Mass Customization: Designing for Successful Learning
Margaret (Maggie) Martinez, Training Place
How do we support successful, lifelong learners and help them competently perform in a rapidly changing world? Some of the answers to this complex question rely on how well we understand key learning differences and their influence on successful learning and performance. Historically, cognitive-rich (how people think) explanations about learning differences have tended to underplay the dominant impact of other key factors on thinking and learning, including affective (how people emotionally respond) and conative (how people realize intentions). Recently, these dimensions have gained considerable importance as contemporary multidisciplinary research has revealed how intentions and emotions can influence, guide, and, at times, override our thinking processes. Since instructors, who previously addressed the conative, affective, and social factors in the classroom, are not always available online, the typical cognitive-based solutions need to be redefined as more and more learners move to the Web. This study examines how learning orientation (meta-level learner-difference profiles) accounts for significant variance, effects, and interactions in a Web learning environment. The study results demonstrate useful ways to differentiate the audience before designing solutions and environments that carefully consider the impact of emotions and intentions on learning.
Champions have expertly honed their winning performance to an extraordinary standard. However, it is passion, commitment, and desire to work hard and win that ultimately leads to true championship. This article introduces learning orientation to explore the dominant sources for successful learning and performance from a new perspective. This perspective highlights the importance of intentions and emotions on learning. It also suggests how these powerful factors work together to develop, guide, manage, and sometimes override cognitive (thinking) processes. It is the different levels of passion to discover new knowledge or willingness to work hard and set harder learning goals that influence different levels of success. This perspective is in contrast to traditional one-size-fits-all cognitive perspectives that may have minimized or isolated the impact and guiding influence of intentions and emotions. Too often contemporary highly cognitive learning solutions focus on learning styles and strategies while emotions and intentions are ignored or subjugated in influence. Clearly, we do not all feel the same about learning nor do we intend to learn the same.
Table 1. Psychological learning factors influencing learner behaviors and performance.
Historical Review of the Individual Difference Research
The question of how people differ in the rate, style, and quality of their learning is one which has concerned psychologists for a great many years. (Gagne, 1967. p. xi)
In 1965, Gagne organized a major conference to discuss and explore individual differences in learning (1967, p. xii). During the conference, Melton (1967, p. 239) suggested "that we frame our hypotheses about individual difference variables in terms of the process constructs of contemporary theories of learning and performance." The conference's consensus was that conceptual processes, that is, information or knowledge processing, intervened between stimuli and response, the prevalent behavioral learning perspective (Frederico, 1980, p. 3). Critical to this research was the view that intelligence and achievement relied heavily on specific intrinsic cognitive processing. "It was suggested strongly that these psychological mechanisms be examined in order to comprehend more completely the processes basic to intellectual behavior. This conference reflected a change in the conceptualization of intelligence as measured performance to mental mechanisms" (Frederico, 1980, p. 3).
Today much of our evolving understanding and research on individual learning differences remains broadly focused on cognitive interests and intrinsic or extrinsic mechanisms for information processing. This research generally examines how the degree of control and management of cognitive processes (Witkin and Goodenough, 1981; Zimmerman, 1989) or learning styles (Kolb, 1984; Gardner, Kornhaber, and Wake, 1997) involve interaction among four classes of phenomena: (a) metacognitive knowledge, (b) goals, (c) metacognitive experience, and (d) actions (Flavell, 1992, 1987, 1979; Masur, McIntyre, and Flavell, 1973). Glaser (1984, 1976, 1972) described similar reasoning when he offered his conceptualization of the "new aptitudes," the cognitive learning processes managed for intellectual competence. Most of the research in this area continues to highlight cognitive aspects, such as cognitive preferences and learning styles, skills, processes, and strategies.
In contrast to the preponderance of primarily cognitive perspectives, many contemporary researchers recognizing the importance of other psychological factors, such as emotions and intentions, eventually extended their investigation to include conative and affective influences on learning differences (Garcia and Pintrich, 1996; McCombs, 1996, 1994, 1993, 1991a, 1991b; Purdie, Hattie, and Douglas, 1996; Pintrich, 1995; Bereiter and Scardamalia, 1993, 1989; Corno, 1993, 1989; Deci et al, 1991; Schunk, 1991; Snow, 1989, 1987; Snow and Farr, 1987; Brown, 1987; Dweck, 1986; Kuhl and Atkinson, 1986; Deci and Ryan, 1985; Bunderson, 1975; Weiner, 1972; Weiner et al., 1971).
This body of research highlights the importance of conative and affective aspects (personal desire, will, striving, motivation, efficacy, pride, fear, frustration, and satisfaction). In this research area, the resulting learning theories, still largely an extension of primarily cognitive study, describe intrinsic, extrinsic, and achievement motivation, and other important influences (to some degree, wanting to set and attain goals, desiring personal or self-development, enjoying learning, or liking to self-direct learning). These perspectives are still largely cognitive because they lack the emphasis of the dominant power of intentions and emotions over thinking processes. This is a critical aspect and useful in distinguishing this perspective from primarily cognitive and constructionist perspectives. From a completely different research perspective other disciplines also describe conative and affective factors as discriminating sources for learning and performance differences. In contrast to traditional cognitive perspectives, emotions and intentions are portrayed as dominant psychological influences (more dominant than cognitive processes) on learning. Joseph Ledoux (1996), neuroscientist at the Center for Neural Science at New York University and author of the "Emotional Brain," and Daniel Goleman (1995), author of "Emotional Intelligence," suggested that emotions and passions influence, guide, and, at times, override our thinking (cognitive) processes. Additionally, child development expert, Amanda Woodward (1998) described how humans are highly goal oriented and use intentions to guide learning and development of cognitive and other processes as early as age six months. This dominant conative and affective perspective is an integral part of this study. As Ledoux (1996), Goleman (1995), and Woodward (1998) would probably advise, recognizing the power of emotions and intentions is also an important lesson for educators. Professionals that can knowingly tap into the audience's emotions and intentions have a powerful advantage.
However, after many years of strong cognitive traditions and secondary emphasis on emotions and intentions, explanations about successful learning are still fuzzy or ambiguous. Snow and Farr (1987, p. 1) suggested that sound learning theories are incomplete or unrealistic if they do not include a whole person view that integrates cognitive, conative, and affective aspects. Although, they championed this critical perspective they were unable to integrate it into their own work successfully. Voicing the concerns of many about fuzzy or ambiguous solutions, Bangert-Drowns and Rudner (1991, p. 1) suggested that for every study that contains a recommendation, "there is another, equally well documented study, challenging the conclusions of the first." No one seems to agree with anyone else's approach. But more distressing: no one seems to know what works." Maddux (1993) proposed that some of the problems are due to lack of sound constructs and ambiguous explanations on how learner and learning variables interact with new teaching variables (such as, new technology, formats, mediums, objectives, and presentation and delivery methods) and affect specific dependent variables. Reeves (1993, p.1) echoed similar sentiments advocating stronger, more reliable theoretical foundations when he suggested that "much of the research in the field of computer-based instruction is pseudoscience because it fails to live up to the theoretical, definitional, methodological, and/or analytic demands of the paradigm upon which it is based."
Even when common sense, research, and experience suggest that people learn differently, many professionals continue to treat learners as a homogeneous audience with a "one-size-fits-all" approach. After a lengthy metareview of research comparing the effectiveness of instructional technology, Russell (1997) proposed that educators should identify and acknowledge learning differences and make "maximum use of the technology to serve them accordingly." He advised that "when lumping all the students together into a fictional 'mass' those who benefit from the technology are balanced by a like number who suffer; when combined with the no-significant-difference majority, the conglomerate yields the widely reported 'no significant difference' results." Clearly, the many ambiguous, conflicting, or inconsistent results from these comparison studies are subtly indicating something critical is missing from the cognitive-rich learning theories, constructs, and solutions.
What are the clearcut, explanations and proven solutions that support successful learning? And following Russell's advice, how do we "maximize the technology" to serve the needs of online learners successfully and economically. Similar to Reeves (1993), this investigator believes that we need to develop theoretical foundations that realistically unveil the broad set of fundamental sources and interrelationships that measureably contribute to successful learning.
After years of strong cognitive traditions, our cognitive-rich constructs, studies, and design methodologies need a strong infusion of conative, affective, and social research. If the cardinal rule for educators is to "know thy audience" then we need sophisticated lifelong and online learning theories and models that identify and support fundamental human needs, such as intentions, emotions, and social relationships. How well we understand and use learner-difference analysis and strategies, sound theoretical foundations, and reliable design methodologies is increasing in importance as schools, universities, and corporations scramble to satisfy the increasing demand for sophisticated online solutions.
New Instructional System Design (ISD) for "Mass Customization" includes Emotions and Intentions
Too often traditional cognitive-rich instructional models foster fuzzy, "one-size-fits-all" solutions for audiences imperceptibly treated as a homogeneous, conglomerate whole. Successful ISD methodology should acknowledge that individuals are feeling, intentional, thinking, and social human beings. In the instructor-led classroom, these essential factors are an integral part of learning and cannot be separated from learning and thinking ability. In contrast, online designs should also include conative, affective, and social strategies that consider and support these essential learning factors. Learning orientations, developed by the author in previous studies (Martinez, 1997, 1999b), enable designers to design solutions based on aggregate learner types, an effective way to mass customize and predict learning. These learner-differences profiles recognize the higher-order, dominant power and influence of intentions and emotions. They represent how individuals, with (to some degree) varying beliefs, emotions, intentionality, and ability, plan and set goals, commit and expend effort, and then autonomously experience learning to attain goals. There are four learning orientations: transforming, performing, conforming, and resistant.
In support of learning orientations, recent neuroscience research explains how intentions and emotions may guide or override our thinking and learning responses, strategies, skills, and processes (Ledoux, 1998). In fact, emotions are found in the part of the brain that evolved first before the thinking parts: the neocortex. Contributing additional evidence on the power of intentions, Woodward (1998) demonstrates how babies are already highly goal-oriented and use intentions to guide learning by six months of age.
This study recognizes that how well we support successful learning depends on how well we recognize and support individual needs and economically individualize or customize solutions that foster increasingly successful learning and performance. Explanations about learning orientation help educators understand learning differences, match and adapt instruction to differences, predict and support learning in different environments, and manage solutions and learning environments with greater sophistication (Martinez, 1999a).
The study purpose is to examine how learners, grouped by learning orientations, perform in specially designed Web learning environments that either match or mismatch their learning orientation (Martinez, 1999b). To accomplish its purpose, this study introduces intentional learning theory and learning orientations to describe higher-order psychological attributes and learner-difference variables for successful learning, examine explanations for fundamental learning differences, and review strategies for matching and accommodating learning.
Intentional Learning Theory
In previous studies, based on a review of the literature (Martinez, 1999b, 1998, 1997) and a blueprint specification about learning differences, the investigator developed the Intentional Learning Theory and Construct (Martinez, 1998, 1997), Learning Orientation Model (Martinez, 1999, 1998, 1997), and Learning Orientation Questionnaire (Martinez, 1999b). The intentional learning theory presents a foundation for understanding learning from a comprehensive perspective that considers the diverse set of psychological factors that influence learning. This perspective includes the wealth of traditional cognitive and social research yet demonstrates the higher-order dominance of conative and affective factors. This theory considers how emotions, attitudes, beliefs, and intentions (in addition to the more commonly recognized cognitive and social factors) influence, support, or undermine learning and performance opportunities. Secondly, it uses the learning orientation construct as a necessary dimension in the consideration of other factors that may influence learning and performance for the targeted audience (e.g., environments, technology, learning objectives and requirements, or disabilities). Finally it provides sophisticated guidelines for other strategies, analyzes, methodologies and models that need to recognize and support key learner-difference variables.
The Learning Orientation Construct (LOC) portrays characteristics, influences, and relationships between three key construct factors: (1) conative and affective aspects, (2) committed learning effort, and (3) learning autonomy. Combined, these three higher-order psychological factors greatly influence an individual's general learning orientations or proclivity to learn.
The Learning Orientation Model (LOM) presents ranges on a continuum for four dominant learner-difference profiles which generally represent an individual's approach to learning to differing degrees of success:
The Learning Orientation Questionnaire (LOQ) is a 25-item measurement tool that has been developed during two previous studies. It measures the three key construct factors (Conative/Affective Aspects, Strategic Planning and Committed Learning Effort, and Learning Autonomy) to measure and identify learning orientation (Martinez, 2000). It considers the key factors that influence learning differences. The results indicate how the individuals scored on each of the three construct factors and provide an overall learning orientation score for one of the four learning orientations.
Descriptions for the Learning Orientations
Transforming learners. At one end of the continuum are transforming learners. Deeply influenced by an awareness of the psychological aspects that motivate them, transforming learners place great importance on personal strengths, intrinsic resources, ability, committed, persistent, assertive effort, sophisticated learning, performance, planing and problem-solving strategies, and positive expectations to self-manage learning successfully. These learners manage holistic to detailed 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 seldom rely heavily on short-term tasks, schedules, deadlines, normative performance standards, expected social or instructional compliance, or others for extrinsic learning motivation. Transforming learners enjoy taking responsibility and control of their learning and willingly become actively involved in managing the learning process. They typically use holistic perspectives, sophisticated problem-solving, and stimulating, intrinsic influences, such as intentions, passions, personal principles, and desires for personal goals and high standards, to self-direct intentional achievement of challenging, long-term goals. Using an autonomous, reflective, goal-oriented, and self-assessment framework, transforming learners expertly adapt suitable strategies to manage the 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 or case study situations; transformative processes; high learning standards, and long-term personal accomplishments and change.
Performing learners. In comparison, a performing learner is a low-risk, skilled learner that consciously, systematically, and capably uses psychological processes, strategies, preferences, and self-regulated learning skills to achieve average-standard learning objectives and tasks. In contrast to transforming learners, performing learners are short-term and task-oriented, and often extrinsically motivated. They take fewer risks with mistakes and challenging or difficult goals, focus on grades, rewards, and normative achievement standards, and most often rely on coaching relationships, available external resources, and social influences to accomplish a task. Performing learners need an important reason to push themselves toward more intentional performance.
Performing learners will selectively commit great effort to learn topics and skills that they highly value and find particularly interesting. Otherwise, more often than not, performing learners will clearly acknowledge that they want to limit or constrain learning effort (for example, they do not have enough time) 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, interaction, and coaching for encouraging self-motivation to learn.
Conforming learners. Compared to transforming or performing learners, conforming learners are more complying and passively accept knowledge, store it, and reproduce it to conform, complete assigned tasks if they can, and please others. The conforming learner does not typically use initiative, think critically, like to make mistakes, reflect on progress, synthesize feedback, or give knowledge new meaning to change themselves or the environment. These learners are less skilled and have difficulty solving complex problems and accepting or managing change. They have little desire to control or manage their learning or set challenging personal learning goals.
Conforming learners prefer to have simple standards set for them, rely on others for guidance, need simple, explicit feedback, and learn best with linear, step-by-step instruction. In supportive, comfortable, uncomplicated learning environments, conforming learners will, with careful guidance, successfully work to achieve simple, clearly explained goals.
Resistant learners. These learners doubt that (1) they can learn or enjoy achieving any goals set by others (2) academic learning and achievement can help them achieve personal goals or initiate desired changes, and (3) their personal values and goals can benefit from academic influence. 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 may 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.
This study examined if learning orientation, time, and learning environment accounted for significant variance, effects, and interactions on the research variables (described in the Data Collection section). Significance levels would indicate the importance of differentiating learning audiences and presenting instruction to match learning orientation. Three of the study’s original five research questions appear here.
1. Do learning orientations influence satisfaction, learning efficacy, achievement, intentional learning performance?
2. Do learners using intentional learning environments (Group EX1) benefit more (measured by the dependent variables) than learners not using intentional learning environments (Control Groups CO1 and CO2)?
3. Do learning orientations influence group interactions (Group EX1 and Control Groups CO1 and CO2)?
The methods to accomplish the study purpose included (a) creating an online learning environment and treatments that matched differentiated audience orientations and provided alternative instructional elements, presentations, environment, and support in three research groups, (b) determining the individual's orientation to learn, (c) using learning orientation as a random selection method to channel Subjects into different research groups, (d) introducing the course and delivering instruction while helping selected learners in the experimental EX1 group understand and manage their individual learning differences, (e) analyzing data and examining effects and interactions on the dependent variables in matched and mismatched learning environments with differing ILO, and (f) making inferences for the second and third study purposes, that is, determine future refinements for the SILPA and guidance for future research.
Intentional Learning Environments
In this study, the investigator designed three learning environments in one (Martinez, 1997) to deliver the same Discovering the World Wide Web course. Each version offered adapted solutions that matched one of three learning orientations: (a) transforming, (b) performing, and (c) conforming.. This three-in-one intentional learning environment, called the System for Intentional Learning and Performance Assessment (SILPA), provided the instructional and research model for the course. The SILPA was developed to support individual learning differences, that is, systemize, match, manage, and measure instructional support and activities for three different learning orientations: Additionally, its purpose was to encourage learners to improve learning ability, including problem solving, task sequencing, goal setting, progress monitoring and critical reflection.
The key to designing the SILPA architecture (Martinez, 1997) was understanding the complex interaction between (a) learning orientation, (b) instructional and assessment objectives, requirements, resources, and situational constraints, (c) intentional learning performance, and (d) preferences for instructional presentation—each element has an strategic role in supporting intentional learning processes. The SILPA design (a) uses a problem-solving, expertise-based, and process-oriented instructional core, (b) adapts to individual learning orientation, performance, and progress, (c) supports, to differing degrees, self-monitored, exploratory, self-assessed, and self-managed learning, and (d) helps learners internalize higher levels of sophisticated intentional learning performance as they progress.
The heart of the SILPA model is a learning management and assessment framework called the iCenter©. It offers resources to examine the content of the course, set goals, reflect on presentation preferences, and review cumulative and comparative information about scores. This learning resource helps the learner manage individual learning performance for the domain of expertise (conceptual, declarative, procedural, conditional, and associated knowledge, skills, and performance) in an organized problem-solving structure integrated with dynamic feedback and assessment opportunities.
A learning progress map, called the iMap©, is also part of the iCenter. It provides information about scores and learning progress and answers questions, including (a) how well am I doing on this lesson or course, (b) what have I completed, (c) how much is left to complete, and (d) how well have I done in comparison to others on this lesson or course?
Before starting the course, the subjects took the SILPA's diagnostic instrument, called the Learning Orientation Questionnaire (developed in a previous study: Martinez, 2000 ) to identify learning orientation. The LOQ provides scores that indicate where the learner may fall across the dimensions of the Learning Orientation Construct (Martinez, 2000) and along the learning orientation continuum. In this study, the learning orientation scores were continuous variables. The computer automatically stored the data collected from the registration form in the SILPA database.
Next, the computer randomly assigned subjects to one of three research groups, including the experimental EX1 group and Control CO1 and CO2 groups. Hence, each group consisted of pretested subjects separated into three categories on the basis of the pretest measure (a) Transforming Learners (Cat1), (b) Performing Learners (Cat2), and (c) Conforming Learners, (Cat3). Descriptions of the three groups appear in Table 2.
Table 2. Description of the Three Learning Environments (Research Groups)
Seventy-one adults (49 women and 22 men; mean age = 22) took the Web course. They were volunteers from local businesses, universities, and households, had very limited or no Web experience, and showed a desire to learn how to use the Web. The sample majority were psychology and sociology undergraduate students attending a large university in the West.
The introductory material included a course introduction with information on taking the Discovering the World Wide Web course in a Web-based intentional learning environment. For the Experimental Group EX1, extra guidance appeared in the course introduction. This guidance introduced the intervention and offered encouragement to foster intentional learning performance (e.g., using the iCenter, iMap, progress monitoring, or task sequencing). The assumption that setting goals, sequencing task, and monitoring progress contributes toward successful learning is part of the course design. Performing learners were not expected to react too positively to guidance about setting higher performance standards and using more effort, but it was important compare the effect on all the orientations.
The Discovering the World Wide Web Basics course is easy-to-use, self-paced computer training delivered on the World Wide Web. The course consists of eight lessons that present instruction integrated with practice, feedback, and assessment activities. During each lesson, subjects had opportunities to accomplish up to seven tasks that helped them learn, review, practice, and test new competencies.
In this course, subjects were expected to accomplish the course objectives by learning how to
After finishing each lesson, learners had the opportunity to practice and take tests with feedback to evaluate their progress. The testing purpose was to evaluate the subjects general understanding of the concepts and ability to understand or perform specific competencies. Each of the eight tests contained a set of multiple choice questions or simulated exercises. Learners clicked the Submit button to have the computer score the practice and lesson’s exercises and provide immediate feedback. The computer stored the data collected from the practices and assessments in the SILPA database.
Experimental Research Design
The investigator developed an experimental 3 X 3 factorial research design and conducted multiple repeated measures univariate analyses of variance (ANOVA) to analyze the independent and interactive effects of two independent variables (learning orientation and intentional learning training) on four dependent variables (satisfaction, learning efficacy, intentional learning performance, and achievement) over three time periods (repeated measures). To allow for the effects of time, the investigator introduced the repeated measure (A1, A2, and A3 in Table 3) for multiple hypotheses testing. The repeated measure design means that the subjects are tested several times for a measure of each independent variable.
The repeated measure ANOVA tests hypotheses about the four dependent variable means measured on different occasions. This multi-variable approach and intervention research design strategy was selected to (a) demonstrate a causal link or interaction between the independent variables (Cat1, Cat2, Cat3 variable in Table 3) and dependent variables (Y Measures in Table 3), (b) study the differential effects and interactions of an instructional intervention treatment upon the various dependent variables over time, and (c) maximize the chances of obtaining statistically significant differences among the three research groups.
Table 3. Research Design.
This research design is unique because it overlays learning orientation (transforming, performing, conforming) as a separate dimension to (1) guide design and development of the research environment, content, presentation, and instruction and (2) differentiate the audience before analyzing the learner, introducing the treatment, and examining the results. This dimension is especially important because it distinguishes learners as individuals with predominant psychological characteristics in comparison to traditional methods that treat learners as a uniform group with generalized, homogeneous conative, affective, and social influences. The introduction of intentional learning and multiple variables examined by orientation is an effort to reflect the diversity of learning differences and then
The repeated measures research design increased data collection points and resulted in four data sets. This more complex collection method was useful in reflecting the dynamics of change in learning, as students realistically experience learning. The first data set came from the pre-course registration and the other three from the practice and assessment activities in the three instructional units. At the end of lessons, learners could write comments or rate themselves on two questions concerning 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 learning system automatically 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 different resources.
The investigator conducted a series of univariate analyses of variance on the data collected from the experimental and two control groups. Since time had been introduced into the research design (Y measures collected on different occasions), the investigator used an analytical model that would treat the time variable as repeated subintervals of the instructional cycle between and among the three research groups. According to Littel et al., "repeated measures data need mixed models because of correlations between measurements on the same subject" (Littel, Milliken, Stroup, and Wolfinger, 1996, p. 97). Following this approach, the investigator used a modified mixed model repeated measures example (with special parameters for learning orientation treated as a continuous subject variable) from Littell, Freund, and Spector (1991) in the SAS system (PROC MIXED).
Multiple Repeated Measure ANOVA Results for Four Dependent Variables
The results exhibited significant GROUP effects and interactions on satisfaction and learning efficacy and time effects. The non-significant results were equally interesting, especially for achievement, when combined with the supplemental evidence gathered by analyzing group means by learning orientation. Table 4 presents the significant main effects and interactions for the dependent variables using ILO (learning orientation), GROUP (EX1, CO1, and CO2), and TIME (three instructional units) 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. ILO * GROUP interactions on satisfaction (p = .0027) and learning efficacy (p = .0245) at a significance level of .01 (99%) and .05 (95%), respectively
3. TIME effects on learning efficacy (p = .0001) and intentional learning performance (p = .0001) both at a significance level of .0001 (99.9%)
Table 4. Analysis of variance for three dependent variables by ILO, GROUP, and TIME.
The results suggest that GROUP, TIME (effects) and ILO * GROUP (interactions) have significant effects and interactions on 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 and improving learner attitudes and learning performance. As expected, the ANOVAs presented nonsignificant results for achievement.
Group Means and Standard Deviations by Time
To supplement the ANOVA analyses, the investigator also examined group means (M) and standard deviations (SD) by time for each of the dependent variables. These results, organized into sections for three of the four dependent variables, appear in Table 5. Additionally, Section 4 in Table 5 exhibits detailed information on achievement organized by learning orientation; this shows how the learning orientations achieved within the groups. Overall, these results show that Group EX1, the intentional learning environment, had higher overall group means for three of the four dependent variables. A closer look at the overall group means (percentage correct) by learning orientation appears in Table 5: Section 4. The results are very similar (M = .83, M = .85, and M = .84). As expected, the achievement means averaged out to this sample's majority orientation (performing learners). More importantly, Table 5: Section 4 reveals that the results for each of the learning orientations were highest in the matching learning environment (EX1: M = 94 for transforming learners, CO1: M = 91 for performing learners, and CO2: M = 87 for conforming learners).
The ANOVA results in Table 4 do not show significant effects or interactions for ILO (learning orientations). These results suggest that learning orientations alone do not impact the four dependent variables. Instead, it is the interaction between ILO and other variables that shows the significant effects on the dependent variables. The third research question discusses how learning orientation "interactions" are more likely to influence learning in different environments with different treatments.
Means for Dependent Variables By Group and Time
Table 5, Section 1. Satisfaction
Means for Satisfaction Dependent Variable by GROUP and TIME using 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.
Table 5, Section 1. Satisfaction.
Section 2. Learning Efficacy
Means for Learning Efficacy Dependent Variable by GROUP and TIME using a 5-point Likert scale (5 = Very Satisfied with my learning progress, 1 = Very Dissatisfied with my learning progress). The higher the rating, the greater the learning efficacy.
Table 5, Section 2. Learning Efficacy
Section 3: Learning Performance (not included)
Section 4. Achievement
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 subgrouped by learning orientation.
Section 4. Achievement
Bivariate Plots of Orientation and Dependent Variables
The ANOVA analyses were unable to describe learning performance by learning orientation. However, bivariate plotting is a useful way to exhibit how individuals, grouped by learning orientations, performed within the GROUP (learning environment) and by TIME. To get this useful information, the investigator plotted eight graphs for each dependent variable. Using the PROC REG procedure in the SAS system and the unstandardized regression weights for the predicted intercept and slope by GROUP or TIME, the investigator used the weights to plot the regression lines between X and Y.
One of the eight plots examined in this study appears in Figure 1. As previously mentioned, the ANOVA results for achievement were not significant and the group achievement means were very similar. However, a closer examination reveals a different story in the bivariate plot which specifically described achievement (by learning orientation) in the three environments (GROUP).
Figure 1. Linear equations for achievement showing the regression of Y on X by GROUP
This evidence suggests that the matching or mismatched environment did impact the achievement means as learning orientation increases or decreases. This evidence shows that as learning orientation increased, the learners in Group EX1 exhibited higher achievement than learners in the other two environments with similar orientations. In contrast, this plot exhibits how Group CO2's restrictive learning environment may limit achievement as learning orientation increased above 5.0. In contrast, Group CO1 and CO2 environments appeared to have supported highest achievement for the lower learning orientations. 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.
Research question 1: Do learning orientations influence satisfaction, learning efficacy, achievement, and intentional learning performance?
The ANOVA results in Table 4 do not show significant effects or interactions for ILO (learning orientations). These results suggest that learning orientations alone do not impact the four dependent variables. Instead, it is the interaction between ILO and other variables that shows the significant effects on the dependent variables. The third research question discusses how learning orientation "interactions" are more likely to influence learning in different environments with different treatments.
Research question 2: Do using intentional learning environments (Group EX1) benefit more than learners not using intentional learning environments (Groups CO1 and CO2)?
Group EX1 offered the learning environment that had the highest group means for three dependent variables: satisfaction, intentional learning performance, and learning efficacy. However, a comparison of the group means by learning orientation for achievement (Table 5: Section 4) showed that individuals did best in the environments which best suited their learning orientation. Figure 1 (and all the plots not shown in this article) also support this evidence as it specifically shows how learners with higher orientations had higher achievement in the more sophisticated learning environments 1 and 2. Additionally, the ANOVA results in Table 4 show statistically significant GROUP effects for satisfaction (F = 5.30, p < 0.01) and learning efficacy (F = 6.64, p < 0.01) and statistically significant ILO * GROUP interactions for satisfaction (F = 6.48, p < 0.01) and learning efficacy (F = 3.93, p < 0.05). These GROUP effects indicate the 99% probability that learning environments influenced learning satisfaction and efficacy and this success depends on how the environment supports and matches the learning orientation. How time is managed is also a relevant factor since the TIME effects were statistically significant (Table 4) for satisfaction, learning efficacy, and learning performance.
Research question 3: Do learning orientations influence group interactions (Groups EX1, CO1, and CO2)?
The ANOVA results in Table 4 show statistically significant ILO * GROUP interactions for satisfaction (F = 6.48, p < 0.01) and learning efficacy (F = 3.93, p < 0.05). These findings indicate how likely the interactions between learning orientation and environment seem to have impacted satisfaction (99%) and learning efficacy (95%). The evidence suggests that recognizing and being sensitive to the learning orientations in advance is useful in guiding the design of instructional solutions and environments. It is also important to note that although students achieved best in the environment which closely suited their learning orientation, those in the two control groups were not in an environment that would help them experiment and improve intentional learning ability. The investigator will use these research findings to guide development of intentional learning environments that are more sensitive to performing and conforming learners. These developmental efforts will focus on making these learning orientations more comfortable, engaged, and willing to perform in an intentional learning environment that uses elements to match their learning orientation and subtly provides support that helps the learner improve learning ability.
This study investigates the importance of learning orientation and (1) using it to determine and explain key learner-difference variables, (2) integrating it into audience analysis and instructional design methodologies to customize solutions that support individual learning difference, and (3) supporting it for more satisfying, successful learning and improved learning performance. The results suggest the need to identify audiences with greater sophistication and specificity then today's primarily cognitive perspectives permitted. The results also provided evidence on specific factors that may impact learning and offer suggestions on customizing better environments for improved learning. The investigator hopes that these results will revitalize the often-ignored, human perspective that recognizes the more dominant conative and affective factors along with the more commonly explored cognitive and social learning factors.
These findings also suggest that the primary purpose of a more sophisticated ISD methodology (especially audience analysis) is not merely to describe a homogenous audience with a "one-size-fits-all" description but rather to identify and discern fundamental "one-on-one" audience attributes using reliable "meta-level" learner-difference or performance-difference criteria. Once recognized, the complete set of important learner-difference variables need to be considered and used to guide the design of successful learning and performance solutions.
These results suggested that learners enjoyed greater success in learning environments that adapted and supported their individual learning orientation. In contrast, the learners learned less successfully in the unmatched environments that conflicted with their learning orientation. With practice, the matched solutions for differentiated audiences will be easier to design and less expensive. Matched instructional solutions offer the promise of better results because the individual should learn to assume greater responsibility for learning and performance, set and attain increasingly higher goals, expend greater, faster effort, and improve learning ability (e.g., problem solving, setting and attaining higher-standard goals, selecting treatments, sequencing tasks, and monitoring goals and progress). These solutions are even more likely to be successful when learners increasingly internalize improved learning ability that leads to higher learning orientation and higher performance standards.
1. Demonstrates the need for sound theoretical foundations that consider and incorporate the influence and relationship between higher-order psychological factors into measurable whole-person learning constructs.
2. Highlights the importance of measuring a comprehensive set of affective, conative, cognitive, and social learner-difference variables that influence learning.
3. Offers explanations on how learners individually adapt to interventions and how some benefit from one type of solution and others do not.
4. Offers analysis and design strategies and models for mass customization, ones that identify and match differentiated audience solutions to foster improved learning and performance.
5. Describes a learning environment that can (1) differentiate the audience by learning orientation, (2) match individual and mass customized solutions, (3) offer components that help learners support and internalize more intentional learning performance, and (4) collect and measure data as the learning occurs.
The findings of this study are limited in generalizability; however, they illuminate the need for additional investigation of creating learning environments (on or offline) that match learning orientations.
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 the treatments and individual together would determine the best payoff because we "can expect some attributes of person to have strong interactions with treatment variables. These attributes have far greater practical importance than the attributes which have little or no interaction." Since then, many others (Linn, 1998; Bereiter and Scardamalia, 1989;1993; Brown, 1989) have sought ways to match differences (scaffolding) in how learners process information differently.
This study suggests that future research should consider principles and guidelines for designing, implementing, and using intentional learning environments, components, resources, and alternative learning paths that match orientations, support individual differences and promote successful, satisfying intentional learning experiences. For example:
Transforming learners need sophisticated, discovery learning situations for assertive, high-standard, high-effort, high learner control, highly skilled learning. The focus is on application and task completion to help improve learning ability.
Performing learners need low-risk, energizing, competitive, interactive settings that obscure the need for extra effort and difficult standards and entice them into internalizing more intentional learning performance. The focus is on long-range planning and problem-solving to help improve learning ability.
Conforming learners need scaffolded, structured, low learner control, non-risk environments that initially help them learn safely and comfortably, then gradually help them internalize more intentional learning performance. The focus is on subtly increasing difficulty and risk to help improve learning ability.
Future replications and extensions of this study will focus on:
1. Individual Learning Research: (a) identifying, examining, and measuring significant psychological influences on successful learning and learning difference, (b) determining a conceptual network or framework structure that describes learning sources, key relationships, and order of influence for learning and individual learning differences, (c) refining the intentional learning theory, constructs, questionnaires, and explanations about learning and how learners approach learning differently, (d) applying information on learning orientations as a necessary dimension of other constructs, theories, and research projects, and (e) integrating learning orientation constructs with older, more established learning constructs.
2. Development: (a) providing principles, resources, and models for designing, developing, implementing, and evaluating enriched learning environments that adapt, match, and support intentional learning performance, (b) developing instructional and assessment models that incorporate the comprehensive set of psychological factors, and (c) providing measurable intentional learning solutions that significantly help learners improve over time.
3. Adaptive Learning Research Program: (a) using the information to guide a long-term adaptive learning research program to personalize learning, (b) developing a formative research methodology, founded on a sound, scientific theory and construct, research design and analytical methodology, instructional/research environment, and measurable improvement cycles, and (c) consider other key factors, (e.g., social, physical and behavioral) that may need inclusion into adaptive learning learning constructs.
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