Klaudia Bovermann
Potentials and limits of gamification in distance education to foster students' motivation
An empirical work using quantitative and qualitative analysis to expand the literature
Dissertation
Kultur- und
Sozialwissen-
schaften
DISTANCE EDUCATION TO FOSTER STUDENTS’
MOTIVATION
An empirical work using quantitative and qualitative analysis to expand the literature
KUMULATIVE DISSERTATION
zur Erlangung des akademischen Grades
eines/einer Doktors/Doktorin der Philosophie (Dr. phil./Dr.in phil.)
an der Fakultät für
Kultur- und Sozialwissenschaften der FernUniversität in Hagen
vorgelegt von
KLAUDIA BOVERMANN
aus Limeshain
Betreuer und Erstgutachter: PROF. DR. THEO BASTIAENS Zweitgutachterin: PROF.*IN DR. CLAUDIA DE WITT
im Promotionsfach: Erziehungswissenschaften
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To my family
„Und jedem Anfang wohnt ein Zauber inne“
“In all beginnings dwells a magic force”
Hermann Hesse “Stufen” (”Stages”)
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The basis of this thesis stems from my passion to motivate students, which no doubt represents an essential aspect for teaching and learning. Thus, developing and conducting instructional strategies to support learners’ motivation has been crucial for my professional acting as an instructor in both face-to-face and online learning environments.
Today’s rapidly changing technologies have brought us to the digital age, which affects all of our daily lives. In the process, it has also paved ways to establish forms of learning such as e-learning and blended learning in the educational field. In addition, the ability to use digital media for learning enables people to learn flexibly from home, greatly influencing the field of distance education. Distance education enables people to engage in life-long learning, continue to qualify years after graduation, and learn and study alongside work and family. In recent years, these advantages have led to a rapid increase in the number of students enrolled in institutions in this area.
I myself have used the advantages of distance education to study alongside work and family and was enrolled in the educational science study program of the FernUniversität in Hagen, Germany. During my studies, I recognized the importance of self- organization for learning, and specifically the aspect of motivation to continue and successfully complete my courses of study. Since then, I have become even more convinced that, in particular, motivational concepts in distance education are of great importance for learners. As such, I followed my conviction and established and examined motivational approaches when working in the department of Instructional Technology and Media at the FernUniversität in Hagen. My passion for research, both quantitative research using statistical analysis and qualitative methods, helped me with this project. In addition, my experience as a distance education student helped me to better understand the needs, thoughts and learning preferences of students in such programs—and in the process, strengthened my competence as a professional teacher in the context of distance education.
In recent years, gamification to foster students’ motivation has gained considerable attention in the educational field. However, it has also experienced mixed results and
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even earned strong criticism. Nevertheless, in sum, it has been claimed to support motivation in e-learning and blend learning settings, and so my interest in this approach was roused. If it represents an appropriate approach, it would certainly be worthwhile to investigate this issue further. Specific gamification concepts were thus planned, designed and investigated for both in-Moodle and on-campus seminars for the target group of students in distance education. In order to evaluate the gamified concepts, I applied mixed-methods models employing either both quantitative and qualitative research or only quantitative approaches, emphasizing my favor for empirical research in social sciences.
Over the past four years, I have researched the topic of gamification, and I have experienced this time I have spent working on my thesis to be highly interesting and deeply constructive in numerous ways. In the beginning, I possessed little knowledge concerning the concept of gamification, although rewards in educational settings were familiar to me. However, I have worked hard all these years, and I have continued my research to achieve results in this realm. Each time an article was accepted and published, this encouraged me to go on to complete this thesis. The resultant work encompasses several empirical studies on gamification to broaden the realm of applying the gamification concept in educational science in the context of distance education.
Klaudia Bovermann
Limeshain, 22 May 2020
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TABLE OF CONTENTS
SECTION I GENERAL INTRODUCTION 1
Chapter 1 General Introduction and Theoretical Foundations 2
SECTION II EMPIRICAL WORK 59
Chapter 2 Online Learning Readiness and Attitudes towards Gaming in Gamified Online Learning: A Mixed Methods Case Study 62
Chapter 3 How Gamification Can Foster Motivation and Collaboration in Blended Learning: A Mixed Methods Case Study 86
Chapter 4 Towards a Motivational Design? Connecting Gamification User Types and Online Learning Activities 110
SECTION III GENERAL DISCUSSION 135
Chapter 5 General Discussion and Practical Implications 136
References 173 Summary 198
Zusammenfassung 205 Danksagung 213
Curriculum Vitae 214 List of Publications 215 Erklärung 217
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LIST OF TABLES AND FIGURES
Tables
Table 1.1 Comparing Independent Learning, Self-Directed Learning and Self- Regulated Learning 19
Table 1.2 Overview of the Empirical Research in this Thesis 57 Table 2.1 The Gamification Concept and Design in Moodle 69 Table 2.2 Analyses of Scales and Statistical Values 73
Table 2.3 Correlations 74
Table 2.4 Extract of the Coding Guideline 75
Table 2.5 Typology of Interviewees Representing a Certain Motivation Type 77
Table 2.6. Correlations 79
Table 3.1 The Two-Phase Explanatory Design Used in this Study 94 Table 3.2 Descriptive Statistics for Age and Gender 97
Table 3.3 Analysis of Intrinsic Motivation Scales and Statistical Values 98 Table 3.4 Analysis of Differences between Control and Treatment Groups 102 Table 4.1 Motivation or Drive, User Types for Gamification, and Associated
Mechanics 117
Table 4.2 Questionnaire 120
Table 4.3 Correlations Between Gamification User Types, Mechanics and Online Learning Activities (n=86) 127
Table 4.4 A Person-Centered Perspective of the Results (User Types 1 -5) 128
Table 4.5 An Environment-Centered Perspective of the Results (Activities A-F) 129
Table 5.1 Overview of the Research Characteristics and Results 141
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Figures
Figure 1.1 Phases and Sub-Phases of Self-Regulation 12
Figure 1.2 The Structural Model of Self-Regulated Learning with Six Components 14
Figure 1.3 Key Phases of Garrison’s Self-Directed Learning Model 17 Figure 1.4 Regulation Types and Internalization in SDT 29
Figure 1.5 Degree of Support for Intrinsic Motivation in Distance Education 32
Figure 2.1 Shares of Motivation Types 76
Figure 2.2 Online Learning Readiness and Types of Motivation 78 Figure 2.3 Agreement to Study-Satisfaction Overall and Gaming 80
Figure 3.1 Kahoot!’ Quiz with Points and Scoreboard in the Introduction Part 91
Figure 3.2 Example of a Ranking List in Class and Recommendations for Peer
Review 92
Figure 3.3 Shares for Intrinsic Motivation Related to the Introduction Part 101 Figure 3.4 Shares for Intrinsic Motivation Related to Collaborative Group Work
102
Figure 4.1 Shares of Degree Program, Gender and Age 122
Figure 4.2 Average Agreement to Gamification User Types 122
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SECTION I
GENERAL INTRODUCTION
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Chapter 1 General Introduction and Theoretical Foundations
Contents
1 Introduction to this Dissertation 4
2 The Special Situation of (Digital) Learning in Distance Education 6
2.1Defining Distance Education 6
2.2Status Quo of Distance Education 7
2.3The Special Situation of (Digital) Learning in Distance Education 8
3 Self-Regulated Learning in Distance Education 9
3.1Defining Self-Regulated Learning 9 3.2Self-Regulated Learning Models 11
3.3Distinguishing Self-Regulated Learning, Self-Directed Learning and Independent Learning 15
3.4The Current Status of Self-Regulated Learning 20
4 Students’ Motivation in Distance Education 23
4.1Defining Motivation 23
4.2Self-Determination Theory: The Meta-Theory for Motivation 26 4.3The Internalization Continuum 27
4.4Three Basic Psychological Needs in SDT 29 4.5Motivation and Self-Regulated Learning 30 4.6Conclusion 33
5 The Use of Gamification to Foster Students’ Motivation 33
5.1The Concept of Gamification 34
5.2Applying Autonomous and Non-Autonomous Motivation in Gamification 36
5.3Social Action and Gamification User Types 38 5.4The Aspects of SDT in Gamification 39
5.5Reasons for the Use of Gamification in Distance Education 40
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6 Research Status of Gamification in Education and Learning 41
6.1 General Overview of Research Results in Gamified Education 41 6.2Research Results on Game Elements Relevant to this Thesis 45
7 The Context of the Empirical Work of this Thesis 48 8 This Dissertation 50
8.1Principles of this Dissertation 51
8.2Research Gaps and their Conclusions 51 8.3 Research Questions in this Thesis 52 8.4 Methodology 53
8.5 Overview of this Thesis 54
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1 Introduction to this Dissertation
Computer technology has developed rapidly over the past three decades, becoming an integral aspect of people’s daily lives. The Internet (the World Wide Web) and multi- functional devices such as smartphones and tablets have fundamentally changed the way people communicate and interact. Social media platforms experience millions of users every day, and people can read or publish information, connect and interact anytime and anywhere around the world. This is made possible through modern devices becoming portable, easy to use, fun and entertaining. In turn, they enable access to all types of information with just a click.
These radical technological transformations in today’s digital age have not only affected people’s daily lives, but also heavily influenced forms of teaching and learning.
This development of new communication and learning tools has in turn brought traditional universities and distance education institutions closer together. Meanwhile, forms of e-learning or blended learning have not only become widespread forms for trainings in companies and distance education institutions, but traditional universities also offer such concepts to their students. Implementing new learning systems supports a wide range of learning methods, such as individual and collaborative learning, formal and informal learning or workplace learning (Valsamidis et al., 2014; Urh et al., 2015).
Learners can easily use their computer, tablet or smartphone to participate in these systems independently of time and place.
The nature of learning in e-learning and blended learning settings has changed from being teacher-centered to learner-centered in learning settings. Thus, in online and distance learning, students require different strategies and skills compared to the traditional face-to-face settings they have become familiar with from their days at school. Learning with digital media, learners are personally responsible for mastering their own tasks and learning process. These aspects also comprise relevant components for self-regulated learning, which needs to be applied to online learning environments.
Today, student motivation represents one of the primary aspects in self-regulated learning, and a crucial aspect in distance education (Hartnett, 2016, 2019). In addition, learners’ satisfaction in higher education has been recognized as an important aspect of learners’ persistence during their courses of studies (Westermann et al., 1996).
For a long time, the focus in learning with digital media, as is needed in distance education, has remained on the technology due to the rapid development in its
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possibilities, leaving users’ needs to be neglected. Urh et al. (2015) offered some considerations for why e-learning might not have been as successful as intended. In their study, they identified reasons such as poorly designed learning environments, ignored instructional design principles, or the use of inappropriate motivational techniques. To counteract learning environments experiencing poor effectiveness, efficiency or motivational incentives for students, the authors depicted the concept of gamification for the educational context. This approach involves applying game elements such as badges, points or leaderboards to contexts other than games. In a wider range of literature, this has been claimed to foster motivation and engagement, enabling broader acceptance of learning via digital media and, in sum, online learning environments (e.g. Kapp, 2012).
The gamification concept is also considered in this thesis to foster students’
motivation. This study’s goal is to systematically investigate the gamification concept within the context of distance education with the need of self-regulated learning. The interest concerned gaining knowledge about the advantages, disadvantages, possibilities and limitations for gamified learning for German learners in distance education.
Participants were enrolled in bachelors’ or masters’ degree programs of a distance education class in educational science. The applied concepts encompassed the game elements of badges, leaderboards, points or progress bars to support students’
motivation. Gamified learning approaches were implemented in both the student learning management system and in on-campus seminars with face-to-face learning.
The aim of the concept was to provide goal-oriented learning and to allow students to monitor and reflect on their own learning process in individual or collaborative learning settings. In doing so, the gamified concepts offered feedback and acknowledgment to support motivation in self-regulated learning. A further aim of this thesis concerns investigating the target group’s interests, needs or preferences based on gamification user types (Marczevski, 2015). These user types and related gamification mechanics correlated with being commonly employed in online learning activities in the students’
learning management system. Investigations should offer further knowledge for developing motivational designs tailored to individuals’ preferences and needs.
This work’s content is divided into three sections. Section I begins this thesis with the general introduction and the framework for the relevant topics. First, distance education and self-regulated learning are explained, followed by the motivation
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construct and the self-determination theory. Next, the gamification approach for fostering student motivation is discussed and the concept’s current research status reported. Afterwards, this study’s context is elaborated, and finally, an overview about this dissertation is illustrated.
Section II encompasses the empirical studies in this thesis. First, the investigation into a gamified concept with badges and progress bars in the student learning management system is presented. Gamified learning in on-campus seminars with points, leaderboards and peer feedback in collaborative learning settings is reported in the subsequent chapter. Finally, the examination into gamification user types, their related gamification mechanics and associated online learning activities is described.
The general discussion of this thesis is located in Section III. First, the key research characteristics and results are presented to provide an overview of this empirical work. Following this, the main findings of the studies and general conclusions are described. In the subsequent chapter, the results are discussed and limitations of this research offered. Finally, practical implications are deduced and avenues for future research suggested.
2 The Special Situation of (Digital) Learning in Distance Education
2.1Defining Distance Education
Distance education is not a new phenomenon. Forms of distance education can be found going back as far as 160 years. Back then, educational institutions provided learners with mail instructions and correspondence for learning. The original target groups for distance education comprised adults with occupational, social, and family commitments (Schlosser & Simonson, 2006), and this group of learners still remains the primary target group today (Black, 2019).
In 1980, Keegan published his work “On Defining Distance Education,” in which he offered an overview of 1970s opinion about distance education in different European countries. Back then, it was already recognized that distance education “gives a radical new meaning to the concept of the independence of the adult learner. In this system, he is responsible for initiating the learning process and, to a large extent, for maintaining it throughout” (Keegan, 1980, p 20).
However, over the last two decades, the form and methods of distance education have considerably changed due to technological developments and the rise of the
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Internet (Black, 2019; Schlosser & Simonson, 2006). This quick technological development allows people today to easily access the World Wide Web from their own devices at any time and from any place. This global processing heavily influences all parts of our lives and has affected the distance education sector. However, certain important characteristics differ from traditional courses. According to Seaman et al.
(2018), distance education “uses one or more technologies to deliver instruction to students who are separated from the instructor and to support regular and substantive interaction between the students and the instructor synchronously or asynchronously”
(p. 5). In sum, distance education instruction employs a variety of technology and tools unavailable two or three decades ago, such as the Internet, audio conferencing, virtual classrooms, or learning management systems (LMS, e.g. Moodle). Examining these presented attributes, distance education can be characterized as institution-based, formal education in which the learning group is separated and interactive telecommunication systems employed (Schlosser & Simonson, 2006). These systems connect learners, resources and instructors, allowing interactions to be synchronous or asynchronous (Simonson et al., 2006). It may also be used on its own or in combination with other forms, such as face-to-face learning (Black, 2019).
2.2 Status Quo of Distance Education
Between 2002 and 2012, overall enrollments in distance education faced an annual increase in the USA. This growth in distance education has continued since 2012, but overall enrollments have declined, especially in the private for-profit sector. Current numbers for total distance enrollments have revealed that 14.9% (3,003,080) of the students were exclusively taking distance courses, while 16.7% (3,356,041) were enrolled in a combination of distance and non-distance courses (Seaman et al., 2018). A similar development was recognized at the FernUniversität in Hagen, Germany, a university for distance education and Germany’s largest state university. Following a period of quick enrollment growth, reaching over 70.000 students in the last few years, a declination of numbers can be observed. However, the total number of students enrolled in distance education remains high (FernUniversität in Hagen, 2019a). In Germany, a total 160.000 learners in 2017/2018 studied at one of Germany’s distance education institutions (Allensbach University, 2020). As such, every second student was enrolled at the FernUniverstät in Hagen. In addition, it can be observed that these
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annually increasing distance education rates in Germany remain higher than those for traditional universities (Allensbach University, 2020). Overall, it must also be recognized in Germany’s field of education or professional education that trainings, e- learning and distance learning are increasingly becoming a normal standard (Schwahn, et al. 2018).
2.3 The Special Situation of (Digital) Learning in Distance Education
The situation of globalization and economic considerations of companies forces employees to engage in life-long learning. Technology’s rapid development and changes in job structures require people to keep up in their knowledge or obtain higher qualification in their profession to retain their jobs. It can be observed that lifelong learning is necessary for people of all ages and in almost every profession (Jarvis, 2009).
For qualification, many people turn to distance education courses in higher education institutions. As a result, students often begin learning years after graduation, and institutions meet students with different learning experiences and biographies. In addition, these learner groups are diverse, possess different levels of available knowledge, and face an extensive work load to be able to study alongside work and family life. Moreover, learning in such online learning environments contrasts learners’
former experiences in traditional classes with teacher-centered learning and a predetermined pace and structure. Therefore, learning via digital media goes beyond previously well-known self-directed media, such as books and school classes. Learning in online learning environments relates to a constructivist learning approach, in which learners work out the content on their own, using their own ability and at their own pace (Kerres, 2013; Reinmann & Mandl, 2006; Zimmermann, 2002).
The key advantages of distance education courses include flexible learning hours and locations, learning through mobile devices, and the possibility of qualifying alongside family and work. However, such learning contexts often challenge students.
Learning mostly occurs in virtual learning settings, and a certain level of online learning readiness (OLR) is required. This aspect encompasses, for example, technical competencies and specific social or communication skills for online learning. In addition, learner-centered environments in distance education require students to be motivated to master their tasks. Moreover, they have to organize their learning and
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monitor, reflect and evaluate the learning process to achieve their learning goals. These characteristics make learning largely autonomous and self-regulated with the application of specific strategies to master and monitor one’s own learning process (Cho
& Heron, 2015; Dettori & Persico, 2011; Zimmerman, 1990, 2002).
Therefore, the learner-centered structure confronts students with the self- regulated learning approach, which carries specific challenges for learners (Kerres, 2013; Reinmann & Mandl, 2006; Zimmerman, 2002). Consequentially, the risk of decreased motivation and subsequent drop-out rates among students in distance education courses due to said challenges can be high (Vogel et al., 2018). Accordingly, the aspect of motivation plays a major role for distance learners and represents a crucial factor for students’ commitment to and engagement in their degree program, leading to the students’ learning situation in distance education.
The following chapter focuses on the self-regulated learning approach, indicatory models and its current research. Additionally, motivation’s primary role is illuminated and highlighted.
3 Self-Regulated Learning in Distance Education
3.1 Defining Self-Regulated Learning
Students enrolled in a distance education programs require a set of key competencies to apply in their academic careers and ensure successful learning in their online degree program. These skills include not only technical competencies, media literacy, or social skills to interact online, but also self-regulated learning with metacognitive skills to apply control measures and reflect on one’s own learning process (Winne, 2015).
In the literature, self-regulated learning and self-determined learning are sometimes used interchangeably, since both approaches address the students’ learning process and involve a learner-directed behavior. The concept of self-determined learning refers to forms of extrinsic and intrinsic motivation, which vary in their relative autonomy. They reflect either external control (extrinsic motivation) or true self- regulation (intrinsic motivation). Thus, motivation can be self-determined when learning is perceived as controlled from the outside. By contrast, true self-regulated motivation is associated with choice and autonomy (Deci & Ryan, 1985, 1993; Ryan &
Deci, 2017). Regardless, motivation plays a crucial role in self-regulation learning and
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its strategies (e.g. Schunk et al., 2014; Schunk & Zimmermann, 2008) within the distance education context (Winnie, 2015).
Next, the self-regulated learning approach (Zimmerman & Schunk, 1989) describes how students become masters of their own learning. This aspect shifts the focus from “student’s learning ability and environments as ‘fixed’ entities to their personally initiated process” (Zimmerman, 1990, p. 4). Responses to the students are designed to improve their learning ability and environment. In this sense, self-regulated learners need to “select and use self-regulated learning strategies to achieve desired academic outcomes on the basis of feedback about learning effectiveness and skill”
(Zimmerman, 1990, pp. 6-7). A precise definition for self-regulated learning can refer to
“the degree learners are metacognitively, motivationally, and behaviorally active participants in their own learning process” (Zimmerman, 2008, p. 167). Moreover, self- regulated learning describes a constructivist approach focusing on the learning environment to activate learners (Zimmerman, 2002). Main aspects of self-regulated learning can be summarized with the following aspects (Kerres, 2013; Zimmerman, 1990, 2002): (1) Learning describes a self-regulated process; (2) learning refers to self- generated behaviors to attain learning objectives; (3) learning represents a constructive process, since it links to prior knowledge; (4) learning comprises a social process, as it is embedded in a cultural background and in interaction with others; (5) learning constitutes an emotional process and influences motivation and self-satisfaction; (6) learners are proactive in their efforts to learn; (7) learners need control skills to monitor and reflect on their learning process; and (8) feedback is used to monitor their learning effectiveness and skills for self-evaluation.
Metacognitive Skills. Since learners perceive a high level of autonomy in self- regulated learning, they require metacognitive skills to manage their learning process and build knowledge (Winne, 2015). Besides cognitive and motivational resources, metacognitive resources form the basis for goal-oriented and self-regulated learning in the sense of an active, constructive acquisition of knowledge (Konrad, 2014). They enable learners to plan, control, monitor, and reflect on their own learning (Winne, 2015). These control measures are necessary to perform one’s own learning process independently and to be able to successfully reach learning objectives. Metacognition features a reflexive view of cognitive strategies and contributes to general, transferable
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knowledge about categories. It also includes decision-making, planning, motivation, affect, and emotion (Winne, 2015). These characteristics support the knowledge development in order to process all information as completely as possible. Instead of the term “metacognitive skills,” Ackerman and Thompson (2015) named these skills
“Meta-Reasoning.” Their model included the aspects “Reasoning” and “Meta- Reasoning” on a timeline. The object-level process of the “Reasoning” category included the identification of components and goals. The control level of the “Meta- Reasoning” component was divided into “Metacognitive Monitoring” and
“Metacognitive Control,” which includes the assessment of knowledge, strategies, and the reflection learning behavior. This framework highlights the learning and problem- solving process of self-regulated learning and the application of metacognitive strategies associated with time.
3.2 Self-Regulated Learning Models
Different self-regulated learning models feature a theoretical and empirical background.
Of relevance to this thesis, four models focusing on motivation, (meta-)cognition or emotion are presented. For a comprehensive overview of self-regulated learning models, Panadero (2017) can be consulted.
Zimmerman and Moylan (2009). One of the first to research self-regulated learning, Barry Zimmerman developed several models and classified his approach as a socio-cognitive theory (Zimmerman, 2013). Here, learners gain knowledge in social interaction and profit from observing others. Zimmerman and Moylan (2009) developed a cyclical model where self-regulated learning contains three phases: (1) forethought, (2) performance, and (3) self-reflection. The forethought phase includes task analysis, goal-setting and strategy. In addition, this step addresses the aspect of self-motivation, which focuses on intrinsic interest and learning goal orientation. The performance phase encompasses components of self-control with strategy and management, as well as self- observation with metacognitive skills. Finally, the third step, the self-reflection phase, centers on the aspects of self-judgement, self-evaluation, self-reaction, and self- satisfaction. All three phases and sub-phases are ordered in a circular process demonstrating that behavior and environmental factors change during self-regulated learning (Aksan, 2009).
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Figure 1.1 Phases and Sub-Phases of Self-Regulation (adopted from Zimmerman &
Moylan, 2009)
Pintrich (2000). Compared to the three phases of Zimmerman and Moylan’s (2009) self-regulation model, Pintrich’s (2000) approach is divided into four steps: (1) forethought with the target and goal setting, (2) monitoring with metacognitive awareness, (3) control in selecting cognitive learning strategies, and (4) reaction and reflection with cognitive judgements for evaluating the task and context. All four phases are associated with motivational and affective aspects to be able to cope with and master the task, and to continue with the learning process. Although, Pintrich identified four rather than three phases, as in the model of Zimmerman and Moylan, the components of his approach can be assigned to the phases and sub-phases of the circular process model by Zimmerman and Moylan (Figure 1.1). In addition, Pintrich’s model considers that students do not always successfully cope with self-regulated learning’s required tasks and strategies. If students are unable to follow the learning process, their effort can decrease, and they may then change or give up on their task, or even leave the context at the end.
Forthought Phase:
-Task Analysis (Goal-Setting, Strategy) -Self-Motivation (Task/Interest)
Performance Phase:
-Self-Control (Strategy, Management) -Self-Observation (Metacognitive Skills) Self-Reflection Phase:
-Self-Judgment (Self-Evaluation) -Self-Reaction (Self-Satisfaction)
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Boekaerts (1996, 2011). In their study about self-regulation, Musso et al. (2019) distinguished between two self-regulated learning approaches: (A) a structural and (B) a dynamic model (also consult Panadero, 2017).
(A) The structural model includes key components for self-regulated learning and is divided into two main dimensions: (1) cognitive self-regulation and (2) motivational self-regulation. For Boekaerts (1996), the two systems are parallel and strongly interrelated, as the cognitive and motivational regulatory systems feature a complementary nature in the students’ attempts. With these two dimensions, the model comprises a total of six components—three for each main dimension (Boekaerts, 1996, Figure 1.2). Dimension one encompasses the components (1) content domain, (2) cognitive strategies, and (3) cognitive regulatory strategies, while dimension two features (4) metacognitive knowledge and motivational beliefs, (5) motivational strategies, and (6) motivational regulatory strategies. All six components are connected and strongly influence each other on each self-regulated learning stage. In addition, the six components are associated, two by two, to a specific process during self-regulated learning: (a) goals with the aspects of cognitive regulatory strategies and motivational strategies, (b) strategy use with cognitive strategies and motivational strategies, and finally, (c) domain-specific knowledge with content-domain and metacognitive knowledge and motivational beliefs.
These six components, with their characteristics, resemble the models of Zimmerman and Moylan (2009) and Pintrich (2000). These aspects can also be more or less identified in both approaches to self-regulated learning.
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Self-Regulated Learning
Dimension 1 (1-3): Dimension 2 (4-6):
Cognitive Self-Regulation
Process (a-c)
Motivational Self-regulation(3) Cognitive Regulatory Strategies
(a) Goals (6) Motivational Regulatory Strategies
(2) Cognitive Strategies (b) Strategy Use (5) Motivation Strategies
(1) Content Domain (c) Domain Specific Knowledge
(4) Metacognitive Knowledge and Motivational Beliefs
Figure 1.2 The Structural Model of Self-Regulated Learning with Six Components (adopted from Boekaerts, 1996)
(B) The dynamic models describe how the aspects of self-regulated learning interact (Musso et al., 2019) and represent the actual process when self-regulated learning occurs. One such dynamic model is the dual processing model of Boekaerts (2011). The two key components of this approach represent learning and coping strategies running in parallel simultaneously. These dimensions interact to master and cope with the learner’s task. In addition, the learner’s knowledge and skills are linked with the learner’s self, either to gain resources to continue in the process or to prevent the loss of resources.
Efklides (2011). The model of Efklides (2011) is based on the classic socio- cognitive theory of self-regulation and extant self-regulated learning approaches. It encompasses the components of metacognition, motivation, and affect, which are found in all self-regulated learning models (Efklides, 2011). The author’s “metacognitive and affective model of SRL” (MASRL) addresses cognition, motivation, and affect, placing less focus on behavioral or environmental factors. However, in the MARSL, strategies also need to be applied to control these factors. The model postulates a person level that targets the learner’s cognitive, metacognitive, motivational, affective, and volitional characteristics. Then, there is a task level, in which the task can objectively be defined
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based on typical features (e.g. novelty, complexity, etc.) and is embedded in a specific context or situation. The task itself remains independent of the task and person level, but influences both levels. Thus, they interact with and form each other by providing feedback between the two levels to enable self-regulated learning while completing the task. The author further pointed out that motivation is not a specific element in the MASRL, which operates before the phases begin to work. The motivation factor rather results from the learner’s metacognitive and affective experiences while performing the task. This aspect differs from some of the other models presented earlier, which identified motivation as crucial for entering a task.
Summary. Examining the self-regulated learning models, several phases and components can be distinguished for this approach. Most prevalent are the elements of metacognition, motivation, and affect operating in self-regulated learning (Baars et al., 2017). In addition, the models postulate that learners require several skills or strategies to master their self-regulated learning process. First, motivation can be named as a crucial aspect to begin and analyze a task. Then, students need to monitor the learning process and control performance in order to master the task. Finally, students have to reflect and judge their learning and task outcome. In summary, they have to apply goal- oriented learning strategies to achieve the goal of successful learning. In doing so, they are actively involved and interested in their task and can identify their own needs.
However, (intrinsic) motivation represents the crucial component and the learner’s
“motor” throughout all phases of self-regulated learning.
3.3 Distinguishing Self-Regulated Learning, Self-Directed Learning and Independent Learning
The self-regulated learning concept can be distinguished from the concepts of self- directed learning and independent learning. However, self-directed learning and self- regulated learning are often used synonymously in literature (Saks & Leijen, 2014). As such, definitions of independent learning and self-directed learning are presented in this subchapter. Afterwards, differences or similarities between the terms of self-directed and self-regulated learning are elaborated.
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Independent Learning. In 1977, independent learning was described by Moore in when he examined the characteristics of distance education. In the eyes of Dron (2019), Moore provided the most precise definition of independent learning, referring to this form of learning as an educational program in which the “learning programme occurs separate in time and place from the teaching programme” (Moore, 1977, p. 11).
Obviously, the definition of distance education (see Section I, Chapter 2) strongly resembles independent learning and encompasses similar characteristics. In independent learning, the learner has the opportunity to determine goals, resources, and evaluation at least in the same share as the teacher. However, independent learning can be conducted without teaching, and the learner does not need to utilize resources or procedures prepared by teachers; rather, the learners can establish their own goals. Moore (1977) pointed out that the term “independent” may be misunderstood, since the student is not isolated on an island. Although learners exhibit an autonomous learning behavior, they remain engaged in an educational program.
Self-Directed Learning. Another phrase used in the literature within the context of distance education would be “self-directed learning.” Knowles (1975, p. 17) described self-directed learning as a “basic human competence-the ability to learn on one’s own.” Based on this definition, Garrison (1997, Figure 1.3) developed a more comprehensive model of self-directed learning encompassing dimensions from the sociological and pedagogical aspects: (1) motivation (entering/task), (2) self- management (control), and (3) self-monitoring (responsibility). All three dimensions eventually lead to self-directed learning. In the model, motivation is required to begin the learning process and is connected to both self-management and self-monitoring.
Self-management involves the performance of goal-directed actions, while the self- monitoring step addresses the cognitive and metacognitive processes with monitoring learning strategies to plan and modify the learning task or goal. Finally, the two components of self-management and self-monitoring comprise an iterative procedure and are mutually dependent.
However, in Garrison’s model (1997), motivation “plays a very significant role”
(p. 26) for learning and for achieving goals. In addition, the entering factors are crucial for the motivation dimension, which encompasses, for example, personal needs, affective state, personal characteristics, and contextual characteristics.
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Figure 1.3 Key Phases of Garrison’s Self-Directed Learning Model (adopted from Garrison, 1997, p. 22)
Then, in 2012 Hiemstra and Brockett reframed the concept of self-directed learning, which previously focused on the process (e.g. Grow, 1991). They suggested an updated version containing three balanced and overlapping elements: (1) “Person,” which includes the characteristics of the individual; (2) “Process,” which involves the teaching-learning transaction; and (3) “Context,” which encompasses the environmental and sociopolitical climate. All three components possess large intersections to pave the way for successful self-directed learning, are treated equally, and should remain in balance.
Self-Directed versus Self-Regulated Learning: Saks and Leijen (2014) distinguished the concept of self-regulated and self-directed learning within the context of e-learning in 30 studies and articles. Their study revealed that the term “self-directed learning” can be seen as a wider concept including the form of self-regulated learning.
In addition, their findings revealed that many similar phrases were employed in the literature synonymously with self-directed learning, such as self-management, learner autonomy, independent learning, or e-learning. By contrast, the concept of self-directed learning was defined and understood more precisely than the form of self-directed learning.
Motivation (Entering/Task)
Self-Monitoring (Respnsibility)
Self- Management
(Control)
Self-Directed Learning
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For an overview, the three approaches of independent learning, self-directed learning, and self-regulated learning, along with their representatives and key components, are compared in Table 1.1.
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Table 1.1 Comparing Independent Learning, Self-Directed Learning and Self- Regulated Learning
Independent Learning (Anderson, 2013; Dron, 2019; Moore, 1997)
SDL (Garrison, 1997;
Hiemstra & Brocket, 2012;
Saks & Leijen, 2014)
SRL (Boekaerts (1996, 2011; Pintrich, 2000;
Zimmerman & Moylan, 2009)
Main aspects
• Instructor is not mandatory
• Learner determines own goals, resources and evaluation
• Self-management
• Learner autonomy
• E-learning and networking
• MOOCs
Main aspects
• Several phases: (1) motivation for entering the tasks, (2) monitoring the learning process, (3) control of the performance with strategies, and (4) self-evaluation and self-reflection of learning
• Goal-oriented learning
• Using strategies to achieve the goal
• Metacognitive skills
• Intrinsic motivation
• Active involvement
• Identification of learners’ own needs Instructor
• Instructor is not mandatory
• Synonym:
Independent Learning
Instructor
• Instructor is present and usually defines learning objectives Phases
• (1) Motivation (Entering/Tasks )
• (2) Self-management (Control)
• (3) Self-monitoring (Responsibility) Balanced Components:
(1) Person, (2) Process, (3) Context
Phases
(1) Forethought: Task analysis and self- motivation
(2) Monitoring: Using strategies
(3) Performance: Self- control and self- observation
(4) Self-Reflection: Self- judgement and self- reaction
Summary. The terms of independent learning, self-directed learning, and self- regulated learning are employed in the context of distance learning, and self-directed and self-regulated learning in particular are often applied synonymously (Saks & Leijen,
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2014). It can be concluded that all three learning forms feature several intersections and similarities to each other. However, independent learning can be classified as the widest definition for learning in distance education. Additionally, self-directed learning still demonstrates a wider concept in the literature compared to self-regulated learning, which features the most detailed explanations (Saks & Leijen, 2014).
Conclusion for this Dissertation. The term of self-regulated learning was followed for the students’ learning context in this thesis. These aspects could best be identified using the self-regulated learning approach, leading to the decisions regarding (1) instructions and goals employed in this thesis, (2) the design of the learning environments, and (3) the skills and requirements the learners required in the context at hand.
Moreover, another distinguishing aspect between independent and self-directed learning is that instructors define the learning objectives, which was also at work in this thesis. Although learners remained free to choose their modules within their courses of study, they needed to follow and achieve specific learning goals set by the institution and teachers of the modules. In doing so, students needed to successfully accomplish modules to be able to finish their courses of study.
Finally, the most prevalent foundation for this thesis was the aspect of (intrinsic) motivation as the key component in the self-regulation models and that motivation represents the central framework for self-regulated learning (Rheinberg et al., 2000) during all phases in the self-regulated process. Since student motivation comprises the most crucial component for distance education and the application of self-regulated learning, motivation was addressed and highlighted in this work.
3.4 The Current Status of Self-Regulated Learning
In the research about self-regulated learning, the aspect of strategies in this approach had often been addressed. For example, in 2015, Broadbent and Poon performed a systematic review of self-regulated learning strategies and their connection to academic achievement in online higher education environments. Their review identified a significant relationship between these two variables. Self-regulated learning strategies such as time management, metacognition, or effort positively correlated with academic outcomes. Therefore, the results indicated that the self-regulated learning approach
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represents a crucial factor for online learning, as had already been illustrated in previous research (e.g. Usta, 2011).
Moreover, Palos et al. (2019) tested the relationship between self-regulated learning and academic performance. Results indicated that the concepts of self- regulation and self-efficacy constituted academic performance predictors. The self- efficacy approach has often been associated with learning and motivation in literature (Schunk & DiBenedetto, 2016). It is defined as people’s beliefs concerning their capabilities of performance or learning, and this determines how people feel, motivate themselves, and behave (Bandura, 1994). Thus, self-efficacy influences motivation (choice, effort), learning, self-regulation, and achievement (Schunk & DiBenedetto, 2016). Palos et al. (2019) highlighted that their findings supported educators in providing suitable learning opportunities to support students in learning better, monitoring their progress, and achieving their objectives.
Furthermore, self-regulated learning strategies were comprehensively investigated by Garcia et al. (2018). In their literature review, they examined a taxonomy of strategies and behaviors for self-regulated learning in e-learning environments. They concluded the following strategies to be most utilized in online learning: (1) self-evaluation, (2) organizing and transforming, (3) goal-setting and planning, or (4) keeping records and monitoring. These findings are in line with the strategies and phases of the most self-regulated learning models.
Lee et al. (2019) conducted a systematic review investigating the concept of self- regulated learning in massive open online courses (MOOCs). Their study determined that self-regulated learning positively influenced learning in MOOCs. They also identified strategies for self-regulated learning, such as motivation, task value, goal- setting, or self-efficacy. In the review, the goal-setting aspect was identified as a metacognitive strategy. However, time management and effort were assigned to behavioral strategies.
Moreover, a systematic review about MOOCs and self-regulated learning was conducted by Wong et al. (2018). They highlighted the highly diverse group of learners in such types of online learning environments, as well as the learners’ requirements to make their own decisions to achieve academic success. Their review results identified human factors as crucial for self-regulated learning and its efficacy. In addition, they recommended for learners’ individual needs to be recognized and understood in order to
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achieve the best possible adaptations for self-regulated learning in online learning settings.
Furthermore, relations between self-regulated learning strategies and motivation were investigated by Mukhtar et al. (2018). They found learners who were more intrinsically motivated reported to utilize more strategies for self-regulated learning. In addition, the research of Baars et al. (2017) investigated the association between autonomous and controlled motivation, affect, and self-regulated learning with different support levels when learning with video instructions. The authors reported that students exhibiting higher autonomous motivation possessed more accurate self-assessments.
Additionally, learners with autonomous motivation revealed a significant positive relation to performance and achieved a higher score in the posttest. Findings also revealed that affect, self-regulated learning skills, and motivation function as predictors for problem-solving performance. Beyond this, Manganellia et al. (2019) investigated the relation between self-determined motivation, self-regulated cognitive strategies, and academic performance. Autonomously motivated students achieved better academic performance than students exhibiting controlled motivation, who obtained lower academic performance.
Finally, Mega et al. (2014) examined how emotions, self-regulated learning, and motivation support academic achievement. Their research suggested that motivation, self-regulated learning, and academic achievement were also influenced by emotions.
However, positive emotions only affected academic achievement when mediated by self-regulated learning and motivation.
Summary. Research on self-regulated learning has revealed strong correlations with self-efficacy and the use of self-regulated learning strategies (Ahn & Bong, 2019;
Zimmerman & Martinez-Pons, 1990) and is supposed to represent a crucial aspect of online learning environments in relation to academic achievement. In particular, self- regulated learning strategies such as self-evaluation, organizing, goal-setting, planning, or monitoring are commonly employed strategies in online learning environments (see Table 1.1 and models in this chapter). The concept of self-efficacy has also revealed strong associations with learning (or academic) motivation, such as task value, interest, and mastery-achievement goals (Ahn & Bong, 2019). Another key aspect in self- regulated online learning is the concept of motivation, which has been highly
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recognized in educational literature (Mukhtar, 2018). Results have also identified human factors and online learners’ individual needs as crucial for motivation, self- regulated learning, and its efficacy.
These obtained and relevant aspects led again to autonomous and intrinsic motivation, one of the crucial aspects of self-regulated learning. The concept of motivation with the meta-theory of motivation—the self-determination theory (SDT) by Deci and Ryan (e.g. 1985, 1993)—represented one of the key issues in this framework, and the basis of empirical research for this thesis. The SDT postulates autonomous forms of motivation determined by autonomy and free-choice versus control and pressure for non-autonomous regulation types. The concept of motivation and the SDT’s meta-theory approach are presented in the subsequent chapter.
4 Students’ Motivation in Distance Education
4.1 Defining Motivation
The motivation concept is multilayered, with numerous definitions that can be consulted (Schunk et al., 2014). Nevertheless, in sum, there is agreement that motivation describes movement in the sense that it helps people to continue performing activities or completing tasks (Schunk et al., 2014).
In 2000(a), Ryan and Deci examined classic definitions of motivation in their study. They stated that motivation’s key message can be described as “to be moved to do something” (p. 54). Thus, people who are activated toward a goal are classified as motivated, while those who are unmotivated feel no inspiration to act. The authors also postulated that the motivation construct is not a “unitary phenomenon” (Ryan & Deci, 2000a, p. 54), but rather reflects that people can possess different levels and types of motivation. Thus, people’s motivation varies in level and orientation (type), which is why actions are carried out.
A further definition for motivation was offered by Schunk et al. (2014). They defined motivation as “the process whereby goal-directed activities are instigated and sustained” (p. 5). The term “process” implies that motivation cannot be directly observed, but rather conducted by actions such as choice of tasks, effort, or persistence.
As such, “goals” in this definition includes that people exhibit goal-directed behavior for activities and strive to attain or avoid tasks. Furthermore, “activities” on the one hand refers to physical activities such as effort, persistence, or actions, and on the other
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hand describes mental activities such as planning, monitoring, making decisions, or solving problems. Additionally, for Schunk et al. (2014), motivation involves making commitments, taking actions, and sustaining activities.
A definition similar to that of Schunk et al. (2014) was offered by Wentzel and Brophy (2014). They defined motivation as “a theoretical construct used to explain the intention, direction, intensity, persistence, and quality of behavior, especially goal- directed behavior” (p. 2). This definition also implies that motivation cannot be directly observed or measured, and therefore, strategies such as behavioral indicators, direct observations, self-reports, or online activities must be used to evaluate the construct (Hartnett, 2012, 2019). Schunk et al. (2014) provided an overview of behavioral indicators that most researchers agree can operate the learners’ motivational process.
Their motivation index encompasses indicators such as choice of tasks, effort, persistence, and (academic) achievement. If free-choice conditions are offered to select tasks, greater motivation can be specified. This aspect can be observed in the case of a behavior exhibiting greater effort or when working for a longer time on tasks. The authors further identified several methods that can be used to measure motivation. In this thesis, direct observation and self-reports via questionnaires and interviews are applied. The direct observations strategy is suitable to measure the choice of task, effort, or persistence. Self-reports, either with questionnaires and item ratings or through interviews and oral responses to questions, also represent appropriate strategies for investigating students’ motivation. In education and learning, high motivation levels are associated with engagement, enjoyment, acceptance of challenge, and academic performance. Research indicates that low motivation levels are a factor of low retention rates in distance education (Hartnett, 2019; Ryan & Deci, 2017).
Historical Overview for Theories of Motivation. Schunk et al. provided a comprehensive historical overview of motivational theories in their work from 2014 (also consult Ryan & Deci, 2017 for a historical report). A summary is subsequently presented to offer some brief insights.
As the authors stated, the study of motivation in the realm of education possesses a long history. It emerged in the discipline of psychology with the topics of volition/will and instincts as its two main conceptualizations. Following this, behavior theories were associated with the concept of motivation. These related to learning
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theories from the paradigm of behaviorism, such as Thorndike’s connectionism, Pavlov’s classic conditioning, and Skinner’s operant conditioning. These representatives operated with positive and negative reinforcement or punishment to achieve certain behaviors.
Furthermore, the motivation concept was influenced by drive theories, which highlighted the internal factors for behavior. Drives can be defined as “internal forces that seek to maintain homeostasis [written in bold in the original]” (Schunk et al, 2014, p. 27). This means that, when the internal force (drive) lessens, the need can be satisfied. Other theories such as purpose behaviorism and human or arousal theories can be named as well in the realm of motivational research. For example, arousal theories suggested designing activities to avoid learners’ boredom or develop positive emotions among students. Finally, Schunk et al. (2014) mentioned the neuroscience of motivation and common themes in current motivational theories, such as (1) motivation involves cognitions, behaviors, and affects; (2) motivation relates to learning, achievement, and self-regulation and the mutually influencing aspects; (3) motivation encompasses personal, social, and contextual aspects; and (4) motivation changes with development.
A well-known and often applied meta-theory for motivation is the self- determination theory, created by Deci and Ryan (e.g. 1985, 1993; Ryan & Deci, 2000a, 2017). This approach is associated with drive theories and reflects the multilayered construct of motivation, with its various different types. A key aspect is intrinsic motivation, suggesting that the energy resides within in human nature as a non-drive- based motivation (Deci & Ryan, 1985). Nevertheless, the theory distinguishes between two motivation dimensions: intrinsic and extrinsic motivation. People who are intrinsically motivated perceive joy and interest for their actions, whereas for extrinsic motivation, action and outcome are separated. Thus, some motivation forms are considered self-determined in SDT due to their potential for choice and autonomy. By contrast, controlled motivation forms are influenced by pressure and control, representing learners’ non-autonomy. By distinguishing between autonomous and non- autonomous learning forms, SDT can explain students’ motivational learning and enables classifying the learning quality.
In this thesis, SDT was employed as a theoretical basis for the empirical work, as this approach represents a broad framework for human motivation and provides a
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metatheory for framing motivational studies (Ryan & Deci, 2017). Its self-reported questionnaires for measuring the different types of motivation are reliable and frequently utilized instruments in empirical research. Furthermore, combined with gamification, this provided a purposeful framework for explaining students’
motivational learning behavior. In the next chapter, SDT is presented and its key statements described.
4.2 The Self-Determination Theory: The Meta-Theory for Motivation
An organismic theory of human behavior and personality development comprises the self-determination theory (SDT) (Deci & Ryan, 1985; 1993; Ryan & Deci, 2017). This empirically based theory provides a broad framework and meta-theory for motivational studies of human motivation and personality. The SDT is one of the most influential motivational theories and has found considerable attention and application in education;
thus, its principles are empirically substantiated in this realm as well (Gagné & Deci, 2005).
The SDT differentiates intrinsic motivation and various forms of extrinsic motivation, as well as their respective roles in individuals (see Figure 1.1). In addition, it focuses on learners’ volition and initiative and their performance quality. Three basic psychological needs—(1) autonomy, (2) competence, and (3) relatedness—must be met to foster the most volitional and high-quality forms of motivation. The degree to which any of these needs are felt by the learners can offer valuable insights into their motivation. The more learners feel autonomous, competent, and related to others, the more they voluntarily engage in activities. This behavior includes enhanced performance, persistence, and creativity.
The SDT postulates a continuum for the different forms of motivational regulation, describing the degree of self-determination and the performance quality. On one side of the continuum, the amotivation form is located. This type of regulation characterizes a lack of motivation with no degree of self-regulation. On the other side of the continuum, intrinsic motivation describes the highest quality of self-determined motivation with true self-regulation. Between these two ends of the continuum determined by amotivation and intrinsic motivation, four various types of internalization and regulation associated with extrinsic motivation are outlined: external regulation,
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introjected regulation, identified regulation, and integrated regulation (Ryan & Deci, 2017).
4.3 The Internalization Continuum
The continuum of the various forms of extrinsic motivation is based on the organismic integration theory (OIT) within the SDT (Ryan & Deci, 2017, see Figure 1.4). This describes the degree of autonomy of the different motivation types and postulates about a process of internalization as a natural tendency. This spans from controlled self- regulation (external motivation) to autonomous self-regulation (integrated motivation).
The continuum explains the extent to which individuals adopt and transform externally conveyed regulation into true self-regulation (Ryan & Deci, 2017). However, the three basic psychological needs of autonomy, competence, and relatedness—essential components in SDT—are still necessary to support this integrative process, which only functions under conditions of these three basic psychological needs. The learning behavior changes from external and controlled toward integrated and fully internalized autonomous learning behavior. Ryan and Deci (2017) described internalization
“as the process of taking in values, beliefs, or behavioral regulations from external sources and transforming them into one’s own. (…) [Internalization]
means assimilating the regulation or value and integrating it with the other values, behaviors, attitudes, and emotions that are themselves inherent and/or have been deeply internalized by the individual. Thus, when a regulation that was originally socially transmitted has been fully internalized, it will largely be in harmony or congruence with other aspects of one’s values and personality, and enacting it will be experienced as autonomous.” (p. 182)
If the behavior is externally regulated, students will perform or act only when they expect a consequence, wither a reward or punishment. Their behavior is controlled and instrumental. Tasks will be accomplished in the least effortful manner and with less attention to quality. An internally regulated behavior comes from within the student and features an autonomous character. Students seek to perform or accomplish tasks because they possess a personal value. They identify themselves with the activities, put forth effort, and pay more attention to their performance quality. On the continuum, these
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various types of extrinsic motivation can be internalized, moving from external motivation to integrated motivation, which is already rather similar to intrinsic motivation. Intrinsic motivation represents the highest quality of regulation type and forms “the prototype of autonomous or self-determined activity” (Ryan & Deci, 2017, p. 198). Learners engage in activities because they are interested in doing so, and they perform out of joy. They experience interest and satisfaction and also feel fully volitional while typically engaging spontaneously in activities. Even if intrinsic motivation is crucial for developing competence, it is typically not the learners’ actual goal. The focus is on the present experience and satisfaction while performing the activities.
Through internalization and integration, it is possible for learners to satisfy their need for autonomy, competence, and relatedness. Even though the different regulation types are ordered on the continuum, it is not necessary to progress through each stage of regulation type, and therefore, it is not a developmental continuum (Ryan & Deci, 2017).
