Running head: SELF REGULATION THEORY IN LEARNING ENVIRONMENTS
Motivational Aspects Of Self Regulation Theory And
Its Impact On Learning Environments
Shobhana Ganapathi
Boise State University
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
LEARNING ENVIRONMENTS
Abstract
This paper is an attempt to consolidate the results of empirical research of various experts to see
if motivational elements make a difference in the learning outcomes of self regulated learners.
First some expert implementations of self regulation theory are studied to find out what strategies
are being used to integrate motivational elements effectively in learning environments. Finally
these tried and tested expert strategies for self regulated learning are summarized. These are the
strong foundational elements which can be used to build any future learning environment, be it
student centered, personal or self organized as self regulation is the key in any of these learning
environments.
Keywords: Self regulation theory, self regulated learning, motivation
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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Motivational Aspects of Self Regulation Theory and
It’s Impact on Learning Environments
As we transition into participatory learning in the 21st century using content from blogs,
RSS feeds, wikis, podcasts, research databases and other web 2.0 tools the emphasis is on self
regulated learning. The demographics of the 21st century learner is changing rapidly. They no
longer fall under any particular age group nor are they limited to the confines of a particular
educational institution. They are lifelong self directed learners. Hence, it is important to know if
motivational factor plays an important role in the design of future learning environments. The
purpose and focus of this paper is to consolidate research efforts from various sources to see 1) If
motivational elements make a difference in the learning outcomes of self regulated learners and
if so 2) What are we doing to promote motivation and scaffolding in the current design of
Student Centered Learning Environments (SCLEs)? Finally, these tried and tested expert
strategies have been summarized to serve as a foundational resource to build future learning
environments.
This is being done in two parts. First data analyzed by some researchers is revisited to see
the effect of motivation on self directed learning then learning environments are examined to see
how theory is being put to practice with special focus on motivational elements.
Self Regulation Theory & Self Directed Learning
SRL Assumptions of Researchers from Various Theoretical Orientations
Self Regulated Learning is a cyclical activity that involves three phases, namely
forethought, performance, and self-reflection (Zimmerman, 1986; 1989; 2002). Self regulated
learners are characterized as active and efficient at managing their own learning through
monitoring and strategy use (Boekaerts, Pintrich, & Zeidner, 2000; Butler & Winnie, 1995;
Efklides, 2011; Greene & Azevedo, 2007; Pintrich, 2000; Winnie, 2001; Zimmerman, 2001).
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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Pintrich’s (2000) taxonomy categorizes SRL research into phases and areas of self
regulation. The phases are task identification and planning, monitoring and control of learning
strategies and reaction and reflection. The areas fall under four broad categories of cognition,
motivation, behavior and context. According to Bandura (1991) ‘Self regulation is a multifaceted
phenomenon operating through a number of subsidiary processes including monitoring, standard
setting, evaluative judgment, self appraisal and affective self reaction. Cognitive regulation of
motivation relies extensively on an anticipatory proactive system rather than simply on a reactive
negative feedback’.
When we relate this to traditional theories of learning it has a socio-cognitive and socioconstructivist component. Cognitive and metacognitive approach to understand and plan goals,
strategies and efforts of learning and, social constructivist approach to modify and adapt the
learning based on motivational and affective factors derived from social support or other
contextual influences. The prominence is given to self motivation by Bandura (1991) based on
data analysis which is discussed in the next section.
A closer look at analyzed data by Bandura (1991), Tuckman (2005), Kaufman (2004)
Azevedo & Hadwin (2005), Keller (1999) to see the effect of motivation on learning in self
regulated learning environments.
Bandura’s (1991) analysis shows that people think ahead, motivate themselves and act
pro-actively to achieve anticipated goals. The influence of cognitively based motivators to think,
reflect appraise one-self and act to produce desired learning outcomes is shown in the figure 2 &
3 below from Bandura’s (1991) research.
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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Data Analysis by Tuckman (2005) to see the effect of scaffolding elements on the
performance in an online environment reveals no change for non procrastinators. The results of
course performance and gain in GPA of high and low procrastinating students in the
motivationally-scaffolded and traditional instructional treatments showed that students who
procrastinated in the online environment because of their inability to understand the structure
performed better in the motivationally-scaffolded versions. It did not have any impact on nonprocrastinating students as they performed equally in both.
Azevedo & Hadwin’s (2005) illustration through five research studies show positive
effects of scaffolding on SRL which is shown in the table below.
SRL has three components cognitive strategy use, metacognitive processing, and
motivational beliefs. Studies were conducted by Kaufman (2004) to investigate what factors had
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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an effect on self regulating strategies in a web based setting. The three components were defined
relative to note-taking methods (cognitive component), self-monitoring prompts (metacognitive
component), and self-efficacy building feedback (motivation component). Results of his study
indicated note-taking method had the strongest influence on both the amount of information
gathered and achievement. Self-efficacy building feedback and self-monitoring prompts
demonstrated modest effects on achievement.
Visser’s (1998) research conducted on a distance learning course in UK while living in
France reiterates the importance of motivational factors in online instruction.“ There is no doubt
that there are serious motivational challenges among distance learners. The attrition rate alone
can be viewed as an indication of motivational problems. Students’ comments often focus on
their feelings of isolation, lack of feeling of making steady progress, and great doubts about
being able to finish the course given their other responsibilities and time constraints.” Visser
(1998). Her adaptation of a motivational strategy developed and validated in an adult education
setting in Mozambique (Visser and Keller, 1990) is discussed in the next section where we look
at how motivational elements are incorporated in learning environments.
Practical Applications in Learning Environments
In this section an attempt is made to look at some implementations provided by experts to
see how self regulation theory is incorporated into Learning Environments that contribute to the
students’ learning experience. In particular the focus is on what strategies experts have used to
embed motivational elements in self regulatory environments.
A Look at the Playing Field Where SRL Strategies have to be applied
Learners have a natural tendency to strive for knowledge equilibrium. Successful
environments are those that mediate the synthetic model of the learning environment with the
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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learner’s current state and their possible future state (Pirnay-Dummer, Ifenthaler, Steel, 2012). If
the three assumptions, a theoretical model about what is to be learned (LE), a theory about the
learners’ models (L) and a theory of learning (instructional factors) are all integrated together
into the design the learners will be able to create new insights without losing flexibility.
Educators and instructional designers are constantly looking for ways to design learning
environments which attain this equilibrium by applying new theories of learning and thinking. In
this section, focus is narrowed down to application of Self Regulation Theory.
Self Regulated Learning Implementations in Learning Environments
The learning environment plays an important role in motivating the learner to self
regulate and want to learn. Within our teaching environments, student concentration, creativity,
effort, and participation are all influenced by how they feel about their surroundings (Ginsberg &
Wlodkowski, 2009).
SRL implementations in Azevedo and colleagues Meta Tutor, Biswas and colleagues
Betty’s Brain, White and Frederiken’s Thinker Tools, and Lester and Colleagues’ Crystal Island
will be examined. Kitsantas and Dabbagh invite educators in postsecondary settings to explore
the self-regulatory benefits of contemporary technological tools. These will also be examined in
this section.
Azevedo & colleagues MetaTutor. Greene, Moos, and Azevedo‘s MetaTutor is a
hypermedia learning environment that investigates how learning environments can scaffold SRL
and metacognition within the context of learning complex biological content. A multitude of
features are embedded in Meta tutor that embody and foster SRL. Four pedagogical agents
corresponding to different SRL processes guide the students through the two hour learning
sessions. For instance learners can type that they do not understand a paragraph or they can use
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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the interface to summarize the circulatory system by providing a static illustration. The agents
provide scaffolding in the form of tutorial dialogue. The system collects information when users
interact with the system to provide adaptive feedback. For instance students are prompted to selfassess their understanding and are then given a quiz. Results from the self assessment and quiz
calibrated between the students self confidence and their actual quiz performance allow the
pedagogical agents to provide adaptive feedback. The design layout also supports SRL
processes. For instance learners can interact with the system by taking notes in the embedded
palate. It helps students set goals and choose their strategies for learning. Cognitive,
Metacognitive and behavioral process aspects of SRL are incorporated but motivational and
affective dimensions are not yet incorporated. This is something for future researchers to look
into and work on.
Biswas & colleagues Betty’s Brain. Betty’s Brain is an agent-based learning
environment that uses learning by teaching and social constructive learning frameworks (Schunk,
2005; Zimmerman & Schunk, 2001). Students play the role of teachers and take the
responsibility of teaching a virtual student Betty about complex topics in middle-school science
using a visual representation called a causal map. The causal map includes concepts and causal
links between concepts in the science domain by providing hyperlinks to relevant science
domains like ecology and thermoregulation. Students access this content to identify the
relationships between the concepts in their learning tasks. They then ask Betty questions about
the cause-and-effect relationships they created in the causal map to see if she understood what
has been taught. Betty responds using text and animation. Her comprehension can also be
checked through quiz results. The quiz is administered by another virtual agent Mr. Davis, who
is her mentor. He grades Betty’s responses based on a hidden expert concept map implemented
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MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON
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into the system. This is not visible to the students or Betty (Biswas et al., 2010). When Betty
makes a mistake students can take hints from Mr. Davis and browse the hypertext content to
teach Betty the correct causal relationship. Betty’s Brain supports reading (the hypermedia
content), editing (teaching concepts to Betty), querying (asking Betty questions), explaining
(prompting Betty to explain her reasoning) and quizzing (having Betty take quizzes). Two
interactive factors that support SRL are the visual shared representation used to teach Betty and
the joint responsibility of teaching and learning between the student and Betty. (Biswas, Roscoe,
Jeong & Sulcer, 2009). The motivation to learn is to share the joy of Betty’s achievement
because she is totally trained by the student.
White & Fredericken’s Thinker Tools. Thinker Tools uses collaborative inquiry as a
platform for SRL and metacognitive development. Students are taken through an inquiry
approach in a cyclical manner through a process of questions, hypothesis, experimentation,
modeling, application, evaluation and again generation of new questions. It uses a social
cognitive model of SRL (Zimmerman & Schunk, 2001). A team of agents offer students advice
and strategies to plan, monitor, reflect and revise through each stage of inquiry based learning.
Students can track and assess their progress of tasks by using built in features of goal sliders,
project journals, progress reports and research notebooks. Opportunities are provided for
students to play the role of advisors which encourages students to internalize the self-regulatory
skills modeled by the agents. Thinker Tool programs coupled with role playing activities
improved student understanding of the purpose and application of SRL (White and Fredricksen,
2005). The motivating factor in this learning environment is that students can design and apply
their personal model of SRL by modifications and role play.
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Lester & colleagues’ Crystal Island. Crystal Island is an innovative Learning
Environment which is narrative-centered with a developed storyline giving it a game-like
environment. It uses inquiry-based approach to teach eighth-grade microbiology. Students create
an avatar and enter an immersive 3D learning environment where they must read and understand
complex texts, evaluate multiple points of view, and solve a science-based mystery to save the
research team based on the island. It uses artificial intelligence (AI) techniques and Human
computer-interaction. It delivers engaging, educational experiences where both the narrative and
educational content is tailored to students’ actions, metacognitive and affective states and
abilities (McQuiggan et al., 2008). The motivational factor in this kind of environment as
identified by Nietfeld et al. (2008) is if the goals of the game were presented as learning-oriented
rather than performance-oriented it generated higher levels of interest towards the game.
Contemporary technological tools. As we move into 21st century learning where
learners are consuming content on the web and finding other non-traditional means to gain
knowledge, we have to revisit the contemporary technology tools to see how they can be
leveraged to improve the effectiveness of learning. This is reflected by the Horizon report (2011)
when they say ‘The abundance of resources and relationships made easily accessible via the
Internet is increasingly challenging us to revisit our roles as educators in sense-making,
coaching, and credentialing.’(Horizon Report, 2011).
In Robertson’s (2011) study involving 113 computer science students, blogs were used
by students to engage with the learning community in the class. Results indicated that students
enhanced their self directed learning skills and also helped to support each other. The strategy
Tuckman (2005) uses for motivational scaffolding is collaboration efforts. Chat was used to run
study skills. Support groups helped students stay on task and instructor office hours were utilized
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for further clarification of doubts. Dabbagh and Kitsantas focused on the contemporary tool of
support provided through web-based pedagogical tools (WBPT) with human instructor supports
provided in the form of individualized feedback. This method of scaffolding provided better
learning outcomes. Visser’s (1998) approach was to implement a program of “motivational
messages” that would be sent to students according to two schedules. She concluded that it was
possible to motivate and bring about positive outcomes by focusing on the student support
system rather than on the instruction, which could not be revised easily.
Summary & Conclusion
The empirical results of research data of various experts were visited to see if
motivational element made a difference in the learning outcomes. Bandura, Kaufman, Azevedo
& Visser’s data analysis showed a positive impact of motivational elements on learning
outcomes. Tuckman’s analysis of data showed that motivational and scaffolding elements did not
have an impact on non-procrastinators but it did make a difference in learning outcomes of
procrastinators.
Several SRL implementations including ones using contemporary technological tools
were visited with particular focus of how motivational elements are being incorporated in student
centered learning environments. In hypermedia systems like Meta Tutor virtual agents provide
scaffolding by dialogue and adaptive feedback to keep students on track. In Betty’s Brain
intrinsic motivation is scaffolded when students’, in their role of teachers, feel a sense of
achievement, when their virtual student Betty who is fully molded by them performs well. In
ThinkerTools, the students ability to personalize and customize according to their needs and role
play motivates them to complete tasks aligned to achievable goals. In Crystal Island, not just the
game-like environment but presenting the goals as learner-oriented rather than performance
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oriented motivated the students to complete tasks and enjoy the educational experiences. Finally
by looking at analyzed data of experts, in usage of contemporary technological tools, it was
determined that contemporary technology tools if used right are powerful elements to provide
motivation and scaffolding elements. Chats, emails, support groups, instant messaging, computer
generated timely scaffolds and human instructor feedback when integrated into learning
environments can supplement self regulated learning and metacognition to have positive learning
outcomes.
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http://dx.doi.org/10.1016/j.bbr.2011.03.031
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