INTRODUCTION

This paper presents an individual learning difference study that uses a learning orientation model to investigate successful learning from a new perspective. Because the research literature on the individual learning difference topic is so extensive, I have highlighted major developments leading up to the perspective described in this article. After this research summary, a discussion about learning orientations and how they were used in this study and the study results will follow. Finally, some of the study results are highlighted to suggest new design strategies for learning environments.

RESEARCH SUMMARY

In the fifties, Cronbach (1957) challenged the field to find "for each individual the treatment to which he can most easily adapt." He suggested that consideration of cognitive treatments and individual together would determine the best payoff because we "can expect some attributes of person to have strong interactions with treatment variables.” Cronbach (1957) stated that “Ultimately we should design treatments, not to fit the average person, but to fit groups of students with particular aptitude patterns.”

Between 1960 and 1970 Cronbach (1975) and others "searched fruitlessly for interactions of abilities." They were looking for "aptitudes" (characteristics that affects responses to the treatment) that explained how to instruct students one way and not another, i.e., evidence that showed regression slopes that differed from treatment to treatment.

In the seventies, Cronbach (1975) still advocated that a closer scrutiny of cognitive processes would be a profitable next phase of work on Aptitude Treatment Interactions (ATIs). He highlighted research that related success to the Ai (Achievement via Independence) and Ac (Achievement via Conformance) scores of Gough's California Personality Inventory. The evidence continued to show that the learning outcomes were better when the environment and instructor's presentation adapted to the student's aptitude and personality (1977). For example, the "constructively motivated student who seeks challenges and takes responsibility is at his best when an instructor challenges him and then leaves him to pursue his own thoughts projects."

In his article, Cronbach (1975) continued to emphasize the important relationship between cognitive aptitudes and treatment interactions. Nevertheless, he states that "Snow and I have been thwarted by the inconsistent findings coming from roughly similar inquiries. Successive studies employing the same treatment variable find different outcome-on-aptitude slopes." He surmised that the inconsistency came from unidentified interactions.

Finally, Snow and Cronbach (1977) concluded that "an understanding of cognitive abilities considered alone would not be sufficient" to explain learning, individual learning differences, and aptitude treatment interactions.

In the early eighties, the cognitive process analysis of aptitudes processes continued with variations. Snow (1980) described the ATI investigation as process-oriented research on individual differences in learning and cognition. Although they were looking for a "whole-person view" of learning, he believed that it was primarily the cognitive processes that should be considered in the design and development of adaptive instructional systems.

Eventually the new "aptitudes" evolved into cognitive styles, commonly called learning styles, to represent the predominant modes of information processing (i.e., preferred learning sets to the acquisition, retention, and retrieval of new knowledge).

ATI critics argued that student performance was too dynamic to be supported by the permanence and pervasiveness of primarily cognitive ATI and that students, e.g., without learner control, would become system dependent on prescribed solutions.

In the late eighties, Snow (1987) described how in cognitive psychology that conation as a learning factor has been "demoted" and "since it seems not really to be a separable function," it is merged with affection.

Together these factors are viewed as "mere associates or products of cognition" and then ignored. He warned that individual difference constructs or "aptitude complexes" needed greater consideration of the joint functioning between cognitive, conative, and affective processes. Snow and other researchers were in search of an information processing model of cognition that would include (still as a secondary consideration) possible cognitive-conative-affective intersections. He was looking for a way to fit realistic "aspects of mental life, such as mood, emotion, impulse, desire, volition, purposive striving" into instructional models.

According to Snow (1989), the best instruction involves treatments that differ in structure and completeness and high or low general ability measures. Highly structured treatments (e.g., high external control, explicit sequences and components) seem to help students with low ability but hinder those with high abilities (relative to low structure treatments).

Cronbach's and Snow's research set the stage for the learning orientation research. The learning orientation research attempts to reveal the dominant influence of affective and conative factors (e.g., emotions and intentions) on guiding and managing cognitive processes (no longer demoted to a secondary or lesser role). It is in understanding the structure and nature of the complex relationships between learning orientations and interactions that we can return to Cronbach's original hypothesis that we should find "for each individual the treatment to which he can most easily adapt." And, "ultimately we should design treatments, not to fit the average person, but to fit groups of students with particular aptitude patterns or aggregated learning dispositions. Conversely, we should seek out the aptitudes which correspond to (interact with) modifiable aspects of the treatment."

As can be expected the new lines of research, especially the neurobiology of learning and memory research, will continue to reopen the old questions, gain from the research accomplished in the past, and pose exciting new questions for the future.

As we look forward to new issues highlighting the importance of emotional and intentional states on cognitive processing, waiting in the wings to be discovered are the treatments that lead toward more interactions and successful learning and performance. And perhaps, as some may have already predicted in the past, the hegemony of cognition over intent and affect may be coming to an end.

NEW RESEARCH FOUNDATIONS

Recent neurobiology of learning and memory research efforts are providing insights for understanding critical sources for learning differences. One powerful, consistent finding to emerge from recent advances in neuroscience is that emotions (e.g., fear, frustration, passion, motivation, and happiness) and intentions (e.g., will, striving, and commitment) greatly impact personality characteristics, including locus of control (Liu etal., 2002) and how learners meet goals, learn and perform tasks, and succeed differently.

Joseph LeDoux (1996, 2002), neuroscientist at the Center for Neural Science at New York University and author of the “Emotional Brain,” and “Synaptic Self,” provides neuroscientific evidence showing that emotions and passions greatly influence, guide, and, at times, override our thinking (cognitive) processes. Similar “brain research” advances (Kandel, 2002; Davidson, 2000; Cahill and McGaugh, 1998) help us identify critical aspects of learning success that are governed by individual differences in learning, memory, and brain development. These advances also help us understand that how much individuals emotionally and intentionally want and commit to learn is a powerful force in how well they purposefully manage information, plan, and set and accomplish goals.

PERSONALIZED FOUNDATIONS FOR INSTRUCTIONAL DESIGN

Many of today's researchers and designers for online learning are investigating more sophisticated learning theories based on research showing how the brain works. A key consideration for online learning is determining dominant “whole-brain” sources influencing individual learning differences, persistence, engagement, and self-motivation. This involves understanding the diverse neurobiological influences on intrinsic or extrinsic mechanisms for information processing and knowledge building. From the whole-brain perspective, one quickly realizes that previous primarily cognitive solutions that traditionally supported classroom instructor roles (i.e., where a classroom instructor manages emotions, intentions, presentation, feedback, and social issues) may be insufficient for online learners and cause higher attrition rates, especially for learners with low intrinsic locus of control (e.g., for those that need more instructor support, guidance, and explicit feedback). For example, consider the social relationships between the instructor, learners, peers, and environments that are traditionally an integral part of the learning process--some learners depend more on these social and learning relationships than others.

Or, consider the experienced instructor who knows that one learner loves to solve problems, is good at problem solving, and needs little assistance. In contrast, the same instructor may respond differently to another learner who hates solving problems, is not very good at it, and needs a different kind of support and encouragement.

In addition to helping the learner solve a particular problem, the instructor’s goal may also include helping the learner improve overall problem solving or critical thinking ability long-term (Center for Critical Thinking, 2003).

The University of Phoenix has been commercially successful understanding important distance learner considerations, especially social issues, as reflected in the following statement by W. Symonds (2003):

But Phoenix Online realized that interaction with humans -- the professor and other students in the class -- was far more important to success than interaction with the digital content.

Thus, Phoenix Online keeps its classes small, averaging just 11 students. And to combat the Achilles heel of distance education -- a high dropout rate -- it offers its students plenty of hand-holding, including round-the-clock tech support. The result: 65% of its students go on to graduate.”

The challenge is improving online learning experiences with more personalized, cost effective e-learning designs, strategies, and technology use. This includes providing designs that consider whole-person considerations and needs, identify and support key success attributes, and tap into sources for self-motivation and self-direction.

LEARNING ORIENTATIONS

This paper uses Learning Orientation Theory to explore whole-person considerations and needs. The Learning Orientation research uses recent advances in the neuroscientific research to represent human learning variability more realistically—from a whole-brain perspective. Learning orientations represent a comprehensive set of psychological factors (conative, affective, cognitive, and social) that influence how individuals approach learning (Martinez, 2000, Martinez, 2003a). This perspective tries to be more robust than primarily cognitive explanations (e.g., learning styles) because it highlights the dominant developing, guiding, and managing influences of emotions and intentions on cognitive and social processes. It suggests that personalization without a whole-person, neurological foundation is unsatisfactory and incomplete.

This Learning Orientation Model presents profiles for four dominant learning orientations, including Transforming, Performing, Conforming, and Resistant (Martinez, 2003b). The differing orientations represent the variability in learning approaches (e.g., differing locus of control) from an individual-learning perspective. These profiles show the degree that learners, following beliefs, values, emotions, and intentions to learn, generally commit effort and self-manage the learning process to attain goals, monitor or assess learning progress, and use reflection to improve future learning opportunities. Depending on the specific learning circumstances and ability, a learner may cover a range of a learning orientation or move downwards or upwards in response to negative or positive responses, conditions, resources, results, expectations, and experiences.

Table 1. Learning Orientations Model

Four Learning Orientations
Transforming Learners	Performing Learners
Conforming Learners	Resistant Learners

Transforming Learners

Transforming Learners are deeply influenced by an awareness of the psychological aspects that motivate them. They place great importance on personal strengths, intrinsic resources, learning ability, committed, persistent, and assertive effort, and problem-solving strategies, and positive expectations to self-manage and accomplish personal goals successfully. These learners manage holistic to partist learning strategies, short- and long-term goals, and enjoy using learning to acquire expertise; they will even risk making mistakes to attain greater expertise.

Transforming Learners are holistic thinkers and seldom rely heavily on short-term tasks, schedules, deadlines, normative performance standards, expected social or instructional compliance, or others for extrinsic learning support. They may find routine activities boring.

Transforming Learners enjoy taking responsibility and control of their learning and willingly become actively involved in managing the learning process (intrinsic locus of control). They typically tap into stimulating, intrinsic influences, such as intentions, passions, personal principles, beliefs, and desires for personal goals and high standards, to self-direct improvement or intentional achievement of challenging, long-term goals.

Using their own well-tested autonomous, reflective, goal-oriented, and self-assessment learning framework, Transforming Learners expertly adapt suitable strategies to manage intrinsic resources and meet the challenges in any learning situation. These learners learn best in open, discovery, or challenging learning environments that encourage and support expertise building; risk-taking; mentoring relationships; self-directed learning; complex, problem-solving situations; transformative processes; high learning standards, and long-term personal accomplishments and change.

This group of learners can improve by paying attention to details and increasing focus on implementation and task completion. In fact, they can improve by focusing on any critical “boring” day-to-day routine activities.

Performing Learners

In comparison, a Performing Learner is a skilled learner that consciously, systematically, and capably uses short-term psychological processes, strategies, preferences, and self-regulated learning skills to achieve learning objectives and tasks. In contrast to Transforming Learners, Performing Learners are task-oriented, more often extrinsically motivated, take fewer risks with mistakes and complex or difficult goals, often focused on grades, rewards, and normative achievement standards, and often ready to rely on coaching relationships, available external resources, and social influences and relationships to accomplish a task.

Performing Learners need an important reason, (one that they value), to push themselves toward more intentional performance and greater levels of expertise. They may selectively commit great effort to learn topics and skills that they highly value and find particularly interesting and beneficial. Often, Performing Learners will clearly acknowledge that they want to limit or constrain effort (for example, they do not have enough time or interest) by only meeting stated objectives, getting the grade, or avoiding exploratory steps beyond learning requirements.

These learners learn best in semi-structured learning environments that add competition, fun, and coaching to foster motivation (i.e., extrinsically). This group of learners can improve by practicing more holistic (big-picture) thinking and problem-solving skills or considering how to look at the forest as well as the trees.

Conforming Learners

Conforming Learners are deeply influenced by an awareness of the social aspects and external resources that surround them and motivate them. Compared to Transforming or Performing Learners, Conforming learners are more compliant with the status quo. They may passively accept knowledge, store it, and reproduce it to conform, complete assigned tasks if they can, and please others.The Conforming Learner is satisfied with routine. They are less complex learners and typically may prefer not to use initiative, think critically, make mistakes, reflect on progress, synthesize feedback, or give knowledge new meaning to change themselves or the environment.

These learners may be less sophisticated or skilled learners and may have difficulty solving complex problems or accepting change. Typically, they may have little desire to control (extrinsic locus of control) or manage their learning or set challenging personal learning goals. Learning in open learning environments, which focus on high learner control, discovery or exploratory learning, complex problem-solving, challenging goals, and inferential direction, may frustrate, demoralize, or demotivate these learners--without sufficient support and scaffolding. In contrast, with sufficient support and scaffolding, these learners can increasingly improve learning ability and accomplishment. They will be able to assume greater responsibility for their learning in more structured environments. These online learners work best with scaffolded structure, guiding direction, simple problems, linear sequencing, and explicit feedback. They would profit most from a variety of blended learning solutions that provide additional support from instructors and peers.

In contrast to other orientations, conforming learners are motivated and learn best in well-structured, collaborative or directive environments using step-by-step procedures. Unlike transforming and performing learners, who have stronger, more positive beliefs about learning and greater learning efficacy, these learners may believe that achievement is often due to luck and that learning is most useful when it helps them avoid risk and meet the basic requirements in their job. In less supportive environments, they may prefer to use minimum effort on simpler goals and task. This group of learners can improve, over time with adequate support, by learning how to take increasingly greater risk in all areas of learning.

Resistant Learners

Resistant Learners may deal with either short-term (temporary) or long-term (permanent) resistance. They may doubt that: (1) they can learn or enjoy achieving any goals set by others (2) compulsory academic learning and achievement can help them achieve personal goals or initiate desired changes, and (3) their personal values, interests, and goals can benefit from academic objectives. Too often Resistant Learners will suffer repeated, long-term frustration from conflicting values, expectations, and goals, painful misunderstandings, perceived academic or social inadequacy, disappointment, or instruction that confuses or does not challenge or help them. They do not believe in formal education or academic institutions as positive, necessary, or enjoyable influences that add value or benefit to their life.

Resistant learners may be passive and disinterested while others may be aggressive and angry. Ironically, some resistant learners find the challenge of not learning far more interesting and rewarding and may commit great effort to resisting goals set by others. Resistant learners are a complex mixture of skilled or unskilled, motivated or bored, satisfied or frustrated, passionate or apathetic. To differing degrees they may be discouraged, defensive, or disobedient learners or in contrast, passionately assertive non-learners.

STUDY PURPOSE

This study extends the learning difference investigation to online learning. The study perspective highlights the influential power of emotions and intentions and uses the learning orientation model to represent human learning variability more realistically. This alternative perspective is more robust than typical primary cognitive explanations (such as learning styles) because it specifically highlights personalization based on the great impact of affective and conative factors on cognitive and social processes.

The study purpose was to investigate learning differences in three environments that either matched or mismatched the student’s learning orientation. The student’s learning orientation was identified in advance using the Learning Orientation Questionnaire LOQ, a 25-item questionnaire that uses the three construct factors (conative/affective aspects, learning autonomy, and strategic and committed learning effort) to measure and identify learning orientation (Martinez, 2000, Martinez & Bunderson, 2001).

The learning orientation model was used to differentiate the audience and then hypothesize about the individual’s disposition to learn--less or more successfully in different environments. Two of the study’s research questions were:

Research Question 1: Do learners using intentional learning environments (Group EX1) that match their learning orientations benefit with higher satisfaction, learning efficacy, and achievement, and more intentional learning performance than learners not using intentional learning environments (Groups CO1 and CO2)?

Research Question 2: Do learning orientations influence group interactions (Groups EX1, CO1, and CO2)?

These questions were helpful in determining if learning orientation, time, and environment would account for statistically significant effects and interactions.

STUDY METHOD

In this study a course was presented in an application that could provide three Web learning environments. The study tested what effects the different environments and learning orientations had on the learner’s attitude, learning performance, learning efficacy, and achievement. The three learning environments served as the three research groups that appear in Table 2.

Table 2. Description of the Three Learning Environments (Research Groups)

Web Learning Environment 1

The experimental group (Group EX1) presented an intentional learning environment which enabled the learners to self-select a transforming, performing, or conforming environment.

It offered resources in the iCenter that could help, if chosen, the learner increasingly self-manage individual learning in the domain of expertise using an organized problem-solving structure integrated with dynamic practice, review, and assessment activities.

These resources enabled the learner to examine the content of the course, set goals, reflect on presentation preferences, and review cumulative information about scores and progress.

Optional Intentional Learning Training (ILT) was briefly offered at the beginning of the course to help and encourage learners to use the special learning resources provided in this environment (intervention treatment).

Web Learning Environment 2

The first control group (Group CO1) presented a transforming / performing learning environment. It offered the same instructional setting presented for Group EX1, including the intentional learning resources (iCenter), but omitted the special ILT intervention instruction. As a result, the learners were not encouraged, directed, or shown how to use the special intentional learning resources.

Web Learning Environment 3

The second control group (Group CO2) presented the conforming learning environment. It offered a restricted, linear-sequenced, menu-driven version. It did not offer the intentional learning resources (iCenter) or the ILT intervention.

After determining the learner’s learning orientation, the application randomly assigned each learner to one of the three learning environments, whether or not it matched their learning orientation. Each of the environments delivered the same course (8 lessons), called Discovering the World Wide Web.

Experimental Research Design

An experimental factorial research design was used to conduct a multiple repeated measures univariate analysis of variance (ANOVA) for the two research questions. Factorial designs allow researchers to analyze two or more variables simultaneously. In this case, the design investigated the independent and interactive effects of two independent variables (learning orientation and ILT training) on four dependent variables (satisfaction, learning efficacy, intentional learning performance, and achievement).

A second advantage of the factorial approach is that one can control variables that you know will influence the analysis, such as the time variable. To allow for the effects of time, the repeated measure aspect for multiple hypothesis testing was used. This design feature means that the subjects are tested several times for a measure of an independent variable. A third advantage of the factorial design is that this approach allows the researcher to control and analyze “interactions,” in addition to “effects.”

Table 3. Repeated Measure Research Design for the Three Research Groups

Step 1 LOQ Pretest
Step 2 Intervention		A₁	A₂	A₃
1 GROUP EX1 with ILT iCenter	Cat. 1	Y Measures	Y Measures	Y Measures
	Cat. 2
	Cat. 3
2 GROUP CO1 without ILT with iCenter	Cat. 1	Y Measures	Y Measures	Y Measures
	Cat. 2
	Cat. 3
3 GROUP CO2 without ILT without iCenter	Cat. 1	Y Measures	Y Measures	Y Measures
	Cat. 2
	Cat. 3
Step 3 Analysis

This research design shows three groups with or without the Intentional Learning Training (independent variable 1) and iCenter resources: Group EX1 is the experimental group, and Groups CO1 and CO2 are control groups. The three orientation categories appear as Cat. 1, Cat. 2, and Cat. 3 to differentiate the subjects within the three research groups by three learning orientations (independent variable 2): transforming, performing, and conforming learners, respectively. Resistant learners are not included in this study. The repeated Y measures for the four dependent variables appear in columns A₁, A_2, and A₃.

This research design is unique because it uses learning orientation (Cat. 1, Cat. 2, and Cat. 3) as a separate dimension to (1) guide development of the research environment and instructional treatment and (2) differentiate the learning audience before introducing the treatment and examining the results. In contrast to a “one-size-fits-all” approach, learning orientation profiles add the human dimension (that is, the differentiated audience) to the treatment and examination of multiple learning variables. This step is especially important because it distinguishes learners as individuals with predominant psychological characteristics or dispositions in comparison to traditional conceptions (limited to cognitive abilities and styles) of learners as a uniform group of human beings with a homogenous set of emotions, intentions, beliefs, goal orientations, and values. The introduction of learning orientation and multiple variables is an effort to reflect a more realistic learning experience. In this study, learners are not expected to have loci of control, persistence, or source of motivation. They are not expected to want or intend to learn, set goals, and benefit alike from the same treatment.

Treatment

Seventy‑one individuals (49 women and 22 men, mean age = 22) volunteered to take the Web course. The group was comprised primarily of undergraduate university students who had very limited or no Web experience and showed a desire to learn how to use the Web.

Other volunteers came from the general public, including white- and blue-collar employees, trainers, young and older housewives, university and high school faculty, retirees, and graduate students. The effort to get a broad volunteer sample was helpful in generalizing the results to the public.

Overall, the treatment was accomplished in three phases:

• Phase 1: Learners visited the research lab where computers (loaded with the Environments and the Web Course) displayed the registration form. Learners registered as first-time users. As part of the registration process, the learners took the 25-item “Learning Orientation Questionnaire,” to identify their learning orientations.

• Phase II: The computer used the stratified random sampling method to assign the subjects, by learning orientation (transforming, performing, and conforming learners) to three independent groups (EX1, CO1, CO2). This step evened the groups out according to learning orientation.

Using the group assignment, the computer displayed specific instructions for each group. The instructions provided the intervention to GROUP EX1, as previously described.

• Phase III: After reading the instructions, learners worked on the course at their own pace, beginning and stopping as necessary. Learners finished the course by completing the assessments for eight lessons, generally in one session. They typically took one and a half to two hours to finish the course. There was no time limitation.

Data Collection

The repeated research design involved a complex collection method (four data sets) to reflect the dynamics of change, that is, as individuals generally experience learning in every day life. The first set came from the pre-course registration and the other three from the practice and assessment activities in the three instructional units. The eight lessons were divided:

• one through four comprised the first unit

• five and six the second unit

• seven and eight the third unit

At the end of lessons three through eight, the learners could write comments or rate themselves on two questions which provided information for two dependent variables.

1. Satisfaction Variable: How would you rate this lesson? (5 = Enjoyable for Me, 1 = Frustrating for Me).

2. Learning Efficacy Variable: How do you feel about your learning progress? (5 = Very Satisfied, 1 = Very Dissatisfied).

The Web application collected and stored the answers and scores for the practice and assessment questions and created an activity log for each learner. The log was a record of the learner's activity during the course. It showed times (learning time per task), sequencing of tasks (learning paths), and frequency of use for the different learning resources. Supplemental qualitative evidence was also collected during an exit interview.

Repeated Measure Univariate ANOVAs

Analysis of variance models were based on a mixed model repeated measures example provided by Littell, Freund, and Spector (1991). Additional parameters were added for learning orientation (treated as a continuous subject variable) to examine the data. The mixed model analysis procedure (PROC MIXED) in the SAS system was used to conduct the series of univariate analyses of variances on the four dependent variables.

To supplement the ANOVA analyses, additional analyses, means and standard deviations by time and overall for each dependent variable, were included. Modified ANOVAs were included to examine the data stratified by learning orientations. These analyses examined how learning orientation groups performed differently within the three online learning environments. Also included were bivariate plots of orientation for each of the dependent variables. This is a method to examine the dependent variable effects by ILO and GROUP or ILO and TIME. This method uses the weights to plot the regression lines between X and Y (y = a + bx). These two types of plots show the group and time effects on the X-axis, respectively, as intentional learning increases. Following is a portion of the study results.

STUDY RESULTS

The analyses were helpful in examining differences by groups and by learning orientation within the groups, between the groups, and over time. The ANOVA results exhibited statistically significant ILO, GROUP, and TIME effects and interactions on the dependent variables. The results suggested the likelihood that learners enjoyed greater success in learning environments that adapted and supported their individual learning orientation. In exit interviews, learners discussed their dissatisfaction with unmatched environments that conflicted with their learning orientation.

Multiple Repeated Measure ANOVA Results for Four Dependent Variables

Table 4 presents the significant ILO (learning orientation), GROUP (EX1, CO1, and CO2), and TIME (three instructional units) main effects and interactions for the four dependent variables. The results show statistically significant

1. GROUP (learning environment) effects on satisfaction (p = .0074) and learning efficacy (p = .0024) at a significance level of .01 (99%)

2. TIME effects on learning efficacy (p = .0001) and intentional learning performance (p = .0001) both at a significance level of .0001 (99.9%)

3. ILO * GROUP interactions on satisfaction (p = .0027) and learning efficacy (p = .0245) at a significance level of .01 (99%) and .05 (95%), respectively

Significance levels exhibit whether there is a statistically significant difference between two means. Significance levels of .0001, .01, and 05 are the values commonly used to show statistical significance. In academic fields, a theory should have at least a 95% chance (.05 significance level) of being true. The first significance level, such as .01, means that the finding has a one percent (.01) chance of not being true, which is the converse of a 99% chance of being true. In contrast, the high significance level for TIME effects (.001) has a 99.9% chance of being true. The results suggested that GROUP, TIME and ILO * GROUP showed significant effects and interactions on the sample population regarding satisfaction, learning efficacy, and learning performance.

Specifically, these results suggested the importance of understanding GROUP and TIME effects and ILO * GROUP interactions as factors in supporting learner attitudes and improving learning efficacy and intentional learning performance.

Table 4. Analysis of Variance for Three Dependent Variables by GROUP, ILO, and TIME

Tests of Fixed Effects

Source NDF DDF Type I F Pr > F

Satisfaction Dependent Variable

GROUP 2 65 5.30 0.0074

ILO * GROUP 2 65 6.48 0.0027

ILO * TIME 2 130 9.80 0.0001

Learning Efficacy Dependent Variable

GROUP 2 65 6.64 0.0024

TIME 2 130 31.82 0.0001

ILO * GROUP 2 65 3.93 0.0245

Intentional Learning Performance

TIME 2 90 14.77 0.0001

NDF = Numerator Degrees of Freedom

DDF = Denominator Degrees of Freedom

To supplement the ANOVA analyses, the investigator also examined group means and standard deviations by time for each of the dependent variables. Results for two of the variables are presented in Table 5 and Table 6. Overall, the results showed that Group EX1, the intentional or learning environment, had the highest overall group means for satisfaction, learning efficacy and intentional learning performance. This is the group in which each of the learners could manage the environment to match their learning orientation. Group EX1 also had the highest group achievement means for transforming learners.

Table 5. Means for the Satisfaction Dependent Variable by GROUP and TIME

Means for Satisfaction Dependent Variable by GROUP and TIME. This table uses a 5-point Likert scale (5 = This lesson is very enjoyable for me, 1 = This lesson is very frustrating for me). The higher the rating, the greater the satisfaction with the course.

Group Means

GROUP N TIME 1 TIME 2 TIME3 OVERALL

EX1 26

M 4.23 4.19 4.62 4.35

SD .82 .88 .70

CO1 23

M 4.04 3.59 4.21 3.95

SD .69 1.06 .67

CO2 22

M 4.06 3.40 3.82 3.76

SD .98 1.20 1.14

OVERALL 4.11 3.73 4.22 4.02

Interestingly, if you look at the overall group means for achievement (Table 6), the results are very similar (M = .83, M = .85, and M = .84). As expected, each group mean averaged out to the group’s majority orientation (performing learners). Yet, if we look closely at the same data (stratified by learning orientation in Table 6), the results show that each of the learning orientation groups performed highest in the matching learning environment (i.e., EX1: M = 94 for transforming learners, CO1: M = 91 for performing learners, and CO2: M = 87 for conforming learners).

Table 6. Means for the Achievement Dependent Variable by GROUP and TIME

Mean Percentage Correct for Achievement Dependent Variable by GROUP and TIME. This table shows the mean achievement scores (1.00 = High, 0 = Low) by GROUP and then subgrouped by three learning orientations.

Group Means

GROUP N TIME 1 TIME 2 TIME3 OVERALL

EX1 26

M 0.88 0.88 0.76 0.84

SD 0.14 0.12 0.16

Transforming Orientation 0.94

Performing Orientation 0.78

Conforming Orientation 0.82

CO1 23

M 0.89 0.92 0.75 0.85

SD 0.10 0.10 0.18

Transforming Orientation .79

Performing Orientation .91

Conforming Orientation .84

CO2 22

M 0.94 0.80 0.76 0.83

SD 0.09 0.20 0.22

Transforming Orientation 0.80

Performing Orientation 0.83

Conforming Orientation 0.87

OVERALL 0.90 0.87 0.76 0.84

Bivariate Plot of Orientation and the Achievement Dependent Variable

Bivariate plotting was also a useful way to exhibit how individuals, grouped by learning orientations, performed within the GROUP and by TIME. One of eight plots appears in Figure 1. The plot describes activity by learning orientation (X –axis: 1 to 7) within the three environments (GROUPs EX1, CO1, CO2) for the achievement dependent research variable (Y-axis: 0 - 1.0).

Figure 1. Linear Equations for Achievement

Showing the Regression of Y on X by GROUP.

The results suggested that as learning orientation (conforming 3.52 - 4.5, performing 4.52 – 5.5, transforming – 5.52 – 7) increased, the learners in Group EX1 exhibited the highest achievement in the matching environment. It appeared that the matched or mismatched environment did impact achievement. In the other two learning environments the learner’s achievement barely improved, regardless of the learning orientation. It is also important to note that the slope of GROUP EX1 is steep enough (Figure 1) to suggest that refinements to the assessment models may contribute to significant effects and interactions in the future.

Also, important in Figure 1 is the evidence showing how Group CO2's restrictive learning environment may limit achievement as learning orientation increased above 5.0. In contrast, Group EX1 and CO1 environments appeared to have supported improved course achievement for the higher learning orientations.

DISCUSSION

The study results suggested that individuals did best in the environments which best suited their learning orientation. Particularly, learners with higher orientations had higher achievement in the more sophisticated learning environments. In Cronbach’s words, these findings suggested that learning outcomes are better when the environment can adapt to match the learner's aptitude and personality. The evidence also suggested that recognizing and being sensitive to the learning orientations (e.g., aptitude patterns or aggregated learning dispositions) in advance is useful in guiding the design of instructional solutions and environments. It is also important to note that although learners did best in the environment which matched their learning orientation, they were not always in an environment that helped them experiment and improve learning ability.

The statistically significant findings suggest that using learning orientation is a rational way to explain the impact environments have on different orientations and describe what specifically works for learners in different environments over time. It is also a useful way to understand the most prominent learning attributes common to the audience before determining, designing, matching, and evaluating solutions and environments for more personalized learning.

Additionally, the evidence demonstrates the importance of adding the learner-difference dimension to the research design (that is, stratifying or differentiating the audience). This is a key step in research design, in contrast to the “one-size-fits-all” approach. Of particular interest was the relationship between learning orientation and achievement. Using the simple assessment model in this study offers key information for understanding this important relationship and improving assessment from a more realistically human perspective.

The study results highlighted areas for refinements, including:

• develop more sophisticated constructs, adequately integrated with conative, affective, social, and cognitive factors, to improve design strategies for online learning environments.

• consider the whole-person perspective in the design and presentation of instruction, practice, feedback, assessment and other learning activities, resources and tools.

• offer more sophisticated solutions, based on sound research foundations, that reliably match the common patterns for learning dispositions.

• provide online learning environments with supportive resources that improve learning experiences and ability.

IMPLICATIONS

This study contributes to the study of individual learning differences with a special emphasis on online environments that match or mismatch common patterns for learning dispositions. Based on advances in neurobiology of learning and memory research, the study suggests that an important topic for future work is in the development of more sophisticated audience analysis, assessment, and learning performance models that integrate a comprehensive set of conative, affective, cognitive, and social aspects.

The new models for online learning will need to reveal the special nuances for audiences differentiated by something more than cognitive aspects. Learning orientation is one way to focus on the common attributes that distinguish key learning dispositions.

There are many ways to personalize online learning environments. Future study efforts will focus on design strategies for environments that match and support the different learning orientations more closely. The goal is to provide online learning environments that satisfy and support learners and help them become more self-motivated, engaged, and willing to set and achieve higher standards.

Specifically, the learning environments should be:

• For transforming learners: sophisticated, discovery learning, high learner control, and mentoring environments for assertive learners who may want to be self-directed, challenged by complex problem solving, and able to self-manage learning and self-monitor progress to attain personal and higher standard, long-term goals.

• For performing learners: lower-risk, energizing, competitive, interactive (hands-on) environments for learners who may want coaching, feedback, scheduling, and social interaction to encourage self-motivation, problem solving, self-monitored progress, and task sequencing. Minimize the need for extra effort and difficult standards, particularly in subject areas of less interest to the learner.

• For conforming learners: simple, scaffolded, structured, non-risk environments for learners who may expect more explicit guidance and social interaction to help individuals learn comfortably, as they increasingly internalize more intentional learning performance. Initially provide low learner control, linear sequences, and ample feedback.

Ultimately, with increased understanding, enriched theoretical foundations, and practice, the matched solutions for differentiated audiences should be less expensive and offer better results. Clearer explanations about the comprehensive set of psychological factors that significantly impact learning will lead to greater discoveries about the right designs for online learning environments, resources, and relationships.

The goal for personalized environments should be to help individuals enjoy greater personal and learning success by gradually:

• expending greater, faster, intentional learning effort.

• improving learning performance (for example, setting high-standard goals, selecting treatments, sequencing tasks, solving complex problems, and self-monitoring goals and progress).

• assuming greater responsibility for learning.

REFERENCES

Cahill, L. & McGaugh, J. (1998). Mechanisms of Emotional Arousal and Lasting Declarative Memory, Trends in Neurosciences, 21, 294-299.

Center for Critical Thinking (2003). Resources. Retrieved June 20, 2003, from

http://www.criticalthinking.org/University/univclass/trc.html

Cronbach, L. (1957). The Two Disciplines of Scientific Psychology. American Psychologist, (12), 671-684.

Cronbach, L. (1975). Beyond the Two Disciplines of Scientific Psychology. American Psychologist, (30), 116-127.

Cronbach, L. & Snow, R. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Irvington Publishers.

Davidson, R. (2000). Cognitive Neuroscience Needs Affective Neuroscience (and Vice Versa). Brain and Cognition, 42, 89–92.

Kandel, E. (2002). Columbia News Video Forum Symposium: Perception, Memory and Art.

Retrieved June 20, 2003, from

http://www.columbia.edu/cu/news/vforum/02/perception_memory_art/

LeDoux, J. (1996). Emotional Brain: The Mysterious Underpinnings of Emotional Life. New York: Simon & Schuster.

LeDoux, J. (2002). Synaptic Self: How Our Brains Become Who We Are. New York:
Viking.

Littell, R., Freund, R., & Spector, P. (1991). SAS Systems for Linear Models. 3rd ed. North Carolina: SAS Institute.

Liu, Y., Lavelle, E., & Andris, J. (2002). Effects of Online Instruction on Locus of Control and Achievement Motivation. Presentation at American Educational Research Association (AERA) 2002 conference, New Orleans, LA. Retrieved June 20, 2003, from

http://www.bamaed.ua.edu/sciteach/AERA_Online_Learning_Papers/Liu.pdf

Martinez, M. (2000). Mass Customization: Designing for Successful Learning. International Journal of Educational Technology, 2(2). Retrieved June 20, 2003, from

http://www.ao.uiuc.edu/ijet/v2n2/martinez/index.html

Martinez, M. & Bunderson, C. (2001). Foundations for Personalized Web Learning Environments. Journal of Asychronous Learning Networks, 4(2). Retrieved June 20, 2003, from

http://www.aln.org/publications/magazine/v4n2/burdenson.asp

Martinez, M. (2003a). Learning Orientations Research (Overview). Retrieved June 20, 2003, from

http://www.trainingplace.com/source/research/overview.htm

Martinez, M. (2003b). Learning Orientations Model: Descriptions for Four Learning Orientations. Retrieved June 20, 2003, from

http://www.trainingplace.com/source/research/lomatrix.htm

Snow, R. (1980). Aptitude Processes. In R. Snow, P. Frederico, & W. Montague (eds.), Aptitude, Learning and Instruction, Conative and Affective Process Analyses, (1), 27-60. Hillsdale, NJ: Erlbaum Associates.

Snow, R. (1987). Aptitude Complexes. In R. Snow & M. Farr (eds.), Aptitude, Learning and Instruction, Cognitive Process Analyses of Aptitude, (3), 11-34. Hillsdale, NJ: Erlbaum Associates.

Snow, R. (1989). Aptitude-Treatment Interaction as a framework on individual differences in learning. In P. Ackermann, R.J. Sternberg, & R. Glaser (eds.), Learning and Individual Differences. New York: W.H. Freeman.

Symonds, W. (2003). University of Phoenix Online: Swift Rise. BusinessWeek Online, June 23, 2003. Retrieved June 20, 2003, from

http://www.businessweek.com/magazine/content/03_25/b3838628.htm

ABSTRACT

ABOUT THE AUTHOR

INTRODUCTION

NEW RESEARCH FOUNDATIONS

REFERENCES