Folder: Personal
Personalizing the Instructional Context
When someone with the authority of a teacher, say, describes the world and you are not in it, there is a moment of psychic disequilibrium, as if you looked into a mirror and saw nothing. (Adrienne Rich, quoted in Rosaldo, 1989, p. ix)
Page Contents
Personalization of instructional context (personal 
Personalization as Vicarious Modeling The effectiveness of modeling Personalization Example Personalization of Instructional Context Personalized Learning Personalized Instruction Personalization as Concrete Context Summary Findings Chart
Personalization of instructional context (personal
Personalization of instructional context (personalization) is not a new instructional strategy. If fact, academics have long been aware that relating new knowledge to students' existing familiarity with the world is an effective way for learners to acquire deeper meaning from new information. Learners' needs, background knowledge, and personal experiences are thus accommodated in the instruction. The use of the term personalization, however, has different meanings. The Personalized System of Instruction (Keller & Sherman, 1974), for example, applies more to individual pacing and the person-to-person interaction between students and facilitator. Personalization is also discussed as a means of incorporating students' goals and choice of topics into a curriculum, particularly for addressing values (Howe & Howe, 1975), and as a model of behavior modification for disruptive students (Mamchak & Mamchak, 1976).
The term is used here in an instructional-design perspective. From this perspective, the domain context of instruction is adapted to facilitate increased relevance and familiarity to students with new content (Ross, 1983). More specifically, the instructional context is individually tailored to students' interests and backgrounds by merging information from biographical inventories into the instructional content. This design model, introduced by Anand and Ross (1987) increases the personal meaningfulness of the content and is referred to in the present study as the Anand/Ross model (see also, Ross & Anand, 1987; Ross, McCormick, Krisak, & Anand, 1985). Miller and Kulhavy (1991) give a concise definition of personalization that is compatible with the Anand/Ross model: personalization refers to "the act of using verbal modifiers and exemplars which have been lifted directly from an individual's own repertoire of life experience" (p. 287).
Personalizing is used in this study within the context of mathematics learning. Various forms of personalizing mathematics learning are shown to be effective for either students of formal, school contexts (Resnick, 1987; Ross, McCormick & Krisak, 1985) or informal, non-school contexts (Carraher, Carraher & Schliemann, 1985; Carraher, Carraher & Schliemann, 1987; Lave, 1985; Lave & Wenger, 1991).
With recent advancements in educational technology, namely the proliferation of computers in the schools, personalization is made more practical. Computer software can be programmed to instantaneously transform data into something meaningful by relating the data to a form or structure that makes sense and is knowable to the individual learner (Anand & Ross, 1987; Ross & Anand, 1987). A major premise of this study is that computer-based personalization gives the learner greater capability to relate to, and make meaning from, new information. While this approach has had success in improving learning, motivation, and attitudes with regard to mathematics word problems (Cordova, 1993; Davis-Dorsey, Ross & Morrison, 1991; Davis-Dorsey, 1989; Lopez, 1989; Lopez & Sullivan, 1992), its potential has not been adequately explored in relation to its effect as a source of self-efficacy information. Particularly, the potential for vicarious experience is expanded when the computer presents information with increasing familiarity, such as with familiar models or characters in an instructional story.
Modeling, in addition to personalization, is also an effective means of conveying vicarious information in both therapeutic and academic self-efficacy research. Modeling refers to someone whose behavior, speech and expressions serve as behavioral cues to the observer. Early studies by Bandura and colleagues at Stanford University revealed that observed modeling of therapeutic behaviors could facilitate changes in percepts of efficacy for clinical patients (Bandura, 1982; Bandura, Adams & Beyer, 1977). In academic settings, Dale Schunk of Purdue University and colleagues consistently found that live and filmed models are effective sources of efficacy information (Schunk & Gunn, 1985; Schunk & Hanson, 1989) Ñespecially when observed models maintain a high degree of familiarity to the research participants (Schunk, 1987; Schunk & Hanson, 1985; Schunk, Hanson & Cox, 1987). Modeling with a high degree of familiarity is made practical as an instructional strategy by computer-based personalization of instructional context.
The present study investigates the premise that computer-based personalized storiesÑby way of character modelingÑcan effectively influence students' mathematics self-efficacy and performance. Instruction in mental computation strategies is selected as the criterial subject matter for this investigation for several reasons: (1) knowledge of strategies in mental computation gives confidence to learners, (2) lack of strategies in mental computation may reduce learners confidence and sense of efficacy, (3) school children readily evaluate their own mathematical capabilities in comparison with peers based on the ability to compute mentally, (4) unlike estimation, mental computation requires an exact answer and facilitates a more accurate view of the hypothesized relationship between self-efficacy and performance, and (5) the National Council of Teachers of Mathematics is calling for renewed interest in mental computation as an important mathematics alternative for the twenty-first century (Reys & Barger, 1994; Reys & Nohda, 1994; Silver, 1994).
Many school children have self-doubts about mathematics. Of course, the reasons are many, but this study suggests that one of the reasons is due to low mathematics self-efficacy and a lack of strategies. Which comes first? A lack of mathematics strategies could certainly influence one's self-efficacy to perform in the domain. From the social cognitive perspective (Bandura, 1977, 1986), however, children's' lack of efficacy to perform can also adversely affect their ability to learn. The problem must be addressed simultaneously; that is, children must acquire task-specific knowledge about their capabilities as they experience learning. This reduces faulty self-doubts and facilitates more accurate appraisals of one's present capabilities. It also demonstrates that learning mathematics improves with the acquisition of strategies and is not solely a matter of innate cognitive ability.
Personalization as Vicarious Modeling
Cognitive self-arousal can take two forms: personalizing the experiences of another or take the perspective of another. In the personalizing form, observers get themselves emotionally aroused by imagining things happening to themselves that either are similar to the model's or have been generalized from previous positive and aversive experiences [...] Research conducted in this framework has been concerned primarily with how role-taking strategies develop and affect social behavior. However, experimental evidence is lacking on how vicarious arousal can be affected by putting oneself in the model's place. What little evidence does exist suggests that personalizing modeled experiences is more vicariously arousing than role-taking. (Bandura, 1986, p. 313)
If humans gained knowledge only through direct experience children would be quite limited intellectually. Fortunately, children can learn from observing others perform and also by observing the consequences of the given performance. This form of vicarious modeling is evidenced in the fact that children can learn from televised depictions of human behavior (Beagles-Roos & Gat, 1983; Meadowcroft & Reeves, 1989; Thelen, Fry, Fehrenbach & Frautschi, 1979). Children also can make judgments about their own capabilities by watching models perform and imagining themselves performing above, equal to, or below the observed level of performance. Children make these judgments based on knowledge about themselves, resulting from past experiences, and perceptions of their own capabilities. The more substantiated evidence individuals gain from observing others, however, depends on the similarity between themselves and the model (Brown & Inouye, 1978; Littlefield & Rieser, 1993; Schunk, 1987; Schunk & Hanson, 1985; Schunk, Hanson & Cox, 1987). If children observe persons of obvious greater physical strength perform a highly physical feat, they do not usually expect that they too can perform up to that standard. If, however, children observe peersÑchildren developmentally similar to the observerÑwhom they perceive to have similar or lesser capabilities perform a requisite act, then their senses inform them that they too possibly can perform at that level. Therefore, model similarity, or peer modeling, is an important source for judging capabilities for performing certain tasks.
We have no peers of greater similarity to us than ourselves. Despite the number of traits possessed by others who are similar to us, we gain considerable knowledge of what we can do from what we have already done. Yet we have neither the time nor the opportunity to do much in our limited lifetimes. From the social cognitive perspective, we cannot be expected to gain our entire life's knowledge based on personal experiences. The resulting dangers alone, experienced by simple trial and error, would have disastrous consequences on our well-being and life expectancy. The challenge for designers of instructional stories is to model learning experiences so the learner vicariously experiences the feelings and cognitions of the protagonist or other characters.
The effectiveness of modeling
The effectiveness of modeling is related to four subprocesses of the observer: attention, retention, production, and motivation (Bandura, 1986; see also Bandura, 1971, for more background on these subprocesses).
Attention requires that the observer attend to the actions of the model. Activities that are modeled should therefore be relevant and engaging to the learner.
Retention requires that the information be relevant and meaningful to the observer. Learners must recognize some feature of new information in order to perceive and classify it as something meaningful (Sainsbury, 1992). From the social cognitive perspective, observers can translate symbolic modeling (e.g. from media) into meaningful behaviors which can be overtly emulated.
Production requires that the observer of a model be developmental capable of emulating the behaviors of the model. Children, therefore, adjust perceptions of efficacy depending on their perceived similarity to the model (Schunk, 1983, 1987).
Motivation processes are often dependent on incentives. Social cognitivists believe that symbolic incentives, including improved social functioning and enhanced self-efficacy, inform observers of the value and effectiveness of emulating modeled behaviors.
One method of modeling that attends to these four subprocesses and demonstrates some success is personalization of instructional context (Anand & Ross, 1987; Lopez & Sullivan, 1992; Ross & Anand, 1987; Ross et al., 1985). Computer-based instruction supports personalization by allowing the learner to determine some of the personal referents in which the content is situated. Unlike televised modeling of instructional information, computers are able to transform the instructional context to reflect individual input. This capability is currently being explored by interactive strategies of computer-based learning, in which the learner is addressed by name or is allowed a certain degree of control in selecting the pacing, sequencing, and characteristics of the instruction. This kind of interactive personalization is described in the literature under the label of learner control (see for a review, Kinzie, 1990).
Personalization, as used in the present study, allows the learner to control the personal referents of instruction, such as character names, in an instructional story. The learner transforms textual information to contain familiar referents. Theoretically, this allows the learner to envision being in the instructional context being depicted and observe a model that is highly similar to the learner. This degree of association enables learners to accommodate new information with existing knowledge structures (Davis-Dorsey, 1989; Ross, 1983; Ross & Anand, 1987).
Another potential benefit of personalized context using models of high similarity is that the learner is able to experience vicariously the emotions and cognitive representations of the models. Using personalized characters in an instructional story, learners can gain significant personal information about their capabilities with regard to the instructional strategies enacted by the modeling characters. Hypothetically, if the depiction is of positive gains in self-efficacy and usage of strategies, then learners are able to picture themselves similarly, thus gaining efficacy and using the strategies.
Personalization Example
Personalization, as used in the present study, refers to manipulating the instructional content to contain personal referents of the learner. Such personal referents may include familiar names, persons, places, or things. By relating the referents of context to familiar, everyday conditions of the learner, the content is made knowable to the learner (Brown, Collins & Duguid, 1989; Resnick, 1987). By modeling a successful learner, the observer believes that the task is achievable.
The purpose of personalization is to stimulate intrinsic interest and facilitate personal meaning of new content. This is accomplished by portraying tasks depicting what real people would do in a realistic situation. For subject matter that is meant to facilitate the instruction of learning strategies, it is important that the complexity of the environments of everyday life not be entirely reduced or abstracted out of context. At the same time, a narrative must be flexible enough to disassociate the concepts and principles from the initial learning context. Encouraging students to construct learning strategies that can be transferred outside of the classroom requires authentic learning environments that can be explored from multiple perspectives, with levels of complexity that approximate the experiential sophistication of the learners (Spiro & Jehng, 1990; Salomon & Perkins, 1989). Similarly, employing multiple perspectives increases the likelihood that the observer can derive multiple sources of efficacy information.
Personalization of Instructional Context
Personalization, as used in the present study, follows a lineage of research on variations of the Anand/Ross Model. This instructional design model enables learners to transform the characteristics of learning and instruction by merging familiar referents with abstract nouns and pronouns as in mathematics word problems and instructional stories.
Anand and Ross (1987) developed three versions of a computer assisted lesson for teaching division of fractions to fifth- and sixth grade children. Participants were assigned to one of three groups: 1)personalized context, 2) concrete context, and 3) abstract context. Personalization was facilitated in this experiment by enabling students to change referents in story problems to personal information, such as personally favored people, places and activities. In the concrete version, names and events were hypothetical (realistic, but unfamiliar). The abstract condition was presented using general referents such as "objects" in place of specified things (such as candy bars). The achievement posttest included items from all three experimental contexts. Attitudes were also assessed on an eight-item Likert-scale asking about students reactions to their respective treatment. Achievement results yielded significant effects for both treatment conditions over the abstract condition, while neither the personalized nor abstract group differed significantly from the concrete group. With regard to attitudes, the personalized group also yielded a significant effect over the concrete group, but did not differ from the abstract group.
In a subsequent investigation, Ross and Anand (1987) sought to compare findings from the first study, in which the instruction was delivered via computer, to printed versions of mathematics story problems using essentially the same treatment design. Participants were again fifth- and sixth-graders. Mathematics achievement was assessed using a three-section posttest containing "context," "transfer," and "recognition" story problems. Attitudes were also assessed. As in the Anand and Ross (1987) study, overall results on the achievement subtests showed the personalized treatment to be the most effective, and was never surpassed by the other conditions. The overall attitude measure was not significant although item analyses mostly favored personalization.
Implications of the two Anand and Ross studies described above for the present study are that personalization is effective in teaching mathematics and in learning to solve word problems.
Personalized Learning
Personalized Learning;. Most research that employs the Anand/Ross Model has been conducted using mathematics word problems.
Lopez and Sullivan (Lopez & Sullivan, 1992; see also Lopez, 1989) demonstrated how personalization of mathematics word problems could improve the mathematics (one- and two-step arithmetical operations) achievement and attitudes of rural Hispanic, seventh graders in Southern Arizona. Participants were grouped by pretest score and gender, and assigned to one of three groups: 1)individualized personalization, 2) group personalization, and 3) nonpersonalized. Students then filled in biographical inventories, detailing familiar nouns and pronouns such as favorite kinds of ice cream and the names of friends. In the individualized treatment, each student received mathematics story problems in which generic nouns and pronouns were merged with personal referents. In the group treatment, common and familiar referents of the majority were merged for one set of story problems for the entire group. In the nonpersonalized version, there was no attempt to familiarize the problems. Substitutions were made using a computer program, however the children received print versions of the story problems. Results show that both the individualized and group personalization treatments were significantly higher than the non-personalized version for two-step arithmetic calculations and mathematics attitudes; although, the treatment versions were not significantly different from each other. There was also a significant attitudinal effect for only the individualized treatment. Attitudinal items consisted of interest, liking, and preference questions. The study suggests that personalization of mathematics story problems is an effective instructional design strategy for improving mathematics achievement and attitudes.
In another study (Davis-Dorsey, Ross, & Morrison, 1991; see also Davis-Dorsey, 1989), researchers investigated whether personalization of mathematics word problems would benefit elementary school children. Personalization of context, in this case, was combined with "problem rewording for explicitness." Treatments were administered as text. Overall significant results show that second graders benefited from the combined intervention of personalization and problem rewording, but personalization itself was not significant. Fifth graders, on the other hand, benefited from the personalization intervention, but not problem rewording. Gender also yielded a significant main effect for fifth graders in favor of females. These results suggest that older children in elementary school may benefit more from personalized context of mathematics story problems, having more developed schemata for processing information in a real-world context. One interesting way to build upon the findings of Lopez and Sullivan (1992) and Davis-Dorsey, Ross, and Morrison (1991), however, is to provide the personalization treatments on computer, and to use personalization as an instructional method instead of as a testing method. The present study employs these variations.
Personalized Instruction
Personalized Instruction;. There are several studies, as of this writing, that examined personalization as a instructional strategy for relating individually to diverse learners.
Herndon (1988) sought to extend on the Anand/Ross Model by comparing three levels of personalized instruction for understanding syllogisms. Participants were high school seniors, assigned to one of three groups: 1) individual personalization, 2) group personalization, and 3) non-personalized. Students completed an inventory that asked them to report their most valued possessions, and other personal referents such as the names of people and things. Individual inventory items were then merged into personalized syllogisms for experimental groups one and two. All instructional versions were delivered to students as text.
Herndon (1988) found that the individual personalization approach had a positive effect of students' attitudes (i,e. whether the instruction appealed to students). There were also significant effects for the two personalization treatments on continuing motivation (i,e.whether students would like more syllogism instruction), but this conclusion should be viewed skeptically because it was based on one "yes" or "no" question. Still, these results suggest that personalized instruction may contribute to improved learner affect which, like cognition, has a reciprocal influence on self-efficacy.
Cordova (1993) used a personalization technique designed to enhance intrinsic motivation and mathematics learning for fourth- and fifth-grade children. Participants were assigned to one of five conditions in a 3 (levels of personalization) by 2 (levels of choice) design. The intervention consisted of a HyperCard-based, computer program designed to teach children arithmetical rules such as order of operations and use of parentheses. Personalization was accomplished by allowing the user to change generic referents in an instructional fantasy story, such as character names, dates corresponding with the users' birthdays, teachers' names, and desired birthday gifts. Choice was accomplished by allowing the user to select the icons representing the user. Children were posttested on a battery of attitudinal measures and a 16-item mathematics test. Significant results showed that students reported liking the personalization and choice features and scored higher on the mathematics test.
Personalization as Concrete Context
The situated nature of learning, remembering, and understanding is a central fact. It may appear obvious that human minds develop in social situations, and that they use the tools and representational media that culture provides to support, extend, and reorganize mental functioning.
(Pea, 1991, p. 11.)
Many studies have shown how skills and knowledge are often better learned, remembered, and understood in the context in which they are acquired.
Ross (1983) conducted two experiments to test the notion that adapting the context of instruction benefits students. In one experiment, 51 college-age, preservice teachers were assigned to one of three groups: 1) adaptive context, 2) nonadaptive context, and 3) abstract context. In the adaptive context, participants were given statistics instruction on probability using explanations and examples from the domain of education. In the nonadaptive context, participants received the instruction from a medical-related perspective. From the abstract perspective, participants learned statistical rules and formulas without reference to any other content domain. Posttests included education, medical, and abstract items. Results of this experiment showed overall significant posttest results in favor of adaptive context over nonadaptive and abstract contexts on education items. Adaptive context was also significantly favored over abstract context on abstract items.
In a follow-up investigation, Ross (1983) sought to replicate the findings in the above study using 50 nursing students. Therefore, the medical domain was now the adaptive context. Results of complex comparisons, using the ScheffŽ method, showed adaptive context significantly superior to the others.
Results of the two Ross (1983) experiments showed that education students performed better in the education adaptive context, and nursing students performed better in the medical adaptive context. Implications of these results are that adaptive contexts are more effective design methods for learning quantitative material. One contributing reason for these phenomena may be that depth of learning is greater when new content is assimilated to prior knowledge structures.
However, Ross, McCormick, and Krisak (1985) further examined the effects of personalization of statistical content on achievement and preferences of college education and nursing students in other experiments and found mixed results. The researchers anticipated that allowing participants to choose their preferred thematic context of instruction (adaptive) versus being given their least preferred context (nonadaptive), would result in higher achievement by the adaptive group. They also sought to examine whether students in the nonadaptive context would suffer detrimental learning. Attitudes and recall of critical information were assessed by posttest questionnaire.
In experiment one, nursing students were randomly assigned to one of four treatment groups: 1) standard-adaptive (automatically given the medical context), 2) standard-nonadaptive (abstract context), 3) learner-control adaptive (given preferred choice of context), and 4) learner-control nonadaptive (given least preferred choice of context). The four instructional contexts were abstract, education, medical, and sports themes. Effects by group on achievement and recall were generally not significant. Item analysis of attitudes generally favored the adaptive context.
In experiment two, 50 education students were used in the same design as in experiment one. Significant results in this case favored the adaptive group, however, unlike the experiment with nursing students, there was no significant attitude effect.
Generally, the mixed results of the two combined studies suggest that personalization (adaptive context) can be an effective method for presenting statistical content to college students, but that this is not a foregone conclusion. The present study, however, changes the instructional context by replacing abstract and generic referents with personal ones, thus placing the learner not only in the context of the story, but in the situated nature of the story problems as well.
Summary
The effects of personalization on self-efficacy (as the two are defined in this study) were assessed in only one previous experiment (Cordova, 1993), using only a few multidimensional items that were domain-general in nature. In this case, self-efficacy was not analyzed according to the same guidelines used in the present study, which purports that self-efficacy is task- and situation-specific. There is, however, solid evidence that various comparable sources of self-efficacy information, such as symbolic modeling with a high degree of personal relevance and using multiple models, can be effectively incorporated into instructional strategies that promote increased self-efficacy and improved subsequent performance.
Modeling is an effective means of raising mathematics self efficacy, and personalization is an effective means of improving mathematics performance. The present study converges these lines of research by specifying an instructional design strategy that personalizes instructional stories where characters model skill acquisition and improved personal changes in self-percepts of efficacy.
There are several reasons that peer-modeling and personalization of instructional context are effective instructional interventions for raising learner self-efficacy and mathematics performance. First, peer modeling provides evidence to the observer that perhaps he or she can too perform successfully at a designated level of performance. Multiple models, too, enable the learner to relate to at least some attributes of the models.
Second, personalization extends on modeling in that it also enables the embedding of instruction in contexts that are familiar and relevant to the learner. Theoretically, learning in situated contexts enables the learner to assimilate new knowledge into existing knowledge structures. Personalization can be viewed as either a form or extension of modeling, as it allows the learner greater control over character referents embedded in instructional stories, but also enables the learner to observe thought patterns of the characters. These thought patterns, or cognitions, can vicariously portray for the observer how one's faulty self-knowledge, or low self-percepts or low self efficacy, may be corrected through strategy acquisition and attention to persuasory information.
Third, there is growing evidence that gender differences in mathematics performance are dissipating; however, questions remain about how self-efficacy influences mathematics performance. The present study includes gender as an attribute variable to further gauge whether personalization is an effective intervention for raising gender based percepts of efficacy and mathematics performance.
The treatment (personalization) and attribute (gender) variables were thus tested on the dependent variables, mathematics self-efficacy and mental computation performance. This experimental matrix provides further explanatory and predictive evidence of the effects of personalized storytelling as an instructional design strategy.
Findings Chart
| Aspect | Subset | Authors | | Findings |
| Modeling Interventions enhancing PSE | Social Comparison | Brown & Inouye |
| 1978 | learned helplessness from helpless models |
| | Social Comparison | Schunk 1983 | PSE influenced by goals, social comparison (peers); also multiple comparisons |
| | Multiple Sources | Schunk & Rice 1987 | effectiveness feedback, specific value was more effective for PSE |
| | Peer Modeling And Academic Performance | Rosekrans (1967) | observers of peer models demonstrated behaviors better |
| | Peer Modeling And Academic Performance | Schunk & Hanson, 1985 | same gender peer modeling resulted in higher math performance and PSE |
| | Peer Modeling And Academic Performance | Schunk, Hanson & Cox, 1987 | sig gains on math perf and PSE for coping and multiple models; no gender effect |
| Model Gender Effects | | Schunk & Hanson, 1985 | same gender peer modeling resulted in higher math performance and PSE |
| | | Schunk, Hanson & Cox, 1987 | see above; elementary kids |
| | | Pintrich & DeGroot, 1990 | no gender effect on several performance and cog vars, except for boys with regard to self-reg and strategy use; but globally investigated confidence and competence |
| Personalization | Intro: Familiarity | Brown, Collins, & Duguid, 1989; Resnick, 1987 | relating referents of context to familiar and everyday conditions makes content knowable to learner |
| | Intro: Multiple Perspectives | Spiro & Jehng, 1990; Salomon & Perkins, 1989 | multiple perspectives improves learning; approx the experience of the learner |
| | Pers Instruction | Herndon, 1988 | individual pers had positive effect on students' attitudes |
| | Pers, Cog, & Affect | Lopez & Sullivan | individualized and group personalization sig better than nonpersonalized on performance and attitudes |
| | | Davis-Dorsey Ross, & Morrison, 1989 | fifth graders benefited from personalization; gender effect in favor of females |
| | Pers And Pse | Cordova, 1993 | sig main effects on pers and choice on two posttest items |
| | Personal Relevance | Anand & Ross, 1987 | ach effects in favor of personalization and concrete over abstract treatment; and pers over concrete |
| | | Ross & Anand, 1987 | pers treatment was most sig effective; gender effect in favor of boys |
| | Story-Based Learning | Anderson, Spiro, & Anderson, 1978 | context schemata aids learning |
| | Mental Computation And Sit Cog | Carraher, Carraher, & Schliemann, 1985 | sit context aids math performance |
| | | Carraher, Carraher, & Schliemann, 1987 | mental computation is more effective in context |
| | | Ross 1983 | adaptive context (personalization) sig favored over abstract context |
| | Mixed Results | Ross, McCormick, & Krisak, 1985 | item analysis of attitudes favored adaptive context, but not sig on recall or ach |
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