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Running head: METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 1 Metacognition, Learning Styles, and OpenScholar @Harvard Adrienne Yapo Bentley University

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Running head: METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 1

Metacognition, Learning Styles, and OpenScholar @Harvard

Adrienne Yapo

Bentley University

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 2

Metacognition, Learning Styles, and OpenScholar @Harvard

Introduction

Metacognition, first defined by Flavell (1979) as “thinking about thinking,” refers to the self-

reflection and self-monitoring of one’s own thinking (Kolencik & Hillwig, 2011; Kay, Kleitman,

Azevedo, 2012). Self-reflection and self-monitoring are crucial processes to learning:

“Metacognition refers to one’s knowledge concerning one’s own cognitive processes or anything related

to them, e.g., the learning-relevant properties of information or data. For example, I am engaging in

metacognition, if I notice that I have more trouble learning A than B; if it strikes me that I should double

check C before accepting it as fact” (Flavel, 1976, p.232). Metacognition runs parallel to primary

cognitive activity; that is, in the example above, learning A & B is the primary cognitive activity, whereas

noticing that I have more trouble learning A than B is the parallel process of metacognition.

In the realm of user experience design, metacognition and learning are part of every interactive

experience, from consumer electronics to kitchen appliances, software products to mobile apps, and quite

obviously, digital (e-) learning environments and games. Every product or system needs to be learned to

some extent by the user in order to ensure correct, effective, and efficient use. Interaction design and user

interface design both have a significant effect on the learnability of a system or product. An astute

designer will consider learning and learning styles in the design process; the complexity of the product,

the target audience, and the product content will all influence the amount and type of learning support

needed (Reeves, 1999). This paper will explore models and theories of metacognition, learning, and

learning styles, particularly in the context of OpenScholar, an open source Drupal-based Content

Management System (CMS) for higher education focusing on university microsites. This review will

specifically focus on OpenScholar @Harvard, the hosted CMS version available for the Harvard

University community.

Metacognition

Flavell’s (1979) seminal work, “Metacognition and Cognitive Monitoring: A New Area of

Cognitive-Developmental Inquiry,” proposed two components of metacognition: metacognitive

knowledge and metacognitive experiences. Metacognitive knowledge is “that segment of your (a child's,

an adult's) stored world knowledge that has to do with people as cognitive creatures and with their diverse

cognitive tasks, goals, actions, and experiences. An example would be a child's acquired belief that

unlike many of her friends, she is better at arithmetic than at spelling” (p.906). Metacognitive

experiences would include “any conscious cognitive or affective experiences that accompany and pertain

to any intellectual enterprise. An example would be the sudden feeling that you do not understand

something another person just said” (Ibid). Since Flavell’s first definition of the term thinking about

thinking, the concept of metacognition has expanded to include: “learning how to learn; knowing how to

learn; controlling one’s own learning; regulating one’s own learning through planning, monitoring,

evaluating, and reflecting on one’s learning; self-awareness of knowledge construction; and ‘knowing

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 3

what to do, when to do it, how to do it, and what to take into consideration’ (DeBono, 1976, p. 51)”

(Kolencik & Hillwig, 2011, p. 4). Kolencik & Hillwig, in their book, Encouraging Metacognition:

Supporting Learners through Metacognitive Teaching Strategies (2011), summarize the components of

metacognition through the work of Brown & Baker (1984), Marzano (1988), and Winn and Synder

(1996). Baker & Brown described metacognition as involving two factors: 1) one’s perception of the

steps necessary to complete a task and 2) one’s self-monitoring of the task – the capacity to gauge task

progress and to self-correct, if appropriate; Marzano emphasized “1) knowledge of control of self, and 2)

knowledge and control of process,” while Winn & Snyder argued that these occur concurrently,

“monitoring your progress as you learn, and making changes and adapting your strategies if you perceive

you are not doing so well,” (Kolencik & Hillwig, 2011, p.5). In other words, metacognition involves goal

setting, self-reflection, self-responsibility, and self-awareness.

Learning and Cognitive styles

If metacognition is the awareness of learning how, knowing how, and controlling how one learns,

learning and cognitive styles can be roughly categorized as the process of how one typically prefers to

learn and approaches learning. Many terms have been used to refer to learning style, including cognitive

style, learning style, sensory preference, personality type, modality, and others (Ehrman et al., 2003).

Learning style and cognitive style have often been used interchangeably in the literature, although some

define the terms distinctly. Cognitive style was first defined in 1937 by Allport to mean “an individual’s

typical or habitual mode of problem solving, thinking, perceiving, and remembering,” (Cassidy, 2004, p.

420). The term “learning style” first appears in 1954 by Thelen to discuss group dynamics, although it

has been adopted to “reflect a concern with the application of cognitive style in a learning situation”

(Cassidy, 2004, p. 421; Ehrman et al., 2003). Curry (1983) noted the fragmentation and confusion

surrounding learning style terminology and proposed the following definitions:

Learning is an intended process that is “adaptive, future focused; and holistic; affecting an

individual's cognitive; affective; social, and moral volitional skills. The product is observable as a

relatively permanent change in behaviour, or potential behavior.”

Learning style is “overused” but refers to the “general area of interest concerning individual

differences in cognitive approach and process of learning.”

Cognitive personality style is “the individual’s approach to adapting and assimilating

information…this is an underlying and relatively permanent personality level dimension that

becomes manifest only indirectly and by looking for universals within an individual’s behaviour

across many learning instances.”

Learning strategy is “a translation-like mechanism by which the individual copes with the

particular learning environment.” (p.2-3)

In contrast, Hartley (1998) proposed simpler definitions for cognitive style, learning style, and learning

strategies. Cognitive style refers to the ways one typically approaches cognitive tasks, and learning style

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 4

refers to the ways one typically approaches learning tasks; learning strategies are the differing strategies

one selects while studying (Cassidy, 2004). Many other definitions of the terms abound; covering all is

beyond the scope of this paper. However, just as learning style terminology varies widely, theories and

models of learning styles do as well.

Models and Theories

Research into cognitive and learning styles has been occurring for well over seventy years in a

range of disciplines, including education, management, medical training, vocational training, and industry

(Cassidy, 2004). Because learning is important to many, if not all domains, research has been conducted

with wide ranging goals and directives; as such, a plethora of models and theories have been proposed,

with overlap in some cases, and seemingly disparate approaches in others. As De Bello (1990) explained,

there is room for “differences” and “dissension” among the models and theories, and “their beliefs are

honest and have integrity” (p.218). However, some have been tested more rigorously through research,

while others remain essentially theoretical.

According to Ehman et al. (2003, p.314), cognitive style research has progressed from Witkin’s

mid-twentieth century field independence-field dependence construct, to cognitive style scales such as

levelling-sharpening and impulsivity-reflectivity through the lens of ego psychology (Schmeck, 1988), to

a focus on personality differences on learning styles, including temperament theory (Thomas and Chess,

1977), the Five Factor Personality Model (Busato, et al. 1999), and the Myers-Briggs Type Indicator

(Myers et al, 1998). Cassidy (2004) tackled the many disparate and overlapping models of learning styles

through a synthesis of central themes and issues. Outlining three major frameworks — Curry (1987),

Riding & Cheema (1991), and Rayner & Riding (1997) — against a matrix of models, theories, and

measures, Cassidy proposed a taxonomy of learning style models to elicit a broad overview of the field.

Curry’s (1983) “onion” model of learning and cognitive styles proposes that cognitive personality style

represents the innermost core; the second layer represents information processing style; the third layer

represents social interaction preferences, and the outermost layer consists of instructional preferences.

Sadler-Smith & Riding’s (1999) research of business studies students indicated that Curry’s innermost

layer, cognitive style, is “fixed” and affects the outermost layer, instructional preferences (p. 356). Riding

& Cheema’s “fundamental dimensions” model proposes two ways that information is represented and

processed: wholist-analytic and verbalizer-imager. The wholist-analytic dimension involves how

individuals process information (as a whole or in parts), while the verbalizer-imager dimension describes

whether individuals are inclined to represent information in memory as words or images (Sadler-Smith &

Riding, 1999; Cassidy, 2004). Rayner & Riding’s framework of learning styles considers personality-

centered, cognitive-centered, and learner-centered approaches (Cassidy, 2004); cognitive-centered

approaches “focus on the identification of styles based on individual difference in cognitive and

perceptual functioning” (Cassidy, 2004, p.424), while learner-centered approaches are concerned with

process-based, preference-based, and cognitive-skills based models. Within these three frameworks,

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 5

Cassidy (2004, p. 425-430) outlined the many models of learning styles along with their measurement

instruments (Figure 1).

Model Definition Field independence – Dependence (Witkin) Preference for learning in isolation vs. learning in groups Impulsivity – Reflexivity (Kagan) Quick decision after a brief scan (cognitive impulsive)

vs. decision reached after scrutiny of all options (cognitive reflective)

Convergent – Divergent (Hudson) Generates one solution vs. many possible solutions Leveller – Sharpener (Holzman & Klein) Oversimplifies perceptions of tasks, assimilates detail vs.

ineffective assimilation, introduces complexity Holist – Serialist (Pask) Focus on whole picture vs. sequential, narrow focus Verbalizer – Visualizer (Pavio) Preference for text-based vs. image-based learning Assimilator – Explorer (Kaufmann) Problem-solving through familiar vs. new strategies Adaption – Innovation (Kirton) “Desire to do things better” vs. “desire to do things

differently” Figure 1: Learning models (content summarized from Cassidy, 2004)

Regardless of the debate surrounding learning/cognitive style models, the essential point is that learning

style varies among individuals; therefore, providing a variety of ways to approach learning ensures the

best outcome for the widest audience. This is relevant to interface and user experience design;

considering the learning styles of users mitigates complexity, supports learning, and ensures that a

product or system can be more easily used and understood.

Learner-Centered Design & Learning Experience Design

In Reeve’s 1999 book, Learner-Centered Design: A Cognitive View of Managing Complexity in

Product, Information, and Environmental Design, he argues that we are living in the “Age of

Complexity” and that design is a fundamental salve to protect against the “scientific, technological,

political, economic, psychological, and sociological complexity that threatens to overwhelm us” (p.xii).

Learner-centered design, he continues “considers scaffolding, or support of understanding, to be essential

in all design, not just the design of systems actively engaged in training on a particular task, skill, or

subject. Successful design now and in the future will be based on the successful communication of

product information in terms of a) the ease of getting started with the product’s intended purpose, b) the

ease of sustained use, and c) the ease of content understanding” (1999, p.2). Due to the increased

complexity in our lives, interfaces in today’s environment need to teach and guide users how to use them.

To some extent, the ubiquity of computers and now mobile devices in our daily lives often force users to

be field-independent learners. Moreover, as a user learns a product or system, the system should support

continued learning as the user moves from novice to expert (Scarr et al, 2011).

The more recent discipline of learning experience design aims to “design a user interface in a way

that supports and enhances the cognitive and affective processes that learning involves” (Peters, 2012).

While learning experience design may be directly concerned with digital learning experiences, one could

argue that the most intuitive interfaces incorporate many of the principles of learning experience design,

even if the principle goal of a product is not learning in and of itself. According to Peters, principles for

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 6

creating effective learning experiences, especially for the novice user, includes (among others): the

multimedia principle – incorporating text and visuals; not separating related text and visuals, and

including explanations in narrative form; avoiding extraneous detail, including text, stories, video, and

audio; utilizing social presence and conversational tones; and chunking information (Peters, 2012; Clark

& Mayer, 2011).

An example of supporting different learning styles in interface design is how “help” is

implemented within the system. A separate help section, while useful to more experienced users and/or

an analytic/verbalizer learning style, may not reach a wider audience. Conventions such as in-line tips

and interface descriptions, visual aids, instructional videos, contextual tips and descriptive defaults, as

well as an overview of the intended process or content accommodates different learning styles and

strategies and aids the user to learn the system while minimizing errors and frustration (Shepard, 2008).

Similarly, as Reeve indicates, the ease of getting started with a product can be directly attributed to how

successfully the product accommodates and supports learning and cognitive styles. OpenScholar

@Harvard successfully employs some of these strategies; however, certain aspects of getting started and

accessing help could be improved to more effectively support learning within the application.

The Case: OpenScholar @Harvard

Figure 2: OpenScholar @ Harvard landing page

OpenScholar @Harvard (openscholar.harvard.edu) is a Drupal-based CMS that enables Harvard

community members to quickly create personal, project, or departmental microsites. The initial “Create

your web site” screen allows users to select a staging URL, type of site, default set-up, and site visibility.

In-line and contextual tips located in close proximity to relevant information support learning in this stage

of the getting started process.

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 7

Figure 3. Create your web site screen in OpenScholar @Harvard

From the “Create your web site” screen, the user is then presented with a modal “Help Center”

dialog box (Figure 4). This dialog box offers several options to support novice and expert users, as well

as different learning styles and strategies. For instance, the three options offered for getting started

include: 1) closing the dialog box to start using the application immediately (experts, explorers, field-

independent learners); 2) watching a video to gain an understanding of the site building process (novices,

visualizers, serialists) or browsing a separate documentation site (verbalizers, analyzers); or 3) visiting an

external site that allows the user to register for training (assimilators and field-dependent learners) and

find “a rich array of resources and guidance.”

Figure 4. OpenScholar @Harvard’s Help Center screen

Option 1 is well executed, especially for experts, explorers, and field-independent learners to take

control of their own learning experience. Options 2 and 3, while conceptually well planned, are not

executed as successfully. In Option 2, the video is long (6:46) and could benefit from chunking. It also

includes a background audio track of classical music that impairs learning because it distracts from the

main content (and negatively impacts cognitive load), and most importantly, the interface displayed on

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 8

the video does not match the current OpenScholar interface. For novices, and especially cognitive

impulsives, serialists, convergent learners, and assimilators, this could potentially lead to confusion and

frustration. The separate documentation site (Figure 5), on the other hand, is well organized, although

certainly may not appeal to all learner types – field independent learners, cognitive reflectives, divergent

learners, verbalizers, and explorers would likely be the most appreciative of the organization of this

content.

Figure 5: OpenScholar Documentation site

Option 3 offers in-person training, which supports field-dependent learners, assimilators, and

perhaps adaptation-focused learners, as well as resources and guidance. However, most of the content on

Option 3 leads back to Option 2 (Figure 6); perhaps the intent of Option 3 is to introduce a “social

presence” – Harvard Web Publishing – into the application to enhance learning and put the user at ease.

Figure 6: Harvard Web Publishing Resources page

Conclusion

The getting started process and help documentation offered in OpenScholar @Harvard is

generally supportive of learning in the application; its getting started process offers several options for a

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 9

variety of learning styles and strategies. However, greater attention should be focused on the actual

content presented and how the content is organized for optimal learning. The introductory video should

match the current interface and potentially be presented in smaller chunks of information; extraneous

audio information should also be removed.

METACOGNITION, LEARNING STYLES, AND OPENSCHOLAR @HARVARD 10

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