MOTIVATIONAL ASPECTS OF SELF REGULATION THEORY AND ITS IMPACT ON LEARNING ENVIRONMENTS 2

Motivational Aspects Of Self Regulation Theory And
Its Impact On Learning Environments
Shobhana Ganapathi
Boise State University

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

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).

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 socio-constructivist 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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FIG. 2. Mean increases in motivational level under conditions of performance feedback alone depending on whether people continue to perform the activity without goals or spontaneously set low or high goals for themselves. Plotted from data of Bandura Cervone (1983).

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 non-procrastinating 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 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 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 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 self-assess 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 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.

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).