in academic tasks are ability, effort, task difficulty, and luck. These attributions can be classified along a continuum of three dimensions: stability, locus of causality, and controllability (Frieze, 1980; Weiner, 1979). Along the dimension of stability, ability and task difficulty are viewed as being relatively stable, whereas effort and luck are relatively unstable. Locus of causality can be either internal or external. Along this dimension, ability and effort are viewed as coming from within, whereas task difficulty and luck are viewed as external in origin. On the controllability dimension, the subjective view of effort is that it is controllable, whereas ability, task difficulty, and luck are uncontrollable (Gredler, 1992).

The choice of attributions for success or failure is made on the basis of the personality of the person making the attributions, as well as on the basis of information available about the situation in which the success or failure occurred (Frieze, Francis, & Hanusa, 1983). Research has shown that some of the more important types of information used to make attributions are history of success on similar tasks, how well others have performed on the same or similar tasks, effort expended, and type of task being evaluated (Frieze, 1975, 1980; Frieze & Snyder, 1980; Frieze & Weiner, 1971; Weiner et al., 1971).

Although the subjective view of causes of success or failure is variable, some patterns have been established. In general, self-esteem is preserved by ascribing success to "internal" factors such as hard work and high ability, while factors outside the self, such as bad luck and task difficulty, are used to explain failure (Johnson, 1981; Kaminsky, 1986).

Both the degree and consistency of past successful performance are important influences on the types of attributions made. Consistency of performance results in attributions to task difficulty and ability (stable factors) while inconsistent previous performance leads to attributions of luck and effort (unstable factors; Ames, Ames, & Felker, 1977; Frieze & Weiner, 1971). Investigating further the relationship between level of success and consistency, Weiner (1974) found that a consistent record of previous success led to the selection of ability as the cause, whereas moderate previous success led to the selection of effort as the main cause. When prior success was rare, luck was seen as the main cause. Therefore it might be expected that, given consistency in success, increased exposure to a task would increase attributions to stable causes. In addition, degree of success would influence attributions along the dimension of locus-of-control, such that a high degree of success would result in attributions to internal factors, whereas a low degree of success would result in attributions to external factors.

Children's attributions and CAI 147

Consistency of success influences the affective reaction which takes place after an attribution has been made. This affective reaction influences expectancies for future success or failure, and in turn, tendencies to behave in particular ways (Bell-Gredler, 1986). For students who have consistent success, the result is high expectation of future success, which enhances feelings of competence and high self-esteem. Individuals who view themselves as being lower in ability may tend to ascribe failure to internal causes, and therefore expect failure in the future, resulting in a decline in performance. Conversely, these individuals may also ascribe failure to external causes, such as bad luck or task difficulty, which, since they are beyond control, may also lead to future failure (Bell-Gredler). Thus, through continued failure, these individuals may learn to see no relationship between their actions and success.

One way of reversing the negative consequences of failure is to provide more opportunities for success in which individuals may associate their actions with their achievement. Success in group learning can provide opportunities for students to develop positive self-images and a sense of efficacy by providing experiences where success is more likely, and where each member of the group feels as if they have contributed to that success. Success within a group may, in turn, help to raise self-esteem and the perception of competence of low- performing students (Ames et al., 1977). Within groups, social factors, such as feedback, social comparison, and modeling, may influence children's motivation to achieve and their sense of competence (Harter, 1982; Schunk, 1983; Schunk & Hanson, 1985). Nevertheless, placing children in groups does not always guarantee positive results. Salomon and Globerson (1989) conducted a study where students were paired on their differing abilities in reading or writing and were asked to work on a task related to that skill. The researchers found that some team members put less effort into the task, relying instead upon the work of the other team member ("freeloader effect"). The effects of such a situation were lower amounts of invested mental effort by the more able members, who may have felt that they were being taken advantage of ("sucker effect"). Although some team members, who were exerting greater effort, may have learned well, the teams may not have been achieving optimally (Salomon & Globerson).

One mode of instruction that is particularly suited to group learning is the microcomputer learning experience. Several researchers have reported the positive effects of computer-assisted instruction (CAI; Chambers & Sprecher, 1980; Edwards, Norton, Taylor, Weiss, & Dusseldorp, 1975; Jamison, Suppes, & Wells, 1974; Vinsohaler & Bass, 1972). Increased student involvement and engagement with CAI, compared with other instructional methods, may account for equal learning in less time (Bright, 1983). Studies of academic learning time have demonstrated a relationship among allocated time, student engagement, student success rate, and achievement (Crist-Whitzel, 1985). In a meta-analysis of several studies, Kulik (1986) found that CAI enhanced learning, reduced learning time, and resulted in more positive attitudes toward learning than traditional modes of instruction. Further, some forms of CAI provide built-in proximal goal-setting, as well as rewards contingent on performance, which are important influences on perceived competence and motivation (Schunk, 1984). Nevertheless, the way in which CAI is presented to children is important to their success at the task. If students have high efficacy expectations, task motivation is enhanced, leading to better performance (Olivier & Shapiro, 1993). Repeated success is an important way to elevate efficacy expectations (Olivier & Shapiro).


Group CAI has been shown to be as effective a learning experience as individual CAI. Introducing computer technology in the classroom has been found to increase both the number and quality of social interactions (Hawkins, 1983, 1987; Hawkins, Homolsky, & Heide, 1984; Hawkins, Sheingold, Gearhart, & Berger, 1982; Krasnor & Mitterer, 1985; Shade, Nida, Lipinski, & Watson, 1986; Sheingold, Kane, & Endrewait, 1983), and a number of studies of group microcomputer learning at high school and college levels found no negative effects for groups (Carrier & Sales, 1987; Cox, 1980; Lebel, 1982; Okey & Majer, 1976; Trowbridge & Durnin, 1984). Studies have found no significant differences in performance between groups and individuals (Baron & Abrami, 1992a, 1992b; Gunterman & Tovar, 1987), and, in addition, reduced study time was the result of working in groups at the computer (Okey & Majer).

Factors related to the context of the group interaction, involving both the social process and the task itself, may also contribute to the development of self- efficacy and motivation to persist in the task (Nastasi & Clements, 1992). The environment which provides social feedback about the quality of performance, comparison of one's own abilities with those of peers, opportunities for cooperation, and/or modeling of motivational orientation and sense of competence is the one most likely to engender both motivation and perceived competence (Nastasi & Clements). Indeed, since group CAI provides feedback from both the computer and one or more peers, the opportunities for developing a greater sense of competence are increased. In addition to varying the number of students in a group, the two studies reported in this paper used different types of software (drill-and-practice and tutorial), allowing for an indirect comparison of the relationship between type of task and attributions.

To summarize, group work has been shown to enhance positive affect and motivation by providing success experiences within a social context. Further, CAI has been shown to be an effective and efficient educational tool and has the advantage of improving motivation by providing consistent feedback and proximal goal setting. Group CAI has been shown to result in enhanced social interaction as well as equally, or more efficient, learning. Questions asked of the research included the following: (a) In addition to exposure time and group size, will the types of attributions made predict performance? (b) Will attributions interact with exposure time and group size to predict performance? and (c) Will there be differences across task in the ability of exposure time, group size, and attributions to predict performance?

STUDY 1

Method

Subjects. In the first study, subjects were 160 fifth-grade and 94 sixth-grade students from three English-first-language schools in metropolitan Montreal. There were 114 girls and 126 boys (14 subjects were absent when gender was recorded). A demographic questionnaire indicated that the majority of subjects had previously used a microcomputer (and for a combination of uses). Most subjects did not have a microcomputer at home, nor had they ever worked on the educational software used in this study. Children participated with parental consent.


Materials. Software. Word Attack (a drill-and-practice program; Davidson & Eckert,

1983) was chosen for this study. Word Attack is a four-part vocabulary-building program that is designed to teach users new words, their meanings, and their usages. It reinforces recall and use of words through a quiz-like format. Word Attack was chosen after reviewing software catalogues published by major educational software developers and publishing companies. It met the following selection criteria: (a) its learning objectives could be measured by a paper-and- pencil test, (b) learner feedback was immediate, (c) it could be teacher-edited, (d) it was age-appropriate and the readability level was at, or below, the targeted grade level, (e) it could be used by groups or individuals, (f) it was user friendly, and (g) it was compatible with the Apple computers used in the study (see Baron & Abrami, 1992a, for more information on selection procedures).

Instrumentation. Before the treatment sessions, all subjects were administered the Basic Word Vocabulary Test (BWVT; Jamestown Publishers, 1975). The BWVT measures vocabulary development using a multiple-choice format. Students are asked to choose the word that has the same meaning as the example. The median correlation coefficient between the BWVT and such standardized tests as the Sequential Tests of Educational Progress is .76. The internal consistency reliability of the BWVT is .96. All subjects also completed a background questionnaire which asked students questions pertaining to whether they prefer working alone or with others, their use of computers as tools or for games, whether they had computers at home, and whether they were familiar with the program they would be using in the study. Pretesting was carried out across the sample classes to determine if they had equal variability in the BWVT.

Posttest measures consisted of an achievement test which was developed by the research staff and patterned after the format used in the Word Attack, using the same words. Subjects were given a 25-item multiple-choice achievement posttest (MCWA) and a 12-item test which assessed understanding (DEFTEST) and use (SENTEST) of the words from Word Attack. In addition, all subjects completed a 10-item Attributions Questionnaire. In the Attributions Ques- tionnaire, subjects responded to items on a Likert scale ranging from 1 to 5. The questionnaire taps into subjects' perceptions of their success in the microcomputer learning experience. Items focused on their attributions about ability (e.g., How smart [bright] do you think you were using the program?), task difficulty (e.g., How hard do you think the words on the program were?), effort (e.g., How hard did you try to do well using the program?), and luck (e.g., How lucky do you think you were using the program?). Factor analysis of the Attributions Questionnaire indicated a three-factor structure. Factor 1 contains items related to ability and task difficulty, both of which represent attributions to uncontrollable causes (the internal structure of Factor 1 is such that attributions to high ability are negatively correlated with task difficulty). The second factor contains items related to effort, an internal, controllable, unstable cause. Factor 3 contains items related to luck, which is external, uncontrollable, and unstable.

Procedure

Subjects were randomly assigned by class to groups of one, two, or four. In addition, the groups were randomly assigned to one half-hour, two half-hours,


or three half-hours of exposure time on the microcomputer (a half-hour at a time). Following completion of the pretests, subjects were brought to the microcomputer room in the school and were exposed to the treatments. Subjects were allowed to talk with other members of their group, and were instructed to solve problems within their own group, calling upon the research staff only as a last resort. At the completion of their one, two, or three half-hour sessions on the microcomputer, subjects completed the posttests in their classroom groups. Four microcomputers were in use at any one session. Software was provided by the research team.

Results

Within each group, preliminary analysis of each group's pretest (BWVT) scores indicated that there were no differences between classroom groups within each sample. Further analysis determined that there were no differences in the results when either group means or individual scores were used as the unit of measurement. Data on individual scores are reported here. Hierarchical multiple regression was used to determine the relative contributions of group size, exposure time, and attributions to achievement on the four posttests.

Hierarchical multiple regression. Hierarchical multiple regressions were performed separately on the three posttest scores (MCWA, DEFTEST, and SENTEST) to determine if exposure time, group size, attributions, and interactions between exposure time and attributions, and between group size and attributions could explain variability in posttest scores. Because exposure time and group size were qualitative (categorical) variables, dummy coding was used. In this type of coding, one category is selected as a reference category, and the other categories are compared against it. Regression statistics indicate differences for each of the contrast categories as compared with the reference category.

Predictors were entered in four steps: exposure time, group size, the three attribution variables (ability and task difficulty, effort, and luck), and the interactions. Because previous research using this sample (Baron & Abrami, 1992a) found a significant difference among groups which had been exposed to the computer programs for three half-hours, versus one or two half-hours, exposure time was entered first. The second variable entered (group size) had been shown by Baron and Abrami to have no effect on achievement. The third step included the three attribution variables as defined by the factor analysis. The fourth step included interactions between exposure time and attributions, and between group size and attributions. Results of the three hierarchical regressions, including the standardized beta weights (13), the correlation (r), the semi-partial correlation (sr) 2, t values, R 2, adjusted R 2, and F value, for each step of the posttest analyses, are shown in Tables 1-3.

The regression on MCWA scores is shown in Table 1. After Step 4, with all independent variables in the equation, R 2 = .56, F(19,212) = 5.32, p < .001. Increments in R 2 were significant for exposure time and attributions. Short exposure time (one half-hour) was negatively related to performance on the MCWA, whereas at t r ibut ions to high ability and low task difficulty (uncontrollable causes) were positively related to MCWA scores.

The regression on DEFTEST scores is shown in Table 2. After Step 4, with all independent variables in the equation, R 2 = .62, F(19,212) = 7.07, p < .001.


Table 1. Hierarchical Regression of Exposure Time, Group Size, and Attributions on MCWA

Variables 13 r (St') 2 t R 2 Adjusted R 2 R2t~ FA

Exposure Time Two hal~houm (ET2) .10 .21 .01 1.38 One hal~hour (ET1) - . 19 - . 2 5 .03 -2 .56*

Group Size Two (GS2) .07 .06 .00 1.03 One (GS1) .06 .06 .00 0.96

AtRdbutions Abil i~/Task(AT) .51 .53 .23 8.20*** Effori (E) .05 .15. 00 .92 Luck(L) - . 0 5 .10 .00 - .81

In~mctions ET1 x AT - . 05 .31 .00 - . 53 ET1 x E - . 09 .06 .00 - .91 ET1 x L - . 0 2 .08 .00 - . 24 ET2 x AT - . 0 6 .32 .00 - .71 ET2 x E - . 0 5 .06 .00 .51 ET2 x L .05 .00 .00 .45 GS1 x AT - .11 .13 .00 -1 .57 GS1 x E1 .08 .13 .01 1.07 GS1 x L - .01 .00 .00 - . 13 GS2 x AT .04 .35 .00 .52 GS2 x E - . 00 .15 .00 - . 04 GS2 x L - . 0 3 .04 .00 - . 40

.07 .06 .07 8.50***

.08 .06 .01 .79

.31 .28 .23 13.86"**

.32 .26 .02 .54

Note. After Step 4, R = .56, F(19,212) = 5.32, p < .001. *p < .05; **p < .01; ***p < .001.

Similar to the MCWA, short exposure time was negatively related to performance on the DEFTEST, whereas attributions to high ability and low task difficulty were positively related to performance.

The regression on SENTEST scores is shown in Table 3. After Step 4, with all independent variables in the equation, R 2 = .61, F(19,212) = 6.69, p < .001. As in the MCWA and DEFTEST, short exposure time was negatively related to performance on SENTEST, whereas attributions to high ability and low task difficulty were positively related to performance.

STUDY 2

Method

Study 2 replicated Study 1 in all aspects of the method, with the exception of subjects, materials, and instrumentation.

Subjects. Subjects in Study 2 consisted of 104 fifth-grade and 105 sixth-grade students from four schools in the Montreal area. There were 98 girls and 111 boys in the second study.

Materials. The computer program used for this study was Analogies Tutorial (Hartley Courseware, 1983). Software selection criteria were identical to those used in Study 1, except that the Analogies Tutorial software was more cognitively demanding than Word Attack. The main goal of Analogies Tutorial is to introduce learners to various forms of analogies. As indicated by the program developers, and characteristic of tutorial programs, the program


Table 2. Hierarchical Regression of Exposure Time, Group Size, and Attributions on DEFTEST

Vanables ~ r (s~ 2 t R 2 A~us tedR 2 R2A FA

Exposure Time Two half-hours (ET2) - . 09 .07 .01 -1 .16 One half-hour (ET1) - . 2 9 - . 25 .06 -3.88***

Group Size Two (GS2) .05 .06 .00 0.80 One(GS1) - .01 - .01 .00 -0.11

Atttributions Ability/Task (AT) .52 .56 .25 8.58*** Effort (E) .07 - . 19 .01 1.14 Luck (L) .03 .21 .00 0.52

Interactions ET1 x AT - . 1 5 .33 .01 -1.61 ET1 x E - . 1 5 .06 .01 -1 .64 ET1 x L .22 .19 .03 2.37 *a ET2 x AT - . 10 .33 .01 -1 .72 ET2 x E .01 .14 .00 .16 ET2 x L .21 .10 .02 2.12 *a GS1 × AT .09 .20 .01 - 1.26 GS1 x E .10 .21 .01 1.13 GS1 x L .07 .06 .01 1.13 GS2 x AT .01 .33 .00 .10 GS2 x E .12 .16 .01 1.67 GS2 x L .13 .09 .02 -1 .88

.07 .06 .07 8.11"**

.07 .05 .00 .37

.33 .31 .26 15.77***

.39 .33 .06 1.67

Note. After Step 4, R = .62, F(19,212) = 7.07, p < .001. aAccording to Cohen and Cohen (1983)individual interactions should not be interpreted as significant unless the entire block is significant. *p < .05; **p < .01; ***p < .001.

assumes no prior knowledge. The tutorial consists of 10 lessons. After 2 introductory lessons, the program proceeds to teach learners the following types of analogies: synonym/antonym, object/group, part/whole, object/description, object/user, cause/effect, and grammar. Once a new type of analogy is introduced, practice is provided on both it and the previously learned analogies.

Instrumentation. As a posttest, subjects in Study 2 completed a 50-item analogies achievement test (POSTANA).

Results

The regression on POSTANA scores is shown in Table 4. After Step 4, with all independent variables in the equation, R 2 = .52, F(19,166) = 3.30, p < .001. In this regression, the only significant predictor of POSTANA scores was attributions to high ability and low task difficulty, which were positively related to performance.

DISCUSSION

A common method for assessing attributional style is to ask subjects to make attributions for their success and failure. The object of these two studies was to determine how children's attributions might predict performance on novel microcomputer tasks, and further, whether group size and exposure time would individually contribute to the prediction of performance, and whether they would interact with attributions in predicting performance. In addition, the use


Table 3. Hierarchical Regression of Exposure Time, Group Size, and Attributions on SENTEST

Variables ~ r (St') 2 t R 2 Adjusted R 2 R2A FA

Exposure Time Two half-hours (ET2) .07 .17 .00 0.89 One half-hour (ET1) - . 1 8 - . 22 .02 -2 .43*

Group Size Two (GS2) .08 .08 .01 1.13 One (GSl) .01 - . 0 0 .00 0.13

Atttributions Ability]Task (AT) .53 .56 .25 8.71"** Effort (E) .04 .16 .00 .64 Luck (L) .08 .22 .01 1.52

Interactions ET1 x AT .12 .33 .01 -1 .28 ET1 x E .10 .08 .01 - .71 ET1 x L .23 .22 .03 2.42 *a ET2 x AT .07 .35 .00 - .81 ET2 x E .01 .10 .00 - . 16 ET2 x L .24 .12 .03 2.36 *a GS1 x AT .02 .23 .00 - . 24 GS1 x E .10 .21 .01 1.38 GS1 x L .04 .06 .00 .60 GS2 x AT .06 .35 .00 .74 GS2 x E .05 .13 .00 .71 GS2 x L .07 .15 .00 .90

.05 .04 .05 6.28**

.06 .04 .01 .65

.33 .31 .28 31.01"**

.41 .35 .03 .93

Note. After Step 4, R = .61,/=(19,212) = 6.69, p < .001. aAccording to Cohen and Cohen (1983) individual interactions should not be interpreted as significant unless the entire block is significant. *p < .05; **p < .01; ***p < .001.

of different types of software in the two studies allowed for some indirect comparison of the ability of exposure time, group size, and attributions to predict performance.

Attributions that students made about their performance were significantly related to their performance on the final tests. Interestingly, only Factor 1 (ability and task difficulty) attributions were significant predictors of posttest scores, indicating that in this microcomputer learning experience, children not only were inclined to make attributions to task difficulty and ability as causes for their success and failure, but that these types of attributions were also good predictors of actual success and failure. In contrast, attributions to luck and effort did not significantly predict final test scores. These results are consistent with the finding that effort becomes a less important attribution as children mature (Harari & Covington, 1981; Nicholls, 1978), and is consistent with the findings of Whitley and Frieze (1985) that luck is also a relatively unimportant attribution.

Whether children worked alone, or in groups of two or four, did not interact with the types of attributions made in predicting final test scores, indicating that children's judgments of the causes of their performance were not directly related to the size of their group. This is consistent with previous research which found no negative effects on achievement for groups (Baron & Abrami, 1992a, 1992b; Cartier & Sales, 1987; Cox, 1980; Lebel, 1982; Okey & Majer, 1976; Trowbridge & Durnin, 1984). Also consistent with those findings, the results of this study showed that group size did not contribute to prediction of final test scores. This might not have been the case if working in groups had contributed to the development of "freeloaders" and "suckers" (Salomon & Globerson, 1989).


Table 4. Hierarchical Regression of Exposure Time, Group Size, and Attributions on POSTANA

Variables J3 r (sr) 2 t R 2 Adjusted R 2 R2z~ F~

Exposure Time .0t - . 0 0 .01 .73 Two half-hours (ET2) - .01 .04 .00 - 0 . 0 7 One half-hour (ET1) - . 0 9 - . 0 9 .01 - 1.08

Group Size .01 - .01 .00 .13 Two (GS2) - .01 - . 0 2 .00 - 0 . 1 2 One (GS1) .03 .03 .00 0.44

Atttributions .21 .18 .20 14.91"** Ability/lask (AT) .44 .43 .19 6.39*** Effort (E) .09 .12 .01 1.36 Luck (L) - . 11 - . 0 5 .02 - 1.65

Interactions .27 .19 .07 1.25 ETI x AT .17 .29 .02 1.73 ET1 x E .07 .05 .00 - 0 . 6 5 ET1 × L .14 .06 .02 1.61 ET2 x AT .20 .34 .02 2.03 *a ET2 × E .05 .06 .00 0.53 ET2 x L .04 - . 0 2 .00 0.47 GS1 x AT .10 .14 .01 - 1 . 1 0 GS1 x E .14 .18 .02 1.71 GS1 x L .05 .06 .00 0.53 GS2 × AT .08 .20 .00 -0 .91 GS2 x E - . 0 3 .01 .00 - 0 . 3 6 GS2 x L - . 01 - . 0 8 .00 - 0 . 1 5

Note. After Step 4, R = .52, F(19,166) = 3.30, p < .001. aAccording to Cohen and Cohen (1983)individual interactions should not be interpreted as significant unless the entire block is significant. *p < .05; **p < .01; ***p < .001.

Although the additional feedback available in group CAI might be expected to contribute to an increased sense of competence (Nastasi & Clements, 1992), and thus contribute to increased attributions to internal causes (i.e., ability), there was no significant interaction between ability and task difficulty attributions and group size in predicting posttest achievement scores.

In contrast to group size, exposure time was shown to be related to performance on all Word Attack posttests, but was not related to performance on the Analogies Tutorial posttest. It would appear that subjects needed at least two half-hours to gain competency on the Word Attack task, but performed equally well on POSTANA whether they had received one, two, or three half- hour sessions. This difference may be related to the nature of the task. Increased exposure time (two or three half-hours) was shown to be beneficial to achievement for subjects in Study 1. In Word Attack, more exposure time means more words learned. In contrast, for subjects who worked with Analogies Tutorial, exposure time was not a significant predictor of posttest scores. In Analogies Tutorial, each successive lesson teaches different types of analogies in a hierarchical fashion, but it would seem that the skill of solving different types of analogies can be acquired in one half-hour.

Increased exposure time did predict performance on Word Attack, but it did not interact with the types of attributions made in predicting posttest scores for Study 1 or Study 2. One might expect that increased exposure might have led to increased attributions to stable causes, as suggested by Frieze (1980). The absence of interactions with exposure time may have been due to the relatively short exposure time in this study (a maximum of three half-hour sessions). More involvement with the software might have resulted in increases in attributions to

Children's attributions and CA1 155

effort, if those interactions were consistently successful. Frequent measures of performance would have indicated whether students' success was consistent over time, and would clarify the relationship between consistency of success and achievement, as suggested by Olivier and Shapiro (1993).

Although the two tasks used in these studies were administered to two different groups, and therefore cannot be compared directly, the similarities between the results are intriguing. The type of software used may be relevant to the development of positive attributions. Tutorial programs, such as Analogies Tutorial, tend to be more cognitively demanding, and give students more control; whereas drill-and-practice programs, such as Word Attack, are largely controlled by the computer. Immediate feedback is one of the positive features of a drill-and-practice program, in that immediate feedback would serve to enhance the connection between actions and success. However, drill-and-practice programs, in contrast to tutorial programs, set goals external to the student, rather than allowing the student to set goals. Opportunities for proximal goal setting are important to motivation and the perception of confidence (Nastasi & Clements, 1992). In addition, setting goals allows students to set the level of task difficulty, further enhancing their sense of control (Merrill et al., 1992). It might be expected that the frequent feedback given in drill-and-practice programs would contribute to attributions to effort for those students who used Word Attack. For the Analogies Tutorial program, the increase in student-controlled goal setting might be expected to lead to more attributions to effort. Neither of these expectations were realized. One likely explanation is that a maximum of three half-hour sessions of contact with the software was not sufficient to influence children's attributions for their performance.

In general, the types of attributions made were very similar across tasks, indicating that the drill-and-practice and tutorial programs used in this research did not differentially affect children's tendency to relate their success to internal versus external, controllable versus uncontrollable, and stable versus unstable causes. Further research is required in order to determine if long-term exposure to different types of software will reveal a relationship between type of software and type of attributions made about performance. Software designers may need to consider ways in which CAI can optimally improve performance. Factors such as efficacy feedback, rewards for effort, and consistency of success must be considered in CAI design.

Acknowledgments - This research was supported by Concordia University and the Fonds Pour la Formation des Chercheurs et l'Aide a la Recherche (Grant EQ-2951), Government of Quebec, Canada. The authors wish to thank Dr. Philip Abrami, Ms. Carol Shattner, and Ms. Linda Wasserman for their contribution to the project, and Ms. Jacky Boivin for assistance with the statistical analysis. Thank you to Davidson and Associates, Inc., and Hartley Courseware, Inc., for contributing the software used in this study. Portions of this work were presented at the Annual Meeting of the Eastern Educational Research Association, (EERA) Sarasota, Florida, February 8-13, 1994.

REFERENCES

Ames, C., Ames, R., & Felker, D. W. (1977). Effects of a competitive reward structure and valence of outcome on children's achievement attributions. Journal of Educational Psychology, 60, 1-8.

Atkinson, J. W. (1958). Towards experimental analysis of human motivation in terms of motives, expectancies, and incentives. In J. W. Atkinson (Ed.), Motives in fantasy, action, and society (pp. 288-305). Princeton, NJ: Van Nostrand.


Atkinson, J. W. (1964). An introduction to motivation. Princeton, NJ: Van Nostrand. Baron, L., & Abrami, P. (1992a). Microcomputer learning with a tutorial p r o g r a m -

manipulating group size and exposure time. Journal of Computing in Childhood Education, 3, 231-245.

Baron, L., & Abrami, P. (1992b). The effect of group size and exposure time on microcomputer learning. Computers in Human Behavior, 8, 353-365.

Bell-Gredler, M. E. (1986). Learning and instruction: Theory into practice. New York: MacMillan. Bright, G. W. (1983). Explaining the efficiency of computer-assisted instruction. Association for

Educational Data Systems Journal, 16, 144-152. Calsyn, R. J., & Kenny, D. A. (1977). Self-concept of ability and perceived evaluation of others:

Cause or effect of academic achievement. Journal of Educational Psychology, 69, 136-145. Carrier, C. A., & Sales, G. C. (1987). Pair versus individual work on the acquisition of concepts in

a computer-based instructional lesson. Journal of Computer-Based Instruction, 14, 11-17. Chambers, J. A., & Sprecher, J. W. (1980). Computer assisted instruction: Current trends and

critical issues. Communications of the Association for Computing Machinery, 23, 332-342. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for behavioral

sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Cox, B. A. (1980). Early adolescent use of selected problem-solving skills using microcomputers.

Unpublished doctoral dissertation, University of Michigan. Crist-Whitzel, J. (1985). Computers for all children: A literature review of equity issues in computer

utilization. San Francisco, CA: Far West Laboratory for Educational Research & Development. (ERIC Document Reproduction Service No. ED 301 159)

Davidson, J., & Eckert, R. (1983). Word Attack [Computer program]. Torrance, CA: Davidson & Associates.

Edwards, J., Norton, S., Taylor, S., Weiss, M., & Dusseldorp, R. (1975). How effective is CAI? A review of the research. Educational Leadership, 33, 147-153.

Frieze, I. H. (1975). The role of information processing in making causal attributions for success and failure. In J. S. Carroll & J. W. Payne (Eds.), Cognition and social behavior (pp. 95-112). Hillsdale, NJ: Lawrence Erlbaum.

Frieze, I. H., (1980). Beliefs about success and failure in the classroom. In J. H. McMillan (Ed.), The social psychology of school learning (pp. 39-78). New York: Academic.

Frieze, I. H., Francis, W., & Hanusa, B. (1983). Defining success in classroom learning. In J. Levine & M. Wang (Eds.), Defining success in classroom settings (pp. 3-28). Hillsdale, NJ: Lawrence Erlbaum.

Frieze, I. H., & Snyder, H. N. (1980). Children's beliefs about the causes of success and failure in school settings. Journal of Educational Psychology, 72, 186-196.

Frieze, I., & Weiner, B. (1971). Cue utilization and attributional judgments for success and failure. Journal of Personality and Social Psychology, 39, 591-606.

Gredler, M. E. (1992). Learning and instruction: Theory in practice (3rd ed.). New York: MacMillan.

Gunterman, E., & Tovar, M. (1987). Collaborative problem-solving with logo: Effects of group size and group composition. Journal of Educational Computer Research, 3, 313-334.

Harari, O., & Covington, M. V. (1981). Reactions to achievement behavior from a teacher and student perspective: a developmental analysis. American Educational Research Journal, 18, 15-28.

Harter, S. (1982). The perceived competence scale for children. ChiM Development, 53, 87-97. Hartley Courseware. (1983). Analogies Tutorial [Computer program]. Dimondale, MI: Author. Hawkins, J. (1983). Learning logo together: The social context. (Report No. 13). New York: Bank

Street College of Education, Center for Children and Technology. Hawkins, J. (1987, April). Collaboration and dissent. Paper presented at the meeting of the Society

for Research in Child Development, Baltimore, MD. Hawkins, J., Homolsky, M., & Heide, P. (1984). Paired problem solving in a computer context.

(Report No. 33). New York: Bank Street College of Education, Center for Children and Technology.

Hawkins, J., Sheingold, K., Gearhart, M., & Berger, C. (1982). Microcomputers in schools: Impact on the social life of elementary classrooms. Journal of Applied Developmental Psychology, 3, 361-373.

Jamestown Publishers (1975). Basic Word Vocabulary Test. Providence, RI: Author. Jamison, D., Suppes, P., & Wells, S. (1974). The effectiveness of alternative instructional media: A

survey. Review of Educational Research, 59, 1-67.


Johnson, D. S. (1981). TI: Naturally acquired learned helplessness: The relationship of school failure to achievement behavior, attributions, and self-concept. Journal of Educational Psychology, 73(2), 174-180.

Kaminsky, S. (1986). High and low achieving fourth graders' perceptions of success and failure. Reading Improvement, 23(2), 124-129.

Krasnor, L., & Mitterer, J. (1985). Computer possibilities for preschool education. The Canadian Journal of Research in Early Childhood Education, 1, 90-94.

Kulik, J. A. (1986). Evaluating the effects of teaching with computers. In P. Campbell & G. Fein (Eds.), Young children and microcomputers (pp. 159-169). Englewood Cliffs, NJ: Prentice-Hall.

Lebel, C. (1982). Cooperation between adolescents in computer-assisted algebraic problem-solving. Unpublished master's thesis, Concordia University, Montreal, Quebec.

Merrill, P. F., Hammons, K., Tolman, M. N., Christensen, L., Vincent., B. R., & Reynolds, P. L. (1992). Computers in Education. Toronto: Allyn & Bacon.

Nastasi, B. K., & Clements, D. H. (1992, April). Motivation and perceived competence in two computer environments. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco.

Nicholls, J. G. (1978). The development of the concepts of effort and ability, perception of academic achievement, and the understanding that difficult tasks require more ability. Child Development, 49, 800-814.

Okey, J. R., & Majer, K. (1976). Individual and small group learning with computer-assisted instruction. Audio Visual Communications Review, 24, 79-86.

Olivier, T. A., & Shapiro, F. (1993). Self-efficacy in computers. Journal of Computer-Based Instruction, 3, 818-885.

Rogers, C. G. (1990). Motivation in the primary years. In C. Rogers & P. Kutnick (Eds.) The social psychology of the primary school (pp. 92-109). New York: Routledge.

Salomon, G., & Globerson, T. (1990). When teams do not function the way they ought to. International Journal of Education Research, 13(1), 89-99.

Schunk, D. H. (1983). Developing children's self-efficacy and skills: The roles of social comparative information and goal setting. Contemporary Educational Psychology, 8, 76-86.

Schunk, D. H. (1984). Enhancing self-efficacy and achievement though rewards and goals: Motivational and information effects. Journal of Education Research, 78, 29-34.

Schunk, D. H., & Hanson, A. R. (1985, April). Influence of peer models on children's self-efficacy. Paper presented at the meeting of the American Educational Research Association, Chicago.

Shade, D., Nida, R., Lipinski, J., & Watson, J. (1986). Microcomputers and preschoolers: Working together in a classroom setting. Computers in the Schools, 3, 53-61.

Shavelson, R. J., & Bolus, R. (1982). Self concept: the interplay of theory and methods. Journal of Educational Psychology, 74, 3-17.

Sheingold, K., Kane, J., & Endrewait, M. (1983). Microcomputers use in schools: Developing a research agenda. Harvard Educational Review, 63, 412-432.

Trowbridge, D., & Durnin, R. (1984). Results from an investigation of groups working at the computer. (Report No. 143). Irvine, CA: California University, Educational Technology Center. (ERIC Document Reproduction Service No. ED 238 724)

Vinsohaler, J. F. & Bass, R. K. (1972). A summary of ten major studies on CAI drill and practice. Educational Technology, 12, 29-32.

Weiner, B. (1972). Theories of motivation: From mechanism to cognition. Chicago: Rand McNally. Weiner, B. (1974). Motivational psychology and educational research. Educational Psychologist,

11, 96-101. Weiner, B. (1979). A theory of motivation for some classroom experiences. Journal of Educational

Psychology, 71, 3-25. Weiner, B., Frieze, I., Kukla, A., Reed, L., Rest, S., & Rosenbaum, R. M. (1971). Perceiving the

causes of success and failure. In E. E. Jones et al. (Eds.), Attribution: Perceiving the causes of success and failure. Morristown, NJ: General Learning Press.

Whitley, B. E., Jr., & Frieze, I. H. (1985). Children's causal attributions for success and failure in achievement settings: a meta-analysis. Journal of Educational Psychology, 77, 608-616.

Wylie, R. C. (1979). The self-concept: Vol. 2. Theory and research on selected topics. Lincoln, NE: University of Nebraska Press.

Download - Attributions, group size, and exposure time as predictors of elementary children's performance on a microcomputer task

Top Related